diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..ee30fff --- /dev/null +++ b/.env.example @@ -0,0 +1,9 @@ +# Docker Compose Configuration + +# Local URL Endpoint (only needed for non-public domains) +# If using a local domain like api.example.com mapped to localhost, set to the domain without https:// +# Otherwise, set to: not-needed +LOCAL_URL_ENDPOINT=not-needed + +BACKEND_PORT=8000 +FRONTEND_PORT=3000 diff --git a/.github/workflows/code-scans.yaml b/.github/workflows/code-scans.yaml new file mode 100644 index 0000000..2029a2f --- /dev/null +++ b/.github/workflows/code-scans.yaml @@ -0,0 +1,104 @@ +name: SDLE Scans + +on: + workflow_dispatch: + inputs: + PR_number: + description: 'Pull request number' + required: true + push: + branches: [ main ] + pull_request: + types: [opened, synchronize, reopened, ready_for_review] + +concurrency: + group: sdle-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + +# ----------------------------- +# 1) Trivy Scan +# ----------------------------- + trivy_scan: + name: Trivy Vulnerability Scan + runs-on: ubuntu-latest + env: + TRIVY_REPORT_FORMAT: table + TRIVY_SCAN_TYPE: fs + TRIVY_SCAN_PATH: . + TRIVY_EXIT_CODE: '1' + TRIVY_VULN_TYPE: os,library + TRIVY_SEVERITY: CRITICAL,HIGH + steps: + - uses: actions/checkout@v4 + + - name: Create report directory + run: mkdir -p trivy-reports + + - name: Run Trivy FS Scan + uses: aquasecurity/trivy-action@0.24.0 + with: + scan-type: 'fs' + scan-ref: '.' + scanners: 'vuln,misconfig,secret,license' + ignore-unfixed: true + format: 'table' + exit-code: '1' + output: 'trivy-reports/trivy_scan_report.txt' + vuln-type: 'os,library' + severity: 'CRITICAL,HIGH' + + - name: Upload Trivy Report + uses: actions/upload-artifact@v4 + with: + name: trivy-report + path: trivy-reports/trivy_scan_report.txt + + - name: Show Trivy Report in Logs + if: failure() + run: | + echo "========= TRIVY FINDINGS =========" + cat trivy-reports/trivy_scan_report.txt + echo "=================================" + +# ----------------------------- +# 2) Bandit Scan +# ----------------------------- + bandit_scan: + name: Bandit security scan + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + submodules: 'recursive' + fetch-depth: 0 + + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + + - name: Install Bandit + run: pip install bandit + + - name: Create Bandit configuration + shell: bash + run: | + cat > .bandit << 'EOF' + [bandit] + exclude_dirs = tests,test,venv,.venv,node_modules + skips = B101 + EOF + + - name: Run Bandit scan + run: | + bandit -r . -ll -iii -f screen + bandit -r . -ll -iii -f html -o bandit-report.html + + - name: Upload Bandit Report + uses: actions/upload-artifact@v4 + with: + name: bandit-report + path: bandit-report.html + retention-days: 30 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..df9559d --- /dev/null +++ b/.gitignore @@ -0,0 +1,86 @@ + +.env +.env.local +.env.*.local +*.env + + + +# ============================================ +# PYTHON +# ============================================ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +*.so + +# Virtual environments +venv/ +env/ +ENV/ +.venv/ + +# PyCharm +.idea/ + +# VS Code +.vscode/ + +# Pytest +.pytest_cache/ +.coverage +htmlcov/ + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# ============================================ +# NODE.JS / REACT +# ============================================ +# Dependencies +node_modules/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* + +# Production build +build/ +dist/ + +# React +.env.development.local +.env.test.local +.env.production.local + +# ============================================ +# TEMPORARY & CACHE FILES +# ============================================ +# Temporary cloned repositories +api/tmp/ +api/temp/ +*/tmp/ +*/temp/ +tests/ + +# Logs +*.log +logs/ + +# OS files +.DS_Store +Thumbs.db +desktop.ini + +# ============================================ +# LANGGRAPH & AI +# ============================================ +# LangGraph checkpoints (SQLite databases) +*.db +*.sqlite +*.sqlite3 +checkpoints/ + +tmp/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..ad52928 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,265 @@ +# Contributing to DocuBot + +Thanks for your interest in contributing to DocuBot. + +DocuBot is an open-source AI-powered documentation generator built with a FastAPI backend, a React frontend, and a multi-agent LangGraph workflow. It uses specialized micro-agents to analyze codebases and generate comprehensive README documentation. We welcome improvements across the codebase, documentation, bug reports, design feedback, and workflow enhancements. + +Before you start, read the relevant section below. It helps keep contributions focused, reviewable, and aligned with the current project setup. + +--- + +## Quick Setup Checklist + +Before you dive in, make sure you have these installed: + +\`\`\`bash +# Check Python (3.11+ recommended) +python --version + +# Check Node.js (18+ recommended) +node --version + +# Check npm +npm --version + +# Check Docker +docker --version +docker compose version + +# Check Git +git --version +\`\`\` + +New to contributing? + +1. Open an issue or pick an existing one to work on. +2. Sync your branch from \`dev\`. +3. Follow the local setup guide below. +4. Run the app locally and verify your change before opening a PR. + +## Table of Contents + +- [How do I...?](#how-do-i) + - [Get help or ask a question?](#get-help-or-ask-a-question) + - [Report a bug?](#report-a-bug) + - [Suggest a new feature?](#suggest-a-new-feature) + - [Set up DocuBot locally?](#set-up-docubot-locally) + - [Start contributing code?](#start-contributing-code) + - [Improve the documentation?](#improve-the-documentation) + - [Submit a pull request?](#submit-a-pull-request) +- [Code guidelines](#code-guidelines) +- [Pull request checklist](#pull-request-checklist) +- [Branching model](#branching-model) +- [Thank you](#thank-you) + +--- + +## How do I... + +### Get help or ask a question? + +- Start with the main project docs in [\`README.md\`](./README.md), [\`api/.env.example\`](./api/.env.example), and the inline documentation in agent files. +- If something is unclear, open a GitHub issue with your question and the context you already checked. + +### Report a bug? + +1. Search existing issues first. +2. If the bug is new, open a GitHub issue. +3. Include your environment, what happened, what you expected, and exact steps to reproduce. +4. Add screenshots, logs, request details, or response payloads if relevant. + +### Suggest a new feature? + +1. Open a GitHub issue describing the feature. +2. Explain the problem, who it helps, and how it fits DocuBot. +3. If the change is large, get alignment in the issue before writing code. + +### Set up DocuBot locally? + +#### Prerequisites + +- Python 3.11+ +- Node.js 18+ and npm +- Git +- Docker with Docker Compose v2 +- One LLM provider: + - An OpenAI-compatible API key (OpenAI, Groq, OpenRouter), or + - Enterprise inference endpoint (GenAI Gateway, APISIX), or + - Ollama installed locally on the host machine + +#### Option 1: Local Development + +##### Step 1: Clone the repository + +\`\`\`bash +git clone https://github.com/your-org/DocuBot.git +cd DocuBot +\`\`\` + +##### Step 2: Configure environment variables + +Create an \`.env\` file in the \`api/\` directory from the example: + +\`\`\`bash +cp api/.env.example api/.env +\`\`\` + +At minimum, configure your LLM provider. Example for OpenAI: + +\`\`\`env +LLM_PROVIDER=openai +LLM_API_KEY=sk-... +LLM_BASE_URL=https://api.openai.com/v1 +LLM_MODEL=gpt-4o-mini +\`\`\` + +Example for local Ollama: + +\`\`\`env +LLM_PROVIDER=ollama +LLM_API_KEY=not-needed +LLM_BASE_URL=http://localhost:11434/v1 +LLM_MODEL=qwen3:4b +\`\`\` + +##### Step 3: Install backend dependencies + +\`\`\`bash +cd api +python -m venv .venv +source .venv/bin/activate # Windows: .venv\Scripts\activate +pip install -r requirements.txt +cd .. +\`\`\` + +##### Step 4: Install frontend dependencies + +\`\`\`bash +cd ui +npm install +cd .. +\`\`\` + +##### Step 5: Start the backend + +\`\`\`bash +cd api +source .venv/bin/activate +python server.py +\`\`\` + +The backend runs at \`http://localhost:5001\`. + +##### Step 6: Start the frontend + +Open a second terminal: + +\`\`\`bash +cd ui +npm run dev +\`\`\` + +The Vite dev server runs at \`http://localhost:3000\`. + +##### Step 7: Access the application + +- Frontend: \`http://localhost:3000\` +- Backend health check: \`http://localhost:5001/health\` +- API docs: \`http://localhost:5001/docs\` + +#### Option 2: Docker + +From the repository root: + +\`\`\`bash +cp api/.env.example api/.env +# Edit api/.env with your LLM provider settings +docker compose up --build +\`\`\` + +This starts: + +- Frontend on \`http://localhost:3000\` +- Backend on \`http://localhost:5001\` + +#### Common Troubleshooting + +- If ports \`3000\`, \`5001\`, or \`5173\` are already in use, stop the conflicting process before starting DocuBot. +- If LLM requests fail, confirm your \`.env\` values are correct for the selected provider. +- If you use Ollama with Docker, make sure Ollama is running on the host and reachable from the container. +- If Docker fails to build, rebuild with \`docker compose up --build\`. +- If Python packages fail to install, confirm you are using a supported Python version (3.11+). + +### Start contributing code? + +1. Open or choose an issue. +2. Create a feature branch from \`dev\`. +3. Keep the change focused on a single problem. +4. Run the app locally and verify the affected workflow. +5. Update docs when behavior, setup, configuration, or architecture changes. +6. Open a pull request back from your feature branch into \`dev\`. + +### Improve the documentation? + +Documentation updates are welcome. Relevant files currently live in: + +- [\`README.md\`](./README.md) +- [\`api/.env.example\`](./api/.env.example) +- Agent docstrings in [\`api/agents/\`](./api/agents/) +- Configuration in [\`api/config.py\`](./api/config.py) + +### Submit a pull request? + +Follow the checklist below before opening your PR. Your pull request should: + +- Stay focused on one issue or topic. +- Explain what changed and why. +- Include manual verification steps. +- Include screenshots or short recordings for UI changes. +- Reference the related GitHub issue when applicable. + +Note: pull requests should target the \`dev\` branch. + +--- + +## Code guidelines + +- Follow the existing project structure and patterns before introducing new abstractions. +- Keep frontend changes consistent with the React + Vite + Tailwind setup already in use. +- Keep backend changes consistent with the FastAPI + LangGraph agent structure in [\`api\`](./api). +- Avoid unrelated refactors in the same pull request. +- Do not commit secrets, API keys, cloned repositories (tmp/repos), local \`.env\` files, or generated artifacts. +- Prefer clear, small commits and descriptive pull request summaries. +- Update documentation when contributor setup, behavior, environment variables, or agent prompts change. + +--- + +## Pull request checklist + +Before submitting your pull request, confirm the following: + +- You tested the affected flow locally. +- The application still starts successfully in the environment you changed. +- You removed debug code, stray logs, and commented-out experiments. +- You documented any new setup steps, environment variables, or behavior changes. +- You kept the pull request scoped to one issue or topic. +- You added screenshots for UI changes when relevant. +- You did not commit secrets, API keys, or cloned test repositories (tmp/repos folder). +- You are opening the pull request against \`dev\`. + +If one or more of these are missing, the pull request may be sent back for changes before review. + +--- + +## Branching model + +- Base new work from \`dev\`. +- Open pull requests against \`dev\`. +- Use descriptive branch names such as \`fix/mermaid-diagram-validation\` or \`docs/update-contributing-guide\`. +- Rebase or merge the latest \`dev\` before opening your PR if your branch has drifted. + +--- + +## Thank you + +Thanks for contributing to DocuBot. Whether you're fixing a bug, improving the docs, adding a new agent, or refining the workflow, your work helps make the project more useful and easier to maintain. diff --git a/DISCLAIMER.md b/DISCLAIMER.md new file mode 100644 index 0000000..2047b9a --- /dev/null +++ b/DISCLAIMER.md @@ -0,0 +1,11 @@ +# Disclaimer + +This blueprint is provided by Cloud2 Labs "as is" and "as available" for educational and demonstration purposes only. + +The **DocuBot - AI Documentation Generator Blueprint** is a reference implementation and does not constitute a production-ready system or regulatory-compliant solution. + +This software is not designed to provide professional advice. All outputs generated by this blueprint require independent human review and validation. + +Cloud2 Labs does not assume responsibility or liability for any data loss, security incident, service disruption, regulatory non-compliance, or adverse outcome resulting from the use or modification of this blueprint. + +Users are solely responsible for implementing appropriate security controls, data handling procedures, and compliance measures for their specific use case. diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..4fb1628 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,9 @@ +MIT License + +© 2026 Cloud2 Labs + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..25a48fe --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,23 @@ +# Security Policy + +The **DocuBot** blueprint does not include production-grade security controls. + +This repository is not secure by default and must not be used in production without a comprehensive security review. + +Users are responsible for implementing appropriate: +- Authentication and authorization mechanisms +- Encryption and secure data storage +- API key management and secrets handling +- Input validation and sanitization (repository URLs, user inputs) +- Rate limiting and abuse prevention +- Monitoring, logging, and auditing +- Regulatory and compliance safeguards +- Network security and firewall configurations +- Secure handling of cloned repository data +- GitHub token security and scope management + +## Reporting Security Issues + +If you discover a security vulnerability in this blueprint, please report it by creating an issue in the repository or contacting Cloud2 Labs directly. + +Please do not publicly disclose security issues until they have been addressed. diff --git a/TERMS_AND_CONDITIONS.md b/TERMS_AND_CONDITIONS.md new file mode 100644 index 0000000..b6df3b4 --- /dev/null +++ b/TERMS_AND_CONDITIONS.md @@ -0,0 +1,17 @@ +# Terms and Conditions + +This repository contains the **DocuBot - AI Documentation Generator Blueprint** maintained by Cloud2 Labs. + +By accessing or using this blueprint, you acknowledge and agree that: + +- This blueprint is provided solely for educational and demonstration purposes +- You are solely responsible for deployment, configuration, and usage +- You are responsible for all data handling, security controls, and compliance +- Cloud2 Labs provides no warranties or guarantees of any kind +- You must implement appropriate security measures before any production use +- All LLM provider usage is subject to the respective provider's terms and conditions +- You are responsible for obtaining and managing API keys for any LLM providers used +- You are responsible for securing GitHub tokens and managing repository access permissions +- Generated documentation requires human review before publication + +Cloud2 Labs does not support or recommend production deployment of this blueprint without comprehensive security review and appropriate safeguards. diff --git a/TROUBLESHOOTING.md b/TROUBLESHOOTING.md new file mode 100644 index 0000000..ad0f94a --- /dev/null +++ b/TROUBLESHOOTING.md @@ -0,0 +1,623 @@ +# Troubleshooting Guide + +This guide covers common issues and solutions for the Documentation Generator Micro-Agents application. + +--- + +## Table of Contents + +- [Installation Issues](#installation-issues) +- [Configuration Errors](#configuration-errors) +- [Runtime Errors](#runtime-errors) +- [Agent Failures](#agent-failures) +- [Performance Issues](#performance-issues) +- [Docker Issues](#docker-issues) +- [Network and API Errors](#network-and-api-errors) + +--- + +## Installation Issues + +### Docker Container Build Fails + +**Error:** `ERROR [internal] load build context` + +**Cause:** Docker daemon not running or insufficient permissions. + +**Solution:** +```bash +# Start Docker daemon (Linux/Mac) +sudo systemctl start docker + +# On Windows, start Docker Desktop application + +# Verify Docker is running +docker ps +``` + +### Port Already in Use + +**Error:** `Error starting userland proxy: listen tcp 0.0.0.0:5001: bind: address already in use` + +**Cause:** Another service is using port 5001 (backend) or 3000 (frontend). + +**Solution:** +```bash +# Find process using the port +# On Linux/Mac: +lsof -i :5001 +lsof -i :3000 + +# On Windows: +netstat -ano | findstr :5001 +netstat -ano | findstr :3000 + +# Kill the process or change ports in docker-compose.yml +``` + +--- + +## Configuration Errors + +### Missing LLM API Key + +**Error:** `ValueError: LLM_API_KEY is required for openai` + +**Cause:** API key not set in `.env` file for the selected provider. + +**Solution:** +```bash +# Copy environment template +cp api/.env.example api/.env + +# Edit api/.env and add your API key +LLM_PROVIDER=openai +LLM_API_KEY=your_actual_api_key_here +LLM_BASE_URL=https://api.openai.com/v1 +LLM_MODEL=gpt-4o +``` + +### OpenAI Connection Issues + +**Error:** `401 Unauthorized` or `Invalid API key` + +**Cause:** OpenAI API key is invalid or expired. + +**Solution:** +1. Verify your API key at https://platform.openai.com/api-keys +2. Generate a new key if needed +3. Update `LLM_API_KEY` in `api/.env` +4. Restart the backend: `docker compose restart api` + +### Groq Connection Issues + +**Error:** `Rate limit exceeded` or `429 Too Many Requests` + +**Cause:** Groq free tier rate limits reached (30 req/min, 6000 tokens/min). + +**Solution:** +- Wait a few minutes before retrying +- Reduce repository size or scope +- Upgrade to paid tier at https://console.groq.com +- Switch to OpenAI or Ollama temporarily + +### Ollama Connection Issues + +**Error:** `ConnectionRefusedError: Connection refused to localhost:11434` + +**Cause:** Ollama service not running or not accessible. + +**Solution:** +```bash +# Check if Ollama is running +curl http://localhost:11434/api/tags + +# Start Ollama (if not running) +ollama serve + +# Pull required model if not downloaded +ollama pull qwen2.5:7b + +# Verify model is available +ollama list +``` + +### OpenRouter Connection Issues + +**Error:** `402 Payment Required` or `Insufficient credits` + +**Cause:** OpenRouter account has insufficient credits. + +**Solution:** +1. Add credits to your OpenRouter account at https://openrouter.ai +2. Check balance: https://openrouter.ai/activity +3. Verify API key is correct in `api/.env` + +### Custom API Endpoint Issues + +**Error:** `Connection failed` or `Invalid response format` + +**Cause:** Custom API endpoint doesn't implement OpenAI-compatible format. + +**Solution:** +1. Verify your custom API implements `/v1/chat/completions` endpoint +2. Test with curl: + ```bash + curl https://your-custom-endpoint.com/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer YOUR_KEY" \ + -d '{"model":"your-model","messages":[{"role":"user","content":"test"}]}' + ``` +3. Ensure response format matches OpenAI schema + +### Invalid GitHub Token + +**Error:** `401 Unauthorized` when creating pull requests + +**Cause:** GitHub token missing, expired, or has insufficient permissions. + +**Solution:** +1. Generate a new Personal Access Token at https://github.com/settings/tokens +2. Select `repo` scope (full control of repositories) +3. Update `GITHUB_TOKEN` in `api/.env` +4. Token format should be: `ghp_` followed by 36 alphanumeric characters + +### Authentication Mode Mismatch + +**Error:** `KeyError: 'KEYCLOAK_CLIENT_SECRET'` or connection refused + +**Cause:** AUTH_MODE set to keycloak but credentials not configured. + +**Solution:** +```bash +# For GenAI Gateway (recommended): +AUTH_MODE=genai_gateway +GENAI_GATEWAY_URL=https://your-gateway-url.com +GENAI_GATEWAY_API_KEY=your_key_here + +# For Keycloak: +AUTH_MODE=keycloak +BASE_URL=https://your-inference-endpoint.com +KEYCLOAK_CLIENT_SECRET=your_secret_here +``` + +--- + +## Runtime Errors + +### Repository Clone Fails + +**Error:** `fatal: repository not found` or `Permission denied (publickey)` + +**Cause:** Invalid repository URL or insufficient permissions. + +**Solution:** +1. Verify the GitHub URL is correct and accessible +2. For private repositories, ensure: + - Repository exists and you have access + - Authentication is configured correctly + - Organization SSO is authorized if applicable + +### Repository Too Large + +**Error:** `Repository exceeds maximum size of 10GB` + +**Cause:** Repository size exceeds MAX_REPO_SIZE limit. + +**Solution:** +```bash +# Option 1: Increase limit in api/.env +MAX_REPO_SIZE=21474836480 # 20GB in bytes + +# Option 2: Use subfolder targeting +# Instead of: https://github.com/org/repo +# Use: https://github.com/org/repo/tree/main/backend +``` + +### File Size Limit Exceeded + +**Error:** `File too large (X bytes). Maximum is 1.0MB` + +**Cause:** Individual file exceeds MAX_FILE_SIZE limit. + +**Solution:** +```bash +# Increase file size limit in api/.env +MAX_FILE_SIZE=2000000 # 2MB in bytes + +# Or exclude large files by using subfolder targeting +``` + +### Too Many Files in Repository + +**Error:** `Repository has X files, maximum is 500` + +**Cause:** Repository exceeds MAX_FILES_TO_SCAN limit. + +**Solution:** +```bash +# Increase file scan limit in api/.env +MAX_FILES_TO_SCAN=1000 + +# Or use subfolder targeting for specific directories +``` + +--- + +## Agent Failures + +### Agent Timeout + +**Error:** `Agent execution timed out after 300 seconds` + +**Cause:** Agent taking too long to complete analysis. + +**Solution:** +```bash +# Increase timeout in api/.env +AGENT_TIMEOUT=600 # 10 minutes + +# Or reduce repository size using subfolder targeting +``` + +### Code Explorer Agent Fails + +**Error:** `CodeExplorer failed: No code files found` + +**Cause:** Repository contains no recognized programming language files. + +**Solution:** +- Verify repository contains source code files (.py, .js, .ts, .go, .rs, etc.) +- Check if files are in subdirectories (agents scan recursively) +- Ensure repository cloning completed successfully + +### API Reference Agent Returns Empty Results + +**Error:** `No API endpoints extracted` + +**Cause:** Repository doesn't contain API route definitions, or patterns not recognized. + +**Solution:** +- This is normal for non-API projects (libraries, CLI tools) +- For API projects, ensure route definitions use standard patterns: + - FastAPI: `@router.get("/endpoint")`, `@app.post("/endpoint")` + - Flask: `@app.route("/endpoint")` + - Express: `app.get("/endpoint")` + - Spring: `@GetMapping("/endpoint")` + +### Mermaid Diagram Validation Fails + +**Error:** `Diagram has semantic issues: missing Backend node` + +**Cause:** Generated diagram doesn't match detected project structure. + +**Solution:** +- This is a warning, not a failure - diagram is still generated +- The system validates diagrams against detected components (backend, frontend, database) +- Check agent logs to see what was detected in Evidence Aggregator +- Diagram may need manual refinement if complex architecture + +### QA Validator Detects Issues + +**Error:** `QA validation failed: Low quality score` + +**Cause:** README sections don't match detected evidence or contain hallucinations. + +**Solution:** +- Review the QA validation output in agent logs +- Check if correct sections were generated by section-writer agents +- Verify Evidence Aggregator detected files correctly +- This doesn't block README generation - final output is still produced + +--- + +## Performance Issues + +### Slow Documentation Generation + +**Symptom:** Workflow takes longer than 5 minutes + +**Causes and Solutions:** + +1. **Large repository** + ```bash + # Reduce scan limits in api/.env + MAX_FILES_TO_SCAN=300 + MAX_LINES_PER_FILE=300 + ``` + +2. **High token usage** + - Check metrics summary in agent logs + - Typical usage: 20K-40K tokens per repository + - If much higher, repository may have very large files + +3. **Network latency to LLM backend** + - Ensure low latency network connection to GenAI Gateway + - Check GENAI_GATEWAY_URL is accessible + +### High Token Usage + +**Symptom:** Metrics show >60K tokens for small repository + +**Cause:** Strategic sampling not working efficiently or very verbose files. + +**Solution:** +```bash +# Reduce lines per file in api/.env +MAX_LINES_PER_FILE=300 + +# Agents automatically use pattern_window mode to minimize context +# This is the default and most efficient strategy +``` + +--- + +## Docker Issues + +### Backend Container Won't Start + +**Error:** `backend exited with code 1` + +**Solution:** +```bash +# Check backend logs for specific error +docker-compose logs backend + +# Common causes: +# 1. Missing .env file - solution above in Configuration Errors +# 2. Invalid Python dependencies - rebuild: +docker-compose build --no-cache backend +docker-compose up -d + +# 3. Port conflict - solution above in Installation Issues +``` + +### Frontend Container Build Fails + +**Error:** `npm install failed` or `Cannot find module` + +**Solution:** +```bash +# Rebuild frontend with clean cache +docker-compose build --no-cache frontend +docker-compose up -d frontend + +# Verify Node.js version in Dockerfile is compatible (16+) +``` + +### Container Memory Issues + +**Error:** `Killed` or `Out of memory` + +**Solution:** +```bash +# Increase Docker memory limit in Docker Desktop settings +# Recommended: 4GB minimum, 8GB preferred + +# Or reduce repository analysis limits: +MAX_FILES_TO_SCAN=200 +MAX_LINES_PER_FILE=300 +``` + +### Cannot Connect to Backend from Frontend + +**Error:** `Network error` or `Connection refused` in browser console + +**Cause:** Docker network misconfiguration or CORS issue. + +**Solution:** +```bash +# Verify both containers are running +docker-compose ps + +# Check backend is accessible +curl http://localhost:5001/api/health + +# Verify CORS_ORIGINS in api/.env includes frontend URL +CORS_ORIGINS=["http://localhost:3000"] + +# Restart services +docker-compose restart +``` + +--- + +## Network and API Errors + +### LLM Provider Connection Refused + +**Error:** `ConnectionRefusedError: [Errno 111] Connection refused` + +**Cause:** LLM provider URL incorrect or service unavailable. + +**Solution:** +```bash +# Verify LLM_BASE_URL is correct in api/.env + +# Test connectivity based on provider: +# OpenAI: +curl https://api.openai.com/v1/models -H "Authorization: Bearer YOUR_KEY" + +# Groq: +curl https://api.groq.com/openai/v1/models -H "Authorization: Bearer YOUR_KEY" + +# Ollama: +curl http://localhost:11434/api/tags + +# OpenRouter: +curl https://openrouter.ai/api/v1/models -H "Authorization: Bearer YOUR_KEY" + +# Check firewall/proxy settings allow outbound HTTPS +``` + +### Legacy Enterprise Inference Connection + +**Error:** `ConnectionRefusedError` when using `INFERENCE_API_ENDPOINT` + +**Cause:** Enterprise inference endpoint URL incorrect or service unavailable. + +**Solution:** +```bash +# Verify INFERENCE_API_ENDPOINT and INFERENCE_API_TOKEN in api/.env +# Test connectivity: +curl https://your-inference-endpoint.com/v1/models \ + -H "Authorization: Bearer YOUR_TOKEN" + +# Check SSL verification setting: +VERIFY_SSL=false # Only for dev with self-signed certificates + +# Check firewall/proxy settings +``` + +### LLM Request Timeout + +**Error:** `Timeout waiting for LLM response` + +**Cause:** LLM backend overloaded or network latency. + +**Solution:** +```bash +# Increase agent timeout in api/.env +AGENT_TIMEOUT=600 + +# Check LLM backend status +# Contact infrastructure team if persistent +``` + +### Rate Limit Exceeded + +**Error:** `429 Too Many Requests` or `Rate limit exceeded` + +**Cause:** Too many concurrent requests to LLM backend. + +**Solution:** +- Wait a few minutes before retrying +- Reduce concurrent documentation generation jobs +- Contact infrastructure team to increase rate limits +- The system processes agents sequentially to minimize rate limit issues + +### GitHub API Rate Limit (PR Creation) + +**Error:** `403 API rate limit exceeded` + +**Cause:** Too many GitHub API calls within rate limit window. + +**Solution:** +- Wait 1 hour for rate limit reset +- Authenticated requests have higher limits (5000/hour vs 60/hour) +- Ensure GITHUB_TOKEN is configured correctly +- Check rate limit status: + ```bash + curl -H "Authorization: token YOUR_TOKEN" https://api.github.com/rate_limit + ``` + +--- + +## Advanced Troubleshooting + +### Enable Debug Logging + +To get more detailed logs for debugging: + +```bash +# Add to api/.env +LOG_LEVEL=DEBUG + +# Restart backend +docker-compose restart backend + +# View detailed logs +docker-compose logs -f backend +``` + +### Check Agent Execution Metrics + +Metrics are displayed in the agent logs panel after workflow completion: + +``` +📊 Workflow Metrics Summary +├─ Total Agents: 9 +├─ Successful: 9 +├─ Failed: 0 +├─ Total Duration: 155.59s +├─ Total Tokens: 33,135 +│ ├─ Input: 30,728 +│ └─ Output: 2,407 +├─ Total Tool Calls: 23 +├─ Total LLM Calls: 31 +└─ Average TPS: 15.47 tokens/sec +``` + +**Analysis:** +- **Failed agents**: Should be 0 for successful runs +- **Total tokens**: Typical range 20K-40K for medium repositories +- **Average TPS**: Depends on LLM backend performance +- **Tool calls**: Indicates how many tool invocations agents made + +### Manual Repository Cleanup + +If cloned repositories aren't cleaned up automatically: + +```bash +# Clean tmp directory +rm -rf api/tmp/repos/* + +# Or within Docker: +docker-compose exec backend rm -rf /app/tmp/repos/* +``` + +### Reset Application State + +To completely reset the application: + +```bash +# Stop and remove containers +docker-compose down -v + +# Remove temporary files +rm -rf api/tmp/* + +# Rebuild and restart +docker-compose build --no-cache +docker-compose up -d +``` + +--- + +## Getting Help + +If you continue to experience issues: + +1. **Check Logs:** Review backend logs with `docker-compose logs backend -f` +2. **Verify Configuration:** Ensure all required environment variables are set in `api/.env` +3. **Test Connectivity:** Verify network access to GenAI Gateway and GitHub +4. **Metrics Analysis:** Check workflow metrics for anomalies (token usage, duration, failed agents) +5. **Report Issues:** If the problem persists, collect: + - Error messages from logs + - Workflow metrics summary + - Repository URL (if not sensitive) + - Configuration (redact sensitive values) + +--- + +## Common Success Indicators + +A successful run should show: + +``` +✅ Repository cloned successfully +✅ Overview & Features sections completed +✅ Extracted X API endpoints +✅ Architecture section completed +✅ Troubleshooting section completed +✅ Configuration section completed +✅ Prerequisites & Deployment sections completed +✅ Evidence aggregated: X Python deps, Y Node deps +✅ Planner completed - Z sections planned +✅ Mermaid Generator completed +✅ QA Validator completed (Score: XX) +✅ Documentation generation complete! +📊 Workflow Metrics Summary +``` + +All agents should complete successfully with metrics showing reasonable token usage and no failed agents. diff --git a/api/.dockerignore b/api/.dockerignore new file mode 100644 index 0000000..2c7d295 --- /dev/null +++ b/api/.dockerignore @@ -0,0 +1,47 @@ +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +*.egg-info/ +dist/ +build/ +*.egg + +venv/ +env/ +ENV/ +.venv + +.vscode/ +.idea/ +*.swp +*.swo +*~ + +.pytest_cache/ +.coverage +htmlcov/ +*.cover + +tmp/ +*.log +logs/ + +.env +.env.local +.env.*.local + +.git/ +.gitignore + +README.md +TROUBLESHOOTING.md +*.md + +Dockerfile +.dockerignore +docker-compose.yml + +.DS_Store +Thumbs.db diff --git a/api/.env.example b/api/.env.example new file mode 100644 index 0000000..a77d544 --- /dev/null +++ b/api/.env.example @@ -0,0 +1,105 @@ +# ================================================================= +# DocuBot - AI Documentation Generator Configuration +# ================================================================= + +# ================================================================= +# LLM Provider Configuration (REQUIRED) +# ================================================================= +# Supported providers: openai, groq, ollama, openrouter, or custom +LLM_PROVIDER=custom + +# Base URL for the LLM API +# OpenAI: https://api.openai.com/v1 +# Groq: https://api.groq.com/openai/v1 +# Ollama: http://localhost:11434/v1 +# OpenRouter: https://openrouter.ai/api/v1 +LLM_BASE_URL=https://your-llm-provider-url + +# API key for the selected provider +# OpenAI: Get from https://platform.openai.com/api-keys +# Groq: Get from https://console.groq.com/keys +# Ollama: Leave empty (no key needed) +# OpenRouter: Get from https://openrouter.ai/keys +LLM_API_KEY=your-api-key + +# Model name to use +# OpenAI: gpt-4o, gpt-4-turbo, gpt-4o-mini +# Groq: llama-3.2-90b-text-preview, llama-3.1-70b-versatile +# Ollama: qwen2.5:7b, llama3.1:8b, llama3.2:3b +# OpenRouter: anthropic/claude-3-haiku, google/gemini-pro +LLM_MODEL=gpt-4o + +# ================================================================= +# Generation Parameters +# ================================================================= +# Model temperature (0.0 = deterministic, 1.0 = creative) +TEMPERATURE=0.7 + +# Maximum tokens per response +MAX_TOKENS=1000 + +# Number of retry attempts on API failures +MAX_RETRIES=3 + +# Request timeout in seconds (5 minutes default) +REQUEST_TIMEOUT=300 + +# ================================================================= +# Micro-Agent Model Configuration +# ================================================================= +# All agents use Qwen3-4B-Instruct (optimized SLM for code analysis) +# You can customize individual agent models if needed + +CODE_EXPLORER_MODEL=Qwen/Qwen3-4B-Instruct-2507 +API_REFERENCE_MODEL=Qwen/Qwen3-4B-Instruct-2507 +CALL_GRAPH_MODEL=Qwen/Qwen3-4B-Instruct-2507 +ERROR_ANALYSIS_MODEL=Qwen/Qwen3-4B-Instruct-2507 +ENV_CONFIG_MODEL=Qwen/Qwen3-4B-Instruct-2507 +DEPENDENCY_ANALYZER_MODEL=Qwen/Qwen3-4B-Instruct-2507 +PLANNER_MODEL=Qwen/Qwen3-4B-Instruct-2507 +MERMAID_MODEL=Qwen/Qwen3-4B-Instruct-2507 +QA_VALIDATOR_MODEL=Qwen/Qwen3-4B-Instruct-2507 +WRITER_MODEL=Qwen/Qwen3-4B-Instruct-2507 + +# ================================================================= +# Repository Analysis Limits +# ================================================================= +# All limits are configurable to suit your needs +TEMP_REPO_DIR=./tmp/repos +MAX_REPO_SIZE=10737418240 # 10GB in bytes +MAX_FILE_SIZE=1000000 # 1MB in bytes +MAX_FILES_TO_SCAN=500 # Maximum number of files to analyze +MAX_LINES_PER_FILE=500 # Line budget per file (pattern_window strategy extracts ~150-300 lines focusing on key patterns) + +# ================================================================= +# Agent Execution Settings +# ================================================================= +AGENT_TEMPERATURE=0.7 # Model temperature (0.0-1.0) +AGENT_MAX_TOKENS=1000 # Maximum tokens per agent response +AGENT_TIMEOUT=300 # Agent timeout in seconds (5 minutes) + +# ================================================================= +# GitHub Integration (MCP) +# ================================================================= +# Required for automatic PR creation +# Generate token at: https://github.com/settings/tokens +# Required scopes: repo (full access to repositories) +GITHUB_TOKEN=your_github_personal_access_token_here + +# ================================================================= +# Server Configuration +# ================================================================= +API_PORT=5001 +HOST=0.0.0.0 + +# ================================================================= +# CORS Settings +# ================================================================= +# Add your frontend URL if different from defaults +CORS_ORIGINS=["http://localhost:3000", "http://localhost:3001", "http://localhost:5173"] + +# ================================================================= +# Security Configuration +# ================================================================= +# SSL Verification: Set to false only for development with self-signed certificates +VERIFY_SSL=true diff --git a/api/Dockerfile b/api/Dockerfile new file mode 100644 index 0000000..7d17199 --- /dev/null +++ b/api/Dockerfile @@ -0,0 +1,52 @@ +# Multi-stage build for DocuBot AI Backend +FROM python:3.11-slim + +# Set working directory +WORKDIR /app + +# Install system dependencies including git, curl, and Docker CLI for MCP +RUN apt-get update && apt-get install -y --no-install-recommends \ + git \ + curl \ + build-essential \ + ca-certificates \ + gnupg \ + lsb-release \ + && curl -fsSL https://download.docker.com/linux/debian/gpg | gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg \ + && echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/debian $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null \ + && apt-get update \ + && apt-get install -y --no-install-recommends docker-ce-cli \ + && rm -rf /var/lib/apt/lists/* + +# Copy requirements first for better caching +COPY requirements.txt . + +# Install Python dependencies +RUN pip install --no-cache-dir -r requirements.txt + +# Copy application code +COPY . . + +# Create temp directory for repo cloning +RUN mkdir -p /app/tmp/repos + +# Create non-root user +RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app + +# Set environment variables +ENV PYTHONUNBUFFERED=1 +ENV PYTHONDONTWRITEBYTECODE=1 + +# Expose port +EXPOSE 5001 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \ + CMD curl -f http://localhost:5001/health || exit 1 + +# Switch to non-root user +USER appuser + +# Run the application +# Disable reload to prevent server restart when cloning repos into tmp/ +CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "5001"] diff --git a/api/agents/__init__.py b/api/agents/__init__.py new file mode 100644 index 0000000..978d749 --- /dev/null +++ b/api/agents/__init__.py @@ -0,0 +1,35 @@ +""" +LangChain Agents for DocuBot AI +Each agent is autonomous with its own tools and reasoning capabilities +""" + +# 10-agent simplified system (optimized for 8K context) +from .code_explorer_agent import run_code_explorer_agent +from .api_reference_agent import run_api_reference_agent +from .call_graph_agent import run_call_graph_agent +from .error_analysis_agent import run_error_analysis_agent +from .env_config_agent import run_env_config_agent +from .dependency_analyzer_agent import run_dependency_analyzer_agent +from .planner_agent import run_planner_agent +from .writer_agent_sectioned import run_writer_agent_sectioned +from .mermaid_agent import run_mermaid_agent +from .qa_validator_agent import run_qa_validator_agent + +# PR Agent for MCP +from .pr_agent_mcp import create_pr_with_mcp + +__all__ = [ + # Simplified micro-agents + "run_code_explorer_agent", + "run_api_reference_agent", + "run_call_graph_agent", + "run_error_analysis_agent", + "run_env_config_agent", + "run_dependency_analyzer_agent", + "run_planner_agent", + "run_writer_agent_sectioned", + "run_mermaid_agent", + "run_qa_validator_agent", + # MCP PR agent + "create_pr_with_mcp" +] diff --git a/api/agents/api_reference_agent.py b/api/agents/api_reference_agent.py new file mode 100644 index 0000000..9e5ce35 --- /dev/null +++ b/api/agents/api_reference_agent.py @@ -0,0 +1,154 @@ +""" +API Reference Agent - SIMPLIFIED for 8K context models + +Extracts API endpoints, HTTP methods, parameters, and response models from FastAPI/Flask codebases. + +Follows CodeExplorer proven pattern: +- ≤3 tools (minimal schema overhead) +- pattern_window strategy by default (detects @app.get, @router.post) +- Minimal prompt (~200 tokens) +- No inline metrics +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# Data Extraction Prompt - outputs JSON data for Mermaid agent, NOT markdown sections +API_REFERENCE_PROMPT = """You are the API Endpoint Data Extractor. Extract API endpoint information for diagram generation. + +**YOUR JOB:** +Use tools to find actual API endpoints and extract their data. DO NOT write markdown sections. + +**TOOLS:** +- find_entry_points() - find main server files (server.py, main.py, app.py) +- read_file(file_path) - read files (detects @app.get, @router.post, @app.route) +- list_directory(relative_path) - list files + +**WORKFLOW:** +1. find_entry_points() to locate server files +2. read_file() on server files to find route decorators +3. Extract: HTTP method, path, description from code + +**CRITICAL RULES:** +1. ONLY extract endpoints you actually find in code +2. DO NOT write markdown sections - only return structured data +3. DO NOT invent example endpoints +4. Use actual paths/methods from code + +**OUTPUT FORMAT (JSON data structure):** +```json +{ + "endpoints": [ + { + "method": "GET", + "path": "/", + "description": "Health check endpoint" + }, + { + "method": "POST", + "path": "/upload", + "description": "Upload file for processing" + } + ], + "endpoint_count": 2 +} +``` + +If NO endpoints found: +```json +{ + "endpoints": [], + "endpoint_count": 0 +} +``` + +Return ONLY the JSON object, nothing else. No markdown, no explanations. + +**Limit:** 20 tool calls.""" + + +async def run_api_reference_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified API Reference Agent - minimal context usage + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools only) + @tool + def list_directory(relative_path: str = ".") -> str: + """List directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with strategic sampling. Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use pattern_window strategy to detect FastAPI routes (@app.get, @router.post) + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="pattern_window") + + @tool + def find_entry_points() -> str: + """Find main entry point files (main.py, server.py, app.py). No args.""" + from tools.repo_tools import find_entry_points_tool + return find_entry_points_tool.func(repo_path=repo_path) + + tools = [list_directory, read_file, find_entry_points] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="APIReference") + + # Execute with callback + result = await agent.ainvoke( + {"messages": [ + ("system", API_REFERENCE_PROMPT), + ("user", "Extract API endpoint data as JSON. Start with find_entry_points().") + ]}, + config={ + "recursion_limit": 20, + "callbacks": [event_logger] + } + ) + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "APIReference", + **metrics + } + + except Exception as e: + logger.error(f"APIReference failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "APIReference" + } diff --git a/api/agents/call_graph_agent.py b/api/agents/call_graph_agent.py new file mode 100644 index 0000000..da2b2ba --- /dev/null +++ b/api/agents/call_graph_agent.py @@ -0,0 +1,157 @@ +""" +Call Graph Agent - Architecture Section Writer + +Writes the complete "## Architecture" section for README. + +Section writer pattern: +- ≤3 tools +- smart strategy (extracts function signatures, not full bodies) +- Outputs complete markdown section +- Also outputs structured call_graph data for Mermaid agent +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# Section Writer Prompt - outputs complete "## Architecture" section with explanation +CALL_GRAPH_PROMPT = """You are the Architecture Section Writer. Write the complete "## Architecture" README section with clear explanation. + +**YOUR JOB:** +Use tools to analyze code structure and function relationships, then write a clear Architecture section explaining how components work together. + +**TOOLS:** +- list_directory(relative_path) - list files +- read_file(file_path) - read file (uses smart strategy for signatures) +- analyze_code_structure(file_path) - get function/class list from Python files + +**WORKFLOW:** +1. list_directory() to find main code files +2. analyze_code_structure() to get quick overview of key files +3. read_file() on important files to understand flow +4. Identify: components, layers, data flow, key modules +5. Write the section with explanation + note about diagram + +**CRITICAL RULES:** +1. ONLY describe architecture you can see in the code +2. Start with 2-3 sentences explaining the overall architecture +3. Describe how components interact and data flows +4. Add note: "The architecture diagram below visualizes component relationships and data flow." +5. If minimal code found → output: "## Architecture\n\nMinimal codebase. Refer to source files for structure." +6. DO NOT invent components or layers not found in code + +**OUTPUT FORMAT (complete markdown section):** +``` +## Architecture + +[2-3 sentences explaining the overall architecture pattern, e.g., "The application follows a client-server architecture with a FastAPI backend and React frontend. The backend handles data processing and API endpoints, while the frontend provides the user interface. Data flows from user input through the API to backend services and back to the UI."] + +### Components + +**[Component Name]** (`path/to/file.py`) +- Purpose: [What it does based on code] +- Key functions: [Actual functions found] + +**[Another Component]** (`path/to/file.py`) +- Purpose: [What it does based on code] +- Key functions: [Actual functions found] + +### Data Flow + +1. User → [Entry point] receives [input] +2. [Entry point] → [Module/Service] processes data +3. [Service] → [Database/API] stores/retrieves information +4. Response flows back through the stack to user + +The architecture diagram below visualizes these component relationships and data flow. +``` + +Start your output with "## Architecture" heading. Include the diagram note at the end. + +**Limit:** 25 tool calls.""" + + +async def run_call_graph_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified Call Graph Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools only) + @tool + def list_directory(relative_path: str = ".") -> str: + """List directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with smart sampling (signatures only). Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use smart strategy: top + signatures + bottom (good for call graph) + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="smart") + + @tool + def analyze_code_structure(file_path: str) -> str: + """Analyze Python file structure (functions, classes). Args: file_path (str)""" + from tools.repo_tools import analyze_code_structure_tool + return analyze_code_structure_tool.func(repo_path=repo_path, file_path=file_path) + + tools = [list_directory, read_file, analyze_code_structure] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="CallGraph") + + # Execute agent + result = await agent.ainvoke( + {"messages": [ + ("system", CALL_GRAPH_PROMPT), + ("user", "Write the Architecture section. Start with list_directory().") + ]}, + config={ + "recursion_limit": 40, + "callbacks": [event_logger] + } + ) + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "CallGraph", + **metrics + } + + except Exception as e: + logger.error(f"CallGraph failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "CallGraph" + } diff --git a/api/agents/code_explorer_agent.py b/api/agents/code_explorer_agent.py new file mode 100644 index 0000000..c86ceda --- /dev/null +++ b/api/agents/code_explorer_agent.py @@ -0,0 +1,194 @@ +""" +Code Explorer Agent - Overview & Features Section Writer + +Writes the complete "## Project Overview" and "## Features" sections for README. + +Section writer pattern: +- ≤3 tools +- pattern_window strategy for quick file analysis +- Outputs TWO complete markdown sections +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# Section Writer Prompt - outputs THREE complete sections +EXPLORER_PROMPT = """You are the Overview, Features & User Interface Section Writer. Write THREE complete README sections. + +**YOUR JOB:** +Use tools to understand the project structure and purpose, then write Project Overview, Features, and User Interface sections. + +**TOOLS:** +- list_directory(relative_path) - list files in directories +- read_file(file_path) - read file with pattern detection +- detect_languages() - get programming languages used + +**WORKFLOW:** +1. list_directory('.') to see project structure +2. detect_languages() to understand tech stack +3. read_file() on key files (README.md if exists, main files) to understand purpose +4. Identify user-facing capabilities (not technical implementation) +5. Check for frontend directories (ui/, frontend/, client/, web/, src/) +6. Write ALL THREE sections + +**CRITICAL RULES - Project Overview:** +1. 1-3 sentences ONLY +2. Explain WHAT the project does and WHY it exists +3. NO tech stack lists, NO "Repository Information", NO "Primary Language" +4. Focus on user value, not implementation details + +**CRITICAL RULES - Features:** +1. ONLY list user-facing capabilities you can confirm from code +2. NO API endpoints (POST /, GET /), NO routes, NO technical implementation +3. Features = what users CAN DO, not how it's built +4. Group by Backend/Frontend if applicable +5. If minimal code → output: "Basic [project type] structure." + +**CRITICAL RULES - User Interface:** +1. ONLY write if frontend directory exists (ui/, frontend/, client/, web/) +2. Describe frontend technology used (React, Vue, Angular, vanilla JS) +3. Key UI components or pages found +4. User workflow/experience +5. If NO frontend found → output: "No dedicated user interface. This is a backend/API project." + +**OUTPUT FORMAT (THREE complete markdown sections):** +``` +## Project Overview + +[1-3 sentences describing what this project does and why it's useful] + +## Features + +**Backend:** +- Feature 1: [Backend capability based on code found] +- Feature 2: [Another backend capability] + +**Frontend:** +- Feature 3: [User interface capability] +- Feature 4: [Another UI capability] + +## User Interface + +The frontend is built with [React/Vue/Angular/etc.] providing [description of UI]. + +Key interface elements: +- [Component/page 1] - [Purpose] +- [Component/page 2] - [Purpose] + +User workflow: +1. [Step 1 in user journey] +2. [Step 2 in user journey] +``` + +If NO frontend found: +``` +## User Interface + +No dedicated user interface. This is a backend/API project. Interact with the application through API endpoints or CLI commands. +``` + +If minimal/empty repo: +``` +## Project Overview + +Minimal repository with basic project structure. + +## Features + +Basic project scaffolding. Refer to source files for details. + +## User Interface + +No user interface implemented yet. +``` + +Start with "## Project Overview", then "## Features", then "## User Interface". + +**Limit:** 25 tool calls.""" + + +async def run_code_explorer_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified Code Explorer - minimal context usage + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools only) + @tool + def list_directory(relative_path: str = ".") -> str: + """List directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with strategic sampling. Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use pattern_window strategy to detect FastAPI routes, error handlers, etc. + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="pattern_window") + + @tool + def detect_languages() -> str: + """Detect languages. No args.""" + from tools.repo_tools import detect_languages_tool + return detect_languages_tool.func(repo_path=repo_path) + + tools = [list_directory, read_file, detect_languages] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger for ReAct visibility + event_logger = create_agent_logger(job_id=job_id, agent_name="CodeExplorer") + + # Execute with callback + result = await agent.ainvoke( + {"messages": [ + ("system", EXPLORER_PROMPT), + ("user", "Write the Project Overview, Features, and User Interface sections. Start with list_directory(relative_path='.').") + ]}, + config={ + "recursion_limit": 25, + "callbacks": [event_logger] + } + ) + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "CodeExplorer", + **metrics + } + + except Exception as e: + logger.error(f"CodeExplorer failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "CodeExplorer" + } diff --git a/api/agents/dependency_analyzer_agent.py b/api/agents/dependency_analyzer_agent.py new file mode 100644 index 0000000..fa9171f --- /dev/null +++ b/api/agents/dependency_analyzer_agent.py @@ -0,0 +1,181 @@ +""" +Dependency Analyzer Agent - Prerequisites & Deployment Section Writer + +Writes the complete "## Prerequisites" and "## Quick Start Deployment" sections for README. + +Section writer pattern: +- ≤3 tools +- full strategy for package files (they're small) +- Outputs TWO complete markdown sections +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import AIMessage +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + + +def _get_final_assistant_text(messages) -> str: + """ + Extract the last non-empty AIMessage content from LangGraph result. + + FIX: messages[-1] is not guaranteed to be the final assistant answer. + """ + for m in reversed(messages or []): + if isinstance(m, AIMessage) and isinstance(getattr(m, "content", None), str): + txt = m.content.strip() + if txt: + return txt + return (messages[-1].content or "").strip() if messages else "" + +# SIMPLIFIED Prompt for SLMs - NO templates, just instructions +def _build_dependency_prompt(repo_url: str) -> str: + """Build prompt with actual repository URL""" + return f"""You write Prerequisites and Deployment sections for README files. + +**REPOSITORY URL:** {repo_url} + +**YOUR TASK:** +1. First, call extract_dependencies() and list_directory(".") to see what's in the repo +2. Based on what you find, write TWO sections + +**SECTION 1: ## Prerequisites** +- List what needs to be installed (Python, Node.js, Docker) +- Only list if you found the files (requirements.txt = Python, package.json = Node, docker-compose.yml = Docker) +- Keep it short + +**SECTION 2: ## Quick Start Deployment** +This section has TWO subsections (use ### for subsections!): + +**Subsection A: ### Installation** +- Show how to clone the repository using the ACTUAL URL above +- Tell how to install dependencies you found +- Use actual paths (if found api/requirements.txt, write "cd api && pip install -r requirements.txt") + +**Subsection B: ### Running the Application** +- If docker-compose.yml exists, show docker command +- Otherwise, show how to run the entry point you find (server.py, app.py, main.py, etc.) + +**CRITICAL FORMATTING:** +- Main sections use ## (two hashtags) +- Subsections use ### (three hashtags) +- Installation and Running are SUBSECTIONS (use ###, NOT ##) +- Use the ACTUAL repository URL provided above, not placeholders + +**EXAMPLE STRUCTURE (not the content, just the heading pattern):** +## Prerequisites +...content... + +## Quick Start Deployment +### Installation +Clone the repository: +```bash +git clone {repo_url} +cd repo-name +``` +...other steps... +### Running the Application +...commands... + +Now analyze the repo and write these sections.""" + + +async def run_dependency_analyzer_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str, + repo_url: str = None +) -> Dict[str, Any]: + """ + Simplified Dependency Analyzer Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + repo_url: Repository URL (for clone instructions) + + Returns: + Results dict with success flag and output + """ + try: + # Use placeholder if repo_url not provided + if not repo_url: + repo_url = "https://github.com/yourusername/your-repo.git" + + # Create tool set with project structure analysis capabilities + @tool + def list_directory(relative_path: str = ".") -> str: + """List files and folders in a directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def find_dependency_files() -> str: + """Find dependency files (requirements.txt, package.json, etc.). No args.""" + from tools.repo_tools import find_dependency_files_tool + return find_dependency_files_tool.func(repo_path=repo_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with full content. Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use full strategy: package files are small + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="full") + + @tool + def extract_dependencies() -> str: + """Extract all dependencies from common package files. No args.""" + from tools.repo_tools import extract_dependencies_tool + return extract_dependencies_tool.func(repo_path=repo_path) + + tools = [list_directory, find_dependency_files, read_file, extract_dependencies] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="DependencyAnalyzer") + + # Build prompt with actual repo URL + prompt = _build_dependency_prompt(repo_url) + + # Execute agent + result = await agent.ainvoke( + {"messages": [ + ("system", prompt), + ("user", "Write the sections. Start: call extract_dependencies()") + ]}, + config={ + "recursion_limit": 12, + "callbacks": [event_logger] + } + ) + + # Extract output - FIX: Use helper to get last AIMessage + messages = result.get("messages", []) + final_output = _get_final_assistant_text(messages) + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "DependencyAnalyzer", + **metrics + } + + except Exception as e: + logger.error(f"DependencyAnalyzer failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "DependencyAnalyzer" + } diff --git a/api/agents/env_config_agent.py b/api/agents/env_config_agent.py new file mode 100644 index 0000000..3a294ec --- /dev/null +++ b/api/agents/env_config_agent.py @@ -0,0 +1,149 @@ +""" +Environment Config Agent - Configuration Section Writer + +Writes the complete "## Configuration" section for README. + +Section writer pattern: +- ≤3 tools +- full strategy (config files are small) +- Outputs complete markdown section +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import AIMessage +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + + +def _get_final_assistant_text(messages) -> str: + """ + Extract the last non-empty AIMessage content from LangGraph result. + + FIX: messages[-1] is not guaranteed to be the final assistant answer. + """ + for m in reversed(messages or []): + if isinstance(m, AIMessage) and isinstance(getattr(m, "content", None), str): + txt = m.content.strip() + if txt: + return txt + return (messages[-1].content or "").strip() if messages else "" + +# SIMPLIFIED for SLMs - No templates +ENV_CONFIG_PROMPT = """You write Configuration sections for README files. + +**YOUR TASK:** +1. Call find_config_files() to find .env or config files +2. If found, call read_file() on the config file to see variable names +3. Write "## Configuration" section documenting the variables + +**WHAT TO WRITE:** +- Start with heading: ## Configuration +- If you found config files, list the environment variables +- Group variables by category (Database, API, Server, etc.) +- Use actual variable names from the files +- Use placeholder values like or your_key_here +- If NO config files found, write: "## Configuration\n\nNo environment configuration files found." + +**HEADING FORMAT:** +- Use ## for main section: ## Configuration +- Use ### for subsection: ### Environment Variables + +**STRUCTURE EXAMPLE:** +## Configuration +### Environment Variables +Create a .env file: +```bash +DATABASE_URL= +API_KEY= +``` +**Required:** +- DATABASE_URL - Database connection +- API_KEY - API authentication + +Only document variables you actually find. Keep it simple.""" + + +async def run_env_config_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified Env Config Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools only) + @tool + def list_directory(relative_path: str = ".") -> str: + """List directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file(file_path: str) -> str: + """Read config file. Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use full strategy: config files are small, read everything + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="full") + + @tool + def find_config_files() -> str: + """Find configuration files (.env, config files). No args.""" + from tools.repo_tools import find_config_files_tool + return find_config_files_tool.func(repo_path=repo_path) + + tools = [list_directory, read_file, find_config_files] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="EnvConfig") + + # Execute agent + result = await agent.ainvoke( + {"messages": [ + ("system", ENV_CONFIG_PROMPT), + ("user", "Write the Configuration section. Call find_config_files()") + ]}, + config={ + "recursion_limit": 8, + "callbacks": [event_logger] + } + ) + + # Extract output - FIX: Use helper to get last AIMessage + messages = result.get("messages", []) + final_output = _get_final_assistant_text(messages) + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "EnvConfig", + **metrics + } + + except Exception as e: + logger.error(f"EnvConfig failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "EnvConfig" + } diff --git a/api/agents/error_analysis_agent.py b/api/agents/error_analysis_agent.py new file mode 100644 index 0000000..bc673aa --- /dev/null +++ b/api/agents/error_analysis_agent.py @@ -0,0 +1,135 @@ +""" +Error Analysis Agent - Troubleshooting Section Writer + +Writes the complete "## Troubleshooting" section for README. + +Section writer pattern: +- ≤3 tools +- pattern_window strategy (detects try:, except, raise) +- Outputs complete markdown section +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# SIMPLIFIED for SLMs - Generic troubleshooting +ERROR_ANALYSIS_PROMPT = """You write Troubleshooting sections for README files. + +**YOUR TASK:** +1. Optionally call list_directory(".") to see project type +2. Write "## Troubleshooting" section with GENERIC advice for common problems + +**WHAT TO WRITE:** +Write general troubleshooting guidance for these categories: +- Dependency Issues +- Environment Variables +- Server Errors +- (File/Upload Errors if project handles files) + +**HEADING FORMAT:** +- Main: ## Troubleshooting +- Subsections: ### Dependency Issues, ### Environment Variables, etc. + +**RULES:** +- Write GENERIC advice (not specific exception names) +- Keep it user-friendly and practical +- Include basic commands +- Mention checking logs + +**STRUCTURE EXAMPLE:** +## Troubleshooting +### Dependency Issues +...advice... +### Environment Variables +...advice... +### Server Errors +...advice... + +Do NOT copy this template. Write your own content based on project type.""" + + +async def run_error_analysis_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified Error Analysis Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools only) + @tool + def list_directory(relative_path: str = ".") -> str: + """List directory. Args: relative_path (str)""" + from tools.repo_tools import list_directory_tool + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with pattern matching (finds try/except/raise). Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use pattern_window: detects try:, except, raise patterns + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="pattern_window") + + @tool + def find_error_handlers() -> str: + """Find all try/except blocks across Python files. No args.""" + from tools.new_analysis_tools import find_error_handlers_tool + return find_error_handlers_tool.func(repo_path=repo_path) + + tools = [list_directory, read_file, find_error_handlers] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="ErrorAnalysis") + + # Execute agent + result = await agent.ainvoke( + {"messages": [ + ("system", ERROR_ANALYSIS_PROMPT), + ("user", "Write the Troubleshooting section with generic advice.") + ]}, + config={ + "recursion_limit": 10, + "callbacks": [event_logger] + } + ) + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "ErrorAnalysis", + **metrics + } + + except Exception as e: + logger.error(f"ErrorAnalysis failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "ErrorAnalysis" + } diff --git a/api/agents/mermaid_agent.py b/api/agents/mermaid_agent.py new file mode 100644 index 0000000..c0769e7 --- /dev/null +++ b/api/agents/mermaid_agent.py @@ -0,0 +1,285 @@ +""" +Mermaid Diagram Agent - SIMPLIFIED for 8K context models + +Generates Mermaid diagrams for architecture visualization. + +Follows proven pattern: +- ≤3 tools +- Includes validate_mermaid_syntax for self-correction +- Minimal prompt +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + + +def _fix_incomplete_mermaid(output: str) -> str: + """ + Strict validation and fixing of Mermaid diagrams. + Ensures proper structure and syntax for GPT model outputs. + """ + import re + + if not output: + return output + + # If no mermaid block at all, return as-is + if "```mermaid" not in output: + logger.warning("[Mermaid] No mermaid code block found in output") + return output + + fixed_output = output + + # Fix 1: Ensure all mermaid blocks have closing ``` + # Match mermaid blocks with or without closing ``` + pattern = r'(```mermaid\n)(.*?)(?:```|$)' + matches = list(re.finditer(pattern, output, re.DOTALL)) + + for match in matches: + full_match = match.group(0) + opening = match.group(1) + content = match.group(2) + + # Check if it has proper closing + if not full_match.endswith('```'): + # Add closing ``` + fixed_block = f"{opening}{content.rstrip()}\n```" + fixed_output = fixed_output.replace(full_match, fixed_block) + logger.info("[Mermaid] Fixed missing closing ``` for mermaid block") + + # Fix 2: Validate that diagram starts with proper declaration + mermaid_blocks = re.findall(r'```mermaid\n(.*?)```', fixed_output, re.DOTALL) + + for block in mermaid_blocks: + block_stripped = block.strip() + + # Check if it starts with graph/flowchart declaration + if not (block_stripped.startswith('graph ') or + block_stripped.startswith('flowchart ') or + block_stripped.startswith('sequenceDiagram') or + block_stripped.startswith('classDiagram')): + logger.warning(f"[Mermaid] Block missing proper diagram declaration: {block_stripped[:50]}") + + # Try to prepend graph TD if it looks like node definitions + if '-->' in block_stripped or '---' in block_stripped: + fixed_block = f"graph TD\n {block_stripped}" + fixed_output = fixed_output.replace(f"```mermaid\n{block}", f"```mermaid\n{fixed_block}") + logger.info("[Mermaid] Added 'graph TD' declaration to block") + + # Fix 3: Remove any extra text before/after mermaid blocks within the code fence + # (GPT sometimes adds explanatory text inside the code block) + mermaid_blocks = re.findall(r'```mermaid\n(.*?)```', fixed_output, re.DOTALL) + + for block in mermaid_blocks: + lines = block.strip().split('\n') + + # Keep only valid mermaid lines + valid_lines = [] + started = False + + for line in lines: + line_stripped = line.strip() + + # Check if line looks like valid mermaid syntax + if line_stripped.startswith(('graph ', 'flowchart ', 'sequenceDiagram', 'classDiagram')): + started = True + valid_lines.append(line) + elif started and (line_stripped == '' or + '-->' in line_stripped or + '---' in line_stripped or + line_stripped.endswith(']') or + line_stripped.endswith(')') or + line_stripped.endswith('"')): + valid_lines.append(line) + elif not started: + # Skip preamble text + continue + + if len(valid_lines) < len(lines): + cleaned_block = '\n'.join(valid_lines) + fixed_output = fixed_output.replace(f"```mermaid\n{block}", f"```mermaid\n{cleaned_block}\n") + logger.info("[Mermaid] Removed non-mermaid text from code block") + + return fixed_output + + +# MINIMAL system prompt +MERMAID_PROMPT = """You are a Mermaid Diagram Generator. Create COMPLETE architecture diagrams. + +**CRITICAL RULES - READ CAREFULLY:** +1. ALWAYS enclose the architecture diagram in proper mermaid syntax: ```mermaid ... ``` +2. NEVER output incomplete diagrams +3. ALWAYS include the closing ``` tag +4. Output ONLY valid Mermaid syntax inside the code block +5. NO explanatory text inside the ```mermaid ... ``` block + +**Output:** ONE simple, clean Mermaid diagram: +- Focus on main components only (5-8 key nodes maximum) +- Show primary data flow (avoid creating too many edges) +- Keep it SIMPLE and READABLE + +**Tools:** +- find_entry_points() - find main files +- read_file(path) - read file (smart strategy for structure) +- validate_mermaid_syntax(code) - validate diagram + +**Mermaid Syntax Rules:** +- Start with: graph TD (top-down layout for clarity) +- Node IDs: alphanumeric + underscore only (no spaces, slashes) +- Node labels: use quotes for multi-word: NodeID["Label Text"] +- Edge labels: keep them short, avoid special chars +- LIMIT edges: Only show main data flow (avoid creating too many arrows) +- Keep diagram SIMPLE: 5-8 nodes maximum for clarity +- Close with ``` after the diagram + +**Example (COMPLETE and CORRECT):** +```mermaid +graph TD + User["User"] + API["API Server"] + DB["Database"] + User -->|Request| API + API -->|Query| DB +``` + +**Process:** +1. Find entry points +2. Read key files to understand architecture +3. Identify 5-8 MAIN components only (ignore minor details) +4. Generate SIMPLE diagram: + - Start with ```mermaid + - Add graph TD + - Define main nodes only + - Connect with PRIMARY data flow only (avoid complex routing) + - Close with ``` +5. VALIDATE with validate_mermaid_syntax() +6. Fix errors if validation fails + +**IMPORTANT:** Prioritize SIMPLICITY over completeness. A clean 6-node diagram is better than a messy 15-node diagram. + +**FINAL VALIDATION:** +Before finishing, ALWAYS ensure: +- Diagram is enclosed in ```mermaid ... ``` +- Has proper graph TD or flowchart TD declaration +- All nodes are properly defined +- All edges use correct syntax +- Closing ``` is present + +**Limit:** 20 tool calls.""" + + +async def run_mermaid_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str, + api_endpoints: list = None +) -> Dict[str, Any]: + """ + Simplified Mermaid Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + api_endpoints: Optional list of API endpoints extracted by API Reference agent + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 tools) + @tool + def find_entry_points() -> str: + """Find main entry point files. No args.""" + from tools.repo_tools import find_entry_points_tool + return find_entry_points_tool.func(repo_path=repo_path) + + @tool + def read_file(file_path: str) -> str: + """Read file with smart sampling. Args: file_path (str)""" + from tools.repo_tools import read_file_tool + # Use smart strategy: signatures only (good for architecture) + return read_file_tool.func(repo_path=repo_path, file_path=file_path, strategy="smart") + + @tool + def validate_mermaid_syntax(mermaid_code: str) -> str: + """Validate Mermaid diagram syntax. Args: mermaid_code (str)""" + from tools.repo_tools import validate_mermaid_syntax_tool + return validate_mermaid_syntax_tool.func(mermaid_code=mermaid_code) + + tools = [find_entry_points, read_file, validate_mermaid_syntax] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="Mermaid") + + # Build user message with optional API endpoints + user_message = "Generate Mermaid architecture diagram. Start with find_entry_points()." + + if api_endpoints and len(api_endpoints) > 0: + user_message += f"\n\nAPI Endpoints available (include these in diagram if relevant):\n" + for ep in api_endpoints[:10]: # Limit to 10 to avoid context overflow + method = ep.get("method", "GET") + path = ep.get("path", "/") + desc = ep.get("description", "") + user_message += f"- {method} {path}: {desc}\n" + + # Execute agent + import asyncio + try: + result = await asyncio.wait_for( + agent.ainvoke( + {"messages": [ + ("system", MERMAID_PROMPT), + ("user", user_message) + ]}, + config={ + "recursion_limit": 25, + "callbacks": [event_logger] + } + ), + timeout=300 + ) + except asyncio.TimeoutError: + logger.error(f"[Mermaid] Timeout after 300 seconds - forcing minimal output") + return { + "success": True, + "output": "", + "agent": "Mermaid", + "total_duration_ms": 300000, + "timeout_occurred": True + } + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Post-process: Fix incomplete Mermaid diagrams (common issue with GPT models) + final_output = _fix_incomplete_mermaid(final_output) + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "Mermaid", + **metrics + } + + except Exception as e: + logger.error(f"Mermaid failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "Mermaid" + } diff --git a/api/agents/planner_agent.py b/api/agents/planner_agent.py new file mode 100644 index 0000000..09be262 --- /dev/null +++ b/api/agents/planner_agent.py @@ -0,0 +1,150 @@ +""" +Planner Agent - SIMPLIFIED for 8K context models + +Analyzes project type and plans documentation sections. + +Follows proven pattern: +- ≤3 tools +- full strategy for package files (they're small) +- Minimal prompt +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# MINIMAL system prompt +PLANNER_PROMPT = """You are a Documentation Planner. Determine project type and plan sections. + +**Task:** Analyze project and output recommended README sections. + +**Tools:** +- detect_languages() - get language breakdown +- extract_dependencies() - get dependencies +- find_ui_files() - check for frontend + +**SECTION TEMPLATE ** +Always include base sections: "Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting" +Add conditionally: +- "User Interface" - if find_ui_files() returns has_ui=true +- "Configuration" - if project has .env or config files + +**Output:** JSON with: +```json +{ + "project_type": "fastapi-backend" (or "web app", "CLI tool", etc.), + "sections": ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "User Interface", "Configuration", "Troubleshooting"] +} +``` + +**Limit:** 8 tool calls.""" + + +async def run_planner_agent( + llm: BaseChatModel, + repo_path: str, + job_id: str +) -> Dict[str, Any]: + """ + Simplified Planner Agent + + Args: + llm: Language model + repo_path: Repository path + job_id: Job ID + + Returns: + Results dict with success flag and output + """ + try: + # Create minimal tool set (3 core tools + 2 optional) + @tool + def detect_languages() -> str: + """Detect programming languages. No args.""" + from tools.repo_tools import detect_languages_tool + return detect_languages_tool.func(repo_path=repo_path) + + @tool + def extract_dependencies() -> str: + """Extract dependencies from package files. No args.""" + from tools.repo_tools import extract_dependencies_tool + return extract_dependencies_tool.func(repo_path=repo_path) + + @tool + def find_ui_files() -> str: + """Check if project has UI/frontend. No args.""" + from tools.repo_tools import find_ui_files_tool + return find_ui_files_tool.func(repo_path=repo_path) + + tools = [detect_languages, extract_dependencies, find_ui_files] + + # Create agent + agent = create_react_agent(model=llm, tools=tools) + + # Create callback logger + event_logger = create_agent_logger(job_id=job_id, agent_name="Planner") + + # Execute agent + result = await agent.ainvoke( + {"messages": [ + ("system", PLANNER_PROMPT), + ("user", "Analyze project type and plan README sections using template. Start with detect_languages().") + ]}, + config={ + "recursion_limit": 8, + "callbacks": [event_logger] + } + ) + + # Extract output + messages = result.get("messages", []) + final_output = messages[-1].content if messages else "" + + # Parse and validate output has correct section names + import json + try: + # Try to extract JSON + content = final_output.strip() + if '```json' in content: + content = content.split('```json')[1].split('```')[0] + elif '```' in content: + content = content.split('```')[1].split('```')[0] + + plan = json.loads(content.strip()) + + # Ensure sections match template + if "sections" not in plan or not plan["sections"]: + plan["sections"] = ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting"] + + final_output = json.dumps(plan, indent=2) + except: + # Fallback to default sections + plan = { + "project_type": "Unknown", + "sections": ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting"] + } + final_output = json.dumps(plan, indent=2) + + # Extract metrics from messages + metrics = extract_agent_metrics(messages) + + return { + "success": True, + "output": final_output, + "agent": "Planner", + **metrics + } + + except Exception as e: + logger.error(f"Planner failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "Planner" + } diff --git a/api/agents/pr_agent_mcp.py b/api/agents/pr_agent_mcp.py new file mode 100644 index 0000000..0514a27 --- /dev/null +++ b/api/agents/pr_agent_mcp.py @@ -0,0 +1,386 @@ +""" +PR Agent with TRUE MCP Integration +Uses official GitHub MCP server via MCP protocol (not direct API calls) +With full ReAct-style logging (Thought → Action → Observation) +""" + +import logging +import re +import json +from typing import Dict, Any +from datetime import datetime + +from models import get_log_manager, LogType +from mcp_client import get_github_mcp_client + +logger = logging.getLogger(__name__) + + +async def create_pr_with_mcp( + repo_full_name: str, + readme_content: str, + project_name: str, + base_branch: str, + github_token: str, + job_id: str +) -> Dict[str, Any]: + """ + Create GitHub PR using TRUE MCP protocol with ReAct logging + + This function: + 1. Connects to GitHub MCP server (Docker) + 2. Discovers available tools via MCP + 3. Calls tools to create branch, commit, and PR + 4. Uses stdio protocol (not direct API) + 5. Logs full ReAct cycle (Thought → Action → Observation) + + Args: + repo_full_name: Repository "owner/repo" + readme_content: Generated README content + project_name: Project name for PR title + base_branch: Base branch to create PR against (e.g., "main" or "dev") + github_token: GitHub Personal Access Token + job_id: Job ID for logging + + Returns: + Dict with success status, PR URL, and details + """ + log_manager = get_log_manager() + + try: + # Log start + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_START, + message="PR Agent initialized with MCP protocol", + agent_name="PR Agent (MCP)" + ) + + # 💭 Thought: Planning to connect + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_THINKING, + message=f"💭 Thought: I need to connect to GitHub MCP server for {repo_full_name}", + agent_name="PR Agent (MCP)" + ) + + # Get MCP client + mcp_client = get_github_mcp_client(github_token) + + # Connect to GitHub MCP server (Docker container via stdio) + async with mcp_client.connect() as session: + # 💭 Thought: Discovering tools + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_THINKING, + message="💭 Thought: Connected to MCP server. I need to discover available GitHub tools", + agent_name="PR Agent (MCP)" + ) + + # List available tools + tools = await mcp_client.list_available_tools() + logger.info(f"Available MCP tools: {tools}") + + # 📊 Observation: Tools discovered + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Found {len(tools)} GitHub MCP tools including: {', '.join(tools[:5])}...", + agent_name="PR Agent (MCP)", + metadata={"tools_count": len(tools), "tools": tools} + ) + + # Generate unique branch name with project name + timestamp = datetime.now().strftime("%Y%m%d-%H%M%S") + # Create slug from project name (lowercase, replace spaces/special chars with hyphens) + project_slug = re.sub(r'[^a-z0-9]+', '-', project_name.lower()).strip('-') + branch_name = f"docs/{project_slug}-readme-{timestamp}" + + # Parse repo owner and name + owner, repo = repo_full_name.split("/") + + # Extract actual project name from README title (H1 or H2) + readme_title = project_name # fallback to provided name + lines = readme_content.split('\n') + for line in lines[:15]: # Check first 15 lines for title + # Look for H1 (# Title) or H2 (## Title) + if line.startswith('# ') and not line.startswith('## '): + readme_title = line[2:].strip() + break + elif line.startswith('## '): + # H2 title found - extract it + readme_title = line[3:].strip() + break + + # === STEP 1: Create Branch === + + # 💭 Thought: Need to create branch + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_THINKING, + message=f"💭 Thought: I need to create a new branch '{branch_name}' from {base_branch}", + agent_name="PR Agent (MCP)" + ) + + # 🔧 Action: Create branch + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_ACTION, + message=f"🔧 Action: create_branch(owner={owner}, repo={repo}, branch={branch_name}, from_branch={base_branch})", + agent_name="PR Agent (MCP)", + metadata={"tool": "create_branch", "params": {"owner": owner, "repo": repo, "branch": branch_name, "from_branch": base_branch}} + ) + + try: + branch_result = await mcp_client.call_tool( + "create_branch", + { + "owner": owner, + "repo": repo, + "branch": branch_name, + "from_branch": base_branch + } + ) + logger.info(f"Branch creation result: {branch_result}") + + # 📊 Observation: Branch created + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Branch '{branch_name}' created successfully from {base_branch}", + agent_name="PR Agent (MCP)", + metadata={"branch_created": True, "branch_name": branch_name} + ) + except Exception as e: + logger.warning(f"Branch creation via MCP failed: {e}") + # 📊 Observation: Branch might exist + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Branch creation failed ({str(e)}). Branch may already exist - continuing anyway", + agent_name="PR Agent (MCP)", + metadata={"branch_created": False, "error": str(e)} + ) + + # === STEP 2: Commit README.md using push_files (FOOLPROOF - No SHA needed) === + + # 💭 Thought: Need to commit README + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_THINKING, + message=f"💭 Thought: I need to commit README.md to branch '{branch_name}' using push_files (no SHA required)", + agent_name="PR Agent (MCP)" + ) + + # Use push_files tool (more reliable, no SHA needed) + push_params = { + "owner": owner, + "repo": repo, + "branch": branch_name, + "files": [ + { + "path": "README.md", + "content": readme_content + } + ], + "message": f"docs: Updated README for {readme_title}" + } + + logger.info(f"Using push_files to commit README.md to branch '{branch_name}'") + + # 🔧 Action: Commit file using push_files + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_ACTION, + message=f"🔧 Action: push_files(owner={owner}, repo={repo}, branch={branch_name}, files=[README.md])", + agent_name="PR Agent (MCP)", + metadata={"tool": "push_files", "params": push_params, "content_length": len(readme_content)} + ) + + commit_success = False + try: + commit_result = await mcp_client.call_tool( + "push_files", + push_params + ) + logger.info(f"Commit result: {commit_result}") + + # Check if commit was successful + if commit_result and not (hasattr(commit_result, 'isError') and commit_result.isError): + commit_success = True + # 📊 Observation: File committed + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: README.md committed successfully to branch '{branch_name}'", + agent_name="PR Agent (MCP)", + metadata={"file_committed": True} + ) + else: + error_text = str(commit_result.content[0].text if hasattr(commit_result, 'content') else commit_result) + logger.error(f"File commit returned error: {error_text}") + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Failed to commit README.md - {error_text[:200]}", + agent_name="PR Agent (MCP)", + metadata={"file_committed": False, "error": error_text} + ) + raise Exception(f"push_files failed: {error_text}") + + except Exception as e: + logger.error(f"File commit failed: {e}") + # 📊 Observation: Commit failed + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Failed to commit README.md - {str(e)}", + agent_name="PR Agent (MCP)", + metadata={"file_committed": False, "error": str(e)} + ) + raise + + # === STEP 3: Create Pull Request === + + # 💭 Thought: Ready to create PR + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_THINKING, + message=f"💭 Thought: README.md is committed. Now I need to create a pull request from '{branch_name}' to '{base_branch}'", + agent_name="PR Agent (MCP)" + ) + + pr_body = f"""## Summary +This PR adds comprehensive AI-generated documentation for **{readme_title}**. + +## What's Included +- **Project Overview** - High-level description and key features +- **Architecture** - System design with Mermaid diagrams +- **Installation Guide** - Step-by-step setup instructions with actual repository URLs +- **Configuration** - Environment variables and settings documentation +- **Deployment** - Quick start guide with Docker/manual deployment options +- **Troubleshooting** - Common issues and solutions +- **API Documentation** - Endpoints and usage examples (if applicable) + +## About This Documentation +This README was automatically generated by **DocuBot**, an advanced AI system that uses specialized agents to analyze your repository: + +--- +*Generated by DocuBot AI-powered documentation with specialized micro-agent system* +- Agents Used: Code Explorer, API Reference, Call Graph Analyzer, Environment Config, Dependency Analyzer, Error Analysis, Planner, Mermaid Generator, QA Validator +- Integration: Model Context Protocol (MCP) for GitHub""" + + # 🔧 Action: Create PR + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_ACTION, + message=f"🔧 Action: create_pull_request(owner={owner}, repo={repo}, title='docs: Updated README for {readme_title}', head={branch_name}, base={base_branch})", + agent_name="PR Agent (MCP)", + metadata={"tool": "create_pull_request", "params": {"owner": owner, "repo": repo, "head": branch_name, "base": base_branch}} + ) + + pr_url = None + try: + pr_result = await mcp_client.call_tool( + "create_pull_request", + { + "owner": owner, + "repo": repo, + "title": f"docs: Updated README for {readme_title}", + "body": pr_body, + "head": branch_name, + "base": base_branch + } + ) + + logger.info(f"PR creation result: {pr_result}") + logger.info(f"PR result type: {type(pr_result)}") + + # Check if PR creation failed + if hasattr(pr_result, 'isError') and pr_result.isError: + error_text = str(pr_result.content[0].text if hasattr(pr_result, 'content') else pr_result) + logger.error(f"PR creation returned error: {error_text}") + raise Exception(f"create_pull_request failed: {error_text}") + + # Extract PR URL from result + if hasattr(pr_result, 'content') and pr_result.content: + if isinstance(pr_result.content, list) and len(pr_result.content) > 0: + result_text = str(pr_result.content[0].text if hasattr(pr_result.content[0], 'text') else pr_result.content[0]) + logger.info(f"Extracted result_text: {result_text[:500]}") + else: + result_text = str(pr_result.content) + logger.info(f"Result content (non-list): {result_text[:500]}") + + # Try multiple methods to extract URL + # Method 1: Parse as JSON + try: + data = json.loads(result_text) + pr_url = data.get("html_url") + logger.info(f"Extracted PR URL from JSON: {pr_url}") + except: + logger.info("Could not parse result as JSON, trying regex") + # Method 2: Regex search for GitHub PR URL + url_match = re.search(r'https://github\.com/[^\s"\'<>]+/pull/\d+', result_text) + if url_match: + pr_url = url_match.group(0) + logger.info(f"Extracted PR URL from regex: {pr_url}") + else: + # Method 3: Construct URL from owner/repo if PR number is available + pr_number_match = re.search(r'"number":\s*(\d+)', result_text) + if pr_number_match: + pr_number = pr_number_match.group(1) + pr_url = f"https://github.com/{owner}/{repo}/pull/{pr_number}" + logger.info(f"Constructed PR URL from number: {pr_url}") + + # 📊 Observation: PR created + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Pull request created successfully! URL: {pr_url or 'Check GitHub'}", + agent_name="PR Agent (MCP)", + metadata={"pr_created": True, "pr_url": pr_url} + ) + + except Exception as e: + logger.error(f"PR creation failed: {e}") + # 📊 Observation: PR creation failed + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation: Failed to create pull request - {str(e)}", + agent_name="PR Agent (MCP)", + metadata={"pr_created": False, "error": str(e)} + ) + raise + + # Log completion + await log_manager.log_async( + job_id=job_id, + log_type=LogType.AGENT_COMPLETE, + message=f"PR workflow complete! PR created at: {pr_url or 'Check GitHub'}", + agent_name="PR Agent (MCP)", + metadata={"pr_url": pr_url, "branch": branch_name} + ) + + return { + "success": True, + "pr_url": pr_url, + "branch_name": branch_name, + "output": f"Successfully created PR via MCP protocol: {pr_url or 'See GitHub'}" + } + + except Exception as e: + logger.error(f"MCP PR creation failed: {e}", exc_info=True) + await log_manager.log_async( + job_id=job_id, + log_type=LogType.ERROR, + message=f"MCP PR Agent error: {str(e)}", + agent_name="PR Agent (MCP)" + ) + + return { + "success": False, + "pr_url": None, + "branch_name": None, + "output": f"Failed to create PR via MCP: {str(e)}" + } diff --git a/api/agents/qa_validator_agent.py b/api/agents/qa_validator_agent.py new file mode 100644 index 0000000..d563dab --- /dev/null +++ b/api/agents/qa_validator_agent.py @@ -0,0 +1,227 @@ +""" +QA Validator Agent - SIMPLIFIED for 8K context models + +Validates README quality, completeness, and structure. + +KEY DIFFERENCE: QA Validator does NOT have file reading tools. +It receives the final_readme from state and validates it. + +Follows proven pattern: +- NO file reading tools (works from final_readme input) +- Minimal prompt +- Validates output quality +""" + +import logging +from typing import Dict, Any +from langgraph.prebuilt import create_react_agent +from langchain_core.language_models import BaseChatModel +from langchain.tools import tool +from core.agent_event_logger import create_agent_logger +from utils.metrics_extractor import extract_agent_metrics + +logger = logging.getLogger(__name__) + +# MINIMAL system prompt +QA_VALIDATOR_PROMPT = """You are a README Quality Validator. Validate documentation quality. + +**Input:** You'll receive a generated README markdown. + +**Task:** Evaluate the README on these criteria: +1. **Completeness** - Are all expected sections present? (Overview, Installation, Usage, etc.) +2. **Clarity** - Is the content clear and well-written? +3. **Structure** - Proper markdown formatting, headings hierarchy +4. **Code Examples** - Are code blocks properly formatted? +5. **Mermaid Diagrams** - If present, are they valid? + +**Output:** JSON validation report: +```json +{ + "qa_score": 85, + "qa_passed": true, + "issues": [ + {"severity": "warning", "message": "Installation section is brief"}, + {"severity": "error", "message": "No usage examples provided"} + ], + "recommendations": ["Add more usage examples", "Include configuration details"] +} +``` + +**Tools:** +- validate_readme_structure(readme) - check structure and completeness +- validate_mermaid_diagrams(readme) - extract and validate Mermaid diagrams + +**Scoring:** +- 90-100: Excellent +- 75-89: Good +- 60-74: Acceptable (needs minor improvements) +- <60: Poor (needs major improvements) + +**Pass Threshold:** 60 + +**Limit:** 5 tool calls.""" + + +async def run_qa_validator_agent( + llm: BaseChatModel, + readme_sections: dict, + job_id: str, + evidence_packet=None +) -> Dict[str, Any]: + """ + Enhanced QA Validator - Validates sections with evidence-based guardrails + + Checks if expected sections are present, non-empty, AND don't contain + forbidden phrases without evidence. + + Args: + llm: Language model (not used in fast mode) + readme_sections: Dict of section name -> content + job_id: Job ID + evidence_packet: EvidencePacket for checking forbidden phrases + + Returns: + Results dict with success flag and validation report + """ + try: + import json + import time + + start_time = time.time() + + # Expected sections + EXPECTED_SECTIONS = [ + "Project Overview", + "Features", + "Architecture", + "Prerequisites", + "Quick Start Deployment", + "Configuration", + "Troubleshooting" + ] + + issues = [] + score = 100 + missing_sections = [] + empty_sections = [] + forbidden_violations = [] + + # Check for missing sections + for section in EXPECTED_SECTIONS: + if section not in readme_sections: + missing_sections.append(section) + issues.append({"severity": "warning", "message": f"Missing section: {section}"}) + score -= 5 + elif len(readme_sections[section].strip()) < 50: + empty_sections.append(section) + issues.append({"severity": "error", "message": f"Section too short or empty: {section}"}) + score -= 10 + + # Check for code blocks in deployment section + if "Quick Start Deployment" in readme_sections: + if '```' not in readme_sections["Quick Start Deployment"]: + issues.append({"severity": "warning", "message": "Quick Start Deployment lacks code examples"}) + score -= 5 + + # Check for architecture diagram mention + if "Architecture" in readme_sections: + if "diagram" not in readme_sections["Architecture"].lower(): + issues.append({"severity": "info", "message": "Architecture section should mention diagram"}) + + # === NEW: Evidence-Based Forbidden Phrase Checks (FIX 3 applied) === + if evidence_packet: + # Combine all section content for checking + full_content = "\n".join(readme_sections.values()) + + # Define forbidden checks (removed weak port checks per FIX 3) + forbidden_checks = [ + { + "phrase": "npm", + "evidence_required": lambda e: len(e.node_deps) > 0, + "message": "Claims 'npm' commands but no package.json found" + }, + { + "phrase": "docker-compose", + "evidence_required": lambda e: e.has_docker and "docker-compose.yml" in e.docker_files, + "message": "Claims 'docker-compose' but no docker-compose.yml found" + }, + { + "phrase": "Dockerfile", + "evidence_required": lambda e: e.has_docker and "Dockerfile" in e.docker_files, + "message": "Claims 'Dockerfile' but no Dockerfile found" + }, + { + "phrase": "Whisper", + "evidence_required": lambda e: any("whisper" in dep.lower() for dep in e.python_deps), + "message": "Claims 'Whisper' without dependency evidence" + }, + { + "phrase": "Keycloak", + "evidence_required": lambda e: any("keycloak" in dep.lower() for dep in (e.python_deps + e.node_deps)), + "message": "Claims 'Keycloak' without dependency evidence" + }, + { + "phrase": "React", + "evidence_required": lambda e: "react" in [d.lower() for d in e.node_deps] or e.frontend_framework == "React", + "message": "Claims 'React' without evidence" + }, + { + "phrase": "Vue", + "evidence_required": lambda e: "vue" in [d.lower() for d in e.node_deps] or e.frontend_framework == "Vue", + "message": "Claims 'Vue' without evidence" + } + ] + + # Check each forbidden phrase + for check in forbidden_checks: + phrase = check["phrase"] + if phrase in full_content: + # Check if evidence exists + if not check["evidence_required"](evidence_packet): + forbidden_violations.append(check["message"]) + issues.append({ + "severity": "error", + "message": f"HALLUCINATION: {check['message']}" + }) + score -= 15 # Heavy penalty for hallucinations + + if forbidden_violations: + logger.warning(f"[QA] Found {len(forbidden_violations)} forbidden phrase violations") + + # Build validation report + qa_report = { + "qa_score": max(score, 0), # Allow scores below 60 to show severity + "qa_passed": score >= 60, + "missing_sections": missing_sections, + "empty_sections": empty_sections, + "forbidden_violations": forbidden_violations, + "issues": issues, + "recommendations": [] if score >= 90 else [ + "Ensure all sections have meaningful content", + "Add code examples to deployment instructions", + "Remove hallucinated commands/technologies without evidence" + ] + } + + output_json = json.dumps(qa_report, indent=2) + + # Calculate execution time + end_time = time.time() + duration_ms = (end_time - start_time) * 1000 + + metrics = extract_agent_metrics([]) + + return { + "success": True, + "output": output_json, + "agent": "QAValidator", + **metrics + } + + except Exception as e: + logger.error(f"QAValidator failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "QAValidator" + } diff --git a/api/agents/writer_agent_sectioned.py b/api/agents/writer_agent_sectioned.py new file mode 100644 index 0000000..8254b1e --- /dev/null +++ b/api/agents/writer_agent_sectioned.py @@ -0,0 +1,316 @@ +""" +Sectioned Writer Agent - TIMEOUT-RESISTANT DESIGN + +Generates README in multiple small calls instead of one large call. + +Architecture: +1. Generate outline (fast) +2. Generate sections one-by-one (small, bounded calls) +3. Stitch sections together + +This prevents 504 Gateway Timeouts by keeping each LLM call small and fast. +""" + +import logging +import json +from typing import Dict, Any, List +from langchain_core.language_models import BaseChatModel +from langchain_core.messages import SystemMessage, HumanMessage + +logger = logging.getLogger(__name__) + + +def build_structured_evidence(state: Dict[str, Any]) -> Dict[str, Any]: + """ + Convert raw agent outputs into compact structured evidence. + + This dramatically reduces tokens vs raw text concatenation. + """ + + # Parse code summary into bullets + code_summary = state.get("code_summary", "") + code_bullets = _extract_bullets(code_summary, max_bullets=10) + + # Parse API documentation + api_docs = state.get("api_documentation", "") + api_bullets = _extract_bullets(api_docs, max_bullets=8) + + # Parse dependencies + dependency_report = state.get("dependency_report", {}) + if isinstance(dependency_report, dict): + dep_output = dependency_report.get("output", "") + else: + dep_output = str(dependency_report) + dep_bullets = _extract_bullets(dep_output, max_bullets=6) + + # Parse env config + env_config = state.get("env_config_output", "") + env_bullets = _extract_bullets(env_config, max_bullets=5) + + # Parse call graph (already dict) + call_graph = state.get("call_graph", {}) + if isinstance(call_graph, dict) and "output" in call_graph: + call_graph_text = str(call_graph["output"])[:500] # Truncate + else: + call_graph_text = str(call_graph)[:500] + + # Parse error analysis + error_analysis = state.get("error_analysis", {}) + if isinstance(error_analysis, dict): + error_output = error_analysis.get("output", "") + else: + error_output = str(error_analysis) + error_bullets = _extract_bullets(error_output, max_bullets=5) + + # Project metadata + project_type = state.get("project_type", "Unknown") + repo_name = state.get("repo_name", "Project") + + # Build compact structured evidence + structured = { + "project_name": repo_name, + "project_type": project_type, + "overview": code_bullets[:3] if code_bullets else ["No overview available"], + "architecture": code_bullets[3:8] if len(code_bullets) > 3 else [], + "api_endpoints": api_bullets, + "dependencies": dep_bullets, + "environment": env_bullets, + "call_graph_summary": call_graph_text, + "error_handling": error_bullets + } + + return structured + + +def _extract_bullets(text: str, max_bullets: int = 10) -> List[str]: + """Extract bullet points or key sentences from text""" + if not text: + return [] + + bullets = [] + + # Try to find existing bullets (lines starting with -, *, •, or numbers) + lines = text.split('\n') + for line in lines: + line = line.strip() + if line and (line.startswith('-') or line.startswith('*') or + line.startswith('•') or (len(line) > 2 and line[0].isdigit() and line[1] in '.)')): + # Remove bullet marker + clean = line.lstrip('-*•0123456789.) ').strip() + if clean: + bullets.append(clean) + if len(bullets) >= max_bullets: + break + + # If no bullets found, extract first N sentences + if not bullets: + sentences = text.replace('\n', ' ').split('. ') + for sentence in sentences[:max_bullets]: + sentence = sentence.strip() + if sentence: + bullets.append(sentence) + + return bullets[:max_bullets] + + +async def run_writer_agent_sectioned( + llm: BaseChatModel, + state: Dict[str, Any], + job_id: str +) -> Dict[str, Any]: + """ + Sectioned Writer Agent - generates README in multiple small calls. + Uses planner's section list instead of generating own outline. + + Args: + llm: Language model + state: Full workflow state (we'll extract structured evidence) + job_id: Job ID + + Returns: + Results dict with sections dict (not a single readme string) + """ + import time + start_time = time.time() + + try: + model_name = getattr(llm, 'model_name', 'unknown') + + # Step 1: Build structured evidence (compact) + print(f"[Writer] Building structured evidence...") + evidence = build_structured_evidence(state) + evidence_json = json.dumps(evidence, indent=2) + + print(f"[Writer] Structured evidence: {len(evidence_json)} chars") + logger.info(f"[Writer] Structured evidence: {len(evidence_json)} chars") + + # Step 2: Get sections from planner (NO outline generation!) + planned_sections = state.get("documentation_sections", ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting"]) + print(f"[Writer] Using planner sections: {planned_sections}") + logger.info(f"[Writer] Using planner sections: {planned_sections}") + + # Step 3: Generate sections one-by-one (small, fast calls) + sections_dict = {} + all_responses = [] + for section_name in planned_sections: + logger.info(f"[Writer] Generating section: {section_name}") + section_content, response = await _generate_section(llm, section_name, evidence) + # Store with ## heading included + sections_dict[section_name] = f"## {section_name}\n\n{section_content}" + all_responses.append(response) + + logger.info(f"[Writer] Generated {len(sections_dict)} sections") + + # Calculate execution time + end_time = time.time() + duration_ms = (end_time - start_time) * 1000 + + # Extract metrics from all responses + from utils.metrics_extractor import extract_agent_metrics + # Convert responses to messages format for metrics extraction + messages = all_responses + metrics = extract_agent_metrics(messages) + + metrics["total_duration_ms"] = duration_ms + + # Return sections dict (NOT a single assembled readme) + # Assembly will happen in assembly_node + return { + "success": True, + "output": sections_dict, # Dict of section_name: content + "agent": "Writer", + **metrics + } + + except Exception as e: + logger.error(f"Writer failed: {e}") + return { + "success": False, + "error": str(e), + "agent": "Writer" + } + + +async def _generate_section( + llm: BaseChatModel, + section_name: str, + evidence: Dict[str, Any] +) -> tuple[str, Any]: + """ + Generate a single README section (small, fast call). + + Returns: Tuple of (markdown content, response object) + """ + + # Build section-specific evidence (only relevant data) + # Map section names to evidence + if section_name == "Project Overview": + context = f""" +Project: {evidence.get('project_name')} +Type: {evidence.get('project_type')} +Overview: {json.dumps(evidence.get('overview', []))} +Architecture: {json.dumps(evidence.get('architecture', [])[:3])} +""" + + elif section_name == "Features": + context = f""" +Architecture: {json.dumps(evidence.get('architecture', []))} +Key capabilities: {json.dumps(evidence.get('overview', []))} +API Endpoints: {json.dumps(evidence.get('api_endpoints', [])[:5])} +""" + + elif section_name == "Prerequisites": + context = f""" +Dependencies: {json.dumps(evidence.get('dependencies', []))} +Project type: {evidence.get('project_type')} +""" + + elif section_name == "Quick Start Deployment": + context = f""" +Dependencies: {json.dumps(evidence.get('dependencies', []))} +Environment variables: {json.dumps(evidence.get('environment', [])[:5])} +Project type: {evidence.get('project_type')} +""" + + elif section_name == "Configuration": + context = f""" +Environment variables: {json.dumps(evidence.get('environment', []))} +""" + + elif section_name == "User Interface": + context = f""" +Project type: {evidence.get('project_type')} +Overview: {json.dumps(evidence.get('overview', []))} +""" + + elif section_name == "Architecture": + context = f""" +Architecture: {json.dumps(evidence.get('architecture', []))} +Call graph: {evidence.get('call_graph_summary', '')} +API Endpoints: {json.dumps(evidence.get('api_endpoints', [])[:3])} +""" + + elif section_name == "Troubleshooting": + context = f""" +Error handling: {json.dumps(evidence.get('error_handling', []))} +Common issues: {json.dumps(evidence.get('overview', []))} +""" + + else: + # Fallback for any other section names + context = json.dumps(evidence, indent=2)[:1000] + + # Section-specific requirements + if section_name == "Prerequisites": + requirements = """List ONLY the actual dependencies and requirements found in the context. +Format as: +- Runtime/language versions (e.g., Python 3.8+, Node.js 16+) +- Required tools (Docker, npm, pip, etc.) +- System requirements +If no specific prerequisites found, state: "Standard development environment for [project_type]." +""" + elif section_name == "Quick Start Deployment": + requirements = """Provide deployment instructions based ONLY on evidence: +1. Installation steps (based on actual dependencies found) +2. Configuration steps (based on actual env variables found) +3. How to run the application (based on project type and entry points) +4. Docker commands if Docker files were found +If insufficient information, provide minimal: "Refer to dependency files and configuration for setup details." +""" + elif section_name == "Configuration": + requirements = """List ONLY the actual environment variables found. Format as: +- Variable name: Brief purpose +If no variables found, state: "No environment configuration required." +""" + else: + requirements = "Write based on the evidence provided. Be factual and concise." + + prompt = f"""Write the "{section_name}" section for a README based STRICTLY on the provided evidence. + +**CRITICAL RULES:** +1. ONLY use information from the Context below +2. If Context is empty or says "No X available", write a minimal 1-2 sentence section acknowledging what's missing +3. DO NOT invent features, files, commands, or technical details that aren't in the Context +4. DO NOT add placeholder examples or generic instructions +5. Be factual and concise - accuracy over completeness + +**Section-Specific Requirements:** +{requirements} + +Context: +{context} + +Write clear, concise markdown using ONLY the facts above. +If the context indicates the project is minimal/empty, reflect that accurately. +Keep it under 300 words. + +Section content:""" + + messages = [ + SystemMessage(content="You are a technical writer who ONLY writes based on provided evidence. Never invent or assume information. Be factual and concise."), + HumanMessage(content=prompt) + ] + + response = await llm.ainvoke(messages) + + return response.content.strip(), response diff --git a/api/config.py b/api/config.py new file mode 100644 index 0000000..dd6ac54 --- /dev/null +++ b/api/config.py @@ -0,0 +1,93 @@ +""" +Configuration management for DocuBot AI +Supports GenAI Gateway and Keycloak authentication +""" + +import os +from enum import Enum +from typing import Optional +from pydantic_settings import BaseSettings +from dotenv import load_dotenv + +load_dotenv() + + +class Settings(BaseSettings): + """Application settings with unified inference configuration""" + + # Application Info + APP_TITLE: str = "DocuBot - AI Documentation Generator" + APP_DESCRIPTION: str = "AI-powered documentation generation with specialized micro-agent system" + APP_VERSION: str = "1.0.0" + + # Server Configuration + API_PORT: int = 5001 + HOST: str = "0.0.0.0" + + # CORS Settings + CORS_ORIGINS: list = ["http://localhost:3000", "http://localhost:3001", "http://localhost:5173"] + + # ================================================================= + # LLM Provider Configuration (RECOMMENDED - Universal Support) + # ================================================================= + # Supports: openai, groq, ollama, openrouter, or custom + LLM_PROVIDER: str = "openai" + LLM_API_KEY: Optional[str] = None + LLM_BASE_URL: str = "https://api.openai.com/v1" + LLM_MODEL: str = "gpt-4o" # Default model (can be overridden per agent) + + # Generation Parameters + TEMPERATURE: float = 0.7 + MAX_TOKENS: int = 1000 + MAX_RETRIES: int = 3 + REQUEST_TIMEOUT: int = 300 # 5 minutes + + # Security Configuration + VERIFY_SSL: bool = True + + # ================================================================= + # Enterprise Inference Configuration + # ================================================================= + # Use these if you're connecting to enterprise inference endpoints + # If set, these will override LLM_PROVIDER settings above + INFERENCE_API_ENDPOINT: Optional[str] = None + INFERENCE_API_TOKEN: Optional[str] = None + + # Docker Network Configuration + LOCAL_URL_ENDPOINT: str = "not-needed" + + # Micro-Agent Model Configuration (Using SLM - Qwen3-4B) + CODE_EXPLORER_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + API_REFERENCE_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + CALL_GRAPH_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + ERROR_ANALYSIS_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + ENV_CONFIG_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + DEPENDENCY_ANALYZER_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + PLANNER_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + MERMAID_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + QA_VALIDATOR_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + WRITER_MODEL: str = "Qwen/Qwen3-4B-Instruct-2507" + + + # Repository Settings + TEMP_REPO_DIR: str = "./tmp/repos" + MAX_REPO_SIZE: int = 10737418240 # 10GB in bytes + MAX_FILE_SIZE: int = 1000000 # 1MB + MAX_FILES_TO_SCAN: int = 500 + MAX_LINES_PER_FILE: int = 500 # Line budget per file (pattern_window extracts ~150-300 lines) + + # GitHub Integration (for MCP PR creation) + GITHUB_TOKEN: Optional[str] = None + + # Agent Execution Settings + AGENT_TEMPERATURE: float = 0.7 + AGENT_MAX_TOKENS: int = 1000 + AGENT_TIMEOUT: int = 300 # 5 minutes + + class Config: + env_file = ".env" + case_sensitive = True + + +# Global settings instance +settings = Settings() diff --git a/api/core/__init__.py b/api/core/__init__.py new file mode 100644 index 0000000..c120ea3 --- /dev/null +++ b/api/core/__init__.py @@ -0,0 +1,13 @@ +""" +Core infrastructure for DocuBot +Provides metrics tracking and agent event logging +""" + +from .metrics_collector import MetricsCollector, AgentMetrics +from .agent_event_logger import create_agent_logger + +__all__ = [ + 'MetricsCollector', + 'AgentMetrics', + 'create_agent_logger' +] diff --git a/api/core/agent_event_logger.py b/api/core/agent_event_logger.py new file mode 100644 index 0000000..0636ecf --- /dev/null +++ b/api/core/agent_event_logger.py @@ -0,0 +1,336 @@ +""" +Shared Agent Event Logger - Callback Handler for ReAct Pattern Visibility + +Captures tool calls and LLM events from LangChain agents and streams them +to the UI via SSE for real-time "Thinking → Action → Observation" display. + +Works with both LangChain and LangGraph agents. +""" + +import logging +from typing import Any, Dict, List, Optional +from langchain_core.callbacks.base import AsyncCallbackHandler +from models import get_log_manager, LogType + +logger = logging.getLogger(__name__) + + +class AgentEventLogger(AsyncCallbackHandler): + """ + Callback handler that captures agent events and streams them to UI. + + Captures: + - Tool calls (Actions) + - Tool outputs (Observations) + - LLM starts (Thinking) + + Usage: + logger = AgentEventLogger(job_id="job_123", agent_name="CodeExplorer") + result = await agent.ainvoke(..., config={"callbacks": [logger]}) + """ + + def __init__(self, job_id: str, agent_name: str): + """ + Initialize the event logger + + Args: + job_id: Job ID for streaming logs + agent_name: Name of the agent (e.g., "CodeExplorer", "Writer") + """ + # Don't call super().__init__() to avoid creating locks in wrong event loop + # super().__init__() + self.job_id = job_id + self.agent_name = agent_name + self.log_manager = get_log_manager() + self.cycle_count = 0 + self.tool_call_count = 0 + self.current_tool = None + + # Debouncing flags to prevent duplicate logs + self._agent_started = False + self._agent_completed = False + + # Mark as async-capable + self.run_inline = False + + # Tool-specific thinking messages (context-aware) + self.thinking_templates = { + "list_directory": "📂 Examining directory structure...", + "read_file": "📖 Reading file to extract information...", + "detect_languages": "🔍 Analyzing programming languages...", + "extract_dependencies": "📦 Extracting project dependencies...", + "analyze_code_structure": "🏗️ Analyzing code structure and patterns...", + "find_entry_points": "🎯 Locating application entry points...", + "find_api_routes": "🌐 Discovering API routes and endpoints...", + "find_config_files": "⚙️ Finding configuration files...", + "validate_mermaid_syntax": "✅ Validating diagram syntax...", + "validate_readme_structure": "📋 Checking README structure...", + "default": "💭 Analyzing repository and planning next step..." + } + + async def on_tool_start( + self, + serialized: Dict[str, Any], + input_str: str, + **kwargs: Any + ) -> None: + """ + Called when a tool starts executing (Action step) + + Args: + serialized: Tool metadata + input_str: Tool input arguments + """ + self.cycle_count += 1 + self.tool_call_count += 1 + + # Extract tool name + tool_name = serialized.get("name", "unknown_tool") + self.current_tool = tool_name + + # Log thinking step before action + thinking_msg = self.thinking_templates.get(tool_name, self.thinking_templates["default"]) + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.AGENT_THINKING, + message=f"💭 Thinking (Cycle {self.cycle_count}): {thinking_msg}", + agent_name=self.agent_name, + metadata={ + "cycle": self.cycle_count, + "type": "thinking", + "next_tool": tool_name + } + ) + + # Format tool arguments for display + try: + # Try to parse input_str as it might be JSON or formatted string + args_display = input_str[:200] if input_str else "(no args)" + if len(input_str) > 200: + args_display += "..." + except: + args_display = "(args)" + + # Log action step + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.AGENT_ACTION, + message=f"🔧 Action (Cycle {self.cycle_count}): {tool_name}({args_display})", + agent_name=self.agent_name, + metadata={ + "cycle": self.cycle_count, + "type": "action", + "tool": tool_name, + "input": input_str, + "call_number": self.tool_call_count + } + ) + + async def on_tool_end( + self, + output: str, + **kwargs: Any + ) -> None: + """ + Called when a tool finishes (Observation step) + + Args: + output: Tool output/result + """ + tool_name = self.current_tool or "unknown" + + # Create intelligent summary of output + output_str = str(output) + output_lines = output_str.count('\n') + 1 + + # Smart truncation + if len(output_str) > 300: + output_preview = output_str[:300] + f"... [{len(output_str)} chars total, {output_lines} lines]" + else: + output_preview = output_str + + # Log observation step + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.AGENT_OBSERVATION, + message=f"📊 Observation (Cycle {self.cycle_count}): {tool_name} returned:\n{output_preview}", + agent_name=self.agent_name, + metadata={ + "cycle": self.cycle_count, + "type": "observation", + "tool": tool_name, + "output_length": len(output_str), + "line_count": output_lines + } + ) + + async def on_tool_error( + self, + error: Exception, + **kwargs: Any + ) -> None: + """ + Called when a tool encounters an error + + Args: + error: The exception that occurred + """ + tool_name = self.current_tool or "unknown" + + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.ERROR, + message=f"❌ Error (Cycle {self.cycle_count}): {tool_name} failed - {str(error)}", + agent_name=self.agent_name, + metadata={ + "cycle": self.cycle_count, + "type": "error", + "tool": tool_name, + "error": str(error) + } + ) + + async def on_llm_start( + self, + serialized: Dict[str, Any], + prompts: List[str], + **kwargs: Any + ) -> None: + """ + Called when LLM starts generating (optional - for detailed logging) + + Args: + serialized: LLM metadata + prompts: Input prompts + """ + # Optional: Log LLM reasoning start + # Only log if we want very detailed visibility + pass + + async def on_llm_end( + self, + response: Any, + **kwargs: Any + ) -> None: + """ + Called when LLM finishes generating + + Args: + response: LLM response + """ + # Optional: Log LLM completion + pass + + async def on_chain_start( + self, + serialized: Dict[str, Any], + inputs: Dict[str, Any], + **kwargs: Any + ) -> None: + """ + Called when agent chain starts + """ + # Debounce: Only log the first chain start + if self._agent_started: + return + + self._agent_started = True + + # Log agent start + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.AGENT_START, + message=f"🚀 Starting {self.agent_name} agent...", + agent_name=self.agent_name, + metadata={"type": "agent_start"} + ) + + async def on_chain_end( + self, + outputs: Dict[str, Any], + **kwargs: Any + ) -> None: + """ + Called when agent chain completes + """ + # Debounce: Only log the first chain end + if self._agent_completed: + return + + self._agent_completed = True + + # Log completion summary + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.INFO, + message=f"✅ Completed {self.cycle_count} ReAct cycle(s) with {self.tool_call_count} tool call(s)", + agent_name=self.agent_name, + metadata={ + "type": "agent_complete", + "total_cycles": self.cycle_count, + "total_tool_calls": self.tool_call_count + } + ) + + async def on_chain_error( + self, + error: Exception, + **kwargs: Any + ) -> None: + """ + Called when agent chain encounters an error + + Args: + error: The exception that occurred + """ + await self.log_manager.log_async( + job_id=self.job_id, + log_type=LogType.ERROR, + message=f"❌ {self.agent_name} agent failed: {str(error)}", + agent_name=self.agent_name, + metadata={ + "type": "agent_error", + "error": str(error) + } + ) + + +def create_agent_logger(job_id: str, agent_name: str) -> AgentEventLogger: + """ + Factory function to create an AgentEventLogger + + Args: + job_id: Job ID for log streaming + agent_name: Name of the agent + + Returns: + Configured AgentEventLogger instance + + Example: + logger = create_agent_logger("job_123", "CodeExplorer") + result = await agent.ainvoke(..., config={"callbacks": [logger]}) + """ + return AgentEventLogger(job_id=job_id, agent_name=agent_name) + + +def create_dual_callbacks(job_id: str, agent_name: str, model_name: str) -> list: + """ + Factory function to create callback handlers for agents + + NOTE: Langfuse integration now uses manual trace creation instead of callbacks. + Use create_langfuse_trace() before agent execution and log_agent_generation() after. + + Args: + job_id: Job ID for log streaming + agent_name: Name of the agent (CodeExplorer, Writer, etc.) + model_name: Model name being used (for Langfuse metadata - currently unused) + + Returns: + List of callback handlers (AgentEventLogger for UI streaming) + + Example: + callbacks = create_dual_callbacks("job_123", "CodeExplorer", "Qwen/Qwen3-4B-Instruct-2507") + result = await agent.ainvoke(..., config={"callbacks": callbacks}) + """ + # Only UI logger - Langfuse is now handled via manual trace creation + return [AgentEventLogger(job_id=job_id, agent_name=agent_name)] diff --git a/api/core/metrics_collector.py b/api/core/metrics_collector.py new file mode 100644 index 0000000..d317698 --- /dev/null +++ b/api/core/metrics_collector.py @@ -0,0 +1,287 @@ +""" +Metrics Collection for Workflow Execution + +Tracks real metrics from execution: +- Token usage per agent +- Time to first token (TTFT) +- Tokens per second (TPS) +- Tool calls per agent +- Workflow-level aggregated metrics + +Key principle: Measure everything, estimate nothing +""" + +import time +import logging +from typing import Dict, Optional, Any +from dataclasses import dataclass, asdict + +logger = logging.getLogger(__name__) + + +@dataclass +class AgentMetrics: + """Metrics for a single agent execution""" + agent_name: str + job_id: str + + # Execution metrics + start_time_ms: float = 0 + end_time_ms: float = 0 + duration_ms: float = 0 + + # Token usage + input_tokens: int = 0 + output_tokens: int = 0 + total_tokens: int = 0 + + # Tool calling + llm_calls: int = 0 + tool_calls: int = 0 + + # Status + success: bool = False + error_message: Optional[str] = None + + def to_dict(self) -> Dict: + """Convert to dictionary""" + return asdict(self) + + def calculate_cost(self, cost_per_million_tokens: float = 0.15) -> float: + """ + Calculate estimated cost in USD + + Args: + cost_per_million_tokens: Cost per 1M tokens (default: $0.15 for Qwen3-4B estimate) + + Returns: + Estimated cost in USD + """ + return (self.total_tokens / 1_000_000) * cost_per_million_tokens + + def calculate_tps(self) -> float: + """ + Calculate tokens per second (TPS) + + Returns: + Tokens per second + """ + if self.duration_ms == 0: + return 0.0 + duration_seconds = self.duration_ms / 1000 + return self.output_tokens / duration_seconds if duration_seconds > 0 else 0.0 + + +class MetricsCollector: + """ + Centralized metrics collection for all agents + + Tracks real execution metrics for workflow analysis + """ + + def __init__(self, job_id: str): + """ + Initialize MetricsCollector + + Args: + job_id: Unique job identifier + """ + self.job_id = job_id + self.agents: Dict[str, AgentMetrics] = {} + self.workflow_start_time = time.time() * 1000 # Convert to ms + self.workflow_end_time: Optional[float] = None + + logger.info(f"[{job_id}] MetricsCollector initialized") + + def start_agent(self, agent_name: str) -> AgentMetrics: + """ + Start tracking an agent + + Args: + agent_name: Name of agent + + Returns: + AgentMetrics instance for this agent + """ + if agent_name in self.agents: + logger.warning(f"[{self.job_id}] Agent {agent_name} already started, resetting") + + metrics = AgentMetrics( + agent_name=agent_name, + job_id=self.job_id, + start_time_ms=time.time() * 1000 + ) + + self.agents[agent_name] = metrics + logger.debug(f"[{self.job_id}] Started tracking: {agent_name}") + + return metrics + + def end_agent( + self, + agent_name: str, + success: bool = True, + input_tokens: int = 0, + output_tokens: int = 0, + llm_calls: int = 0, + tool_calls: int = 0, + error_message: Optional[str] = None + ): + """ + End tracking an agent + + Args: + agent_name: Name of agent + success: Whether agent succeeded + input_tokens: Input tokens used + output_tokens: Output tokens generated + llm_calls: Number of LLM calls + tool_calls: Number of tool calls + error_message: Error message if failed + """ + if agent_name not in self.agents: + logger.error(f"[{self.job_id}] Agent {agent_name} not started") + return + + metrics = self.agents[agent_name] + metrics.end_time_ms = time.time() * 1000 + metrics.duration_ms = metrics.end_time_ms - metrics.start_time_ms + metrics.success = success + metrics.input_tokens = input_tokens + metrics.output_tokens = output_tokens + metrics.total_tokens = input_tokens + output_tokens + metrics.llm_calls = llm_calls + metrics.tool_calls = tool_calls + metrics.error_message = error_message + + tps = metrics.calculate_tps() + + logger.info( + f"[{self.job_id}] {agent_name} completed: " + f"success={success}, tokens={metrics.total_tokens}, " + f"duration={metrics.duration_ms:.0f}ms, TPS={tps:.2f}" + ) + + def finalize_workflow(self): + """Mark workflow as complete and calculate final metrics""" + self.workflow_end_time = time.time() * 1000 + workflow_duration = self.workflow_end_time - self.workflow_start_time + + logger.info( + f"[{self.job_id}] Workflow completed in {workflow_duration:.0f}ms " + f"({len(self.agents)} agents)" + ) + + def get_summary(self) -> Dict[str, Any]: + """ + Get comprehensive metrics summary with percentiles + + Returns: + Dictionary with all metrics including latency percentiles + """ + if not self.workflow_end_time: + self.finalize_workflow() + + # Calculate totals + total_tokens = sum(m.total_tokens for m in self.agents.values()) + total_input_tokens = sum(m.input_tokens for m in self.agents.values()) + total_output_tokens = sum(m.output_tokens for m in self.agents.values()) + total_tool_calls = sum(m.tool_calls for m in self.agents.values()) + total_llm_calls = sum(m.llm_calls for m in self.agents.values()) + workflow_duration_ms = self.workflow_end_time - self.workflow_start_time + + # Agent-level metrics + agent_metrics = [m.to_dict() for m in self.agents.values()] + + # Calculate workflow-level TPS + workflow_duration_seconds = workflow_duration_ms / 1000 + workflow_tps = total_output_tokens / workflow_duration_seconds if workflow_duration_seconds > 0 else 0.0 + + # Count failed agents + failed_agents = [m for m in self.agents.values() if not m.success] + successful_agents = [m for m in self.agents.values() if m.success] + + # Calculate latency percentiles (p50, p90, p95, p99) + latency_percentiles = self.calculate_latency_percentiles() + + return { + "job_id": self.job_id, + "workflow": { + "total_agents": len(self.agents), + "successful_agents": len(successful_agents), + "failed_agents": len(failed_agents), + "total_duration_ms": workflow_duration_ms, + "total_duration_seconds": round(workflow_duration_seconds, 2), + "total_tokens": total_tokens, + "total_input_tokens": total_input_tokens, + "total_output_tokens": total_output_tokens, + "total_tool_calls": total_tool_calls, + "total_llm_calls": total_llm_calls, + "average_tps": round(workflow_tps, 2), + # Latency percentiles + "p50_latency_ms": latency_percentiles.get("p50", 0), + "p90_latency_ms": latency_percentiles.get("p90", 0), + "p95_latency_ms": latency_percentiles.get("p95", 0), + "p99_latency_ms": latency_percentiles.get("p99", 0) + }, + "agents": agent_metrics, + "failed_agent_names": [m.agent_name for m in failed_agents] + } + + def calculate_latency_percentiles(self) -> Dict[str, float]: + """ + Calculate latency percentiles (p50, p90, p95, p99) from successful agents + + Returns: + Dictionary with percentile values in milliseconds + """ + import statistics + + # Get latencies from successful agents only + latencies = [ + m.duration_ms for m in self.agents.values() + if m.success and m.duration_ms > 0 + ] + + if not latencies: + return {"p50": 0, "p90": 0, "p95": 0, "p99": 0} + + # Sort latencies for percentile calculation + latencies.sort() + + # Calculate percentiles + try: + p50 = statistics.median(latencies) + + # For p90, p95, p99, use quantiles if we have enough data points + if len(latencies) >= 20: + quantiles = statistics.quantiles(latencies, n=100) # 100 quantiles = percentiles + p90 = quantiles[89] # 90th percentile (0-indexed) + p95 = quantiles[94] # 95th percentile + p99 = quantiles[98] # 99th percentile + else: + # Fallback: use approximate percentiles for small datasets + n = len(latencies) + p90_idx = int(0.90 * n) + p95_idx = int(0.95 * n) + p99_idx = int(0.99 * n) + p90 = latencies[min(p90_idx, n - 1)] + p95 = latencies[min(p95_idx, n - 1)] + p99 = latencies[min(p99_idx, n - 1)] + + return { + "p50": round(p50, 2), + "p90": round(p90, 2), + "p95": round(p95, 2), + "p99": round(p99, 2) + } + + except Exception as e: + logger.warning(f"Failed to calculate percentiles: {e}") + # Fallback to simple values + return { + "p50": round(statistics.median(latencies), 2) if latencies else 0, + "p90": round(max(latencies), 2) if latencies else 0, + "p95": round(max(latencies), 2) if latencies else 0, + "p99": round(max(latencies), 2) if latencies else 0 + } diff --git a/api/mcp_client/__init__.py b/api/mcp_client/__init__.py new file mode 100644 index 0000000..15cca45 --- /dev/null +++ b/api/mcp_client/__init__.py @@ -0,0 +1,8 @@ +""" +MCP Client Module +Provides connections to MCP servers (Model Context Protocol) +""" + +from .github_mcp_client import GitHubMCPClient, get_github_mcp_client + +__all__ = ['GitHubMCPClient', 'get_github_mcp_client'] diff --git a/api/mcp_client/github_mcp_client.py b/api/mcp_client/github_mcp_client.py new file mode 100644 index 0000000..cf235d3 --- /dev/null +++ b/api/mcp_client/github_mcp_client.py @@ -0,0 +1,131 @@ +""" +GitHub MCP Client - Connects to official GitHub MCP server +Uses MCP protocol (stdio) to communicate with Docker container +""" + +import os +import asyncio +import logging +from typing import Dict, Any, List, Optional +from contextlib import asynccontextmanager + +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +logger = logging.getLogger(__name__) + + +class GitHubMCPClient: + """Client for GitHub MCP Server""" + + def __init__(self, github_token: str): + """ + Initialize GitHub MCP client + + Args: + github_token: GitHub Personal Access Token + """ + self.github_token = github_token + self.session: Optional[ClientSession] = None + self._client_context = None + + @asynccontextmanager + async def connect(self): + """ + Connect to GitHub MCP server running in Docker + + Yields: + ClientSession for making tool calls + """ + # Define server parameters - run GitHub MCP server via Docker + server_params = StdioServerParameters( + command="docker", + args=[ + "run", + "-i", + "--rm", + "-e", f"GITHUB_PERSONAL_ACCESS_TOKEN={self.github_token}", + "ghcr.io/github/github-mcp-server:latest" + ], + env=None + ) + + logger.info("Starting GitHub MCP server via Docker...") + + async with stdio_client(server_params) as (read, write): + async with ClientSession(read, write) as session: + self.session = session + + # Initialize session + await session.initialize() + + # List available tools + tools = await session.list_tools() + logger.info(f"Connected to GitHub MCP server. Available tools: {[t.name for t in tools.tools]}") + + yield session + + logger.info("Disconnected from GitHub MCP server") + self.session = None + + async def call_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Any: + """ + Call a tool on the GitHub MCP server + + Args: + tool_name: Name of the tool to call + arguments: Tool arguments + + Returns: + Tool result + """ + if not self.session: + raise RuntimeError("Not connected to MCP server. Use 'async with client.connect():'") + + logger.debug(f"Calling MCP tool: {tool_name} with args: {arguments}") + + result = await self.session.call_tool(tool_name, arguments=arguments) + + logger.debug(f"MCP tool {tool_name} result: {result}") + + return result + + async def list_available_tools(self) -> List[str]: + """ + Get list of available tools from GitHub MCP server + + Returns: + List of tool names + """ + if not self.session: + raise RuntimeError("Not connected to MCP server") + + tools = await self.session.list_tools() + return [tool.name for tool in tools.tools] + + +# Singleton instance +_github_mcp_client: Optional[GitHubMCPClient] = None + + +def get_github_mcp_client(github_token: Optional[str] = None) -> GitHubMCPClient: + """ + Get or create GitHub MCP client instance + + Args: + github_token: GitHub PAT (uses env var if not provided) + + Returns: + GitHubMCPClient instance + """ + global _github_mcp_client + + if _github_mcp_client is None: + token = github_token or os.getenv("GITHUB_TOKEN") + if not token: + raise ValueError("GitHub token required. Set GITHUB_TOKEN env var or pass token parameter.") + + _github_mcp_client = GitHubMCPClient(token) + logger.info("GitHub MCP client created") + + return _github_mcp_client diff --git a/api/models/__init__.py b/api/models/__init__.py new file mode 100644 index 0000000..dbfb07a --- /dev/null +++ b/api/models/__init__.py @@ -0,0 +1,28 @@ +""" +Data models for DocuBot AI +""" + +from .state import DocGenState +from .schemas import ( + GenerateDocsRequest, + GenerateDocsResponse, + JobStatusResponse, + ProjectSelectionRequest, + ProjectSelectionResponse, + AgentLog, + LogType +) +from .log_manager import LogManager, get_log_manager + +__all__ = [ + "DocGenState", + "GenerateDocsRequest", + "GenerateDocsResponse", + "JobStatusResponse", + "ProjectSelectionRequest", + "ProjectSelectionResponse", + "AgentLog", + "LogType", + "LogManager", + "get_log_manager" +] diff --git a/api/models/evidence.py b/api/models/evidence.py new file mode 100644 index 0000000..b8818d1 --- /dev/null +++ b/api/models/evidence.py @@ -0,0 +1,126 @@ +""" +Evidence Packet - Central Evidence Store for README Generation + +This module defines the schema for storing all repository evidence +with provenance tracking to prevent hallucinations. + +Key Principles: +- Single source of truth for all README content +- Every evidence item includes source_files for traceability +- Deterministic derivation (no LLM-generated values) +- Supports both file-based and structured agent outputs +""" + +from dataclasses import dataclass, field +from typing import List, Dict, Any + + +@dataclass +class EvidenceItem: + """Single piece of evidence with provenance.""" + category: str # "dependency", "config", "route", "tool", etc. + key: str # Identifier (e.g., "fastapi", "PORT", "/upload") + value: str # The evidence value + source_files: List[str] = field(default_factory=list) # Where it was found + confidence: str = "high" # "high", "medium", "low" + + +@dataclass +class EvidencePacket: + """ + Central evidence store for README generation. + + Contains all factual information extracted from repository + with provenance tracking. + """ + + # Repository metadata (deterministic) + repo_name: str = "" + repo_path: str = "" + + # Language detection + languages: Dict[str, int] = field(default_factory=dict) # {language: file_count} + + # Dependencies (from package files) + python_deps: List[str] = field(default_factory=list) # From requirements.txt + node_deps: List[str] = field(default_factory=list) # From package.json + + # Technology flags + has_docker: bool = False + has_frontend: bool = False + has_backend: bool = False + + # Configuration + env_files: List[str] = field(default_factory=list) # [".env.example", ".env"] + env_vars: Dict[str, str] = field(default_factory=dict) # {VAR: description} + + # Docker evidence + docker_files: List[str] = field(default_factory=list) # ["Dockerfile", "docker-compose.yml"] + + # API endpoints (if backend detected) + api_endpoints: List[Dict[str, str]] = field(default_factory=list) # [{method, path, file}] + + # Entry points + entry_points: List[str] = field(default_factory=list) # ["server.py", "main.py"] + + # Frontend info + frontend_framework: str = "" # "React", "Vue", "Angular", "" + frontend_files: List[str] = field(default_factory=list) + + # Raw evidence items + items: List[EvidenceItem] = field(default_factory=list) + + def add_evidence(self, item: EvidenceItem) -> None: + """Add evidence item with deduplication.""" + # Avoid duplicate evidence + for existing in self.items: + if existing.key == item.key and existing.category == item.category: + return + self.items.append(item) + + def has_evidence_for(self, key: str, category: str = None) -> bool: + """Check if evidence exists for a key.""" + for item in self.items: + if item.key == key: + if category is None or item.category == category: + return True + return False + + def get_evidence(self, key: str, category: str = None) -> List[EvidenceItem]: + """Get all evidence items matching key and optional category.""" + results = [] + for item in self.items: + if item.key == key: + if category is None or item.category == category: + results.append(item) + return results + + def to_dict(self) -> Dict[str, Any]: + """Convert to dictionary for JSON serialization.""" + return { + "repo_name": self.repo_name, + "repo_path": self.repo_path, + "languages": self.languages, + "python_deps": self.python_deps, + "node_deps": self.node_deps, + "has_docker": self.has_docker, + "has_frontend": self.has_frontend, + "has_backend": self.has_backend, + "env_files": self.env_files, + "env_vars": self.env_vars, + "docker_files": self.docker_files, + "api_endpoints": self.api_endpoints, + "entry_points": self.entry_points, + "frontend_framework": self.frontend_framework, + "frontend_files": self.frontend_files, + "items": [ + { + "category": item.category, + "key": item.key, + "value": item.value, + "source_files": item.source_files, + "confidence": item.confidence + } + for item in self.items + ] + } diff --git a/api/models/log_manager.py b/api/models/log_manager.py new file mode 100644 index 0000000..7c03e97 --- /dev/null +++ b/api/models/log_manager.py @@ -0,0 +1,217 @@ +""" +Log Manager for storing and streaming agent activity logs +Supports Server-Sent Events (SSE) for real-time streaming +""" + +import asyncio +import logging +import threading +from collections import defaultdict +from typing import Dict, List, Optional +from datetime import datetime +from .schemas import AgentLog, LogType + +logger = logging.getLogger(__name__) + + +class LogManager: + """ + Manages agent activity logs with SSE streaming support. + Stores logs per job_id and provides async iteration for SSE. + """ + + def __init__(self, max_logs_per_job: int = 1000): + """ + Initialize log manager + + Args: + max_logs_per_job: Maximum logs to keep per job (circular buffer) + """ + self.max_logs_per_job = max_logs_per_job + self._logs: Dict[str, List[AgentLog]] = defaultdict(list) + self._subscribers: Dict[str, List[asyncio.Queue]] = defaultdict(list) + # Use threading.Lock for thread-safe access + self._lock = threading.Lock() + + async def add_log(self, log: AgentLog) -> None: + """ + Add a log entry and notify all subscribers + + Args: + log: Agent log entry + """ + with self._lock: + # Add to storage + job_logs = self._logs[log.job_id] + job_logs.append(log) + + # Maintain circular buffer + if len(job_logs) > self.max_logs_per_job: + job_logs.pop(0) + + # Get subscriber queues (copy to avoid modification during iteration) + subscribers = list(self._subscribers[log.job_id]) + + # Notify all subscribers for this job (outside lock to allow async operations) + dead_queues = [] + for queue in subscribers: + try: + await asyncio.wait_for(queue.put(log), timeout=1.0) + except (asyncio.TimeoutError, asyncio.QueueFull): + dead_queues.append(queue) + logger.warning(f"Queue full or timeout for job {log.job_id}") + + # Clean up dead queues + if dead_queues: + with self._lock: + for queue in dead_queues: + if queue in self._subscribers[log.job_id]: + self._subscribers[log.job_id].remove(queue) + + # Log to console for debugging + logger.info(f"[{log.job_id}] {log.log_type.value}: {log.message}") + + def log_sync( + self, + job_id: str, + log_type: LogType, + message: str, + agent_name: Optional[str] = None, + metadata: Optional[Dict] = None + ) -> None: + """ + Synchronous logging helper (for use in sync code) + + Args: + job_id: Job identifier + log_type: Type of log + message: Log message + agent_name: Name of the agent (if applicable) + metadata: Additional metadata + """ + log = AgentLog( + job_id=job_id, + log_type=log_type, + message=message, + agent_name=agent_name, + metadata=metadata + ) + # Schedule the async add_log in the event loop + try: + loop = asyncio.get_event_loop() + if loop.is_running(): + asyncio.create_task(self.add_log(log)) + else: + loop.run_until_complete(self.add_log(log)) + except RuntimeError: + # No event loop, just log to console + logger.info(f"[{job_id}] {log_type.value}: {message}") + + async def log_async( + self, + job_id: str, + log_type: LogType, + message: str, + agent_name: Optional[str] = None, + metadata: Optional[Dict] = None + ) -> None: + """ + Asynchronous logging helper + + Args: + job_id: Job identifier + log_type: Type of log + message: Log message + agent_name: Name of the agent (if applicable) + metadata: Additional metadata + """ + log = AgentLog( + job_id=job_id, + log_type=log_type, + message=message, + agent_name=agent_name, + metadata=metadata + ) + await self.add_log(log) + + def get_logs(self, job_id: str, limit: Optional[int] = None) -> List[AgentLog]: + """ + Get all logs for a job + + Args: + job_id: Job identifier + limit: Maximum number of logs to return (most recent) + + Returns: + List of logs + """ + logs = self._logs.get(job_id, []) + if limit: + return logs[-limit:] + return logs + + async def subscribe(self, job_id: str) -> asyncio.Queue: + """ + Subscribe to real-time logs for a job (for SSE) + + Args: + job_id: Job identifier + + Returns: + Queue that will receive new logs + """ + queue: asyncio.Queue = asyncio.Queue(maxsize=100) + with self._lock: + self._subscribers[job_id].append(queue) + existing_logs = list(self._logs.get(job_id, [])) + + # Send existing logs immediately (outside lock to allow async operations) + for log in existing_logs: + try: + await queue.put(log) + except asyncio.QueueFull: + logger.warning(f"Initial logs queue full for job {job_id}") + break + + return queue + + async def unsubscribe(self, job_id: str, queue: asyncio.Queue) -> None: + """ + Unsubscribe from logs + + Args: + job_id: Job identifier + queue: Queue to remove + """ + with self._lock: + if queue in self._subscribers[job_id]: + self._subscribers[job_id].remove(queue) + + def clear_job_logs(self, job_id: str) -> None: + """ + Clear all logs for a job + + Args: + job_id: Job identifier + """ + if job_id in self._logs: + del self._logs[job_id] + if job_id in self._subscribers: + del self._subscribers[job_id] + + +# Global log manager instance +_log_manager: Optional[LogManager] = None + + +def get_log_manager() -> LogManager: + """ + Get or create the global log manager instance + + Returns: + LogManager instance + """ + global _log_manager + if _log_manager is None: + _log_manager = LogManager() + return _log_manager diff --git a/api/models/schemas.py b/api/models/schemas.py new file mode 100644 index 0000000..7668e56 --- /dev/null +++ b/api/models/schemas.py @@ -0,0 +1,75 @@ +""" +Pydantic schemas for API requests and responses +""" + +from datetime import datetime +from enum import Enum +from typing import Optional, Dict, Any, List +from pydantic import BaseModel, HttpUrl, Field + + +class GenerateDocsRequest(BaseModel): + """Request to generate documentation""" + repo_url: HttpUrl = Field(..., description="GitHub repository URL") + + +class GenerateDocsResponse(BaseModel): + """Response with job ID for tracking""" + job_id: str + status: str + message: str + + +class JobStatusResponse(BaseModel): + """Job status information""" + job_id: str + status: str + progress_percentage: int + current_agent: Optional[str] = None + error_message: Optional[str] = None + readme_preview: Optional[str] = None + awaiting_project_selection: Optional[bool] = False + detected_projects: Optional[List[Dict[str, Any]]] = None + skipped_folders: Optional[List[Dict[str, Any]]] = None + + +class ProjectSelectionRequest(BaseModel): + """User's project selection""" + selected_project_paths: List[str] = Field(..., description="List of selected project paths") + + +class ProjectSelectionResponse(BaseModel): + """Response after project selection""" + status: str + message: str + + +class LogType(str, Enum): + """Types of log entries""" + AGENT_START = "agent_start" + AGENT_THINKING = "agent_thinking" + AGENT_ACTION = "agent_action" # Agent calling a tool (ReAct Action step) + AGENT_OBSERVATION = "agent_observation" # Agent receiving tool result (ReAct Observation step) + AGENT_TOOL_USE = "agent_tool_use" + AGENT_DECISION = "agent_decision" + AGENT_COMPLETE = "agent_complete" + WORKFLOW_PROGRESS = "workflow_progress" + INFO = "info" + WARNING = "warning" + ERROR = "error" + SUCCESS = "success" + + +class AgentLog(BaseModel): + """Agent activity log entry""" + timestamp: datetime = Field(default_factory=datetime.now) + job_id: str + log_type: LogType + agent_name: Optional[str] = None + message: str + metadata: Optional[Dict[str, Any]] = None + + class Config: + json_encoders = { + datetime: lambda v: v.isoformat() + } diff --git a/api/models/state.py b/api/models/state.py new file mode 100644 index 0000000..9f1e86c --- /dev/null +++ b/api/models/state.py @@ -0,0 +1,101 @@ +""" +LangGraph State Definition for DocuBot +Enhanced with agent messaging, evidence packs, and metrics tracking +""" + +from typing import TypedDict, Dict, List, Any, Optional +from models.evidence import EvidencePacket + + +class DocGenState(TypedDict): + """ + Shared state for the micro-agent documentation generation workflow. + Each agent reads from and writes to this state. + """ + + # Job Information + job_id: str + repo_url: str + + # Repository Data (populated by clone step) + repo_path: Optional[str] + repo_name: Optional[str] + default_branch: Optional[str] + is_subfolder_target: Optional[bool] + + # Project Detection (populated by project detector) + is_monorepo: Optional[bool] + detected_projects: Optional[List[Dict[str, Any]]] + skipped_folders: Optional[List[Dict[str, Any]]] + selected_projects: Optional[List[str]] + awaiting_project_selection: Optional[bool] + + # Evidence-Based Architecture + evidence_packet: Optional[EvidencePacket] # Central evidence store + + # Analysis Agent Outputs + file_structure: Optional[str] + languages: Optional[Dict[str, int]] + key_files: Optional[List[str]] + code_summary: Optional[str] + + # Dependency Analysis (NEW - from DependencyAnalyzer) + dependency_report: Optional[Dict[str, Any]] # Full dependency analysis + security_warnings: Optional[List[Dict[str, str]]] # Vulnerability warnings + + # Environment Config (NEW - from EnvConfigParser) + env_variables: Optional[List[Dict[str, Any]]] # Extracted env vars + config_files_found: Optional[List[str]] + + # Code Analysis Agent Outputs + # API Reference (NEW - from APIReferenceAgent) + api_endpoints: Optional[List[Dict[str, Any]]] # Detected endpoints + api_documentation: Optional[str] + + # Call Graph (NEW - from CallGraphAgent) + call_graph: Optional[Dict[str, Any]] # Function call relationships + + # Error Analysis (NEW - from ErrorAnalysisAgent) + error_handlers: Optional[List[Dict[str, Any]]] # Exception handlers found + error_analysis: Optional[Dict[str, Any]] + + # Planning + project_type: Optional[str] + documentation_sections: Optional[List[str]] + section_instructions: Optional[Dict[str, str]] + + # Content Generation + readme_sections: Optional[Dict[str, str]] + + # Visualization + mermaid_diagrams: Optional[Dict[str, str]] + + # Quality Assurance + qa_validation_result: Optional[Dict[str, Any]] # QA validator output + qa_score: Optional[int] + qa_passed: Optional[bool] + + # Final Output + final_readme: Optional[str] + + # Agent-to-Agent Communication (NEW) + agent_messages: Optional[List[Dict[str, Any]]] # Messages between agents + # Format: [{"from": "APIReference", "to": "Mermaid", "type": "data", "content": {...}}] + + # Performance Metrics (NEW) + agent_metrics: Optional[Dict[str, Dict[str, Any]]] # Per-agent metrics + blast_radius_report: Optional[Dict[str, Any]] # Blast radius calculation + total_tokens_used: Optional[int] + total_duration_ms: Optional[float] + + # Error Handling + error: Optional[str] + retry_count: int + failed_agents: Optional[List[str]] # List of agents that failed + + # Langfuse Observability + langfuse_trace_id: Optional[str] # Root trace ID for hierarchical tracing + + # Metadata + workflow_status: str # pending, in_progress, completed, failed + current_agent: Optional[str] diff --git a/api/requirements.txt b/api/requirements.txt new file mode 100644 index 0000000..7d4b310 --- /dev/null +++ b/api/requirements.txt @@ -0,0 +1,39 @@ +# Web Framework +fastapi>=0.109.0 +uvicorn[standard]>=0.27.0 +python-multipart>=0.0.22 +sse-starlette>=1.6.5 + +# Environment & Config +python-dotenv>=1.0.0 +pydantic>=2.5.0 +pydantic-settings>=2.1.0 + +# LangChain & LangGraph (Agentic Framework) +langchain>=0.1.0 +langchain-openai>=0.0.5 +langgraph>=0.0.20 +langgraph-checkpoint-sqlite>=1.0.0 +langchain-community>=0.0.20 + +# LLM APIs +openai>=1.10.0 +httpx>=0.24.0 +requests>=2.31.0 + +# Git Operations +gitpython>=3.1.40 + +# GitHub Integration +PyGithub>=2.1.1 + +# MCP (Model Context Protocol) +mcp>=0.9.0 + +# Code Analysis +astroid>=3.0.0 + +# Utilities +aiofiles>=23.0.0 +aiosqlite>=0.20.0 +tenacity>=8.2.3 diff --git a/api/server.py b/api/server.py new file mode 100644 index 0000000..a900638 --- /dev/null +++ b/api/server.py @@ -0,0 +1,564 @@ +""" +FastAPI Server for DocuBot +AI-Powered Documentation Intelligence Platform +Provides REST API and SSE streaming for real-time agent visibility +""" + +import asyncio +import json +import logging +import sys +import uuid +from contextlib import asynccontextmanager +from typing import Dict, List + +# Disable output buffering for real-time log streaming +sys.stdout.reconfigure(line_buffering=True) +sys.stderr.reconfigure(line_buffering=True) + +from fastapi import FastAPI, BackgroundTasks, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import StreamingResponse +from sse_starlette.sse import EventSourceResponse + +from config import settings +from models import ( + GenerateDocsRequest, + GenerateDocsResponse, + JobStatusResponse, + ProjectSelectionRequest, + ProjectSelectionResponse, + LogType, + get_log_manager +) +from workflow import get_workflow +from agents.pr_agent_mcp import create_pr_with_mcp + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + +# Job storage +job_storage: Dict[str, Dict] = {} + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Lifespan context for startup/shutdown""" + logger.info("DocuBot AI starting up...") + logger.info(f"Inference endpoint: {settings.INFERENCE_API_ENDPOINT}") + + # Check if GitHub token is configured for MCP PR creation + github_token = settings.GITHUB_TOKEN + if github_token: + logger.info("GitHub token configured - MCP PR creation enabled") + else: + logger.warning("GITHUB_TOKEN not configured - PR creation will be disabled") + + yield + logger.info("DocuBot AI shutting down...") + + +# Initialize FastAPI +app = FastAPI( + title=settings.APP_TITLE, + description=settings.APP_DESCRIPTION, + version=settings.APP_VERSION, + lifespan=lifespan +) + +# CORS +app.add_middleware( + CORSMiddleware, + allow_origins=settings.CORS_ORIGINS, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/") +def root(): + """Root endpoint""" + return { + "name": settings.APP_TITLE, + "version": settings.APP_VERSION, + "status": "running" + } + + +@app.get("/health") +def health_check(): + """Health check""" + return { + "status": "healthy", + "inference_endpoint": settings.INFERENCE_API_ENDPOINT + } + + +@app.post("/api/generate-docs", response_model=GenerateDocsResponse) +async def generate_documentation(request: GenerateDocsRequest, background_tasks: BackgroundTasks): + """ + Start documentation generation workflow + + Returns job_id for tracking and SSE streaming + """ + try: + job_id = str(uuid.uuid4()) + + # Initialize job + job_storage[job_id] = { + "repo_url": str(request.repo_url), + "status": "pending", + "workflow_paused": False, + "current_agent": None, + "final_readme": None, + "error": None, + "awaiting_project_selection": False, + "detected_projects": None + } + + # Start workflow in background + background_tasks.add_task(run_workflow, job_id, str(request.repo_url)) + + logger.info(f"Documentation generation started: {job_id}") + + return GenerateDocsResponse( + job_id=job_id, + status="started", + message="Documentation generation workflow started. Connect to SSE stream to monitor progress." + ) + + except Exception as e: + logger.error(f"Failed to start workflow: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=str(e)) + + +async def run_workflow(job_id: str, repo_url: str): + """ + Run the documentation workflow + Executes with human-in-the-loop interrupts + """ + try: + workflow = await get_workflow() + + # Initial state + initial_state = { + "job_id": job_id, + "repo_url": repo_url, + "repo_path": None, + "repo_name": None, + "default_branch": None, + "is_monorepo": None, + "detected_projects": None, + "skipped_folders": None, # List of skipped folders with reasons + "selected_projects": None, + "awaiting_project_selection": None, + "is_subfolder_target": None, # NEW: Track if user provided subfolder URL + "file_structure": None, + "languages": None, + "dependencies": None, + "key_files": None, + "code_summary": None, + "project_type": None, + "documentation_sections": None, + "section_instructions": None, + "readme_sections": None, + "mermaid_diagrams": None, + "final_readme": None, + "error": None, + "retry_count": 0, + "workflow_status": "pending", + "current_agent": None + } + + # Run workflow (will interrupt after each agent) + config = {"configurable": {"thread_id": job_id}} + + # Execute workflow - it will pause at interrupt points + async for event in workflow.graph.astream(initial_state, config): + logger.info(f"Workflow event: {event}") + + # Update job storage with latest state + for node_name, node_state in event.items(): + if isinstance(node_state, dict): + # Always update status from workflow_status + workflow_status = node_state.get("workflow_status", "in_progress") + job_storage[job_id]["status"] = workflow_status + job_storage[job_id]["current_agent"] = node_state.get("current_agent") + + # Store repo_name for PR creation + if node_state.get("repo_name"): + job_storage[job_id]["repo_name"] = node_state["repo_name"] + + # CRITICAL: Update final_readme whenever present + if node_state.get("final_readme"): + job_storage[job_id]["final_readme"] = node_state["final_readme"] + logger.info(f"✅ Updated job_storage[{job_id}]['final_readme'] = {len(node_state['final_readme'])} chars (workflow_status={workflow_status})") + + if node_state.get("error"): + job_storage[job_id]["error"] = node_state["error"] + + # Handle project selection + if node_state.get("awaiting_project_selection"): + job_storage[job_id]["awaiting_project_selection"] = True + job_storage[job_id]["detected_projects"] = node_state.get("detected_projects") + job_storage[job_id]["skipped_folders"] = node_state.get("skipped_folders", []) + + # DEBUG: Log what we're storing + logger.info(f"DEBUG server.py: Storing skipped_folders: {job_storage[job_id]['skipped_folders']}") + logger.info(f"DEBUG server.py: Number of skipped folders: {len(job_storage[job_id]['skipped_folders'])}") + + logger.info(f"Workflow paused for project selection: {job_id}") + return # Stop workflow execution until user selects projects + + logger.info(f"Workflow completed for job: {job_id}") + + except Exception as e: + logger.error(f"Workflow failed for job {job_id}: {e}", exc_info=True) + job_storage[job_id]["status"] = "failed" + job_storage[job_id]["error"] = str(e) + + +@app.get("/api/status/{job_id}", response_model=JobStatusResponse) +def get_job_status(job_id: str): + """Get current job status""" + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + job = job_storage[job_id] + + # DEBUG: Log what we're returning + skipped = job.get("skipped_folders", []) + logger.info(f"DEBUG get_job_status: Returning skipped_folders with {len(skipped)} items") + logger.info(f"DEBUG get_job_status: skipped_folders = {skipped}") + + return JobStatusResponse( + job_id=job_id, + status=job["status"], + progress_percentage=50, # Simplified + current_agent=job.get("current_agent"), + error_message=job.get("error"), + readme_preview=job.get("final_readme", "")[:500] if job.get("final_readme") else None, + awaiting_project_selection=job.get("awaiting_project_selection", False), + detected_projects=job.get("detected_projects"), + skipped_folders=job.get("skipped_folders", []) + ) + + +@app.get("/api/logs/{job_id}") +async def stream_logs(job_id: str): + """ + Server-Sent Events (SSE) endpoint for streaming agent activity logs + + Client usage: + ```javascript + const eventSource = new EventSource(`/api/logs/${jobId}`) + eventSource.onmessage = (event) => { + const log = JSON.parse(event.data) + console.log(log) + } + ``` + """ + log_manager = get_log_manager() + + async def event_generator(): + """Generate SSE events from log queue""" + # Subscribe to logs for this job + queue = await log_manager.subscribe(job_id) + + try: + while True: + # Wait for new log + try: + log = await asyncio.wait_for(queue.get(), timeout=30.0) + + # Convert log to JSON + log_data = { + "timestamp": log.timestamp.isoformat(), + "log_type": log.log_type.value, + "agent_name": log.agent_name, + "message": log.message, + "metadata": log.metadata + } + + yield { + "data": json.dumps(log_data) + } + + except asyncio.TimeoutError: + # Send keep-alive ping + yield { + "data": json.dumps({"type": "keepalive"}) + } + + except asyncio.CancelledError: + logger.info(f"SSE stream cancelled for job: {job_id}") + finally: + # Unsubscribe + await log_manager.unsubscribe(job_id, queue) + + return EventSourceResponse(event_generator()) + + +@app.post("/api/approve/{job_id}") +async def approve_agent_output(job_id: str): + """ + Approve current agent's output and continue workflow + Used for human-in-the-loop control + """ + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + # Resume workflow by invoking with continue command + workflow = await get_workflow() + config = {"configurable": {"thread_id": job_id}} + + try: + # Continue workflow from last checkpoint + state = workflow.graph.get_state(config) + + # Resume execution + async for event in workflow.graph.astream(None, config): + logger.info(f"Resumed workflow: {event}") + + return {"status": "approved", "message": "Workflow continued"} + + except Exception as e: + logger.error(f"Failed to continue workflow: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/api/reject/{job_id}") +async def reject_agent_output(job_id: str, feedback: str = ""): + """ + Reject current agent's output and provide feedback + Agent will retry with feedback + """ + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + # Update state with feedback and retry + workflow = await get_workflow() + config = {"configurable": {"thread_id": job_id}} + + try: + state = workflow.graph.get_state(config) + + # Update state with feedback + # (Implementation depends on how you want to handle retries) + + return {"status": "rejected", "message": "Agent will retry with feedback"} + + except Exception as e: + logger.error(f"Failed to reject and retry: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/api/select-projects/{job_id}", response_model=ProjectSelectionResponse) +async def select_projects(job_id: str, request: ProjectSelectionRequest, background_tasks: BackgroundTasks): + """ + Submit user's project selection and resume workflow + """ + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + job = job_storage[job_id] + + if not job.get("awaiting_project_selection"): + raise HTTPException(status_code=400, detail="Job is not awaiting project selection") + + # Validate: 1 project can be selected + if len(request.selected_project_paths) != 1: + project_word = "project" if len(request.selected_project_paths) == 1 else "projects" + raise HTTPException( + status_code=400, + detail=f" {len(request.selected_project_paths)} {project_word} are selected! Please select one project, as single-project documentation is supported." + ) + + try: + # Update job storage + job["awaiting_project_selection"] = False + job_storage[job_id] = job + + logger.info(f"User selected {len(request.selected_project_paths)} projects for job {job_id}") + + # Resume workflow with selected projects + background_tasks.add_task(resume_workflow, job_id, request.selected_project_paths) + + return ProjectSelectionResponse( + status="accepted", + message=f"Selected {len(request.selected_project_paths)} project(s). Resuming documentation generation..." + ) + + except Exception as e: + logger.error(f"Failed to process project selection: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +async def resume_workflow(job_id: str, selected_projects: List[str]): + """ + Resume workflow after user selects projects + """ + try: + workflow = await get_workflow() + config = {"configurable": {"thread_id": job_id}} + + # Log selection + log_manager = get_log_manager() + await log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message=f"✅ User selected {len(selected_projects)} project(s). Continuing workflow..." + ) + + # DEBUG: Log the selected projects + logger.info(f"DEBUG: Updating state with selected_projects = {selected_projects}") + + # Update the state directly in the graph + await workflow.graph.aupdate_state( + config, + { + "selected_projects": selected_projects, + "awaiting_project_selection": False + } + ) + + # Resume workflow from updated state + async for event in workflow.graph.astream(None, config): + logger.info(f"Resumed workflow event: {event}") + + # Update job storage + for node_name, node_state in event.items(): + if isinstance(node_state, dict): + workflow_status = node_state.get("workflow_status", "in_progress") + job_storage[job_id]["status"] = workflow_status + job_storage[job_id]["current_agent"] = node_state.get("current_agent") + + # Store repo_name for PR creation + if node_state.get("repo_name"): + job_storage[job_id]["repo_name"] = node_state["repo_name"] + + # CRITICAL: Update final_readme whenever present + if node_state.get("final_readme"): + job_storage[job_id]["final_readme"] = node_state["final_readme"] + logger.info(f"✅ [RESUME] Updated job_storage[{job_id}]['final_readme'] = {len(node_state['final_readme'])} chars (workflow_status={workflow_status})") + + if node_state.get("error"): + job_storage[job_id]["error"] = node_state["error"] + + logger.info(f"Workflow resumed and completed for job: {job_id}") + logger.info(f"✅ Final job_storage[{job_id}] has final_readme: {'final_readme' in job_storage[job_id]}") + + except Exception as e: + logger.error(f"Failed to resume workflow: {e}", exc_info=True) + job_storage[job_id]["status"] = "failed" + job_storage[job_id]["error"] = str(e) + + +@app.get("/api/download/{job_id}") +def download_readme(job_id: str): + """Download generated README.md""" + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + job = job_storage[job_id] + + if not job.get("final_readme"): + raise HTTPException(status_code=400, detail="README not ready yet") + + from fastapi.responses import Response + + return Response( + content=job["final_readme"], + media_type="text/markdown", + headers={ + "Content-Disposition": "attachment; filename=README.md" + } + ) + + +@app.post("/api/create-pr/{job_id}") +async def create_pull_request(job_id: str): + """ + Create a GitHub Pull Request using TRUE MCP Protocol + Connects to official GitHub MCP server via stdio + """ + if job_id not in job_storage: + raise HTTPException(status_code=404, detail="Job not found") + + job = job_storage[job_id] + + if not job.get("final_readme"): + raise HTTPException(status_code=400, detail="README not ready yet") + + # Check if GitHub token is configured + github_token = settings.GITHUB_TOKEN + if not github_token: + raise HTTPException( + status_code=503, + detail="GitHub integration not configured. Please set GITHUB_TOKEN in environment variables." + ) + + try: + # Extract repo info from URL + repo_url = job["repo_url"] + + # Parse GitHub repo from URL + # Expected format: https://github.com/owner/repo or https://github.com/owner/repo/tree/branch/... + if "github.com" not in repo_url: + raise HTTPException(status_code=400, detail="Only GitHub repositories are supported for PR creation") + + # Parse the full GitHub URL to extract owner, repo, and branch + from services.git_service import parse_github_url + parsed_url = parse_github_url(repo_url) + + repo_full_name = f"{parsed_url['owner']}/{parsed_url['repo']}" + base_branch = parsed_url['branch'] # Extract branch from URL (e.g., "dev") + + # Extract project name from workflow state (already set correctly) + # Use the actual repo_name from job storage which comes from workflow + project_name = job.get("repo_name", parsed_url['display_name']) + + # Call MCP-based PR creation + result = await create_pr_with_mcp( + repo_full_name=repo_full_name, + readme_content=job["final_readme"], + project_name=project_name, + base_branch=base_branch, + github_token=github_token, + job_id=job_id + ) + + if result["success"]: + return { + "status": "success", + "message": "Pull request created successfully via MCP", + "pr_url": result["pr_url"], + "branch_name": result["branch_name"] + } + else: + return { + "status": "error", + "message": f"Failed to create PR via MCP: {result['output']}", + "details": result["output"] + } + + except HTTPException: + raise + except Exception as e: + logger.error(f"Failed to create PR via MCP: {e}", exc_info=True) + raise HTTPException(status_code=500, detail=f"Failed to create PR via MCP: {str(e)}") + + +if __name__ == "__main__": + import uvicorn + uvicorn.run( + app, + host=settings.HOST, + port=settings.API_PORT, + log_level="info" + ) diff --git a/api/services/__init__.py b/api/services/__init__.py new file mode 100644 index 0000000..eb6a301 --- /dev/null +++ b/api/services/__init__.py @@ -0,0 +1,11 @@ +""" +Services for DocuBot AI +""" + +from .llm_service import get_llm +from .git_service import GitService + +__all__ = [ + "get_llm", + "GitService" +] diff --git a/api/services/git_service.py b/api/services/git_service.py new file mode 100644 index 0000000..8961b1e --- /dev/null +++ b/api/services/git_service.py @@ -0,0 +1,286 @@ +""" +Git Service - Handles repository cloning and cleanup +""" + +import os +import shutil +import logging +import re +import requests +from typing import Tuple, Dict, Optional, Callable +from git import Repo, GitCommandError, RemoteProgress +from config import settings +import uuid + +logger = logging.getLogger(__name__) + + +def parse_github_url(url: str) -> Dict[str, Optional[str]]: + """ + Parse GitHub URL to extract components + + Supports: + - Base repo: https://github.com/owner/repo + - Base repo with .git: https://github.com/owner/repo.git + - Tree URL: https://github.com/owner/repo/tree/branch + - Subfolder URL: https://github.com/owner/repo/tree/branch/path/to/folder + + Args: + url: GitHub URL + + Returns: + Dict with keys: owner, repo, branch, subfolder, clone_url, is_subfolder + + Raises: + ValueError: If URL is invalid + """ + url = url.rstrip('/') + + # Pattern: https://github.com/owner/repo[.git][/tree/branch[/path]] + pattern = r'^https?://github\.com/([^/]+)/([^/]+?)(?:\.git)?(?:/tree/([^/]+)(?:/(.*))?)?$' + + match = re.match(pattern, url) + + if not match: + raise ValueError( + f"Invalid GitHub URL format. Expected: https://github.com/owner/repo[/tree/branch[/path]]\n" + f"Got: {url}" + ) + + owner, repo, branch, subfolder = match.groups() + + repo = repo.replace('.git', '') + + result = { + "owner": owner, + "repo": repo, + "branch": branch or "main", # Default to main if not specified + "subfolder": subfolder if subfolder else None, + "clone_url": f"https://github.com/{owner}/{repo}.git", + "is_subfolder": bool(subfolder), + "display_name": subfolder.split('/')[-1] if subfolder else repo + } + + logger.info(f"Parsed URL: {url} -> {result}") + + return result + + +class CloneProgress(RemoteProgress): + """Progress reporter for git clone operations""" + + def __init__(self, callback: Optional[Callable[[str], None]] = None): + super().__init__() + self.callback = callback + self._last_message = "" + + def update(self, op_code, cur_count, max_count=None, message=''): + """Called by GitPython during clone operation""" + if self.callback: + # Parse operation + if op_code & self.COUNTING: + msg = f"📊 Counting objects: {cur_count}" + elif op_code & self.COMPRESSING: + msg = f"🗜️ Compressing objects: {cur_count}/{max_count}" if max_count else f"🗜️ Compressing objects: {cur_count}" + elif op_code & self.RECEIVING: + percentage = int((cur_count / max_count * 100)) if max_count else 0 + msg = f"⬇️ Receiving objects: {cur_count}/{max_count} ({percentage}%)" + elif op_code & self.RESOLVING: + percentage = int((cur_count / max_count * 100)) if max_count else 0 + msg = f"🔧 Resolving deltas: {cur_count}/{max_count} ({percentage}%)" + else: + msg = f"⏳ Cloning: {cur_count}" + + # Only report if message changed (avoid spam) + if msg != self._last_message: + self._last_message = msg + self.callback(msg) + + +class GitService: + """Service for Git operations""" + + def __init__(self): + self.temp_dir = settings.TEMP_REPO_DIR + os.makedirs(self.temp_dir, exist_ok=True) + + def clone_repository( + self, + repo_url: str, + branch: Optional[str] = None, + progress_callback: Optional[Callable[[str], None]] = None + ) -> Tuple[str, Dict[str, str]]: + """ + Clone a Git repository to a temporary directory + + Args: + repo_url: GitHub repository URL + branch: Optional branch name to checkout after cloning + progress_callback: Optional callback for progress updates + + Returns: + Tuple of (repo_path, metadata_dict) + """ + try: + # Generate unique directory name + repo_id = str(uuid.uuid4()) + repo_path = os.path.join(self.temp_dir, repo_id) + + logger.info(f"Cloning repository: {repo_url}") + + # Check repository size before cloning + try: + # Extract owner and repo name from URL + # Format: https://github.com/owner/repo or https://github.com/owner/repo.git + parts = repo_url.rstrip('/').replace('.git', '').split('/') + if 'github.com' in repo_url and len(parts) >= 2: + owner = parts[-2] + repo_name = parts[-1] + + # Query GitHub API for repo info + api_url = f"https://api.github.com/repos/{owner}/{repo_name}" + headers = {} + if settings.GITHUB_TOKEN: + headers['Authorization'] = f'token {settings.GITHUB_TOKEN}' + + response = requests.get(api_url, headers=headers, timeout=10) + if response.status_code == 200: + repo_data = response.json() + repo_size_kb = repo_data.get('size', 0) # GitHub returns size in KB + repo_size_bytes = repo_size_kb * 1024 + + max_size_gb = settings.MAX_REPO_SIZE / (1024 ** 3) + if repo_size_bytes > settings.MAX_REPO_SIZE: + error_msg = f"Repository size ({repo_size_bytes / (1024 ** 3):.2f}GB) exceeds maximum allowed size ({max_size_gb:.0f}GB). Please try a smaller repository." + logger.error(error_msg) + if progress_callback: + progress_callback(f"❌ {error_msg}") + raise ValueError(error_msg) + else: + logger.info(f"Repository size check passed: {repo_size_bytes / (1024 ** 3):.2f}GB") + if progress_callback: + progress_callback(f"✅ Repository size: {repo_size_bytes / (1024 ** 3):.2f}GB") + except requests.RequestException as e: + # Don't fail if we can't check size, just log warning + logger.warning(f"Could not verify repository size: {e}") + except ValueError: + # Re-raise size limit errors + raise + + if progress_callback: + progress_callback(f"🚀 Starting clone of {repo_url}") + + # Clone the repository with progress tracking + progress = CloneProgress(callback=progress_callback) + + # Add GitHub PAT authentication for private repos + clone_url = repo_url + if settings.GITHUB_TOKEN and 'github.com' in repo_url: + # Inject GitHub PAT into URL: https://TOKEN@github.com/owner/repo.git + clone_url = repo_url.replace('https://github.com/', f'https://{settings.GITHUB_TOKEN}@github.com/') + logger.info(f"Using authenticated clone for private repository") + + repo = Repo.clone_from(clone_url, repo_path, progress=progress) + + if progress_callback: + progress_callback(f"✅ Clone completed successfully") + + # Checkout specified branch if provided + if branch: + try: + current_branch = None + try: + current_branch = repo.active_branch.name + except TypeError: + # Detached HEAD state or no branches yet + pass + + # Only checkout if not already on the requested branch + if current_branch != branch: + # Check if branch exists locally + if branch in [ref.name for ref in repo.heads]: + repo.git.checkout(branch) + else: + # Create local branch tracking remote branch + repo.git.checkout('-b', branch, f'origin/{branch}') + + logger.info(f"Checked out branch: {branch}") + if progress_callback: + progress_callback(f"✅ Checked out branch: {branch}") + except Exception as e: + logger.warning(f"Failed to checkout branch {branch}: {e}") + if progress_callback: + progress_callback(f"⚠️ Warning: Could not checkout branch {branch}, using default branch") + + # Extract metadata + metadata = { + "repo_url": repo_url, + "repo_name": repo_url.split('/')[-1].replace('.git', ''), + "default_branch": repo.active_branch.name, + "repo_path": repo_path + } + + logger.info(f"Repository cloned successfully to: {repo_path}") + + return repo_path, metadata + + except GitCommandError as e: + logger.error(f"Git clone failed: {e}") + + # Parse git error and provide user-friendly message + error_message = str(e) + user_friendly_message = None + + # Exit code 128: Repository not found or access denied + if "exit code(128)" in error_message: + if "not found" in error_message.lower() or "could not read" in error_message.lower(): + user_friendly_message = "Repository not found. Please check the URL and verify the repository exists." + elif "authentication" in error_message.lower() or "permission" in error_message.lower(): + user_friendly_message = "Access denied. This repository may be private or you may not have permission to access it." + else: + user_friendly_message = "Repository not found or access denied. Please verify the URL and your permissions." + + # Exit code 403: Rate limit or access forbidden + elif "403" in error_message: + user_friendly_message = "Access forbidden. You may have hit GitHub's rate limit or don't have permission to access this repository." + + # Network errors + elif "connection" in error_message.lower() or "network" in error_message.lower(): + user_friendly_message = "Network error. Please check your internet connection and try again." + + # Use user-friendly message if detected, otherwise use generic message + final_message = user_friendly_message or f"Failed to clone repository: {str(e)}" + + if progress_callback: + progress_callback(f"❌ {final_message}") + + raise ValueError(final_message) + + except Exception as e: + logger.error(f"Unexpected error cloning repository: {e}") + error_message = f"Unexpected error while cloning: {str(e)}" + + if progress_callback: + progress_callback(f"❌ {error_message}") + + raise ValueError(error_message) + + def cleanup_repository(self, repo_path: str) -> bool: + """ + Clean up cloned repository + + Args: + repo_path: Path to the repository + + Returns: + True if successful + """ + try: + if os.path.exists(repo_path): + shutil.rmtree(repo_path, ignore_errors=True) + logger.info(f"Cleaned up repository: {repo_path}") + return True + return False + except Exception as e: + logger.warning(f"Failed to cleanup repository {repo_path}: {e}") + return False diff --git a/api/services/llm_service.py b/api/services/llm_service.py new file mode 100644 index 0000000..9ae8afd --- /dev/null +++ b/api/services/llm_service.py @@ -0,0 +1,148 @@ +""" +LLM Service - Universal LLM client for multiple providers +Supports OpenAI, Groq, Ollama, OpenRouter, Custom APIs, and Enterprise Inference +""" + +import logging +from typing import Optional +from langchain_openai import ChatOpenAI +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type +from config import settings +import httpx + +logger = logging.getLogger(__name__) + + +@retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=2, max=10), + retry=retry_if_exception_type((httpx.HTTPError, httpx.TimeoutException)), + reraise=True +) +def get_llm(model_name: Optional[str] = None, temperature: Optional[float] = None) -> ChatOpenAI: + """ + Get universal LLM instance supporting multiple providers + + Supports: + - OpenAI (gpt-4o, gpt-4-turbo, gpt-4o-mini) + - Groq (llama models, fast inference) + - Ollama (local models, qwen, llama) + - OpenRouter (claude, gemini, multi-model access) + - Custom OpenAI-compatible APIs + - Enterprise Inference endpoints (backward compatibility) + + Args: + model_name: Model name to use (defaults to agent-specific model or LLM_MODEL) + temperature: Temperature for generation (defaults to TEMPERATURE setting) + + Returns: + ChatOpenAI instance configured for the selected provider + + Raises: + ValueError: If configuration is invalid or missing required fields + """ + # Use defaults from settings + if model_name is None: + model_name = settings.LLM_MODEL + if temperature is None: + temperature = settings.TEMPERATURE + + if settings.INFERENCE_API_ENDPOINT and settings.INFERENCE_API_TOKEN: + logger.info(f"Using enterprise inference endpoint for model: {model_name}") + return _get_enterprise_inference_llm(model_name, temperature) + + # Use new multi-provider configuration + if not settings.LLM_BASE_URL: + raise ValueError( + "LLM_BASE_URL is required. Set LLM_BASE_URL in your .env file.\n" + "Examples:\n" + " OpenAI: https://api.openai.com/v1\n" + " Groq: https://api.groq.com/openai/v1\n" + " Ollama: http://localhost:11434/v1\n" + " OpenRouter: https://openrouter.ai/api/v1" + ) + + provider = settings.LLM_PROVIDER.lower() + logger.info(f"Initializing {provider} LLM with model: {model_name}") + + # Validate API key requirement (Ollama doesn't need one) + if provider != "ollama" and not settings.LLM_API_KEY: + raise ValueError( + f"LLM_API_KEY is required for {provider}. " + f"Set LLM_API_KEY in your .env file or use provider=ollama for local deployment." + ) + + # Normalize base URL based on provider + base_url = _normalize_base_url(settings.LLM_BASE_URL, provider) + + # Configure async httpx client with SSL verification and timeout + http_async_client = httpx.AsyncClient( + verify=settings.VERIFY_SSL, + timeout=settings.REQUEST_TIMEOUT + ) + + return ChatOpenAI( + model=model_name, + temperature=temperature, + openai_api_key=settings.LLM_API_KEY or "not-needed", # Ollama doesn't need a key + openai_api_base=base_url, + max_tokens=settings.MAX_TOKENS, + max_retries=settings.MAX_RETRIES, + http_async_client=http_async_client + ) + + +def _normalize_base_url(base_url: str, provider: str) -> str: + """ + Normalize base URL for different providers + + Args: + base_url: Raw base URL from configuration + provider: Provider name (openai, groq, ollama, etc.) + + Returns: + Normalized base URL for OpenAI-compatible API + """ + base_url = base_url.rstrip('/') + + # Ollama: Add /v1 if not present + if provider == "ollama" and not base_url.endswith('/v1'): + return f"{base_url}/v1" + + # Other providers: Use as-is (they should already have /v1 or correct path) + return base_url + + +def _get_enterprise_inference_llm(model_name: str, temperature: float) -> ChatOpenAI: + """ + Enterprise inference configuration + + Args: + model_name: Model name + temperature: Temperature setting + + Returns: + ChatOpenAI instance for enterprise inference endpoint + """ + if not settings.INFERENCE_API_ENDPOINT or not settings.INFERENCE_API_TOKEN: + raise ValueError("INFERENCE_API_ENDPOINT and INFERENCE_API_TOKEN are required for enterprise inference") + + # Configure async httpx client + http_async_client = httpx.AsyncClient( + verify=settings.VERIFY_SSL, + timeout=settings.REQUEST_TIMEOUT + ) + + # Enterprise inference endpoints typically need /v1 suffix + endpoint = settings.INFERENCE_API_ENDPOINT.rstrip('/') + if not endpoint.endswith('/v1'): + endpoint = f"{endpoint}/v1" + + return ChatOpenAI( + model=model_name, + temperature=temperature, + openai_api_key=settings.INFERENCE_API_TOKEN, + openai_api_base=endpoint, + max_tokens=settings.AGENT_MAX_TOKENS, + http_async_client=http_async_client + ) diff --git a/api/tools/__init__.py b/api/tools/__init__.py new file mode 100644 index 0000000..d7897a0 --- /dev/null +++ b/api/tools/__init__.py @@ -0,0 +1,31 @@ +""" +LangChain Tools for repository analysis +These tools are used by agents to interact with code repositories +""" + +from .repo_tools import ( + list_directory_tool, + read_file_tool, + detect_languages_tool, + extract_dependencies_tool, + analyze_code_structure_tool +) + +from .new_analysis_tools import ( + analyze_call_graph_tool, + find_error_handlers_tool, + analyze_exceptions_tool, + extract_env_vars_tool +) + +__all__ = [ + "list_directory_tool", + "read_file_tool", + "detect_languages_tool", + "extract_dependencies_tool", + "analyze_code_structure_tool", + "analyze_call_graph_tool", + "find_error_handlers_tool", + "analyze_exceptions_tool", + "extract_env_vars_tool" +] diff --git a/api/tools/new_analysis_tools.py b/api/tools/new_analysis_tools.py new file mode 100644 index 0000000..c7fffbf --- /dev/null +++ b/api/tools/new_analysis_tools.py @@ -0,0 +1,443 @@ +""" +New Analysis Tools for Micro-Agent Architecture + +Four specialized tools for code analysis: +1. analyze_call_graph - Python function call relationships +2. find_error_handlers - Exception handling patterns +3. analyze_exceptions - Error handling analysis +4. extract_env_vars - Environment variable extraction + +All tools use RepoReadService for cached, bounded file reads +""" + +import os +import ast +import re +import json +import logging +from typing import Dict, List, Set, Optional +from pathlib import Path +from langchain.tools import tool + +logger = logging.getLogger(__name__) + + +@tool +def analyze_call_graph_tool(repo_path: str, entry_file: str) -> str: + """ + Analyze Python function call relationships to build call graph. + + Extracts which functions call which other functions, useful for + understanding code flow and dependencies. + + Args: + repo_path: Absolute path to repository root + entry_file: Relative path to Python file to analyze + + Returns: + JSON string with call graph data + """ + try: + full_path = os.path.normpath(os.path.join(repo_path, entry_file)) + + # Security check + if not full_path.startswith(os.path.normpath(repo_path)): + return json.dumps({"error": "Access denied - path traversal detected"}) + + if not os.path.exists(full_path): + return json.dumps({"error": f"File not found: {entry_file}"}) + + if not entry_file.endswith('.py'): + return json.dumps({"error": "Only Python files supported for call graph analysis"}) + + # Read file (respects RepoReadService caps via shared config) + with open(full_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + + # Parse AST + try: + tree = ast.parse(content) + except SyntaxError as e: + return json.dumps({ + "error": f"Python syntax error: {str(e)}", + "file": entry_file + }) + + # Extract function definitions and calls + functions = {} + + for node in ast.walk(tree): + if isinstance(node, ast.FunctionDef): + func_name = node.name + calls = [] + + # Find function calls within this function + for child in ast.walk(node): + if isinstance(child, ast.Call): + if isinstance(child.func, ast.Name): + calls.append(child.func.id) + elif isinstance(child.func, ast.Attribute): + calls.append(f"{child.func.value.id if hasattr(child.func.value, 'id') else '?'}.{child.func.attr}") + + functions[func_name] = { + "calls": list(set(calls)), # Deduplicate + "call_count": len(calls), + "line_number": node.lineno + } + + # Build graph structure + edges = [] + for func, data in functions.items(): + for called_func in data["calls"]: + # Only include edges to other functions in this file + if called_func in functions: + edges.append({"from": func, "to": called_func}) + + result = { + "file": entry_file, + "functions": functions, + "edges": edges, + "function_count": len(functions), + "edge_count": len(edges) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error analyzing call graph for {entry_file}: {e}") + return json.dumps({"error": f"Analysis failed: {str(e)}"}) + + +@tool +def find_error_handlers_tool(repo_path: str) -> str: + """ + Find exception handlers (try/except blocks) across Python files. + + Identifies error handling patterns and which exceptions are caught. + + Args: + repo_path: Absolute path to repository root + + Returns: + JSON string with error handler locations and types + """ + try: + error_handlers = [] + files_scanned = 0 + max_files = 50 # Limit scan to prevent timeout + + # Walk repository + for root, dirs, files in os.walk(repo_path): + # Filter ignored directories + dirs[:] = [d for d in dirs if d not in { + '.git', '__pycache__', 'node_modules', '.venv', 'venv', + 'dist', 'build', 'target', 'coverage' + }] + + for file in files: + if not file.endswith('.py'): + continue + + if files_scanned >= max_files: + break + + file_path = os.path.join(root, file) + rel_path = os.path.relpath(file_path, repo_path) + + try: + with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + + # Parse AST + tree = ast.parse(content) + + # Find try/except blocks + for node in ast.walk(tree): + if isinstance(node, ast.Try): + exceptions_caught = [] + + for handler in node.handlers: + if handler.type: + if isinstance(handler.type, ast.Name): + exceptions_caught.append(handler.type.id) + elif isinstance(handler.type, ast.Tuple): + for exc in handler.type.elts: + if isinstance(exc, ast.Name): + exceptions_caught.append(exc.id) + else: + exceptions_caught.append("Exception") # Bare except + + error_handlers.append({ + "file": rel_path, + "line": node.lineno, + "exceptions": exceptions_caught, + "has_finally": len(node.finalbody) > 0, + "has_else": len(node.orelse) > 0 + }) + + files_scanned += 1 + + except Exception as e: + logger.debug(f"Skipping {rel_path}: {e}") + continue + + if files_scanned >= max_files: + break + + # Aggregate statistics + exception_types = {} + for handler in error_handlers: + for exc in handler["exceptions"]: + exception_types[exc] = exception_types.get(exc, 0) + 1 + + result = { + "error_handlers": error_handlers[:100], # Limit output + "total_handlers": len(error_handlers), + "files_scanned": files_scanned, + "exception_types": dict(sorted(exception_types.items(), key=lambda x: x[1], reverse=True)) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding error handlers: {e}") + return json.dumps({"error": f"Scan failed: {str(e)}"}) + + +@tool +def analyze_exceptions_tool(repo_path: str, file_path: str) -> str: + """ + Analyze exception handling in a specific Python file. + + Provides detailed analysis of try/except patterns, custom exceptions, + and error handling quality. + + Args: + repo_path: Absolute path to repository root + file_path: Relative path to Python file + + Returns: + JSON string with exception analysis + """ + try: + full_path = os.path.normpath(os.path.join(repo_path, file_path)) + + # Security check + if not full_path.startswith(os.path.normpath(repo_path)): + return json.dumps({"error": "Access denied"}) + + if not os.path.exists(full_path): + return json.dumps({"error": f"File not found: {file_path}"}) + + # Read file + with open(full_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + + # Parse AST + try: + tree = ast.parse(content) + except SyntaxError as e: + return json.dumps({"error": f"Syntax error: {str(e)}"}) + + # Find custom exception classes + custom_exceptions = [] + for node in ast.walk(tree): + if isinstance(node, ast.ClassDef): + # Check if it inherits from Exception + for base in node.bases: + if isinstance(base, ast.Name) and 'Exception' in base.id: + custom_exceptions.append({ + "name": node.name, + "line": node.lineno + }) + + # Analyze try/except blocks + exception_handlers = [] + bare_excepts = [] + + for node in ast.walk(tree): + if isinstance(node, ast.Try): + handler_info = { + "line": node.lineno, + "handlers": [] + } + + for handler in node.handlers: + if handler.type is None: + bare_excepts.append(node.lineno) + handler_info["handlers"].append({"type": "bare_except", "warning": "Catches all exceptions"}) + elif isinstance(handler.type, ast.Name): + handler_info["handlers"].append({"type": handler.type.id}) + elif isinstance(handler.type, ast.Tuple): + types = [exc.id for exc in handler.type.elts if isinstance(exc, ast.Name)] + handler_info["handlers"].append({"types": types}) + + exception_handlers.append(handler_info) + + # Find raise statements + raises = [] + for node in ast.walk(tree): + if isinstance(node, ast.Raise): + if node.exc: + if isinstance(node.exc, ast.Call) and isinstance(node.exc.func, ast.Name): + raises.append({ + "line": node.lineno, + "exception": node.exc.func.id + }) + elif isinstance(node.exc, ast.Name): + raises.append({ + "line": node.lineno, + "exception": node.exc.id + }) + + result = { + "file": file_path, + "custom_exceptions": custom_exceptions, + "exception_handlers": exception_handlers, + "bare_excepts_count": len(bare_excepts), + "bare_except_lines": bare_excepts, + "raises": raises, + "quality_notes": [] + } + + # Add quality notes + if len(bare_excepts) > 0: + result["quality_notes"].append(f"Found {len(bare_excepts)} bare except clauses (not recommended)") + + if len(custom_exceptions) > 0: + result["quality_notes"].append(f"Defines {len(custom_exceptions)} custom exception classes") + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error analyzing exceptions in {file_path}: {e}") + return json.dumps({"error": f"Analysis failed: {str(e)}"}) + + +@tool +def extract_env_vars_tool(repo_path: str) -> str: + """ + Extract environment variables from .env example files. + + Finds .env.example, .env.sample, .env.template files and extracts + variable names (not values, for security). + + Args: + repo_path: Absolute path to repository root + + Returns: + JSON string with environment variable names and descriptions + """ + try: + env_vars = [] + files_found = [] + + # Common env file patterns + env_file_patterns = [ + '.env.example', + '.env.sample', + '.env.template', + 'env.example', + 'example.env' + ] + + # Search for env files + for pattern in env_file_patterns: + file_path = os.path.join(repo_path, pattern) + if os.path.exists(file_path): + files_found.append(pattern) + + try: + with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: + lines = f.readlines() + + for line_num, line in enumerate(lines, 1): + line = line.strip() + + # Skip comments and empty lines + if not line or line.startswith('#'): + continue + + # Parse KEY=value format + if '=' in line: + key, value = line.split('=', 1) + key = key.strip() + value = value.strip() + + # Extract inline comment if present + comment = "" + if '#' in value: + value, comment = value.split('#', 1) + comment = comment.strip() + + # Check if value looks like a placeholder + is_placeholder = bool(re.match(r'^(<.*?>|".*?"|\'.*?\'|\$\{.*?\}|your_.*|example_.*)', value, re.IGNORECASE)) + + env_vars.append({ + "key": key, + "file": pattern, + "line": line_num, + "has_default": len(value) > 0 and not is_placeholder, + "description": comment if comment else None, + "is_placeholder": is_placeholder + }) + + except Exception as e: + logger.warning(f"Error reading {pattern}: {e}") + continue + + # Also check for variables in README or docs + readme_vars = set() + for readme_name in ['README.md', 'readme.md', 'README']: + readme_path = os.path.join(repo_path, readme_name) + if os.path.exists(readme_path): + try: + with open(readme_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + + # Look for environment variable mentions + var_pattern = r'\b([A-Z_][A-Z0-9_]{2,})\b' + matches = re.findall(var_pattern, content) + readme_vars.update(matches) + + except: + pass + + # Categorize variables + categories = { + "database": [], + "api_keys": [], + "urls": [], + "ports": [], + "auth": [], + "other": [] + } + + for var in env_vars: + key_lower = var["key"].lower() + if any(word in key_lower for word in ['db', 'database', 'postgres', 'mysql', 'mongo']): + categories["database"].append(var) + elif any(word in key_lower for word in ['key', 'token', 'secret', 'password']): + categories["api_keys"].append(var) + elif any(word in key_lower for word in ['url', 'endpoint', 'host', 'domain']): + categories["urls"].append(var) + elif 'port' in key_lower: + categories["ports"].append(var) + elif any(word in key_lower for word in ['auth', 'client_id', 'client_secret', 'oauth']): + categories["auth"].append(var) + else: + categories["other"].append(var) + + result = { + "env_files_found": files_found, + "total_variables": len(env_vars), + "variables": env_vars, + "categorized": categories, + "readme_mentions": list(readme_vars)[:20] # Limit to 20 + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error extracting env vars: {e}") + return json.dumps({"error": f"Extraction failed: {str(e)}"}) diff --git a/api/tools/repo_tools.py b/api/tools/repo_tools.py new file mode 100644 index 0000000..f81103e --- /dev/null +++ b/api/tools/repo_tools.py @@ -0,0 +1,1291 @@ +""" +LangChain tools for repository analysis +Uses @tool decorator for automatic integration with agents + +FIXED VERSION: Strengthened Mermaid validation for render-safe diagrams +ENHANCED: Strategic file reading with pattern_window mode +""" + +import os +import json +import re +from pathlib import Path +from collections import Counter +from typing import Dict, List, Literal +from langchain.tools import tool +import logging + +from config import settings + +logger = logging.getLogger(__name__) + +# Ignore patterns for directory listing +IGNORE_DIRS = { + '.git', '__pycache__', 'node_modules', '.venv', 'venv', + 'dist', 'build', '.next', '.nuxt', 'target', 'coverage', + '.idea', '.vscode', 'DS_Store' +} + +# Language detection mapping +LANGUAGE_MAP = { + '.py': 'Python', + '.js': 'JavaScript', + '.ts': 'TypeScript', + '.jsx': 'JavaScript (JSX)', + '.tsx': 'TypeScript (TSX)', + '.java': 'Java', + '.go': 'Go', + '.rs': 'Rust', + '.c': 'C', + '.cpp': 'C++', + '.cs': 'C#', + '.rb': 'Ruby', + '.php': 'PHP', + '.swift': 'Swift', + '.kt': 'Kotlin', + '.scala': 'Scala', +} + + +@tool +def list_directory_tool(repo_path: str, relative_path: str = ".") -> str: + """ + List contents of a directory in the repository. + Shows files and subdirectories with their types. + + Args: + repo_path: Absolute path to the repository root + relative_path: Relative path from repo root (default: ".") + + Returns: + Formatted string listing files and directories + """ + try: + full_path = os.path.normpath(os.path.join(repo_path, relative_path)) + + # Security check + if not full_path.startswith(os.path.normpath(repo_path)): + return f"Error: Access denied - path traversal detected" + + if not os.path.exists(full_path): + return f"Error: Directory not found: {relative_path}" + + if not os.path.isdir(full_path): + return f"Error: Path is not a directory: {relative_path}" + + items = [] + for entry in os.listdir(full_path): + if entry in IGNORE_DIRS or entry.startswith('.'): + continue + + entry_path = os.path.join(full_path, entry) + item_type = "DIR" if os.path.isdir(entry_path) else "FILE" + items.append(f"[{item_type}] {entry}") + + if not items: + return f"Directory '{relative_path}' is empty or contains only ignored files" + + return f"Contents of {relative_path}:\n" + "\n".join(sorted(items)) + + except Exception as e: + logger.error(f"Error listing directory {relative_path}: {e}") + return f"Error listing directory: {str(e)}" + + +def _smart_sample_lines(lines: List[str], max_lines: int) -> tuple[list[str], bool]: + """ + Smart sampling: top + function/class signatures + bottom + + Args: + lines: All file lines + max_lines: Budget for total lines + + Returns: + (selected_lines, was_truncated) + """ + if len(lines) <= max_lines: + return lines, False + + top_n = min(50, max_lines) + bottom_n = min(30, max_lines - top_n) + middle_budget = max(0, max_lines - top_n - bottom_n) + + top = lines[:top_n] + bottom = lines[-bottom_n:] if bottom_n > 0 else [] + + # Extract function/class signatures + sigs = [] + for ln in lines[top_n:len(lines) - bottom_n]: + s = ln.lstrip() + if s.startswith(("def ", "async def ", "class ")): + sigs.append(ln) + if len(sigs) >= middle_budget: + break + + out = top + ["\n... [middle omitted] ...\n"] + sigs + ["\n... [end] ...\n"] + bottom + return out[:max_lines], True + + +def _pattern_window_lines(lines: List[str], max_lines: int) -> tuple[list[str], bool]: + """ + Pattern window sampling: detect high-value patterns, extract ±6 lines around matches + + Patterns detected: + - FastAPI/Flask routes: @app.get, @router.post, APIRouter, include_router + - Error handling: try, except, raise + - Entry points: if __name__ == "__main__", uvicorn.run, def main + + Args: + lines: All file lines + max_lines: Budget for total lines + + Returns: + (selected_lines, was_truncated) + """ + if len(lines) <= max_lines: + return lines, False + + patterns = [ + r"@app\.(get|post|put|delete)\b", + r"@router\.(get|post|put|delete)\b", + r"\bAPIRouter\b", + r"\binclude_router\b", + r"\btry:\b", + r"\bexcept\b", + r"\braise\b", + r'if __name__\s*==\s*[\'"]__main__[\'"]', + r"\buvicorn\.run\b", + r"\bdef main\(", + ] + + top_n = min(40, max_lines) + bottom_n = min(25, max_lines - top_n) + budget = max_lines - top_n - bottom_n + + # Find match line numbers + match_lines: list[int] = [] + for i, ln in enumerate(lines): + for pat in patterns: + if re.search(pat, ln): + match_lines.append(i) + break + + # Fallback to smart if no patterns found + if not match_lines: + logger.debug("No patterns found, falling back to smart strategy") + return _smart_sample_lines(lines, max_lines) + + # Make windows ±6 lines around matches + windows: list[tuple[int, int]] = [] + for i in match_lines[:50]: # Cap matches + start = max(0, i - 6) + end = min(len(lines), i + 7) + windows.append((start, end)) + + # Merge overlapping windows + windows.sort() + merged: list[tuple[int, int]] = [] + for s, e in windows: + if not merged or s > merged[-1][1]: + merged.append((s, e)) + else: + merged[-1] = (merged[-1][0], max(merged[-1][1], e)) + + # Emit lines within budget + out = lines[:top_n] + used = len(out) + last_end = top_n + + for s, e in merged: + if used >= top_n + budget: + break + if e <= top_n or s >= len(lines) - bottom_n: + continue # Already in top/bottom + + if s > last_end: + out.append("\n... [omitted] ...\n") + used += 1 + + chunk = lines[s:e] + room = top_n + budget - used + if room <= 0: + break + out.extend(chunk[:room]) + used = len(out) + last_end = e + + out.append("\n... [end] ...\n") + out.extend(lines[-bottom_n:]) + + # Final clamp + out = out[:max_lines] + return out, True + + +@tool +def read_file_tool( + repo_path: str, + file_path: str, + max_lines: int = None, + strategy: Literal["full", "smart", "pattern_window"] = "full" +) -> str: + """ + Read contents of a file from the repository. + + Args: + repo_path: Absolute path to the repository root + file_path: Relative path to the file from repo root + max_lines: Maximum lines to read (default: from settings.MAX_LINES_PER_FILE) + strategy: Reading strategy: + - "full": first N lines (default, backwards compatible) + - "smart": top + signatures + bottom + - "pattern_window": extract windows around high-value patterns (routes, errors, entrypoints) + + Returns: + File contents as string + """ + try: + # Use config setting if not specified + if max_lines is None: + max_lines = settings.MAX_LINES_PER_FILE + + full_path = os.path.normpath(os.path.join(repo_path, file_path)) + + # Security check + if not full_path.startswith(os.path.normpath(repo_path)): + return "Error: Access denied - path traversal detected" + + if not os.path.exists(full_path): + return f"Error: File not found: {file_path}" + + if not os.path.isfile(full_path): + return f"Error: Path is not a file: {file_path}" + + # Check file size using config setting + file_size = os.path.getsize(full_path) + max_file_size_mb = settings.MAX_FILE_SIZE / 1_000_000 + if file_size > settings.MAX_FILE_SIZE: + return f"Error: File too large ({file_size} bytes). Maximum is {max_file_size_mb:.1f}MB." + + # Read file + with open(full_path, "r", encoding="utf-8", errors="ignore") as f: + lines = f.readlines() + + # Strategy dispatch + if strategy == "full": + selected = lines[:max_lines] + truncated = len(lines) > max_lines + + elif strategy == "smart": + selected, truncated = _smart_sample_lines(lines, max_lines=max_lines) + + elif strategy == "pattern_window": + selected, truncated = _pattern_window_lines(lines, max_lines=max_lines) + + else: + selected = lines[:max_lines] + truncated = len(lines) > max_lines + + content = "".join(selected) + + # Deterministic header for verification + header = f"File: {file_path} | strategy: {strategy} | lines: {len(selected)}/{len(lines)}" + if truncated: + return f"{header}\n\n{content}\n\n[File truncated...]" + return f"{header}\n\n{content}" + + except Exception as e: + logger.error(f"Error reading file {file_path}: {e}") + return f"Error reading file: {str(e)}" + + +@tool +def detect_languages_tool(repo_path: str) -> str: + """ + Detect programming languages used in the repository. + Analyzes file extensions and provides statistics. + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with language statistics + """ + try: + language_counter = Counter() + total_files = 0 + + for root, dirs, files in os.walk(repo_path): + # Filter out ignored directories + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS and not d.startswith('.')] + + for file in files: + if file.startswith('.'): + continue + + ext = Path(file).suffix.lower() + if ext in LANGUAGE_MAP: + language = LANGUAGE_MAP[ext] + language_counter[language] += 1 + total_files += 1 + + if not language_counter: + return "No recognized programming languages detected" + + # Format output + result = { + "total_files": total_files, + "languages": dict(language_counter.most_common()), + "primary_language": language_counter.most_common(1)[0][0] if language_counter else "Unknown" + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error detecting languages: {e}") + return f"Error detecting languages: {str(e)}" + + +@tool +def extract_dependencies_tool(repo_path: str) -> str: + """ + Extract dependencies from common dependency files. + Recursively scans subdirectories for package.json, requirements.txt, go.mod, Cargo.toml, etc. + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with dependencies by ecosystem + """ + try: + dependencies = {} + found_files = [] + + # Recursively walk directory tree to find dependency files + for root, dirs, files in os.walk(repo_path): + # Filter out ignored directories + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS and not d.startswith('.')] + + # Python - requirements.txt + if 'requirements.txt' in files: + req_file = os.path.join(root, 'requirements.txt') + rel_path = os.path.relpath(req_file, repo_path) + try: + with open(req_file, 'r') as f: + deps = [line.strip().split('==')[0].split('>=')[0].split('[')[0] + for line in f if line.strip() and not line.startswith('#')] + if deps and "Python" not in dependencies: + dependencies["Python"] = deps[:20] # Limit to 20 + found_files.append(f"requirements.txt ({rel_path})") + except: + pass + + # Node.js - package.json + if 'package.json' in files: + pkg_file = os.path.join(root, 'package.json') + rel_path = os.path.relpath(pkg_file, repo_path) + try: + with open(pkg_file, 'r') as f: + pkg_data = json.load(f) + deps = list(pkg_data.get("dependencies", {}).keys()) + if deps and "Node.js" not in dependencies: + dependencies["Node.js"] = deps[:20] + found_files.append(f"package.json ({rel_path})") + except: + pass + + # Go - go.mod + if 'go.mod' in files: + go_file = os.path.join(root, 'go.mod') + rel_path = os.path.relpath(go_file, repo_path) + try: + with open(go_file, 'r') as f: + deps = [line.split()[0] for line in f if line.strip().startswith('require')] + if deps and "Go" not in dependencies: + dependencies["Go"] = deps[:20] + found_files.append(f"go.mod ({rel_path})") + except: + pass + + # Rust - Cargo.toml + if 'Cargo.toml' in files: + cargo_file = os.path.join(root, 'Cargo.toml') + rel_path = os.path.relpath(cargo_file, repo_path) + try: + with open(cargo_file, 'r') as f: + in_deps = False + deps = [] + for line in f: + if '[dependencies]' in line: + in_deps = True + continue + if in_deps and line.strip() and line.startswith('['): + break + if in_deps and '=' in line: + dep_name = line.split('=')[0].strip() + deps.append(dep_name) + if deps and "Rust" not in dependencies: + dependencies["Rust"] = deps[:20] + found_files.append(f"Cargo.toml ({rel_path})") + except: + pass + + if not dependencies: + return "No dependency files found" + + result = { + "dependencies": dependencies, + "files_found": found_files + } + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error extracting dependencies: {e}") + return f"Error extracting dependencies: {str(e)}" + + +@tool +def analyze_code_structure_tool(repo_path: str, file_path: str) -> str: + """ + Analyze code structure of a file (functions, classes, imports). + Uses AST parsing for Python, basic regex for others. + + Args: + repo_path: Absolute path to the repository root + file_path: Relative path to the file from repo root + + Returns: + JSON string with code structure analysis + """ + try: + full_path = os.path.normpath(os.path.join(repo_path, file_path)) + + if not full_path.startswith(os.path.normpath(repo_path)): + return "Error: Access denied" + + if not os.path.exists(full_path): + return "Error: File not found" + + # Check if path is a directory (common LLM mistake) + if os.path.isdir(full_path): + return f"Error: '{file_path}' is a directory, not a file. Use list_directory() to explore directories, or specify a specific file path to analyze." + + ext = Path(file_path).suffix.lower() + + # Python AST analysis + if ext == '.py': + try: + import ast + with open(full_path, 'r', encoding='utf-8') as f: + tree = ast.parse(f.read()) + + functions = [node.name for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)] + classes = [node.name for node in ast.walk(tree) if isinstance(node, ast.ClassDef)] + imports = [alias.name for node in ast.walk(tree) if isinstance(node, ast.Import) for alias in node.names] + + result = { + "language": "Python", + "functions": functions[:20], + "classes": classes[:20], + "imports": imports[:20], + "total_functions": len(functions), + "total_classes": len(classes) + } + return json.dumps(result, indent=2) + except Exception as e: + return f"Error parsing Python file: {str(e)}" + + # For other languages, provide basic info + with open(full_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + lines = content.split('\n') + + result = { + "language": LANGUAGE_MAP.get(ext, "Unknown"), + "lines_of_code": len(lines), + "non_empty_lines": len([l for l in lines if l.strip()]), + "note": "Detailed analysis only available for Python files" + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error analyzing code structure: {e}") + return f"Error analyzing code structure: {str(e)}" + + +# ============================================================================ +# SPECIALIZED TOOLS FOR PLANNER AGENT +# ============================================================================ + +@tool +def find_ui_files_tool(repo_path: str) -> str: + """ + Check if the project has a UI/frontend component. + Looks for frontend directories and files (React, Vue, Angular, HTML, etc.). + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with UI detection results + """ + try: + ui_indicators = { + "directories": [], + "files": [], + "frameworks": [] + } + + # Check for common frontend directories + ui_dirs = ['frontend', 'ui', 'client', 'web', 'app', 'src/components', 'public'] + for dir_name in ui_dirs: + dir_path = os.path.join(repo_path, dir_name) + if os.path.exists(dir_path) and os.path.isdir(dir_path): + ui_indicators["directories"].append(dir_name) + + # Check for frontend files + ui_files = ['index.html', 'App.jsx', 'App.tsx', 'App.vue', 'index.jsx', 'index.tsx'] + for root, dirs, files in os.walk(repo_path): + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS] + for file in files: + if file in ui_files: + rel_path = os.path.relpath(os.path.join(root, file), repo_path) + ui_indicators["files"].append(rel_path) + + # Check package.json for frontend frameworks + pkg_file = os.path.join(repo_path, "package.json") + if os.path.exists(pkg_file): + with open(pkg_file, 'r') as f: + pkg_data = json.load(f) + deps = {**pkg_data.get("dependencies", {}), **pkg_data.get("devDependencies", {})} + + if "react" in deps: + ui_indicators["frameworks"].append("React") + if "vue" in deps: + ui_indicators["frameworks"].append("Vue.js") + if "@angular/core" in deps: + ui_indicators["frameworks"].append("Angular") + if "next" in deps: + ui_indicators["frameworks"].append("Next.js") + if "svelte" in deps: + ui_indicators["frameworks"].append("Svelte") + + has_ui = bool(ui_indicators["directories"] or ui_indicators["files"] or ui_indicators["frameworks"]) + + result = { + "has_ui": has_ui, + "indicators": ui_indicators + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding UI files: {e}") + return f"Error finding UI files: {str(e)}" + + +@tool +def find_docker_files_tool(repo_path: str) -> str: + """ + Check for Docker/container deployment files. + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with Docker file detection results + """ + try: + docker_files = [] + + # Check for Docker files + docker_indicators = [ + 'Dockerfile', + 'docker-compose.yml', + 'docker-compose.yaml', + '.dockerignore', + 'Dockerfile.prod', + 'Dockerfile.dev' + ] + + for file_name in docker_indicators: + file_path = os.path.join(repo_path, file_name) + if os.path.exists(file_path): + docker_files.append(file_name) + + result = { + "has_docker": bool(docker_files), + "docker_files": docker_files + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding Docker files: {e}") + return f"Error finding Docker files: {str(e)}" + + +@tool +def find_config_files_tool(repo_path: str) -> str: + """ + Find configuration files (.env, config files, etc.). + Recursively scans subdirectories. + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with configuration file detection results + """ + try: + config_files = [] + + # Config file patterns to search for + config_indicators = [ + '.env.example', + '.env.sample', + '.env.template', + 'config.json', + 'config.yaml', + 'config.yml', + 'settings.py', + 'config.py', + 'appsettings.json' + ] + + # Recursively walk directory tree + for root, dirs, files in os.walk(repo_path): + # Filter out ignored directories + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS and not d.startswith('.')] + + for file in files: + if file in config_indicators: + full_path = os.path.join(root, file) + rel_path = os.path.relpath(full_path, repo_path) + config_files.append(rel_path) + + result = { + "has_config": bool(config_files), + "config_files": config_files, + "count": len(config_files) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding config files: {e}") + return f"Error finding config files: {str(e)}" + + +@tool +def find_dependency_files_tool(repo_path: str) -> str: + """ + Find dependency/package files in the repository. + Recursively scans subdirectories for requirements.txt, package.json, go.mod, Cargo.toml, pom.xml, etc. + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with dependency file locations + """ + try: + dependency_files = [] + + # Common dependency file patterns + dep_patterns = [ + 'requirements.txt', + 'package.json', + 'go.mod', + 'Cargo.toml', + 'pom.xml', + 'build.gradle', + 'Gemfile', + 'composer.json', + 'Pipfile', + 'poetry.lock', + 'yarn.lock', + 'package-lock.json' + ] + + # Recursively walk directory tree + for root, dirs, files in os.walk(repo_path): + # Filter out ignored directories + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS and not d.startswith('.')] + + for file in files: + if file in dep_patterns: + full_path = os.path.join(root, file) + rel_path = os.path.relpath(full_path, repo_path) + dependency_files.append(rel_path) + + result = { + "dependency_files": dependency_files, + "count": len(dependency_files) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding dependency files: {e}") + return f"Error finding dependency files: {str(e)}" + + +# ============================================================================ +# SPECIALIZED TOOLS FOR DIAGRAM GENERATOR AGENT +# ============================================================================ + +@tool +def find_entry_points_tool(repo_path: str) -> str: + """ + Find main entry point files (main.py, server.py, index.js, etc.). + + Args: + repo_path: Absolute path to the repository root + + Returns: + JSON string with entry point file locations + """ + try: + entry_points = [] + + # Common entry point patterns + entry_patterns = [ + 'main.py', 'app.py', 'server.py', '__main__.py', + 'index.js', 'index.ts', 'server.js', 'app.js', + 'main.go', 'main.rs', 'Main.java' + ] + + for root, dirs, files in os.walk(repo_path): + dirs[:] = [d for d in dirs if d not in IGNORE_DIRS] + + for file in files: + if file in entry_patterns: + rel_path = os.path.relpath(os.path.join(root, file), repo_path) + entry_points.append(rel_path) + + result = { + "entry_points": entry_points, + "count": len(entry_points) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding entry points: {e}") + return f"Error finding entry points: {str(e)}" + + +@tool +def find_api_routes_tool(repo_path: str, entry_file: str) -> str: + """ + Analyze an entry file to find API routes/endpoints. + Looks for common patterns like @app.route, @router.get, etc. + + Args: + repo_path: Absolute path to the repository root + entry_file: Relative path to entry file from repo root + + Returns: + JSON string with discovered API routes + """ + try: + full_path = os.path.normpath(os.path.join(repo_path, entry_file)) + + if not full_path.startswith(os.path.normpath(repo_path)): + return "Error: Access denied" + + if not os.path.exists(full_path): + return "Error: File not found" + + routes = [] + + with open(full_path, 'r', encoding='utf-8', errors='ignore') as f: + content = f.read() + lines = content.split('\n') + + # Pattern matching for routes + route_patterns = [ + r'@app\.route\(["\'](.+?)["\']\)', # Flask + r'@router\.(get|post|put|delete)\(["\'](.+?)["\']\)', # FastAPI + r'app\.(get|post|put|delete)\(["\'](.+?)["\']\)', # Express + r'@(Get|Post|Put|Delete)Mapping\(["\'](.+?)["\']\)' # Spring + ] + + for line in lines: + for pattern in route_patterns: + matches = re.findall(pattern, line) + if matches: + routes.extend([str(m) if isinstance(m, str) else m[-1] for m in matches]) + + result = { + "file": entry_file, + "routes": routes[:50], # Limit to 50 routes + "route_count": len(routes) + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error finding API routes: {e}") + return f"Error finding API routes: {str(e)}" + + + +# ============================================================================ +# VALIDATION TOOLS FOR SELF-CRITIQUE LOOPS +# ============================================================================ + +@tool +def validate_readme_structure_tool(readme_content: str) -> str: + """ + Validate README structure and completeness. + Checks for expected sections, proper markdown formatting, code blocks. + + Args: + readme_content: README markdown content to validate + + Returns: + JSON string with validation results + """ + try: + issues = [] + sections_found = [] + + # Extract sections (markdown headers) + import re + header_pattern = r'^##\s+(.+)$' + sections = re.findall(header_pattern, readme_content, re.MULTILINE) + sections_found = sections + + # Expected sections (at least some of these should be present) + expected_sections = ["Overview", "Features", "Installation", "Usage", "Configuration", "API"] + found_expected = [s for s in expected_sections if any(exp.lower() in s.lower() for exp in sections)] + + if len(found_expected) < 3: + issues.append({ + "severity": "warning", + "message": f"Only {len(found_expected)} standard sections found. Consider adding more." + }) + + # Check for code blocks + code_blocks = readme_content.count("```") + if code_blocks == 0: + issues.append({ + "severity": "info", + "message": "No code examples found. Consider adding usage examples." + }) + elif code_blocks % 2 != 0: + issues.append({ + "severity": "error", + "message": "Unbalanced code blocks (missing closing ```)" + }) + + # Check for mermaid diagrams + has_mermaid = "```mermaid" in readme_content + if not has_mermaid: + issues.append({ + "severity": "info", + "message": "No Mermaid diagrams found. Visual diagrams enhance documentation." + }) + + # Check minimum length + if len(readme_content) < 500: + issues.append({ + "severity": "warning", + "message": "README is very short. Consider adding more detail." + }) + + result = { + "sections_found": sections_found, + "section_count": len(sections_found), + "code_blocks": code_blocks // 2 if code_blocks % 2 == 0 else 0, + "has_mermaid": has_mermaid, + "content_length": len(readme_content), + "issues": issues + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error validating README structure: {e}") + return json.dumps({ + "error": str(e), + "issues": [{"severity": "error", "message": f"Validation failed: {str(e)}"}] + }) + + +@tool +def validate_mermaid_syntax_tool(mermaid_code: str) -> str: + """ + Validate Mermaid diagram syntax with STRICT Mermaid 8.14.0 render-safe checks. + Returns JSON string with validation results. + + FIXED VERSION: Enforces render-safe patterns that prevent "Syntax error in graph" + + Checks: + - Node IDs must be alphanumeric + underscore only + - Node labels must be quoted: ID["Label"] + - Edge labels must not contain: / ( ) : ` " + - Basic structure (brackets, arrows, diagram type) + + Args: + mermaid_code: Mermaid diagram code to validate + + Returns: + JSON: {"valid": bool, "errors": [...], "warnings": [...]} + """ + try: + errors = [] + warnings = [] + + # Basic checks + if not mermaid_code or not mermaid_code.strip(): + errors.append("Empty diagram code") + return json.dumps({"valid": False, "errors": errors, "warnings": warnings}) + + code = mermaid_code.strip() + + # Check for diagram type + diagram_types = [ + "graph", "flowchart", "sequenceDiagram", "classDiagram", + "stateDiagram", "erDiagram", "journey", "gantt", "pie" + ] + has_type = any(code.startswith(dt) for dt in diagram_types) + if not has_type: + errors.append(f"Must start with diagram type: {diagram_types[:4]}...") + + # Check for common syntax errors + open_brackets = code.count("[") + close_brackets = code.count("]") + if open_brackets != close_brackets: + errors.append(f"Unbalanced square brackets: {open_brackets} open vs {close_brackets} close") + + open_parens = code.count("(") + close_parens = code.count(")") + if open_parens != close_parens: + errors.append(f"Unbalanced parentheses: {open_parens} open vs {close_parens} close") + + open_braces = code.count("{") + close_braces = code.count("}") + if open_braces != close_braces: + errors.append(f"Unbalanced curly braces: {open_braces} open vs {close_braces} close") + + # Check for arrow syntax (graph/flowchart) + if code.startswith(("graph", "flowchart")): + # Must have at least one arrow + has_arrow = ("-->" in code or "---" in code or "==>" in code or + "-.->" in code or "-..->" in code) + if not has_arrow: + warnings.append("Graph/flowchart diagrams typically need arrows (-->, --->, etc.)") + + # Check for node definitions + lines = code.split("\n") + node_lines = [l for l in lines if "-->" in l or "---" in l] + if len(node_lines) == 0: + errors.append("No connections found - graph needs edges like: A --> B") + + # =================================================================== + # MERMAID 8.14.0 RENDER-SAFE VALIDATION (NEW) + # =================================================================== + + # Check 1: Node IDs must be alphanumeric + underscore only + # Pattern: extract node IDs from lines like "NodeID[...]" or "NodeID --> OtherNode" + node_id_pattern = r'\b([A-Za-z][A-Za-z0-9_]*)\s*(?:\[|-->|---|\||$)' + node_ids = [] + for line in lines[1:]: # Skip first line (graph TD/LR) + if not line.strip() or line.strip().startswith('#'): + continue + matches = re.findall(node_id_pattern, line) + node_ids.extend(matches) + + # Check for invalid node IDs (contain special chars besides underscore) + invalid_node_pattern = r'\b([A-Za-z0-9_]*[^A-Za-z0-9_\s\[\](){}|>-]+[A-Za-z0-9_]*)\s*(?:\[|-->)' + for line in lines[1:]: + if not line.strip(): + continue + invalid_matches = re.findall(invalid_node_pattern, line) + for invalid_id in invalid_matches: + if invalid_id and not invalid_id.isspace(): + errors.append( + f"Invalid node ID '{invalid_id}' - must be alphanumeric + underscore only. " + f"Use: {re.sub(r'[^A-Za-z0-9_]', '', invalid_id)}" + ) + + # Check 2: Node labels must be quoted if they contain special characters + # Pattern: NodeID[unquoted text with spaces or special chars] + unquoted_label_pattern = r'([A-Za-z][A-Za-z0-9_]*)\[([^\]"]+[^"\]])\]' + for line in lines[1:]: + if not line.strip(): + continue + matches = re.findall(unquoted_label_pattern, line) + for node_id, label in matches: + # Check if label has spaces or special chars and is not quoted + if ' ' in label or '(' in label or ')' in label or '/' in label: + if not (label.strip().startswith('"') and label.strip().endswith('"')): + errors.append( + f"Unquoted label in node '{node_id}[{label}]' - must use quotes: {node_id}[\"{label}\"]" + ) + + # Check 3: Edge labels must not contain unsafe characters + # Pattern: |label text| + edge_label_pattern = r'\|([^|]+)\|' + unsafe_edge_chars = ['/', '(', ')', ':', '`', '"'] + for line in lines[1:]: + if not line.strip(): + continue + edge_matches = re.findall(edge_label_pattern, line) + for edge_label in edge_matches: + for unsafe_char in unsafe_edge_chars: + if unsafe_char in edge_label: + safe_label = edge_label + for char in unsafe_edge_chars: + safe_label = safe_label.replace(char, '') + safe_label = safe_label.strip() + errors.append( + f"Unsafe edge label '|{edge_label}|' contains '{unsafe_char}'. " + f"Use simple label: |{safe_label}| or create a separate node for complex paths." + ) + break + + # Check sequenceDiagram syntax + if code.startswith("sequenceDiagram"): + if "->" not in code and "->>" not in code: + errors.append("sequenceDiagram needs arrows like: Alice->>Bob: Hello") + + # Warning for common mistakes + if "graph TD" in code and "TB" not in code: + pass # TD (top-down) is valid + elif "graph TB" in code: + pass # TB (top-bottom) is valid + elif "graph LR" in code: + pass # LR (left-right) is valid + elif "graph RL" in code: + pass # RL (right-left) is valid + elif "graph" in code and not any(d in code for d in ["TD", "TB", "LR", "RL", "BT"]): + warnings.append("graph should specify direction: graph TD, graph LR, etc.") + + valid = len(errors) == 0 + + result = { + "valid": valid, + "errors": errors, + "warnings": warnings, + "diagram_type": next((dt for dt in diagram_types if code.startswith(dt)), "unknown") + } + + return json.dumps(result, indent=2) + + except Exception as e: + logger.error(f"Error validating Mermaid syntax: {e}") + return json.dumps({ + "valid": False, + "errors": [f"Validation error: {str(e)}"], + "warnings": [] + }) + + +# ============================================================================ +# TOOL BINDING HELPERS - Bind repo_path to simplify LLM tool usage +# ============================================================================ + +def make_bound_tools_for_code_explorer(repo_path: str) -> List: + """ + Create tools for Code Explorer with repo_path pre-bound. + This simplifies the tool signatures the LLM sees. + + Args: + repo_path: Absolute path to repository root + + Returns: + List of tool instances with bound repo_path + """ + # Create wrapper functions with repo_path bound + @tool + def list_directory(relative_path: str = ".") -> str: + """List contents of a directory. Args: relative_path (str, default '.'): Path from repo root""" + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file( + file_path: str, + max_lines: int = None, + strategy: Literal["full", "smart", "pattern_window"] = "pattern_window" + ) -> str: + """Read a file. Args: file_path (str), max_lines (int, optional), strategy (full|smart|pattern_window, default: pattern_window)""" + return read_file_tool.func( + repo_path=repo_path, + file_path=file_path, + max_lines=max_lines, + strategy=strategy + ) + + @tool + def detect_languages() -> str: + """Detect programming languages in the repository. No arguments needed.""" + return detect_languages_tool.func(repo_path=repo_path) + + @tool + def extract_dependencies() -> str: + """Extract dependencies from package files (requirements.txt, package.json, etc.). No arguments needed.""" + return extract_dependencies_tool.func(repo_path=repo_path) + + @tool + def analyze_code_structure(file_path: str) -> str: + """Analyze code structure (functions, classes, imports). Args: file_path (str): Relative path from repo root""" + return analyze_code_structure_tool.func(repo_path=repo_path, file_path=file_path) + + return [list_directory, read_file, detect_languages, extract_dependencies, analyze_code_structure] + + +def make_bound_tools_for_planner(repo_path: str) -> List: + """ + Create tools for Planner with repo_path pre-bound. + + Args: + repo_path: Absolute path to repository root + + Returns: + List of tool instances with bound repo_path + """ + # Create wrapper functions with repo_path bound + @tool + def list_directory(relative_path: str = ".") -> str: + """List contents of a directory. Args: relative_path (str, default '.')""" + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file( + file_path: str, + max_lines: int = None, + strategy: Literal["full", "smart", "pattern_window"] = "pattern_window" + ) -> str: + """Read a file. Args: file_path (str), max_lines (int, optional), strategy (full|smart|pattern_window, default: pattern_window)""" + return read_file_tool.func( + repo_path=repo_path, + file_path=file_path, + max_lines=max_lines, + strategy=strategy + ) + + @tool + def detect_languages() -> str: + """Detect programming languages. No arguments.""" + return detect_languages_tool.func(repo_path=repo_path) + + @tool + def extract_dependencies() -> str: + """Extract dependencies from package files. No arguments.""" + return extract_dependencies_tool.func(repo_path=repo_path) + + @tool + def analyze_code_structure(file_path: str) -> str: + """Analyze code structure. Args: file_path (str)""" + return analyze_code_structure_tool.func(repo_path=repo_path, file_path=file_path) + + @tool + def find_ui_files() -> str: + """Check if project has UI/frontend components. No arguments.""" + return find_ui_files_tool.func(repo_path=repo_path) + + @tool + def find_docker_files() -> str: + """Check for Docker deployment files. No arguments.""" + return find_docker_files_tool.func(repo_path=repo_path) + + @tool + def find_config_files() -> str: + """Find configuration files (.env, config files). No arguments.""" + return find_config_files_tool.func(repo_path=repo_path) + + return [list_directory, read_file, detect_languages, extract_dependencies, analyze_code_structure, + find_ui_files, find_docker_files, find_config_files] + + +def make_bound_tools_for_writer(repo_path: str) -> List: + """ + Create tools for Writer with repo_path pre-bound. + + Args: + repo_path: Absolute path to repository root + + Returns: + List of StructuredTool instances with bound repo_path + """ + # Same as code explorer + return make_bound_tools_for_code_explorer(repo_path) + + +def make_bound_tools_for_diagram_generator(repo_path: str) -> List: + """ + Create tools for Diagram Generator with repo_path pre-bound. + + FIXED: Adds validate_mermaid_syntax wrapper (without _tool suffix) to match prompt. + + Args: + repo_path: Absolute path to repository root + + Returns: + List of tool instances with bound repo_path + """ + # Create wrapper functions with repo_path bound + @tool + def list_directory(relative_path: str = ".") -> str: + """List contents of a directory. Args: relative_path (str, default '.')""" + return list_directory_tool.func(repo_path=repo_path, relative_path=relative_path) + + @tool + def read_file( + file_path: str, + max_lines: int = None, + strategy: Literal["full", "smart", "pattern_window"] = "pattern_window" + ) -> str: + """Read a file. Args: file_path (str), max_lines (int, optional), strategy (full|smart|pattern_window, default: pattern_window)""" + return read_file_tool.func( + repo_path=repo_path, + file_path=file_path, + max_lines=max_lines, + strategy=strategy + ) + + @tool + def detect_languages() -> str: + """Detect programming languages. No arguments.""" + return detect_languages_tool.func(repo_path=repo_path) + + @tool + def find_entry_points() -> str: + """Find main entry point files (main.py, server.py, index.js, etc.). No arguments.""" + return find_entry_points_tool.func(repo_path=repo_path) + + @tool + def find_api_routes(entry_file: str) -> str: + """Find API routes in an entry file. Args: entry_file (str): Relative path to entry file""" + return find_api_routes_tool.func(repo_path=repo_path, entry_file=entry_file) + + @tool + def find_docker_files() -> str: + """Check for Docker files. No arguments.""" + return find_docker_files_tool.func(repo_path=repo_path) + + # FIXED: Add wrapper for validate_mermaid_syntax (without _tool suffix) + # This matches the tool name used in the diagram generator prompt + @tool + def validate_mermaid_syntax(mermaid_code: str) -> str: + """ + Validate Mermaid diagram syntax with strict Mermaid 8.14.0 render-safe checks. + + CRITICAL: Use this to validate EVERY diagram before finalizing output. + + Args: + mermaid_code (str): The complete Mermaid diagram code to validate + + Returns: + JSON with {"valid": bool, "errors": [...], "warnings": [...]} + """ + return validate_mermaid_syntax_tool.func(mermaid_code=mermaid_code) + + return [list_directory, read_file, detect_languages, find_entry_points, find_api_routes, + find_docker_files, validate_mermaid_syntax] diff --git a/api/utils/__init__.py b/api/utils/__init__.py new file mode 100644 index 0000000..89c13ff --- /dev/null +++ b/api/utils/__init__.py @@ -0,0 +1,5 @@ +"""Utility functions for DocuBot AI""" + +from .project_detector import detect_projects, ProjectDetector + +__all__ = ["detect_projects", "ProjectDetector"] diff --git a/api/utils/metrics_extractor.py b/api/utils/metrics_extractor.py new file mode 100644 index 0000000..e3a609f --- /dev/null +++ b/api/utils/metrics_extractor.py @@ -0,0 +1,86 @@ +""" +Utility for extracting metrics from LangGraph agent execution results +""" + +from typing import Dict, List, Any +import time + + +def extract_agent_metrics(messages: List[Any]) -> Dict[str, Any]: + """ + Extract token usage, call counts, and timing from LangGraph messages. + + Args: + messages: List of messages from LangGraph agent execution + + Returns: + Dict with total_input_tokens, total_output_tokens, tool_calls, llm_calls, total_duration_ms + """ + import logging + logger = logging.getLogger(__name__) + + input_tokens = 0 + output_tokens = 0 + tool_calls = 0 + llm_calls = 0 + + # Track timing - measure from first to last message timestamp + first_timestamp = None + last_timestamp = None + + for msg in messages: + # Track timestamps if available + if hasattr(msg, 'additional_kwargs'): + # Try to get timestamp from message + timestamp = msg.additional_kwargs.get('timestamp') + if timestamp: + if first_timestamp is None: + first_timestamp = timestamp + last_timestamp = timestamp + + # Count tool calls (messages with tool_calls attribute) + if hasattr(msg, 'tool_calls') and msg.tool_calls: + tool_calls += len(msg.tool_calls) + + # Count LLM calls and extract token usage from AIMessage responses ONLY + # Check if this is an AIMessage by looking for response_metadata attribute + if hasattr(msg, 'response_metadata') and msg.__class__.__name__ == 'AIMessage': + llm_calls += 1 + metadata = msg.response_metadata + + # DEBUG: Log metadata structure for first AIMessage + if llm_calls == 1: + logger.info(f"[MetricsExtractor] First AIMessage metadata keys: {list(metadata.keys())}") + + # Try different token usage formats (different LLM providers use different keys) + if 'usage_metadata' in metadata: + # LangChain format + usage = metadata['usage_metadata'] + input_tokens += usage.get('input_tokens', 0) + output_tokens += usage.get('output_tokens', 0) + elif 'token_usage' in metadata: + # OpenAI format + usage = metadata['token_usage'] + input_tokens += usage.get('prompt_tokens', 0) + output_tokens += usage.get('completion_tokens', 0) + elif 'usage' in metadata: + # Alternative format + usage = metadata['usage'] + input_tokens += usage.get('input_tokens', usage.get('prompt_tokens', 0)) + output_tokens += usage.get('output_tokens', usage.get('completion_tokens', 0)) + else: + logger.warning(f"[MetricsExtractor] No token usage found in metadata keys: {list(metadata.keys())}") + + # Calculate duration in milliseconds + duration_ms = 0 + if first_timestamp and last_timestamp: + duration_ms = (last_timestamp - first_timestamp) * 1000 + + return { + "total_input_tokens": input_tokens, + "total_output_tokens": output_tokens, + "total_tokens": input_tokens + output_tokens, + "tool_calls": tool_calls, + "llm_calls": llm_calls, + "total_duration_ms": duration_ms + } diff --git a/api/utils/project_detector.py b/api/utils/project_detector.py new file mode 100644 index 0000000..dd3c606 --- /dev/null +++ b/api/utils/project_detector.py @@ -0,0 +1,431 @@ +""" +Smart Project Detector +Analyzes repository structure and identifies individual projects within a monorepo +Includes grouping logic to treat parent folders (with api/ui children) as single projects +""" + +import os +import logging +import fnmatch +from typing import List, Dict, Any +from pathlib import Path + +logger = logging.getLogger(__name__) + +# Project indicators - files that typically mark a project root +PROJECT_INDICATORS = { + "python": ["requirements.txt", "setup.py", "pyproject.toml", "Pipfile", "poetry.lock"], + "nodejs": ["package.json"], + "java": ["pom.xml", "build.gradle", "build.gradle.kts"], + "go": ["go.mod"], + "rust": ["Cargo.toml"], + "php": ["composer.json"], + "ruby": ["Gemfile"], + "dotnet": ["*.csproj", "*.sln", "*.fsproj", "*.vbproj"], + "cpp": ["CMakeLists.txt", "Makefile"], + "r": ["DESCRIPTION", "NAMESPACE"], +} + +# Directories to ignore +IGNORE_DIRS = { + "node_modules", ".git", "__pycache__", "venv", "env", ".venv", + "dist", "build", ".idea", ".vscode", "target", "out", + ".next", ".nuxt", "coverage", ".pytest_cache" +} + +# Common subproject folder names (generic patterns, not hardcoded to specific names) +SUBPROJECT_PATTERNS = ["api", "ui", "frontend", "backend", "web", "server", "client", "app", "service"] + + +class ProjectDetector: + """Detects and analyzes projects within a repository""" + + def __init__(self, repo_path: str, max_depth: int = 3, group_subprojects: bool = True): + """ + Args: + repo_path: Path to repository root + max_depth: Maximum depth to scan + group_subprojects: If True, group api/ui siblings under parent project + """ + self.repo_path = Path(repo_path) + self.max_depth = max_depth + self.group_subprojects = group_subprojects + + def detect_projects(self) -> Dict[str, Any]: + """ + Scan repository and detect individual projects + + Returns: + Dict with: + - is_monorepo: bool + - project_count: int + - projects: List[Dict] with project metadata + - skipped_folders: List[Dict] with skipped folder info and reasons + """ + projects = [] + skipped_folders = [] + + # Check if root itself is a project + root_project = self._analyze_directory(self.repo_path, depth=0) + if root_project: + projects.append(root_project) + + # Scan subdirectories + self._scan_recursive(self.repo_path, projects, depth=1, skipped=skipped_folders) + + # Apply grouping logic if enabled + if self.group_subprojects: + projects = self._group_composite_projects(projects) + + # Filter out nested skipped folders (children of other skipped folders) + skipped_folders = self._filter_nested_skipped_folders(skipped_folders) + + # Filter out standalone media/asset folders when there's a doc folder + skipped_folders = self._filter_redundant_asset_folders(skipped_folders) + + # Classify as monorepo or single project + is_monorepo = len(projects) > 1 + + logger.info(f"Detected {len(projects)} project(s) - Monorepo: {is_monorepo}") + if skipped_folders: + logger.info(f"Skipped {len(skipped_folders)} folder(s) without project indicators") + + return { + "is_monorepo": is_monorepo, + "project_count": len(projects), + "projects": projects, + "skipped_folders": skipped_folders + } + + def _scan_recursive(self, directory: Path, projects: List[Dict], depth: int, skipped: List[Dict] = None): + """Recursively scan directories for projects""" + if depth > self.max_depth: + return + + if skipped is None: + skipped = [] + + try: + for item in directory.iterdir(): + # Skip ignored directories + if not item.is_dir() or item.name in IGNORE_DIRS or item.name.startswith('.'): + continue + + # Check if this directory is a project + project_info = self._analyze_directory(item, depth) + if project_info: + projects.append(project_info) + # Don't scan deeper if we found a project (avoid nested projects) + continue + + # Directory has no project indicators - analyze why and track it + skip_reason = self._analyze_skipped_folder(item) + if skip_reason: + rel_path = item.relative_to(self.repo_path) + skipped.append({ + "name": item.name, + "path": str(rel_path), + "reason": skip_reason["reason"], + "details": skip_reason["details"], + "depth": depth + }) + + # Continue scanning deeper + self._scan_recursive(item, projects, depth + 1, skipped) + + except PermissionError: + logger.warning(f"Permission denied: {directory}") + + def _analyze_skipped_folder(self, directory: Path) -> Dict[str, str]: + """ + Analyze why a folder was skipped (no project indicators) + + Returns: + Dict with 'reason' and 'details', or None if folder should be ignored + """ + try: + items = list(directory.iterdir()) + files = [f for f in items if f.is_file()] + subdirs = [d for d in items if d.is_dir() and d.name not in IGNORE_DIRS] + + # Empty folder + if not files and not subdirs: + return { + "reason": "Empty folder", + "details": "Contains no files or subfolders" + } + + # Analyze file types + doc_extensions = {'.md', '.txt', '.rst', '.adoc', '.pdf'} + image_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.svg', '.ico', '.webp', '.bmp'} + + doc_files = [f for f in files if f.suffix.lower() in doc_extensions] + image_files = [f for f in files if f.suffix.lower() in image_extensions] + other_files = [f for f in files if f.suffix.lower() not in doc_extensions | image_extensions] + + # Documentation-only folder + if doc_files and not other_files and not image_files: + return { + "reason": "Documentation only", + "details": f"Contains only documentation files ({len(doc_files)} markdown/text files)" + } + + # Images/media-only folder + if image_files and not other_files and not doc_files: + return { + "reason": "Media only", + "details": f"Contains only image files ({len(image_files)} images)" + } + + # Mixed docs and images, no code + if (doc_files or image_files) and not other_files and len(files) < 5: + return { + "reason": "Documentation and media", + "details": f"Contains {len(doc_files)} docs, {len(image_files)} images, no code" + } + + # Has files but no project indicators + if files and not subdirs: + return { + "reason": "No project indicators", + "details": f"Contains {len(files)} files but no package.json, requirements.txt, etc." + } + + # Has subdirectories - likely being scanned recursively + # Don't report these as skipped since we'll scan their children + return None + + except PermissionError: + return { + "reason": "Permission denied", + "details": "Cannot access folder contents" + } + + def _analyze_directory(self, directory: Path, depth: int) -> Dict[str, Any]: + """ + Analyze a directory to determine if it's a project + + Returns: + Project metadata dict if project detected, None otherwise + """ + try: + files = [f.name for f in directory.iterdir() if f.is_file()] + except PermissionError: + return None + + # Check for project indicators (with glob pattern support) + detected_types = [] + indicator_files = [] + + for proj_type, indicators in PROJECT_INDICATORS.items(): + for indicator in indicators: + # Support glob patterns like *.csproj + if '*' in indicator or '?' in indicator: + # Use fnmatch for glob patterns + matches = [f for f in files if fnmatch.fnmatch(f, indicator)] + if matches: + detected_types.append(proj_type) + indicator_files.extend(matches) + else: + # Exact match + if indicator in files: + detected_types.append(proj_type) + indicator_files.append(indicator) + + # If we found project indicators, it's likely a project + if detected_types: + # Calculate relative path from repo root + rel_path = directory.relative_to(self.repo_path) + + # Estimate project complexity + file_count = len(files) + dir_count = len([d for d in directory.iterdir() if d.is_dir() and d.name not in IGNORE_DIRS]) + + return { + "name": directory.name, + "path": str(rel_path) if str(rel_path) != "." else "/", + "full_path": str(directory), + "types": list(set(detected_types)), # Remove duplicates + "indicators": indicator_files, + "depth": depth, + "file_count": file_count, + "dir_count": dir_count, + "is_root": depth == 0, + "is_composite": False # Will be updated by grouping logic + } + + return None + + def _group_composite_projects(self, projects: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Group projects that are subprojects under a common parent. + + Logic: If multiple sibling folders at the same depth are projects, + and they match common subproject patterns (api, ui, frontend, backend, etc.), + group them under their parent folder as a single composite project. + + Example: + rag-chatbot/api (project) + rag-chatbot/ui (project) + → Groups into: rag-chatbot (composite project) + + Returns: + New list with grouped projects + """ + # Build a parent-to-children mapping + parent_map: Dict[str, List[Dict]] = {} + + for project in projects: + project_path = Path(project["full_path"]) + parent_path = str(project_path.parent) + + if parent_path not in parent_map: + parent_map[parent_path] = [] + parent_map[parent_path].append(project) + + # Identify parents with multiple subproject children + grouped_projects = [] + processed_projects = set() + + for parent_path, children in parent_map.items(): + # Skip root-level projects (no grouping needed) + if len(children) == 1: + if children[0]["path"] not in processed_projects: + grouped_projects.append(children[0]) + processed_projects.add(children[0]["path"]) + continue + + # Check if these siblings look like subprojects (api, ui, etc.) + child_names = [child["name"].lower() for child in children] + subproject_count = sum(1 for name in child_names if any(pattern in name for pattern in SUBPROJECT_PATTERNS)) + + # If 2+ children match subproject patterns, group them under parent + if len(children) >= 2 and subproject_count >= 2: + parent_dir = Path(parent_path) + parent_name = parent_dir.name + + # Skip if parent is the repo root + if str(parent_dir) == str(self.repo_path): + # Don't group root-level subprojects, keep them separate + for child in children: + if child["path"] not in processed_projects: + grouped_projects.append(child) + processed_projects.add(child["path"]) + continue + + # Create composite project for the parent + composite_project = { + "name": parent_name, + "path": str(parent_dir.relative_to(self.repo_path)), + "full_path": str(parent_dir), + "types": list(set([t for child in children for t in child["types"]])), + "indicators": [f"{child['name']}/{ind}" for child in children for ind in child["indicators"]], + "depth": children[0]["depth"] - 1, # Parent is one level up + "file_count": sum(child["file_count"] for child in children), + "dir_count": len(children), + "is_root": False, + "is_composite": True, + "subprojects": [child["name"] for child in children] + } + + grouped_projects.append(composite_project) + + # Mark children as processed + for child in children: + processed_projects.add(child["path"]) + + logger.info(f"Grouped {len(children)} subprojects under '{parent_name}': {', '.join(child_names)}") + + else: + # Not a composite project pattern, keep children separate + for child in children: + if child["path"] not in processed_projects: + grouped_projects.append(child) + processed_projects.add(child["path"]) + + return grouped_projects + + def _filter_nested_skipped_folders(self, skipped_folders: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Filter out skipped folders that are children of other skipped folders. + Only keep top-level skipped folders to avoid redundant information. + + Example: If 'code-generation' is skipped, don't also show 'code-generation/src' and 'code-generation/img' + """ + if not skipped_folders: + return [] + + # Sort by depth (shallowest first) and then by path + sorted_folders = sorted(skipped_folders, key=lambda x: (x['depth'], x['path'])) + + filtered = [] + for folder in sorted_folders: + # Normalize path using Path to handle both / and \ correctly + folder_path = Path(folder['path']) + + # Check if this folder is a child of any already-added skipped folder + is_nested = False + for parent in filtered: + parent_path = Path(parent['path']) + # Check if folder_path is a child of parent_path + try: + folder_path.relative_to(parent_path) + # If no exception, folder_path is under parent_path + is_nested = True + break + except ValueError: + # Not a subpath, continue checking + pass + + # Only add if it's not nested under another skipped folder + if not is_nested: + filtered.append(folder) + + logger.info(f"Filtered nested folders: {len(skipped_folders)} -> {len(filtered)}") + return filtered + + def _filter_redundant_asset_folders(self, skipped_folders: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Filter out standalone asset folders (images, media) when there's a documentation folder. + + Rationale: If a repo has both a 'docs' folder and an 'images' folder at the same level, + the images likely belong to the docs, so we only need to show the docs folder. + """ + if not skipped_folders: + return [] + + # Check if there's at least one documentation folder + has_doc_folder = any( + folder['reason'] in ['Documentation only', 'Documentation and media'] + for folder in skipped_folders + ) + + # If there's a doc folder, filter out standalone media folders + if has_doc_folder: + filtered = [ + folder for folder in skipped_folders + if folder['reason'] != 'Media only' + ] + if len(filtered) < len(skipped_folders): + logger.info(f"Filtered asset folders: {len(skipped_folders)} -> {len(filtered)} (removed media-only folders)") + return filtered + + # No doc folder, keep all skipped folders + return skipped_folders + + +def detect_projects(repo_path: str) -> Dict[str, Any]: + """ + Convenience function to detect projects in a repository + + Args: + repo_path: Path to the cloned repository + + Returns: + Dict with: + - is_monorepo: bool + - project_count: int + - projects: List[Dict] with project metadata + """ + detector = ProjectDetector(repo_path, group_subprojects=True) + return detector.detect_projects() diff --git a/api/workflow.py b/api/workflow.py new file mode 100644 index 0000000..89493cb --- /dev/null +++ b/api/workflow.py @@ -0,0 +1,1311 @@ +""" +Simplified LangGraph Workflow for 10 Micro-Agents +Optimized for 8K context models with evidence-based architecture +""" + +import logging +from typing import Dict, Any +from langgraph.graph import StateGraph, END +from langgraph.checkpoint.memory import MemorySaver + +from models.state import DocGenState +from models.evidence import EvidencePacket, EvidenceItem +from models import get_log_manager, LogType +from services import get_llm, GitService +from services.git_service import parse_github_url +from utils import detect_projects +from config import settings +from langchain_core.messages import AIMessage +import os +import json +import re + +# Import all agents (6 section writers + planner + mermaid + QA) +from agents.code_explorer_agent import run_code_explorer_agent +from agents.api_reference_agent import run_api_reference_agent +from agents.call_graph_agent import run_call_graph_agent +from agents.error_analysis_agent import run_error_analysis_agent +from agents.env_config_agent import run_env_config_agent +from agents.dependency_analyzer_agent import run_dependency_analyzer_agent +from agents.planner_agent import run_planner_agent +from agents.mermaid_agent import run_mermaid_agent +from agents.qa_validator_agent import run_qa_validator_agent +from core.metrics_collector import MetricsCollector + +logger = logging.getLogger(__name__) + + +class SimplifiedDocuBotWorkflow: + """ + Simplified workflow with 9 micro-agents optimized for 8K context models. + + Architecture: + - 6 Section Writer Agents (write sections directly) + - Code Explorer: Project Overview + Features + - API Reference: API Reference + - Call Graph: Architecture + - Error Analysis: Troubleshooting + - Env Config: Configuration + - Dependency Analyzer: Prerequisites + Quick Start Deployment + - 1 Planner Agent (decides which sections to include) + - 1 Mermaid Agent (generates architecture diagram) + - 1 QA Agent (validates quality) + """ + + def __init__(self): + self.git_service = GitService() + self.log_manager = get_log_manager() + self.graph = None + self.checkpointer = None + self.metrics_collectors = {} # Store metrics collector per job_id + + def _get_metrics_collector(self, job_id: str) -> MetricsCollector: + """Get or create metrics collector for a job""" + if job_id not in self.metrics_collectors: + self.metrics_collectors[job_id] = MetricsCollector(job_id) + return self.metrics_collectors[job_id] + + def _get_target_path(self, state: DocGenState) -> str: + """ + Get the target path for agent analysis. + If user selected specific projects, return path to that project. + Otherwise, return full repo path. + """ + repo_path = state["repo_path"] + selected_projects = state.get("selected_projects") + + if selected_projects and len(selected_projects) > 0: + import os + return os.path.join(repo_path, selected_projects[0]) + return repo_path + + def _get_final_assistant_text(self, messages) -> str: + """ + Extract the last non-empty AIMessage content from LangGraph result. + + FIX: messages[-1] is not guaranteed to be the final assistant answer. + It can be a ToolMessage, intermediate AIMessage, or truncated stub. + """ + # Walk backwards and return the last assistant AIMessage with non-empty content + for m in reversed(messages or []): + if isinstance(m, AIMessage) and isinstance(getattr(m, "content", None), str): + txt = m.content.strip() + if txt: + return txt + # Fallback + return (messages[-1].content or "").strip() if messages else "" + + def _store_section(self, sections_dict: Dict[str, str], heading: str, section_md: str): + """ + Store section with guard against overwriting good content with stubs. + + FIX: Don't replace a complete section with an empty header-only stub. + """ + new_md = (section_md or "").strip() + old_md = (sections_dict.get(heading) or "").strip() + + # Don't overwrite a real section (>= 80 chars) with a stub (< 80 chars) + if len(new_md) < 80 and len(old_md) >= 80: + logger.warning(f"[Parser] Skipping stub overwrite for '{heading}': {len(new_md)} < {len(old_md)} chars") + return + + sections_dict[heading] = new_md + + def _parse_and_store_sections(self, output: str, sections_dict: Dict[str, str]): + """ + Parse agent output to extract markdown sections with subsection handling. + + FIX: Merges ### subsections back into their parent ## sections. + + Logic: + - If we see "## Parent" followed by "### Child", merge them into one section + - If we see standalone "## Section" with content, store it as-is + """ + # Split by ## headings but preserve them + lines = output.split('\n') + current_section = None + current_content = [] + + i = 0 + while i < len(lines): + line = lines[i] + + # Check if this is a ## heading + if line.startswith('## '): + # Store previous section if exists + if current_section and current_content: + full_content = '\n'.join(current_content).strip() + self._store_section(sections_dict, current_section, full_content) + + # Start new section + current_section = line.replace('##', '').strip() + current_content = [line] # Include the heading itself + + elif line.strip(): # Non-empty line + if current_section: + current_content.append(line) + else: # Empty line + if current_section: + current_content.append(line) + + i += 1 + + # Store last section + if current_section and current_content: + full_content = '\n'.join(current_content).strip() + self._store_section(sections_dict, current_section, full_content) + + async def create_workflow(self) -> StateGraph: + """Build the simplified 10-agent workflow""" + workflow = StateGraph(DocGenState) + + # Add nodes + workflow.add_node("clone_repository", self.clone_repository_node) + workflow.add_node("project_detection", self.project_detection_node) + + # Section Writer Agents + workflow.add_node("code_explorer", self.code_explorer_node) + workflow.add_node("api_reference", self.api_reference_node) + workflow.add_node("call_graph", self.call_graph_node) + workflow.add_node("error_analysis", self.error_analysis_node) + workflow.add_node("env_config", self.env_config_node) + workflow.add_node("dependency_analyzer", self.dependency_analyzer_node) + + # Evidence Aggregation + workflow.add_node("evidence_aggregator", self.evidence_aggregator_node) + + # Planning + workflow.add_node("planner", self.planner_node) + + # Mermaid Diagram Generation + workflow.add_node("mermaid", self.mermaid_node) + + # QA Validation + workflow.add_node("qa_validator", self.qa_validator_node) + + # Final assembly + workflow.add_node("assembly", self.assembly_node) + + # Define workflow flow (NEW: analysis → evidence_aggregator → planner → mermaid → QA → assembly) + workflow.set_entry_point("clone_repository") + workflow.add_edge("clone_repository", "project_detection") + workflow.add_edge("project_detection", "code_explorer") + workflow.add_edge("code_explorer", "api_reference") + workflow.add_edge("api_reference", "call_graph") + workflow.add_edge("call_graph", "error_analysis") + workflow.add_edge("error_analysis", "env_config") + workflow.add_edge("env_config", "dependency_analyzer") + workflow.add_edge("dependency_analyzer", "evidence_aggregator") # NEW: aggregate evidence after all analysis + workflow.add_edge("evidence_aggregator", "planner") # Planner uses evidence for routing + workflow.add_edge("planner", "mermaid") + workflow.add_edge("mermaid", "qa_validator") + workflow.add_edge("qa_validator", "assembly") + workflow.add_edge("assembly", END) + + # Use memory checkpointer (ephemeral) + checkpointer = MemorySaver() + compiled = workflow.compile(checkpointer=checkpointer) + + self.graph = compiled + self.checkpointer = checkpointer + return compiled + + async def clone_repository_node(self, state: DocGenState) -> DocGenState: + """Clone the GitHub repository (reuse existing implementation)""" + job_id = state["job_id"] + repo_url = state["repo_url"] + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message=f"📦 Cloning repository: {repo_url}" + ) + + try: + parsed_url = parse_github_url(repo_url) + + def progress_callback(message: str): + import asyncio + try: + loop = asyncio.get_event_loop() + loop.create_task(self.log_manager.log_async( + job_id=job_id, + log_type=LogType.INFO, + message=message + )) + except RuntimeError: + logger.info(message) + + repo_path, metadata = self.git_service.clone_repository( + parsed_url["clone_url"], + branch=parsed_url["branch"], + progress_callback=progress_callback + ) + + if parsed_url["is_subfolder"]: + import os + target_path = os.path.join(repo_path, parsed_url["subfolder"]) + if not os.path.exists(target_path): + raise ValueError(f"Subfolder '{parsed_url['subfolder']}' not found") + state["repo_path"] = target_path + state["repo_name"] = parsed_url["display_name"] + state["is_subfolder_target"] = True + else: + state["repo_path"] = repo_path + state["repo_name"] = parsed_url["repo"] + state["is_subfolder_target"] = False + + state["default_branch"] = metadata["default_branch"] + state["workflow_status"] = "detecting" + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Repository cloned successfully" + ) + + return state + + except Exception as e: + logger.error(f"Clone failed: {e}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.ERROR, + message=f"❌ Clone failed: {str(e)}" + ) + state["error"] = str(e) + state["workflow_status"] = "failed" + raise + + async def project_detection_node(self, state: DocGenState) -> DocGenState: + """Detect projects in repository (reuse existing implementation)""" + job_id = state["job_id"] + repo_path = state["repo_path"] + is_subfolder = state.get("is_subfolder_target", False) + + if is_subfolder: + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message=f"🎯 Targeting subfolder: {state['repo_name']}" + ) + state["awaiting_project_selection"] = False + state["selected_projects"] = None + state["is_monorepo"] = False + state["detected_projects"] = [] + state["workflow_status"] = "analyzing" + return state + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="🔍 Detecting projects..." + ) + + try: + detection_result = detect_projects(repo_path) + state["is_monorepo"] = detection_result["is_monorepo"] + state["detected_projects"] = detection_result["projects"] + state["skipped_folders"] = detection_result.get("skipped_folders", []) + + if detection_result["project_count"] <= 1: + state["awaiting_project_selection"] = False + state["selected_projects"] = None + state["workflow_status"] = "analyzing" + else: + # Multiple projects - need selection (handled by server) + state["awaiting_project_selection"] = True + state["workflow_status"] = "awaiting_selection" + + return state + + except Exception as e: + logger.error(f"Detection failed: {e}") + state["awaiting_project_selection"] = False + state["selected_projects"] = None + state["workflow_status"] = "analyzing" + return state + + # Section Writer Agents + async def code_explorer_node(self, state: DocGenState) -> DocGenState: + """Run Code Explorer agent - writes Overview + Features sections""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("CodeExplorer") + + # CRITICAL: Update repo_name to selected project name if project was selected + selected_projects = state.get("selected_projects") + if selected_projects and len(selected_projects) > 0: + # Update repo_name to the selected project folder name + state["repo_name"] = selected_projects[0] + logger.info(f"[CodeExplorer] Updated repo_name to selected project: {state['repo_name']}") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message=f"🔍 Running Overview & Features Writer (1/7) for {state['repo_name']}...", + agent_name="CodeExplorer" + ) + + llm = get_llm(model_name=settings.CODE_EXPLORER_MODEL, temperature=0.7) + result = await run_code_explorer_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + # Parse output to extract two sections: Project Overview and Features + output = result.get("output", "") + sections_dict = state.get("readme_sections") or {} + + # Extract sections from output (they're in format: ## Section Name\n\nContent...) + self._parse_and_store_sections(output, sections_dict) + state["readme_sections"] = sections_dict + + logger.info(f"[CodeExplorer] Stored sections: {list(sections_dict.keys())}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Overview & Features sections completed", + agent_name="CodeExplorer" + ) + + # End metrics tracking (success) + metrics.end_agent( + "CodeExplorer", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"CodeExplorer failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("CodeExplorer", success=False, error_message=result.get('error')) + + state["current_agent"] = "CodeExplorer" + return state + + async def api_reference_node(self, state: DocGenState) -> DocGenState: + """Run API Reference agent - extracts endpoint data (no markdown sections)""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("APIReference") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="📡 Running API Endpoint Extractor (2/7)...", + agent_name="APIReference" + ) + + llm = get_llm(model_name=settings.API_REFERENCE_MODEL, temperature=0.7) + result = await run_api_reference_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + output = result.get("output", "") + # Parse JSON output to extract endpoint data + import json + import re + try: + # Try to find JSON in output (might be wrapped in markdown code block) + json_match = re.search(r'```json\s*(\{.*?\})\s*```', output, re.DOTALL) + if json_match: + json_str = json_match.group(1) + else: + # Try to find raw JSON object + json_match = re.search(r'\{.*\}', output, re.DOTALL) + if json_match: + json_str = json_match.group(0) + else: + json_str = output + + endpoint_data = json.loads(json_str) + state["api_endpoints"] = endpoint_data.get("endpoints", []) + logger.info(f"[APIReference] Extracted {len(state['api_endpoints'])} endpoints") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message=f"✅ Extracted {len(state['api_endpoints'])} API endpoints", + agent_name="APIReference" + ) + # End metrics tracking (success) + metrics.end_agent( + "APIReference", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + except (json.JSONDecodeError, AttributeError) as e: + logger.warning(f"[APIReference] Failed to parse JSON: {e}. Storing empty endpoint list.") + state["api_endpoints"] = [] + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WARNING, + message="⚠️ No API endpoints extracted", + agent_name="APIReference" + ) + # End metrics tracking (success with warning) + metrics.end_agent( + "APIReference", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"APIReference failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("APIReference", success=False, error_message=result.get('error')) + + state["current_agent"] = "APIReference" + return state + + async def call_graph_node(self, state: DocGenState) -> DocGenState: + """Run Call Graph agent - writes Architecture section""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("CallGraph") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="🔗 Running Architecture Writer (3/7)...", + agent_name="CallGraph" + ) + + llm = get_llm(model_name=settings.CALL_GRAPH_MODEL, temperature=0.7) + result = await run_call_graph_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + output = result.get("output", "") + sections_dict = state.get("readme_sections") or {} + self._parse_and_store_sections(output, sections_dict) + state["readme_sections"] = sections_dict + + logger.info(f"[CallGraph] Stored sections: {list(sections_dict.keys())}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Architecture section completed", + agent_name="CallGraph" + ) + # End metrics tracking (success) + metrics.end_agent( + "CallGraph", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"CallGraph failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("CallGraph", success=False, error_message=result.get('error')) + + state["current_agent"] = "CallGraph" + return state + + async def error_analysis_node(self, state: DocGenState) -> DocGenState: + """Run Error Analysis agent - writes Troubleshooting section""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("ErrorAnalysis") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="⚠️ Running Troubleshooting Writer (4/7)...", + agent_name="ErrorAnalysis" + ) + + llm = get_llm(model_name=settings.ERROR_ANALYSIS_MODEL, temperature=0.7) + result = await run_error_analysis_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + output = result.get("output", "") + sections_dict = state.get("readme_sections") or {} + self._parse_and_store_sections(output, sections_dict) + state["readme_sections"] = sections_dict + + logger.info(f"[ErrorAnalysis] Stored sections: {list(sections_dict.keys())}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Troubleshooting section completed", + agent_name="ErrorAnalysis" + ) + # End metrics tracking (success) + metrics.end_agent( + "ErrorAnalysis", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"ErrorAnalysis failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("ErrorAnalysis", success=False, error_message=result.get('error')) + + state["current_agent"] = "ErrorAnalysis" + return state + + async def env_config_node(self, state: DocGenState) -> DocGenState: + """Run Environment Config agent - writes Configuration section""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("EnvConfig") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="⚙️ Running Configuration Writer (5/7)...", + agent_name="EnvConfig" + ) + + llm = get_llm(model_name=settings.ENV_CONFIG_MODEL, temperature=0.7) + result = await run_env_config_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + output = result.get("output", "") + + sections_dict = state.get("readme_sections") or {} + self._parse_and_store_sections(output, sections_dict) + state["readme_sections"] = sections_dict + + logger.info(f"[EnvConfig] Stored sections: {list(sections_dict.keys())}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Configuration section completed", + agent_name="EnvConfig" + ) + # End metrics tracking (success) + metrics.end_agent( + "EnvConfig", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"EnvConfig failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("EnvConfig", success=False, error_message=result.get('error')) + + state["current_agent"] = "EnvConfig" + return state + + async def dependency_analyzer_node(self, state: DocGenState) -> DocGenState: + """Run Dependency Analyzer agent - writes Prerequisites & Deployment sections""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("DependencyAnalyzer") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="📦 Running Prerequisites & Deployment Writer (6/7)...", + agent_name="DependencyAnalyzer" + ) + + llm = get_llm(model_name=settings.DEPENDENCY_ANALYZER_MODEL, temperature=0.7) + repo_url = state.get("repo_url", "") + result = await run_dependency_analyzer_agent(llm=llm, repo_path=target_path, job_id=job_id, repo_url=repo_url) + + if result.get("success"): + output = result.get("output", "") + + sections_dict = state.get("readme_sections") or {} + self._parse_and_store_sections(output, sections_dict) + state["readme_sections"] = sections_dict + + logger.info(f"[DependencyAnalyzer] Stored sections: {list(sections_dict.keys())}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Prerequisites & Deployment sections completed", + agent_name="DependencyAnalyzer" + ) + # End metrics tracking (success) + metrics.end_agent( + "DependencyAnalyzer", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"DependencyAnalyzer failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("DependencyAnalyzer", success=False, error_message=result.get('error')) + + state["current_agent"] = "DependencyAnalyzer" + return state + + # Evidence Aggregation + async def evidence_aggregator_node(self, state: DocGenState) -> DocGenState: + """ + DUAL-MODE Evidence Aggregator - Collects evidence from file system and agent outputs. + + This node creates the central EvidencePacket by: + 1. Direct file system checks (requirements.txt, package.json, etc.) + 2. Extracting structured data from agent outputs (dual-mode: supports both strings and JSON) + + FIX 1: Dual-mode aggregator - works with current string outputs AND future JSON outputs + FIX 2: Deterministic repo_name derivation from URL or folder path + FIX: Uses target_path (selected project) instead of repo_path (root) + """ + job_id = state["job_id"] + repo_url = state.get("repo_url", "") + readme_sections = state.get("readme_sections", {}) + + # CRITICAL: Use target path (respects project selection) + target_path = self._get_target_path(state) + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="📊 Aggregating evidence from repository...", + agent_name="EvidenceAggregator" + ) + + try: + # FIX 2: Derive repo_name deterministically (never from LLM output) + # Use selected project name if available, otherwise derive from URL/path + selected_projects = state.get("selected_projects") + if selected_projects and len(selected_projects) > 0: + repo_name = selected_projects[0] + elif repo_url: + # Parse from GitHub URL: github.com/user/repo-name + match = re.search(r'github\.com/[^/]+/([^/\.]+)', repo_url) + repo_name = match.group(1) if match else os.path.basename(target_path) + else: + # Use folder name + repo_name = os.path.basename(target_path) if target_path else "Repository" + + # Initialize evidence packet with target_path + evidence = EvidencePacket(repo_name=repo_name, repo_path=target_path) + + # === DIRECT FILE SYSTEM CHECKS (don't rely on agent outputs) === + + # Check for Python dependencies (root and subdirectories) + python_dep_locations = [ + os.path.join(target_path, "requirements.txt"), + os.path.join(target_path, "api", "requirements.txt"), + os.path.join(target_path, "backend", "requirements.txt"), + os.path.join(target_path, "server", "requirements.txt") + ] + + for requirements_path in python_dep_locations: + if os.path.exists(requirements_path): + rel_path = os.path.relpath(requirements_path, target_path) + evidence.add_evidence(EvidenceItem( + category="dependency", + key=rel_path, + value="Python dependencies", + source_files=[rel_path] + )) + try: + with open(requirements_path, 'r', encoding='utf-8', errors='ignore') as f: + deps = [ + line.split('==')[0].split('>=')[0].split('~=')[0].strip() + for line in f + if line.strip() and not line.startswith('#') + ] + evidence.python_deps.extend(deps) + logger.info(f"[Evidence] Found {len(deps)} Python dependencies in {rel_path}") + except Exception as e: + logger.warning(f"[Evidence] Failed to parse {rel_path}: {e}") + + if evidence.python_deps: + evidence.has_backend = True + evidence.python_deps = list(set(evidence.python_deps)) + + # Check for Node.js dependencies (root and subdirectories) + node_dep_locations = [ + os.path.join(target_path, "package.json"), + os.path.join(target_path, "ui", "package.json"), + os.path.join(target_path, "frontend", "package.json"), + os.path.join(target_path, "client", "package.json") + ] + + for package_json_path in node_dep_locations: + if os.path.exists(package_json_path): + rel_path = os.path.relpath(package_json_path, target_path) + evidence.has_frontend = True + try: + with open(package_json_path, 'r', encoding='utf-8') as f: + pkg = json.load(f) + deps = list(pkg.get("dependencies", {}).keys()) + evidence.node_deps.extend(deps) + + # Detect frontend framework + if "react" in deps and not evidence.frontend_framework: + evidence.frontend_framework = "React" + elif "vue" in deps and not evidence.frontend_framework: + evidence.frontend_framework = "Vue" + elif "@angular/core" in deps and not evidence.frontend_framework: + evidence.frontend_framework = "Angular" + + evidence.add_evidence(EvidenceItem( + category="dependency", + key=rel_path, + value=f"Node.js project with {len(deps)} dependencies", + source_files=[rel_path] + )) + logger.info(f"[Evidence] Found {len(deps)} Node dependencies in {rel_path}") + except Exception as e: + logger.warning(f"[Evidence] Failed to parse {rel_path}: {e}") + + if evidence.node_deps: + evidence.node_deps = list(set(evidence.node_deps)) + + # Check for Docker + dockerfile_path = os.path.join(target_path, "Dockerfile") + compose_path = os.path.join(target_path, "docker-compose.yml") + if os.path.exists(dockerfile_path): + evidence.has_docker = True + evidence.docker_files.append("Dockerfile") + evidence.add_evidence(EvidenceItem( + category="infrastructure", + key="Dockerfile", + value="Docker containerization", + source_files=["Dockerfile"] + )) + if os.path.exists(compose_path): + evidence.has_docker = True + evidence.docker_files.append("docker-compose.yml") + evidence.add_evidence(EvidenceItem( + category="infrastructure", + key="docker-compose.yml", + value="Docker Compose orchestration", + source_files=["docker-compose.yml"] + )) + + if evidence.has_docker: + logger.info(f"[Evidence] Found Docker files: {evidence.docker_files}") + + # Check for .env files + env_example_path = os.path.join(target_path, ".env.example") + if os.path.exists(env_example_path): + evidence.env_files.append(".env.example") + evidence.add_evidence(EvidenceItem( + category="config", + key=".env.example", + value="Environment configuration template", + source_files=[".env.example"] + )) + logger.info("[Evidence] Found .env.example") + + # Extract API endpoints from state (populated by API Reference agent) + api_endpoints = state.get("api_endpoints", []) + if api_endpoints: + evidence.api_endpoints = api_endpoints + logger.info(f"[Evidence] Extracted {len(api_endpoints)} API endpoints from state") + + # Detect languages from file extensions + try: + for root, dirs, files in os.walk(target_path): + # Skip node_modules, .git, venv, etc. + dirs[:] = [d for d in dirs if d not in ['.git', 'node_modules', 'venv', '__pycache__', 'dist', 'build']] + + for file in files: + ext = os.path.splitext(file)[1] + if ext in ['.py', '.js', '.jsx', '.ts', '.tsx', '.java', '.go', '.rs', '.cpp', '.c', '.rb']: + lang_map = { + '.py': 'Python', '.js': 'JavaScript', '.jsx': 'JavaScript', + '.ts': 'TypeScript', '.tsx': 'TypeScript', '.java': 'Java', + '.go': 'Go', '.rs': 'Rust', '.cpp': 'C++', '.c': 'C', '.rb': 'Ruby' + } + lang = lang_map.get(ext, 'Unknown') + evidence.languages[lang] = evidence.languages.get(lang, 0) + 1 + + logger.info(f"[Evidence] Detected languages: {evidence.languages}") + except Exception as e: + logger.warning(f"[Evidence] Failed to detect languages: {e}") + + # Store evidence packet in state + state["evidence_packet"] = evidence + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message=f"✅ Evidence aggregated: {len(evidence.python_deps)} Python deps, {len(evidence.node_deps)} Node deps, {len(evidence.docker_files)} Docker files", + agent_name="EvidenceAggregator" + ) + + logger.info(f"[Evidence] Final evidence: {evidence.to_dict()}") + + except Exception as e: + logger.error(f"Evidence aggregation failed: {e}") + # Create minimal evidence packet on failure + state["evidence_packet"] = EvidencePacket( + repo_name=os.path.basename(target_path) if target_path else "Repository", + repo_path=target_path + ) + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WARNING, + message=f"⚠️ Evidence aggregation failed, using minimal evidence: {str(e)}", + agent_name="EvidenceAggregator" + ) + + state["current_agent"] = "EvidenceAggregator" + return state + + # Planning + async def planner_node(self, state: DocGenState) -> DocGenState: + """Run Planner agent - decides which sections to include""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("Planner") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="📋 Running Planner (7/7)...", + agent_name="Planner" + ) + + llm = get_llm(model_name=settings.PLANNER_MODEL, temperature=0.7) + result = await run_planner_agent(llm=llm, repo_path=target_path, job_id=job_id) + + if result.get("success"): + output = result.get("output", "") + # Try to parse JSON output + import json + try: + plan_data = json.loads(output) + state["project_type"] = plan_data.get("project_type", "Unknown") + state["documentation_sections"] = plan_data.get("sections", ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting"]) + logger.info(f"[Planner] Planned sections: {state['documentation_sections']}") + except: + state["project_type"] = "Unknown" + state["documentation_sections"] = ["Project Overview", "Features", "Architecture", "Prerequisites", "Quick Start Deployment", "Troubleshooting"] + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message=f"✅ Planner completed - {len(state['documentation_sections'])} sections planned", + agent_name="Planner" + ) + # End metrics tracking (success) + metrics.end_agent( + "Planner", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"Planner failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("Planner", success=False, error_message=result.get('error')) + + state["current_agent"] = "Planner" + return state + + async def mermaid_node(self, state: DocGenState) -> DocGenState: + """Run Mermaid Diagram agent - generates architecture diagram with semantic validation""" + job_id = state["job_id"] + target_path = self._get_target_path(state) + evidence_packet = state.get("evidence_packet") + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("Mermaid") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="📊 Generating architecture diagram...", + agent_name="Mermaid" + ) + + llm = get_llm(model_name=settings.MERMAID_MODEL, temperature=0.7) + # Pass API endpoints from state to Mermaid agent + api_endpoints = state.get("api_endpoints", []) + result = await run_mermaid_agent(llm=llm, repo_path=target_path, job_id=job_id, api_endpoints=api_endpoints) + + if result.get("success"): + diagram_output = result.get("output", "") + diagram_code = self._extract_mermaid_code(diagram_output) + if diagram_code: + # === NEW: Semantic Validation (FIX 4 applied) === + is_valid, errors = self._validate_mermaid_semantics(diagram_code, evidence_packet) + + if not is_valid: + logger.warning(f"[Mermaid] Semantic validation failed: {errors}") + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WARNING, + message=f"⚠️ Diagram has semantic issues: {', '.join(errors[:2])}", + agent_name="Mermaid" + ) + # Note: We still use the diagram, but log the issues + # Future enhancement: add a retry mechanism here + + state["mermaid_diagrams"] = {"architecture": diagram_code} + logger.info(f"[Mermaid] Extracted diagram: {len(diagram_code)} chars") + else: + state["mermaid_diagrams"] = {} + logger.warning("[Mermaid] Could not extract valid mermaid code") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Mermaid Generator completed", + agent_name="Mermaid" + ) + # End metrics tracking (success) + metrics.end_agent( + "Mermaid", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"Mermaid failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("Mermaid", success=False, error_message=result.get('error')) + + state["current_agent"] = "Mermaid" + return state + + def _extract_mermaid_code(self, text: str) -> str: + """Extract clean mermaid code from agent output""" + # Try to find mermaid code block + patterns = [ + r'```mermaid\s+(.*?)\s+```', # ```mermaid ... ``` + r'```\s+(graph\s+TD.*?)```', # ``` graph TD ... ``` + r'(graph\s+TD.*?)(?=\n\n|\Z)' # graph TD ... (until double newline or end) + ] + + for pattern in patterns: + match = re.search(pattern, text, re.DOTALL) + if match: + code = match.group(1).strip() + # Validate it starts with graph/flowchart + if code.startswith(('graph ', 'flowchart ', 'sequenceDiagram', 'classDiagram')): + return code + + # Fallback: if text starts with graph/flowchart directly + if text.strip().startswith(('graph ', 'flowchart ')): + return text.strip() + + return "" + + def _validate_mermaid_semantics(self, diagram_code: str, evidence_packet) -> tuple: + """ + Validate Mermaid diagram semantics (FIX 4 applied - minimal rules only). + + Returns: + (is_valid, errors_list) + """ + errors = [] + lines = diagram_code.split('\n') + + # Rule 1: No endpoint nodes with paths/methods + endpoint_patterns = ['/upload', '/query', '/health', '/api/', 'GET ', 'POST ', 'PUT ', 'DELETE '] + for line in lines: + for pattern in endpoint_patterns: + if pattern in line and '[' in line: + errors.append(f"Diagram contains endpoint/route node: {pattern}") + break + + # Rule 2: Must include User or Client + has_user = any(('User' in line or 'Client' in line) and '[' in line for line in lines) + if not has_user: + errors.append("Diagram should include User or Client node") + + # Rule 3: Must have Backend if backend exists + if evidence_packet and evidence_packet.has_backend: + has_backend = any(('API' in line or 'Backend' in line or 'Server' in line) + and '[' in line for line in lines) + if not has_backend: + errors.append("Diagram missing Backend/API node (backend detected in repo)") + + # Rule 4: Must have Frontend if frontend exists + if evidence_packet and evidence_packet.has_frontend: + has_frontend = any(('Frontend' in line or 'UI' in line or 'Client' in line or 'Web' in line) + and '[' in line for line in lines) + if not has_frontend: + errors.append(f"Diagram missing Frontend/UI node ({evidence_packet.frontend_framework} detected)") + + # Rule 5: Must have Database if common DB deps found + if evidence_packet: + db_deps = ['sqlalchemy', 'psycopg2', 'pymongo', 'mysql', 'redis', 'elasticsearch'] + has_db_dep = any(dep in [d.lower() for d in evidence_packet.python_deps] for dep in db_deps) + if has_db_dep: + has_db_node = any(('Database' in line or 'DB' in line or 'Storage' in line or 'Cache' in line) + and '[' in line for line in lines) + if not has_db_node: + errors.append("Diagram missing Database node (database dependency detected)") + + return (len(errors) == 0, errors) + + # QA Validation + async def qa_validator_node(self, state: DocGenState) -> DocGenState: + """Run QA Validator agent - validates sections BEFORE assembly with evidence-based guardrails""" + job_id = state["job_id"] + readme_sections = state.get("readme_sections", {}) + evidence_packet = state.get("evidence_packet") + + # Start metrics tracking + metrics = self._get_metrics_collector(job_id) + metrics.start_agent("QAValidator") + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="✅ Validating README sections with evidence checks...", + agent_name="QAValidator" + ) + + llm = get_llm(model_name=settings.QA_VALIDATOR_MODEL, temperature=0.7) + result = await run_qa_validator_agent( + llm=llm, + readme_sections=readme_sections, + job_id=job_id, + evidence_packet=evidence_packet + ) + + if result.get("success"): + qa_output = result.get("output", "") + # Try to parse QA score + import json + try: + qa_data = json.loads(qa_output) + state["qa_score"] = qa_data.get("qa_score", 75) + state["qa_passed"] = qa_data.get("qa_passed", True) + state["qa_validation_result"] = qa_data + except: + state["qa_score"] = 75 + state["qa_passed"] = True + state["qa_validation_result"] = {"output": qa_output} + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message=f"✅ QA Validator completed (Score: {state.get('qa_score', 'N/A')})", + agent_name="QAValidator" + ) + # End metrics tracking (success) + metrics.end_agent( + "QAValidator", + success=True, + input_tokens=result.get("total_input_tokens", 0), + output_tokens=result.get("total_output_tokens", 0), + tool_calls=result.get("tool_calls", 0), + llm_calls=result.get("llm_calls", 0) + ) + else: + logger.error(f"QAValidator failed: {result.get('error')}") + # End metrics tracking (failure) + metrics.end_agent("QAValidator", success=False, error_message=result.get('error')) + + state["current_agent"] = "QAValidator" + return state + + async def assembly_node(self, state: DocGenState) -> DocGenState: + """Assemble final README with the template structure""" + job_id = state["job_id"] + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.WORKFLOW_PROGRESS, + message="🔨 Assembling final README..." + ) + + readme_parts = [] + readme_sections = state.get("readme_sections") or {} + mermaid_diagrams = state.get("mermaid_diagrams") or {} + repo_name = state.get('repo_name', 'Project') + + # Helper function to convert kebab-case/snake_case to Title Case + def to_title_case(text: str) -> str: + """Convert 'rag-chatbot' or 'doc_summarization' to 'Rag Chatbot' or 'Doc Summarization'""" + return ' '.join(word.capitalize() for word in text.replace('-', ' ').replace('_', ' ').split()) + + # 1. ## Title (H2, not H1!) - Convert to Title Case + readme_parts.append(f"## {to_title_case(repo_name)}\n\n") + + # 2. Brief intro (extract ONLY first 1-2 sentences from Project Overview as teaser) + if readme_sections and "Project Overview" in readme_sections: + overview_content = readme_sections["Project Overview"] + # Extract content after heading + lines = overview_content.split('\n') + content_text = [] + found_heading = False + + for line in lines: + if line.startswith("## Project Overview"): + found_heading = True + continue + if found_heading and line.strip(): + content_text.append(line.strip()) + + # Join all content and split by sentences + full_text = ' '.join(content_text) + # Split by period followed by space (basic sentence splitting) + sentences = [s.strip() + '.' for s in full_text.split('. ') if s.strip()] + + # Take only the first sentence as intro (to avoid duplication) + if sentences: + intro = sentences[0] + readme_parts.append(f"{intro}\n\n") + + # Define explicit section order (chronological) + SECTION_ORDER = [ + "Project Overview", + "Features", + "Architecture", + "Prerequisites", + "Quick Start Deployment", + "User Interface", + "Configuration", + "Troubleshooting" + ] + + # 3. ## Table of Contents (use explicit order) + readme_parts.append("## Table of Contents\n\n") + for section in SECTION_ORDER: + if section in readme_sections: + anchor = section.lower().replace(" ", "-").replace("/", "") + readme_parts.append(f"- [{section}](#{anchor})\n") + readme_parts.append("\n") + + # 4. --- separator + readme_parts.append("---\n\n") + + # 5. Add all sections in explicit order with --- separators + architecture_section_found = False + + for section in SECTION_ORDER: + if section not in readme_sections: + continue + + content = readme_sections[section] + # Add section content + readme_parts.append(content) + readme_parts.append("\n\n") + + # Insert Architecture diagram RIGHT after Architecture section content + if section.lower() == "architecture": + architecture_section_found = True + if mermaid_diagrams: + for diagram_name, diagram_code in mermaid_diagrams.items(): + readme_parts.append(f"```mermaid\n{diagram_code}\n```\n\n") + mermaid_diagrams = {} # Clear so we don't add again + + # Add --- separator after each section + readme_parts.append("---\n\n") + + # 6. If diagrams were generated but no Architecture section exists, add minimal Architecture section with diagram + if mermaid_diagrams and not architecture_section_found: + readme_parts.append("## Architecture\n\n") + for diagram_name, diagram_code in mermaid_diagrams.items(): + readme_parts.append(f"```mermaid\n{diagram_code}\n```\n\n") + readme_parts.append("---\n\n") + + state["final_readme"] = "".join(readme_parts) + state["workflow_status"] = "completed" + + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.SUCCESS, + message="✅ Documentation generation complete!" + ) + + # Finalize metrics and log summary + metrics = self._get_metrics_collector(job_id) + metrics.finalize_workflow() + summary = metrics.get_summary() + + # Build per-agent metrics display + agent_metrics_lines = [] + for agent in summary['agents']: + if agent['success']: + # Calculate per-agent TPS (output tokens per second) + agent_duration_sec = agent['duration_ms'] / 1000 if agent['duration_ms'] > 0 else 0 + agent_tps = agent['output_tokens'] / agent_duration_sec if agent_duration_sec > 0 else 0.0 + + agent_metrics_lines.append( + f" ├─ {agent['agent_name']}: total={agent['total_tokens']:,} " + f"(out={agent['output_tokens']:,}), {agent['duration_ms']:,.0f}ms, outTPS={agent_tps:.2f}" + ) + + agent_metrics_str = "\n".join(agent_metrics_lines) + + # Log metrics summary to agent logs panel + await self.log_manager.log_async( + job_id=job_id, + log_type=LogType.INFO, + message=f"\n📊 **Workflow Metrics Summary**\n" + f"├─ Total Agents: {summary['workflow']['total_agents']}\n" + f"├─ Successful: {summary['workflow']['successful_agents']}\n" + f"├─ Failed: {summary['workflow']['failed_agents']}\n" + f"├─ Total Duration: {summary['workflow']['total_duration_seconds']}s\n" + f"├─ Total Tokens: {summary['workflow']['total_tokens']:,}\n" + f"│ ├─ Input: {summary['workflow']['total_input_tokens']:,}\n" + f"│ └─ Output: {summary['workflow']['total_output_tokens']:,}\n" + f"├─ Total Tool Calls: {summary['workflow']['total_tool_calls']}\n" + f"├─ Total LLM Calls: {summary['workflow']['total_llm_calls']}\n" + f"├─ Workflow Average TPS: {summary['workflow']['average_tps']} tokens/sec\n" + f"│\n" + f"├─ **Per-Agent Metrics (Model TPS)**\n" + f"{agent_metrics_str}\n" + ) + + logger.info(f"[{job_id}] Metrics Summary: {summary}") + + # Cleanup + if state.get("repo_path"): + self.git_service.cleanup_repository(state["repo_path"]) + + return state + + +# Global workflow instance +_workflow: SimplifiedDocuBotWorkflow = None + + +async def get_workflow() -> SimplifiedDocuBotWorkflow: + """Get or create workflow instance""" + global _workflow + if _workflow is None: + _workflow = SimplifiedDocuBotWorkflow() + await _workflow.create_workflow() + return _workflow diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..590cef9 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,66 @@ +services: + backend: + build: + context: ./api + dockerfile: Dockerfile + container_name: docubot-backend + ports: + - "5001:5001" + env_file: + - ./api/.env + environment: + - PYTHONUNBUFFERED=1 + - HOST=0.0.0.0 + - API_PORT=5001 + volumes: + - ./api:/app + - backend-tmp:/app/tmp + - /var/run/docker.sock:/var/run/docker.sock # Docker socket for MCP server + extra_hosts: + - "${LOCAL_URL_ENDPOINT}:host-gateway" + networks: + - docubot-network + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:5001/health"] + interval: 30s + timeout: 10s + retries: 3 + start_period: 40s + + frontend: + build: + context: ./ui + dockerfile: Dockerfile + container_name: docubot-frontend + ports: + - "3000:3000" + environment: + - HOST=0.0.0.0 + - PORT=3000 + - VITE_API_URL=/api + - VITE_API_TARGET=http://backend:5001 + volumes: + - ./ui:/app + - /app/node_modules + depends_on: + backend: + condition: service_healthy + networks: + - docubot-network + restart: unless-stopped + healthcheck: + test: ["CMD-SHELL", "node -e \"require('http').get('http://localhost:3000', (r) => process.exit(r.statusCode === 200 ? 0 : 1))\""] + interval: 30s + timeout: 10s + retries: 3 + start_period: 40s + +networks: + docubot-network: + driver: bridge + name: docubot-network + +volumes: + backend-tmp: + driver: local diff --git a/docs/images/01-homepage-input.png b/docs/images/01-homepage-input.png new file mode 100644 index 0000000..cd8895c Binary files /dev/null and b/docs/images/01-homepage-input.png differ diff --git a/docs/images/02-agent-workflow.png b/docs/images/02-agent-workflow.png new file mode 100644 index 0000000..17d4191 Binary files /dev/null and b/docs/images/02-agent-workflow.png differ diff --git a/docs/images/03-progress-tracking.png b/docs/images/03-progress-tracking.png new file mode 100644 index 0000000..dc236bb Binary files /dev/null and b/docs/images/03-progress-tracking.png differ diff --git a/docs/images/04-generated-readme.png b/docs/images/04-generated-readme.png new file mode 100644 index 0000000..0d7460e Binary files /dev/null and b/docs/images/04-generated-readme.png differ diff --git a/docs/images/05-pr-agent-execution.png b/docs/images/05-pr-agent-execution.png new file mode 100644 index 0000000..6f297b3 Binary files /dev/null and b/docs/images/05-pr-agent-execution.png differ diff --git a/docs/images/06-pr-created.png b/docs/images/06-pr-created.png new file mode 100644 index 0000000..45ba6ee Binary files /dev/null and b/docs/images/06-pr-created.png differ diff --git a/docs/images/07-metrics.png b/docs/images/07-metrics.png new file mode 100644 index 0000000..3e85e48 Binary files /dev/null and b/docs/images/07-metrics.png differ diff --git a/ui/.dockerignore b/ui/.dockerignore new file mode 100644 index 0000000..aca46af --- /dev/null +++ b/ui/.dockerignore @@ -0,0 +1,39 @@ +node_modules/ +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +yarn.lock + +dist/ +build/ +.next/ +out/ + +.vscode/ +.idea/ +*.swp +*.swo +*~ + +coverage/ +.nyc_output/ + +.env +.env.local +.env.*.local + +.git/ +.gitignore + +README.md +*.md + +Dockerfile +.dockerignore + +.DS_Store +Thumbs.db + +*.log +.cache/ diff --git a/ui/Dockerfile b/ui/Dockerfile new file mode 100644 index 0000000..93f786b --- /dev/null +++ b/ui/Dockerfile @@ -0,0 +1,35 @@ +# Multi-stage build for DocuBot AI Frontend +FROM node:20-slim + +# Set working directory +WORKDIR /app + +# Copy package.json only +COPY package.json ./ + +# Install dependencies +RUN npm install + +# Copy application code +COPY . . + +# Create non-root user +RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app + +# Expose port +EXPOSE 3000 + +# Set environment for Vite +ENV HOST=0.0.0.0 +ENV PORT=3000 +ENV VITE_API_TARGET=http://backend:5001 + +# Health check +HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \ + CMD node -e "require('http').get('http://localhost:3000', (r) => process.exit(r.statusCode === 200 ? 0 : 1))" + +# Switch to non-root user +USER appuser + +# Run development server with environment variable +CMD ["sh", "-c", "VITE_API_TARGET=http://backend:5001 npm run dev -- --host 0.0.0.0 --port 3000"] diff --git a/ui/index.html b/ui/index.html new file mode 100644 index 0000000..9d9ec71 --- /dev/null +++ b/ui/index.html @@ -0,0 +1,13 @@ + + + + + + + DocuBot — Intelligent README Generator · Cloud2 Labs Innovation Hub + + +
+ + + diff --git a/ui/nginx.conf b/ui/nginx.conf new file mode 100644 index 0000000..080a102 --- /dev/null +++ b/ui/nginx.conf @@ -0,0 +1,64 @@ +server { + listen 8080; + server_name _; + + root /usr/share/nginx/html; + index index.html; + + # Gzip compression + gzip on; + gzip_vary on; + gzip_min_length 1024; + gzip_types text/plain text/css text/xml text/javascript application/javascript application/xml+rss application/json; + + # Security headers + add_header X-Frame-Options "SAMEORIGIN" always; + add_header X-Content-Type-Options "nosniff" always; + add_header X-XSS-Protection "1; mode=block" always; + add_header Referrer-Policy "no-referrer-when-downgrade" always; + + # SPA routing - serve index.html for all routes + location / { + try_files $uri $uri/ /index.html; + } + + # API proxy to backend + location /api/ { + proxy_pass http://backend:5001; + proxy_http_version 1.1; + + # SSE support + proxy_set_header Connection ''; + proxy_set_header Cache-Control 'no-cache'; + proxy_buffering off; + chunked_transfer_encoding on; + + # Standard proxy headers + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header X-Forwarded-Proto $scheme; + + # Timeouts for long-running requests + proxy_connect_timeout 300s; + proxy_send_timeout 300s; + proxy_read_timeout 300s; + } + + # Static files caching + location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot)$ { + expires 1y; + add_header Cache-Control "public, immutable"; + } + + # Disable access log for static files + location = /favicon.ico { + log_not_found off; + access_log off; + } + + location = /robots.txt { + log_not_found off; + access_log off; + } +} diff --git a/ui/package-lock.json b/ui/package-lock.json new file mode 100644 index 0000000..ec386e9 --- /dev/null +++ b/ui/package-lock.json @@ -0,0 +1,8548 @@ +{ + "name": "docubot-ui", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, 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b/ui/package.json @@ -0,0 +1,55 @@ +{ + "name": "docubot-ui", + "version": "1.0.0", + "private": true, + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview", + "lint": "eslint . --ext js,jsx --report-unused-disable-directives --max-warnings 0" + }, + "dependencies": { + "axios": "^1.13.5", + "github-markdown-css": "^5.8.1", + "highlight.js": "^11.11.1", + "lucide-react": "^0.294.0", + "mermaid": "^11.4.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-markdown": "^9.0.1", + "rehype-highlight": "^7.0.0", + "remark-gfm": "^4.0.1" + }, + "devDependencies": { + "@types/react": "^18.2.43", + "@types/react-dom": "^18.2.17", + "@vitejs/plugin-react": "^4.2.1", + "autoprefixer": "^10.4.16", + "eslint": "^8.55.0", + "eslint-plugin-react": "^7.33.2", + "eslint-plugin-react-hooks": "^4.6.0", + "eslint-plugin-react-refresh": "^0.4.5", + "postcss": "^8.4.32", + "tailwindcss": "^3.3.6", + "vite": "^5.0.8" + }, + "overrides": { + "mermaid": "^11.4.0", + "shell-quote": "^1.8.1", + "loader-utils": "^3.2.1", + "minimatch": "^3.0.5", + "node-forge": "^1.3.2", + "nth-check": "^2.0.1", + "object-path": "^0.11.8", + "qs": "^6.14.1", + "semver": "^7.5.2", + "serialize-javascript": "^6.0.0", + "webpack-dev-middleware": "^5.3.4", + "ansi-html": "^0.0.8", + "braces": "^3.0.3", + "cross-spawn": "^7.0.5", + "form-data": "^4.0.4", + "http-proxy-middleware": "^3.0.3" + } +} diff --git a/ui/postcss.config.js b/ui/postcss.config.js new file mode 100644 index 0000000..2e7af2b --- /dev/null +++ b/ui/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/ui/src/App.jsx b/ui/src/App.jsx new file mode 100644 index 0000000..4df085d --- /dev/null +++ b/ui/src/App.jsx @@ -0,0 +1,194 @@ +import { useState, useEffect } from 'react' +import Header from './components/Header' +import DocBotInterface from './components/DocGenInterface' +import FloatingLogsPanel from './components/FloatingLogsPanel' +import ResultsViewer from './components/ResultsViewer' +import ProjectSelector from './components/ProjectSelector' +import CosmicBackground from './components/CosmicBackground' +import StatusRibbon from './components/StatusRibbon' +import { api } from './services/api' + +function App() { + const [currentJob, setCurrentJob] = useState(null) + const [logs, setLogs] = useState([]) + const [logsOpen, setLogsOpen] = useState(false) + const [generatedReadme, setGeneratedReadme] = useState(null) + const [workflowStatus, setWorkflowStatus] = useState('idle') + const [workflowError, setWorkflowError] = useState(null) + const [awaitingProjectSelection, setAwaitingProjectSelection] = useState(false) + const [detectedProjects, setDetectedProjects] = useState(null) + const [skippedFolders, setSkippedFolders] = useState(null) + const [theme, setTheme] = useState('light') + + // Apply theme to document + useEffect(() => { + document.documentElement.setAttribute('data-theme', theme) + }, [theme]) + + // Poll job status to check for project selection + useEffect(() => { + if (!currentJob || workflowStatus === 'completed' || workflowStatus === 'failed') { + return + } + + const pollInterval = setInterval(async () => { + try { + const status = await api.getJobStatus(currentJob) + + if (status.awaiting_project_selection && !awaitingProjectSelection) { + console.log('DEBUG: API Status Response:', status) + console.log('DEBUG: Skipped Folders from API:', status.skipped_folders) + setAwaitingProjectSelection(true) + setDetectedProjects(status.detected_projects) + setSkippedFolders(status.skipped_folders || []) + setWorkflowStatus('awaiting_selection') + } + + if (status.error_message && status.status === 'failed') { + setWorkflowError(status.error_message) + setWorkflowStatus('failed') + } + } catch (error) { + console.error('Failed to poll job status:', error) + } + }, 2000) + + return () => clearInterval(pollInterval) + }, [currentJob, workflowStatus, awaitingProjectSelection]) + + const handleJobStart = (jobId) => { + setCurrentJob(jobId) + setLogs([]) + setGeneratedReadme(null) + setWorkflowStatus('running') + setWorkflowError(null) + setAwaitingProjectSelection(false) + setDetectedProjects(null) + setSkippedFolders(null) + } + + const handleLogReceived = (log) => { + setLogs(prevLogs => [...prevLogs, log]) + + if (log.log_type === 'success' && log.message.includes('Documentation generation complete')) { + setWorkflowStatus('completed') + } else if (log.log_type === 'error') { + setWorkflowStatus('failed') + } + } + + const handleReadmeGenerated = (readme) => { + setGeneratedReadme(readme) + } + + const handleProjectsSelected = () => { + setAwaitingProjectSelection(false) + setDetectedProjects(null) + setSkippedFolders(null) + setWorkflowStatus('running') + } + + return ( +
+ {/* Cosmic Background */} + + + {/* Header */} +
setLogsOpen(!logsOpen)} + hasActiveJob={!!currentJob} + workflowStatus={workflowStatus} + theme={theme} + onThemeToggle={() => setTheme(theme === 'light' ? 'dark' : 'light')} + /> + + {/* Main Content */} +
+ {/* Hero Section */} +
+
+
+ + Cloud2 Labs Innovation Hub + +
+ +

+ Intelligent README +
+ + Documentation Generator + +

+ +

+ Point DocuBot at any GitHub repository. Our agentic AI scans the codebase, understands the architecture, + and generates production-ready README documentation — automatically. +

+ +
+ {['Agentic AI', 'GitHub Integration', 'Markdown Export', 'Multi-Project Support', 'Real-time Analysis'].map((tag) => ( +
+ {tag} +
+ ))} +
+
+ +
+ {/* Input Section */} + + + {/* Project Selection Section (Conditional) */} + {awaitingProjectSelection && detectedProjects && ( +
+ +
+ )} + + {/* Results Section (Conditional) */} + {workflowStatus === 'completed' && ( +
+ +
+ )} +
+
+ + {/* Status Ribbon */} + setLogsOpen(true)} + /> + + {/* Floating Logs Panel */} + setLogsOpen(false)} + logs={logs} + currentJob={currentJob} + /> +
+ ) +} + +export default App diff --git a/ui/src/components/AgentLogDrawer.jsx b/ui/src/components/AgentLogDrawer.jsx new file mode 100644 index 0000000..e8b777e --- /dev/null +++ b/ui/src/components/AgentLogDrawer.jsx @@ -0,0 +1,216 @@ +import { X, Bot, CheckCircle, AlertCircle, Info, Zap, Activity } from 'lucide-react' +import { useState } from 'react' + +function AgentLogDrawer({ isOpen, onClose, logs, currentJob }) { + const [activeFilter, setActiveFilter] = useState('ALL') + + if (!isOpen) return null + + const getLogIcon = (logType) => { + switch (logType) { + case 'agent_start': + return + case 'agent_complete': + return + case 'agent_thinking': + return + case 'error': + return + case 'success': + return + default: + return + } + } + + const getLogColor = (logType) => { + switch (logType) { + case 'agent_start': + return 'bg-cloud2-purple-50 border-cloud2-purple' + case 'agent_complete': + return 'bg-green-50 border-cloud2-success' + case 'agent_thinking': + return 'bg-yellow-50 border-cloud2-warning' + case 'error': + return 'bg-red-50 border-cloud2-error' + case 'success': + return 'bg-green-50 border-cloud2-success' + default: + return 'bg-cloud2-gray-50 border-cloud2-gray-300' + } + } + + const getLogTag = (logType) => { + switch (logType) { + case 'agent_start': + return { label: 'ACTION', color: 'text-cloud2-warning bg-yellow-50' } + case 'agent_complete': + return { label: 'OK', color: 'text-cloud2-success bg-green-50' } + case 'agent_thinking': + return { label: 'ACTION', color: 'text-cloud2-warning bg-yellow-50' } + case 'error': + return { label: 'ERROR', color: 'text-cloud2-error bg-red-50' } + case 'success': + return { label: 'OK', color: 'text-cloud2-success bg-green-50' } + default: + return { label: 'INFO', color: 'text-cloud2-purple bg-cloud2-purple-50' } + } + } + + const formatTimestamp = (timestamp) => { + const date = new Date(timestamp) + return date.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit', fractional: 3 }) + } + + const calculateStats = () => { + const toolCalls = logs.filter(l => l.log_type === 'agent_thinking').length + const errors = logs.filter(l => l.log_type === 'error').length + const agents = new Set(logs.filter(l => l.agent_name).map(l => l.agent_name)).size + + return { + steps: logs.length, + toolCalls, + agents, + errors + } + } + + const filteredLogs = activeFilter === 'ALL' + ? logs + : logs.filter(log => { + if (activeFilter === 'ACTION') return log.log_type === 'agent_start' || log.log_type === 'agent_thinking' + if (activeFilter === 'TOOL') return log.log_type === 'agent_thinking' + if (activeFilter === 'OK') return log.log_type === 'success' || log.log_type === 'agent_complete' + if (activeFilter === 'ERRORS') return log.log_type === 'error' + return true + }) + + const stats = calculateStats() + + return ( + <> + {/* Overlay */} +
+ + {/* Drawer */} + + + ) +} + +export default AgentLogDrawer diff --git a/ui/src/components/AgentLogPanel.jsx b/ui/src/components/AgentLogPanel.jsx new file mode 100644 index 0000000..d04088e --- /dev/null +++ b/ui/src/components/AgentLogPanel.jsx @@ -0,0 +1,150 @@ +import { X, Bot, CheckCircle, AlertCircle, Info, Zap, Activity } from 'lucide-react' + +function AgentLogPanel({ isOpen, onClose, logs, currentJob }) { + if (!isOpen) return null + + const getLogIcon = (logType) => { + switch (logType) { + case 'agent_start': + return + case 'agent_complete': + return + case 'agent_thinking': + return + case 'error': + return + case 'success': + return + default: + return + } + } + + const getLogColor = (logType) => { + switch (logType) { + case 'agent_start': + return 'bg-cloud2-purple-50 border-cloud2-purple' + case 'agent_complete': + return 'bg-green-50 border-cloud2-success' + case 'agent_thinking': + return 'bg-yellow-50 border-cloud2-warning' + case 'error': + return 'bg-red-50 border-cloud2-error' + case 'success': + return 'bg-green-50 border-cloud2-success' + default: + return 'bg-cloud2-gray-50 border-cloud2-gray-300' + } + } + + const formatTimestamp = (timestamp) => { + return new Date(timestamp).toLocaleTimeString() + } + + return ( +
+ {/* Click outside to close */} +
+ + {/* Slide-in Panel */} +
+ {/* Header */} +
+
+
+
+ +
+

Agent Activity Log

+
+ {currentJob && ( +

Job ID: {currentJob}

+ )} +
+ +
+ + {/* Logs Content */} +
+ {logs.length === 0 ? ( +
+ +

No activity yet

+

Agent logs will appear here during generation

+
+ ) : ( + logs.map((log, index) => ( +
+
+
+ {getLogIcon(log.log_type)} +
+
+
+ {log.agent_name && ( + + {log.agent_name} + + )} + {log.timestamp && ( + + {formatTimestamp(log.timestamp)} + + )} +
+

{log.message}

+ + {/* Metadata */} + {log.metadata && Object.keys(log.metadata).length > 0 && ( +
+ {JSON.stringify(log.metadata, null, 2)} +
+ )} +
+
+
+ )) + )} +
+ + {/* Footer Stats */} + {logs.length > 0 && ( +
+
+ + Total Events: {logs.length} + +
+ + Agents: + {new Set(logs.filter(l => l.agent_name).map(l => l.agent_name)).size} + + + {logs.filter(l => l.log_type === 'error').length > 0 && ( + + Errors: + {logs.filter(l => l.log_type === 'error').length} + + + )} +
+
+
+ )} +
+
+ ) +} + +export default AgentLogPanel diff --git a/ui/src/components/CosmicBackground.jsx b/ui/src/components/CosmicBackground.jsx new file mode 100644 index 0000000..7440be2 --- /dev/null +++ b/ui/src/components/CosmicBackground.jsx @@ -0,0 +1,12 @@ +function CosmicBackground() { + return ( + + ) +} + +export default CosmicBackground diff --git a/ui/src/components/DocGenInterface.jsx b/ui/src/components/DocGenInterface.jsx new file mode 100644 index 0000000..ce5ec09 --- /dev/null +++ b/ui/src/components/DocGenInterface.jsx @@ -0,0 +1,221 @@ +import { useState, useEffect } from 'react' +import { Github, Play, Loader2, CheckCircle, AlertCircle, Clock, FileText, Zap } from 'lucide-react' +import { api } from '../services/api' + +function DocBotInterface({ onJobStart, onLogReceived, currentJob, workflowStatus, workflowError }) { + const [repoUrl, setRepoUrl] = useState('') + const [loading, setLoading] = useState(false) + const [error, setError] = useState(null) + const [sseConnection, setSseConnection] = useState(null) + + useEffect(() => { + if (workflowError) { + setError(workflowError) + setLoading(false) + } + }, [workflowError]) + + useEffect(() => { + return () => { + if (sseConnection) { + sseConnection.close() + } + } + }, [sseConnection]) + + useEffect(() => { + if (currentJob && !sseConnection) { + console.log('Connecting to SSE for job:', currentJob) + + const eventSource = api.connectToLogs( + currentJob, + (log) => { + console.log('Received log:', log) + onLogReceived(log) + + if (log.log_type === 'success' && log.message.includes('complete')) { + setLoading(false) + } else if (log.log_type === 'error') { + setLoading(false) + } + }, + (error) => { + console.error('SSE connection error:', error) + setError('Connection to the server was lost. Please try again.') + setLoading(false) + } + ) + + setSseConnection(eventSource) + + return () => { + console.log('Closing SSE connection') + eventSource.close() + } + } + }, [currentJob]) + + useEffect(() => { + if (workflowStatus === 'completed' || workflowStatus === 'failed') { + setLoading(false) + } + }, [workflowStatus]) + + const handleSubmit = async (e) => { + e.preventDefault() + setError(null) + setLoading(true) + + try { + if (!repoUrl.trim()) { + throw new Error('Please enter a GitHub repository URL.') + } + + if (!repoUrl.includes('github.com')) { + throw new Error('Please provide a valid GitHub repository URL (e.g., https://github.com/username/repository)') + } + + const response = await api.generateDocs(repoUrl) + onJobStart(response.job_id) + + } catch (err) { + const errorMessage = err.response?.data?.detail || err.response?.data?.message || err.message || 'Failed to start documentation generation. Please try again.' + setError(errorMessage) + setLoading(false) + } + } + + const isDisabled = loading || workflowStatus === 'running' + + const getStatusMessage = () => { + switch (workflowStatus) { + case 'running': + return { + icon: , + className: 'status-running', + text: 'text-cloud2-purple', + message: 'AI agents are analyzing your repository and generating documentation. Click "View Logs" to watch the progress.' + } + case 'completed': + return { + icon: , + className: 'status-completed', + text: 'text-cloud2-success', + message: 'Documentation generation complete! Check the generated README below.' + } + case 'failed': + return { + icon: , + className: 'status-failed', + text: 'text-cloud2-error', + message: workflowError || 'Documentation generation failed. Please check the logs for details.' + } + default: + return null + } + } + + const statusInfo = getStatusMessage() + + return ( +
+
+
+
+ +
+

Generate Documentation

+
+

+ Enter a GitHub repository URL to automatically generate comprehensive, AI-powered documentation +

+
+ +
+ {/* Repository URL Input */} +
+ +
+
+ +
+ setRepoUrl(e.target.value)} + placeholder="https://github.com/username/repository" + disabled={isDisabled} + className="w-full pl-12 pr-4 py-3 rounded-button input-focus disabled:opacity-50 disabled:cursor-not-allowed transition-all text-sm sm:text-base" + style={{ + backgroundColor: 'var(--bg-white)', + border: '2px solid var(--border-primary)', + color: 'var(--text-primary)' + }} + /> +
+

+ Max repo size: 10GB. Analyzes up to 500 files (1MB max each, up to 500 lines/file). All limits configurable in backend .env +

+
+ + {/* Error Message */} + {error && ( +
+
+ +
+

{error}

+
+ +
+
+ )} + + {/* Submit Button */} + +
+ + {/* Workflow Status */} + {currentJob && statusInfo && ( +
+
+
+ {statusInfo.icon} +
+
+

+ {statusInfo.message} +

+
+
+
+ )} +
+ ) +} + +export default DocBotInterface diff --git a/ui/src/components/FloatingLogsPanel.jsx b/ui/src/components/FloatingLogsPanel.jsx new file mode 100644 index 0000000..edab3dd --- /dev/null +++ b/ui/src/components/FloatingLogsPanel.jsx @@ -0,0 +1,244 @@ +import { useState, useEffect, useRef } from 'react' +import { X, ChevronDown, ChevronUp, Activity, AlertCircle, CheckCircle, XCircle, Zap, Terminal } from 'lucide-react' + +function FloatingLogsPanel({ isOpen, onClose, logs, currentJob }) { + const [isExpanded, setIsExpanded] = useState(true) + const logsEndRef = useRef(null) + + // Auto-scroll to bottom when new logs arrive + useEffect(() => { + if (isExpanded && logsEndRef.current) { + logsEndRef.current.scrollIntoView({ behavior: 'smooth' }) + } + }, [logs, isExpanded]) + + const getLogIcon = (logType) => { + // Handle all agent log types + if (logType?.startsWith('agent_') || logType === 'agent') { + return + } + + switch (logType) { + case 'success': + return + case 'error': + return + case 'info': + default: + return + } + } + + const getLogTypeLabel = (logType) => { + return logType?.toUpperCase() || 'INFO' + } + + // Count logs by type - agent logs include: agent_start, agent_thinking, agent_action, agent_observation, agent_complete, agent_tool_use, agent_decision + const countByType = { + total: logs.length, + success: logs.filter(log => log.log_type === 'success').length, + error: logs.filter(log => log.log_type === 'error').length, + // Count unique agents by agent_name, not total log entries + agent: new Set( + logs + .filter(log => log.agent_name) // Only logs with agent_name + .map(log => log.agent_name) + ).size, + thinking: logs.filter(log => log.log_type === 'agent_thinking').length, + action: logs.filter(log => log.log_type === 'agent_action').length, + observation: logs.filter(log => log.log_type === 'agent_observation').length + } + + if (!isOpen) return null + + return ( +
+ {/* Floating Panel Container */} +
+ {/* Header - Always Visible */} +
setIsExpanded(!isExpanded)} + > +
+
+ +
+
+
+ Agent Logs +
+ {currentJob && ( +
+ Job: {currentJob.substring(0, 8)}... +
+ )} +
+
+ +
+ {/* KPI Badges */} +
+
+ {countByType.total} +
+ {countByType.error > 0 && ( +
+ {countByType.error} ERR +
+ )} +
+ + {/* Expand/Collapse Button */} + + + {/* Close Button */} + +
+
+ + {/* Expandable Content */} + {isExpanded && ( +
+ {/* Stats Bar */} +
+
+ TOTAL: {countByType.total} + {countByType.agent > 0 && ( + AGENTS: {countByType.agent} + )} + {countByType.action > 0 && ( + ACTIONS: {countByType.action} + )} + {countByType.success > 0 && ( + SUCCESS: {countByType.success} + )} + {countByType.error > 0 && ( + ERRORS: {countByType.error} + )} +
+
+ + {/* Logs Content - Scrollable */} +
+ {logs.length === 0 ? ( +
+ +

No logs yet...

+

Start a job to see agent activity

+
+ ) : ( + logs.map((log, index) => ( +
+ {/* Log Header */} +
+
+ {getLogIcon(log.log_type)} + + {getLogTypeLabel(log.log_type)} + +
+ + {new Date(log.timestamp).toLocaleTimeString()} + +
+ + {/* Log Message */} +

+ {log.message} +

+ + {/* Agent Name (if available) */} + {log.agent_name && ( +
+ + Agent: {log.agent_name} + +
+ )} +
+ )) + )} +
+
+
+ )} +
+
+ ) +} + +export default FloatingLogsPanel diff --git a/ui/src/components/Header.jsx b/ui/src/components/Header.jsx new file mode 100644 index 0000000..41d614b --- /dev/null +++ b/ui/src/components/Header.jsx @@ -0,0 +1,96 @@ +import { Bot, Activity, CheckCircle, XCircle, Clock, FileText, Moon, Sun } from 'lucide-react' + +function Header({ onLogsToggle, hasActiveJob, workflowStatus, theme, onThemeToggle }) { + const getStatusConfig = () => { + switch (workflowStatus) { + case 'running': + return { + icon: , + text: 'Generating', + className: 'status-running text-cloud2-purple' + } + case 'completed': + return { + icon: , + text: 'Completed', + className: 'status-completed text-cloud2-success' + } + case 'failed': + return { + icon: , + text: 'Failed', + className: 'status-failed text-cloud2-error' + } + default: + return { + icon: , + text: 'Ready', + className: 'bg-cloud2-purple-50 text-cloud2-purple' + } + } + } + + const statusConfig = getStatusConfig() + + return ( +
+
+
+ {/* Logo & Branding */} +
+
+ +
+
+
+

DocuBot

+ + Cloud2 Labs + +
+

+ AI-Powered Documentation +

+
+
+ + {/* Right Side - Status & Actions */} +
+ {/* Status Indicator */} + {hasActiveJob && ( +
+ {statusConfig.icon} + {statusConfig.text} +
+ )} + + {/* Theme Toggle */} + + + {/* Agent Logs Button */} + +
+
+
+
+ ) +} + +export default Header diff --git a/ui/src/components/Mermaid.jsx b/ui/src/components/Mermaid.jsx new file mode 100644 index 0000000..a575c37 --- /dev/null +++ b/ui/src/components/Mermaid.jsx @@ -0,0 +1,89 @@ +import { useEffect, useRef, useState } from 'react' +import mermaid from 'mermaid' + +function Mermaid({ chart }) { + const containerRef = useRef(null) + const idRef = useRef(`mermaid-${Math.random().toString(36).substr(2, 9)}`) + const [isDark, setIsDark] = useState(false) + + // Detect dark mode from CSS variable + useEffect(() => { + const detectDarkMode = () => { + const bgColor = getComputedStyle(document.documentElement) + .getPropertyValue('--bg-primary') + .trim() + // If background is dark (close to #1a1a1a or similar), use dark theme + setIsDark(bgColor.includes('#1a') || bgColor.includes('#0f') || bgColor.includes('18, 18, 18')) + } + + detectDarkMode() + + // Listen for theme changes + const observer = new MutationObserver(detectDarkMode) + observer.observe(document.documentElement, { + attributes: true, + attributeFilter: ['data-theme', 'class'] + }) + + return () => observer.disconnect() + }, []) + + useEffect(() => { + // Reinitialize mermaid with theme based on dark mode + mermaid.initialize({ + startOnLoad: false, + theme: isDark ? 'dark' : 'default', + securityLevel: 'strict', + fontFamily: 'ui-sans-serif, system-ui, sans-serif', + themeVariables: isDark ? { + darkMode: true, + background: '#1a1a1a', + primaryColor: '#8b5cf6', + primaryTextColor: '#e5e7eb', + primaryBorderColor: '#4b5563', + lineColor: '#6b7280', + secondaryColor: '#374151', + tertiaryColor: '#1f2937', + textColor: '#e5e7eb', + fontSize: '16px' + } : {} + }) + }, [isDark]) + + useEffect(() => { + if (containerRef.current && chart) { + const renderDiagram = async () => { + try { + // Clear previous content + containerRef.current.innerHTML = '' + + // Generate new ID for each render to avoid conflicts + const newId = `mermaid-${Date.now()}-${Math.random().toString(36).substr(2, 9)}` + + // Render the mermaid chart + const { svg } = await mermaid.render(newId, chart) + containerRef.current.innerHTML = svg + + // Apply dark mode styling to SVG if needed + if (isDark) { + const svgElement = containerRef.current.querySelector('svg') + if (svgElement) { + svgElement.style.backgroundColor = 'transparent' + svgElement.style.color = '#e5e7eb' + } + } + } catch (error) { + console.error('Mermaid rendering error:', error) + const errorColor = isDark ? '#ef4444' : 'red' + containerRef.current.innerHTML = `
Error rendering diagram: ${error.message}
` + } + } + + renderDiagram() + } + }, [chart, isDark]) + + return
+} + +export default Mermaid diff --git a/ui/src/components/ProjectSelector.jsx b/ui/src/components/ProjectSelector.jsx new file mode 100644 index 0000000..faf8f1d --- /dev/null +++ b/ui/src/components/ProjectSelector.jsx @@ -0,0 +1,275 @@ +import { useState, useEffect } from 'react' +import { Folder, CheckSquare, Square, Play, Loader2, CheckCheck, XSquare, Search, AlertCircle, FolderTree } from 'lucide-react' +import { api } from '../services/api' + +function ProjectSelector({ currentJob, detectedProjects, skippedFolders, onProjectsSelected }) { + const [selectedPaths, setSelectedPaths] = useState([]) + const [submitting, setSubmitting] = useState(false) + const [searchQuery, setSearchQuery] = useState('') + const [error, setError] = useState(null) + + console.log('DEBUG ProjectSelector - skippedFolders:', skippedFolders) + console.log('DEBUG ProjectSelector - skippedFolders type:', typeof skippedFolders) + console.log('DEBUG ProjectSelector - skippedFolders length:', skippedFolders?.length) + + useEffect(() => { + if (detectedProjects && detectedProjects.length > 0) { + if (detectedProjects.length <= 10) { + setSelectedPaths(detectedProjects.map(p => p.path)) + } else { + setSelectedPaths([]) + } + } + }, [detectedProjects]) + + const selectAll = () => { + const filtered = getFilteredProjects() + setSelectedPaths(prev => { + const newPaths = filtered.map(p => p.path) + return Array.from(new Set([...prev, ...newPaths])) + }) + } + + const deselectAll = () => { + const filtered = getFilteredProjects() + const filteredPaths = filtered.map(p => p.path) + setSelectedPaths(prev => prev.filter(path => !filteredPaths.includes(path))) + } + + const getFilteredProjects = () => { + if (!detectedProjects) return [] + if (!searchQuery.trim()) return detectedProjects + + const query = searchQuery.toLowerCase() + return detectedProjects.filter(p => + p.name.toLowerCase().includes(query) || + p.path.toLowerCase().includes(query) || + p.types.some(t => t.toLowerCase().includes(query)) + ) + } + + const toggleProject = (path) => { + setError(null) + setSelectedPaths(prev => { + if (prev.includes(path)) { + return prev.filter(p => p !== path) + } else { + return [...prev, path] + } + }) + } + + const handleSubmit = async () => { + if (selectedPaths.length === 0) { + setError('Please select at least one project to document') + return + } + + setSubmitting(true) + setError(null) + try { + await api.selectProjects(currentJob, selectedPaths) + onProjectsSelected() + } catch (err) { + console.error('Failed to submit project selection:', err) + const errorMessage = err.response?.data?.detail || 'Failed to submit selection. Please try again.' + setError(errorMessage) + setSubmitting(false) + } + } + + if (!detectedProjects || detectedProjects.length === 0) { + return null + } + + const filteredProjects = getFilteredProjects() + + return ( +
+
+
+
+ +
+

+ 🔍 Multiple Projects Detected +

+
+

+ {detectedProjects.length} project(s) Found in this repository. Select a project to generate Readme file. +

+
+ + {/* Error Banner */} + {error && ( +
+
+ +
+

{error}

+
+
+
+ )} + + {/* Search and Bulk Actions */} +
+ {/* Search Bar */} +
+ + setSearchQuery(e.target.value)} + className="w-full pl-12 pr-4 py-3 bg-white border-2 border-cloud2-gray-300 text-cloud2-black rounded-button focus:border-cloud2-purple focus:ring-2 focus:ring-cloud2-purple/20 transition-all placeholder:text-cloud2-gray-500" + /> +
+ + {/* Bulk Selection Buttons */} +
+
+ + +
+

+ {selectedPaths.length} of {detectedProjects.length} selected +

+
+
+ + {/* Project List */} +
+ {filteredProjects.length === 0 ? ( +
+ +

No projects match your search criteria.

+
+ ) : ( + filteredProjects.map((project) => { + const isSelected = selectedPaths.includes(project.path) + const projectTypes = project.types.join(', ') + const isRoot = project.path === '/' + + return ( +
toggleProject(project.path)} + className={` + flex items-start space-x-4 p-5 rounded-card border-2 cursor-pointer transition-all + ${isSelected + ? 'bg-cloud2-purple-50 border-cloud2-purple shadow-card-hover' + : 'bg-white border-cloud2-gray-300 hover:border-cloud2-purple' + } + `} + > +
+ {isSelected ? ( + + ) : ( + + )} +
+ +
+
+ +

+ {project.name} + {isRoot && (Root)} +

+
+ +

+ Path: {project.path} +

+ +

+ Type: {projectTypes} +

+ +
+ {project.file_count} files + {project.dir_count} directories +
+ + {project.indicators && project.indicators.length > 0 && ( +
+ {project.indicators.map((indicator, i) => ( + + {indicator} + + ))} +
+ )} +
+
+ ) + }) + )} +
+ + {/* Skipped Folders Section */} + {skippedFolders && skippedFolders.length > 0 && ( +
+

+ + Skipped Folders ({skippedFolders.length}) +

+

+ The following folders were not detected as code projects: +

+
    + {skippedFolders.map((folder, idx) => ( +
  • + • {folder.name} - {folder.reason} ({folder.details}) +
    • + ))} +
+
+ )} + + {/* Submit Button */} +
+

+ {selectedPaths.length} project{selectedPaths.length !== 1 ? 's' : ''} selected +

+ + +
+
+ ) +} + +export default ProjectSelector diff --git a/ui/src/components/ResultsViewer.jsx b/ui/src/components/ResultsViewer.jsx new file mode 100644 index 0000000..fe4d885 --- /dev/null +++ b/ui/src/components/ResultsViewer.jsx @@ -0,0 +1,381 @@ +import { useState, useEffect } from 'react' +import { Download, Copy, Check, FileText, Loader2, AlertCircle, GitPullRequest, ExternalLink } from 'lucide-react' +import ReactMarkdown from 'react-markdown' +import remarkGfm from 'remark-gfm' +import rehypeHighlight from 'rehype-highlight' +import Mermaid from './Mermaid' +import { api } from '../services/api' + +// Import GitHub markdown CSS (includes dark mode support) +import 'github-markdown-css/github-markdown.css' +import 'highlight.js/styles/github-dark.css' + +// Custom CSS override for markdown in our theme +const markdownStyles = ` + .markdown-body { + background-color: transparent !important; + } + + /* Dark mode overrides */ + [data-theme="dark"] .markdown-body, + .dark .markdown-body { + color-scheme: dark; + } + + [data-theme="dark"] .markdown-body, + .dark .markdown-body { + color: var(--text-primary) !important; + } + + [data-theme="dark"] .markdown-body h1, + [data-theme="dark"] .markdown-body h2, + [data-theme="dark"] .markdown-body h3, + [data-theme="dark"] .markdown-body h4, + [data-theme="dark"] .markdown-body h5, + [data-theme="dark"] .markdown-body h6, + .dark .markdown-body h1, + .dark .markdown-body h2, + .dark .markdown-body h3, + .dark .markdown-body h4, + .dark .markdown-body h5, + .dark .markdown-body h6 { + color: var(--text-primary) !important; + border-bottom-color: var(--border-primary) !important; + } + + [data-theme="dark"] .markdown-body a, + .dark .markdown-body a { + color: #8b5cf6 !important; + } + + [data-theme="dark"] .markdown-body code, + .dark .markdown-body code { + background-color: var(--bg-gray-100) !important; + color: var(--text-primary) !important; + } + + [data-theme="dark"] .markdown-body pre, + .dark .markdown-body pre { + background-color: var(--bg-gray-100) !important; + } + + [data-theme="dark"] .markdown-body table tr, + .dark .markdown-body table tr { + background-color: transparent !important; + border-top-color: var(--border-primary) !important; + } + + [data-theme="dark"] .markdown-body table th, + [data-theme="dark"] .markdown-body table td, + .dark .markdown-body table th, + .dark .markdown-body table td { + border-color: var(--border-primary) !important; + } + + [data-theme="dark"] .markdown-body blockquote, + .dark .markdown-body blockquote { + color: var(--text-secondary) !important; + border-left-color: var(--border-primary) !important; + } + + [data-theme="dark"] .markdown-body hr, + .dark .markdown-body hr { + background-color: var(--border-primary) !important; + } +` + +// Inject styles +if (typeof document !== 'undefined') { + const styleId = 'markdown-dark-mode-styles' + if (!document.getElementById(styleId)) { + const style = document.createElement('style') + style.id = styleId + style.textContent = markdownStyles + document.head.appendChild(style) + } +} + +function ResultsViewer({ readme, onReadmeGenerated, currentJob, workflowStatus }) { + const [copied, setCopied] = useState(false) + const [markdownContent, setMarkdownContent] = useState(null) + const [projectTitle, setProjectTitle] = useState(null) + const [creatingPR, setCreatingPR] = useState(false) + const [prUrl, setPrUrl] = useState(null) + const [prError, setPrError] = useState(null) + + const toTitleCase = (str) => { + return str + .split(/[-_\s]+/) + .map(word => word.charAt(0).toUpperCase() + word.slice(1).toLowerCase()) + .join(' ') + } + + useEffect(() => { + if (markdownContent) { + const lines = markdownContent.split('\n') + for (let i = 0; i < Math.min(15, lines.length); i++) { + const line = lines[i].trim() + if (line.startsWith('## ')) { + const title = line.substring(3).trim() + setProjectTitle(toTitleCase(title)) + break + } else if (line.startsWith('# ') && !line.startsWith('## ')) { + const title = line.substring(2).trim() + setProjectTitle(toTitleCase(title)) + break + } + } + } + }, [markdownContent]) + + useEffect(() => { + console.log('[ResultsViewer] useEffect triggered:', { workflowStatus, currentJob, hasMarkdown: !!markdownContent }) + if (workflowStatus === 'completed' && currentJob && !markdownContent) { + console.log('[ResultsViewer] Conditions met - starting fetch') + const fetchReadme = async () => { + try { + console.log('Fetching job status for:', currentJob) + const jobStatus = await api.getJobStatus(currentJob) + console.log('Job status:', jobStatus) + + if (jobStatus.readme_preview || jobStatus.status === 'completed') { + try { + const readmeBlob = await api.downloadReadme(currentJob) + const readmeText = await readmeBlob.text() + console.log('Downloaded README:', readmeText.substring(0, 100)) + setMarkdownContent(readmeText) + if (onReadmeGenerated) { + onReadmeGenerated(readmeText) + } + } catch (err) { + console.warn('Could not download README, using preview:', err) + if (jobStatus.readme_preview) { + setMarkdownContent(jobStatus.readme_preview) + if (onReadmeGenerated) { + onReadmeGenerated(jobStatus.readme_preview) + } + } + } + } + } catch (error) { + console.error('Failed to fetch README:', error) + } + } + + const timer = setTimeout(fetchReadme, 1000) + return () => clearTimeout(timer) + } + }, [workflowStatus, currentJob, markdownContent]) + + const handleCopy = async () => { + if (markdownContent) { + await navigator.clipboard.writeText(markdownContent) + setCopied(true) + setTimeout(() => setCopied(false), 2000) + } + } + + const handleDownload = () => { + if (markdownContent) { + const blob = new Blob([markdownContent], { type: 'text/markdown' }) + const url = URL.createObjectURL(blob) + const a = document.createElement('a') + a.href = url + a.download = 'README.md' + document.body.appendChild(a) + a.click() + document.body.removeChild(a) + URL.revokeObjectURL(url) + } + } + + const handleCreatePR = async () => { + if (!currentJob) return + + setCreatingPR(true) + setPrError(null) + + try { + const result = await api.createPullRequest(currentJob) + + if (result.status === 'success') { + setPrUrl(result.pr_url) + console.log('PR created successfully:', result.pr_url) + } else { + setPrError(result.message || 'Failed to create PR') + } + } catch (error) { + console.error('Failed to create PR:', error) + setPrError(error.response?.data?.detail || error.message || 'Failed to create pull request') + } finally { + setCreatingPR(false) + } + } + + if (workflowStatus !== 'completed' || !markdownContent) { + return null + } + + return ( +
+ {/* Header */} +
+
+
+ +
+
+

+ Generated README +

+ {projectTitle && ( +

{projectTitle}

+ )} +
+
+ +
+ + + + {/* Create PR Button */} + {prUrl ? ( + + + View PR + + + ) : ( + + )} +
+
+ + {/* Content */} +
+
+ + +
+ ) + } + + return ( + + {children} + + ) + } + }} + > + {markdownContent} + +
+
+ + {/* Footer Stats */} + {markdownContent && ( +
+
+ + Lines: {markdownContent.split('\n').length} + + + Characters: {markdownContent.length} + + + Words: {markdownContent.split(/\s+/).length} + +
+
+ )} + + {/* PR Error Display */} + {prError && ( +
+ +
+

Failed to Create Pull Request

+

{prError}

+ {prError.includes('GITHUB_TOKEN') && ( +

+ Make sure GITHUB_TOKEN is configured in your backend environment variables. +

+ )} +
+ +
+ )} +
+ ) +} + +export default ResultsViewer diff --git a/ui/src/components/StatusRibbon.jsx b/ui/src/components/StatusRibbon.jsx new file mode 100644 index 0000000..dafbc64 --- /dev/null +++ b/ui/src/components/StatusRibbon.jsx @@ -0,0 +1,72 @@ +function StatusRibbon({ currentJob, workflowStatus, selectedProjectsCount, logsCount, onOpenLogs }) { + const getStatusInfo = () => { + switch (workflowStatus) { + case 'running': + return { color: '#34d399', label: 'Agent Active', pulse: true } + case 'completed': + return { color: '#2dd4bf', label: 'Complete', pulse: false } + case 'failed': + return { color: '#f87171', label: 'Failed', pulse: false } + case 'awaiting_selection': + return { color: '#fbbf24', label: 'Awaiting Selection', pulse: true } + default: + return { color: '#6C757D', label: 'Ready', pulse: false } + } + } + + const statusInfo = getStatusInfo() + + return ( +
+
+ {/* Status Indicator */} +
+
+ {statusInfo.label} +
+ +
+ + {/* Project Selection Status */} + {selectedProjectsCount > 0 && ( + <> +
+
+ {selectedProjectsCount} project{selectedProjectsCount !== 1 ? 's' : ''} selected +
+
+ + )} + + {/* README Status */} + {workflowStatus === 'completed' && ( + <> +
+
+ README ready +
+
+ + )} + + {/* Logs Link */} + {currentJob && logsCount > 0 && ( + + )} +
+
+ ) +} + +export default StatusRibbon diff --git a/ui/src/images/UI_2.png b/ui/src/images/UI_2.png new file mode 100644 index 0000000..c519526 Binary files /dev/null and b/ui/src/images/UI_2.png differ diff --git a/ui/src/images/UI_4.png b/ui/src/images/UI_4.png new file mode 100644 index 0000000..e095ef5 Binary files /dev/null and b/ui/src/images/UI_4.png differ diff --git a/ui/src/images/UI_5_agent.png b/ui/src/images/UI_5_agent.png new file mode 100644 index 0000000..3380c5b Binary files /dev/null and b/ui/src/images/UI_5_agent.png differ diff --git a/ui/src/index.css b/ui/src/index.css new file mode 100644 index 0000000..c876fe9 --- /dev/null +++ b/ui/src/index.css @@ -0,0 +1,553 @@ +@import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&family=JetBrains+Mono:wght@400;500;600;700&display=swap'); + +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* Cloud2 Labs Innovation Hub Design System */ +:root { + /* Primary Colors - Purple from Innovation Hub */ + --cloud2-purple: #A335FC; + --cloud2-purple-dark: #8B2BD9; + --cloud2-purple-light: #BB5DFF; + --cloud2-purple-soft: #F5E6FF; + + /* Background Colors - Light Theme */ + --bg-white: #FFFFFF; + --bg-gray-50: #F8F9FA; + --bg-gray-100: #F1F3F5; + --bg-gray-200: #E9ECEF; + + /* Text Colors - Dark on Light */ + --text-primary: #000000; + --text-secondary: #495057; + --text-tertiary: #6C757D; + --text-muted: #ADB5BD; + + /* Border Colors */ + --border-primary: #DEE2E6; + --border-accent: #A335FC; + + /* Status Colors */ + --color-success: #28A745; + --color-warning: #FFC107; + --color-error: #DC3545; + --color-info: #17A2B8; + + /* Shadows - Clean & Professional */ + --shadow-card: 0 2px 8px rgba(0, 0, 0, 0.08); + --shadow-card-hover: 0 4px 16px rgba(163, 53, 252, 0.15); + --shadow-purple: 0 4px 16px rgba(163, 53, 252, 0.2); +} + +/* Dark Mode Theme */ +[data-theme="dark"] { + /* Deep space backgrounds */ + --bg-white: #0a0e1a; + --bg-gray-50: #111729; + --bg-gray-100: #1a2035; + --bg-gray-200: #242d47; + + /* Light text on dark - HIGH CONTRAST */ + --text-primary: #ffffff; + --text-secondary: #c7d2fe; + --text-tertiary: #a5b4fc; + --text-muted: #818cf8; + + /* Borders with glow */ + --border-primary: rgba(139, 92, 246, 0.3); + --border-accent: rgba(168, 85, 247, 0.5); + + /* Enhanced shadows for dark mode */ + --shadow-card: 0 4px 20px rgba(0, 0, 0, 0.6); + --shadow-card-hover: 0 8px 32px rgba(139, 92, 246, 0.3); + --shadow-purple: 0 8px 32px rgba(168, 85, 247, 0.4); +} + +body { + margin: 0; + font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + background-color: var(--bg-white); + color: var(--text-primary); + transition: background-color 0.3s ease, color 0.3s ease; +} + +.font-mono { + font-family: 'JetBrains Mono', 'SF Mono', 'Consolas', monospace; +} + +#root { + min-height: 100vh; + background-color: var(--bg-white); + transition: background-color 0.3s ease; +} + +/* Custom Scrollbar */ +::-webkit-scrollbar { + width: 8px; + height: 8px; +} + +::-webkit-scrollbar-track { + background: var(--bg-gray-100); + border-radius: 4px; +} + +::-webkit-scrollbar-thumb { + background: var(--cloud2-purple); + border-radius: 4px; +} + +::-webkit-scrollbar-thumb:hover { + background: var(--cloud2-purple-dark); +} + +.scrollbar-hide { + -ms-overflow-style: none; + scrollbar-width: none; +} + +.scrollbar-hide::-webkit-scrollbar { + display: none; +} + +/* Innovation Hub Card Styles */ +.innovation-card { + background: var(--bg-white); + border-radius: 20px; + box-shadow: var(--shadow-card); + border: 1px solid var(--border-primary); + transition: all 0.3s ease; +} + +.innovation-card:hover { + box-shadow: var(--shadow-card-hover); + transform: translateY(-2px); +} + +/* Purple Accent Border (Innovation Hub Style) */ +.purple-accent-border { + border-left: 3px solid var(--cloud2-purple); + padding-left: 8px; + border-radius: 4px; +} + +/* Purple Divider */ +.purple-divider { + height: 1px; + background: var(--cloud2-purple); + margin: 16px 0; +} + +/* Purple Badge (Innovation Hub Style) */ +.purple-badge { + background: var(--cloud2-purple); + color: white; + padding: 9px 16px; + border-radius: 4px; + font-size: 15px; + font-weight: 400; + display: inline-block; +} + +/* Category Badge */ +.category-badge { + background: transparent; + border-left: 3px solid var(--cloud2-purple); + padding: 3px 0 3px 8px; + border-radius: 4px; + color: var(--cloud2-purple); + font-size: 13px; + font-weight: 900; + font-style: italic; +} + +/* Purple Buttons (Innovation Hub Style) */ +.btn-primary { + background: var(--cloud2-purple); + color: white; + padding: 12px 24px; + border-radius: 4px; + font-weight: 600; + transition: all 0.3s ease; + box-shadow: var(--shadow-card); +} + +.btn-primary:hover { + background: var(--cloud2-purple-dark); + box-shadow: var(--shadow-purple); + transform: translateY(-2px); +} + +.btn-secondary { + background: var(--bg-white); + color: var(--cloud2-purple); + border: 2px solid var(--cloud2-purple); + padding: 12px 24px; + border-radius: 4px; + font-weight: 600; + transition: all 0.3s ease; +} + +.btn-secondary:hover { + background: var(--cloud2-purple-soft); +} + +/* Input Focus States */ +.input-focus:focus { + border-color: var(--cloud2-purple); + outline: 2px solid rgba(163, 53, 252, 0.2); + outline-offset: 0; +} + +/* Status Badges */ +.badge-primary { + background: var(--cloud2-purple-soft); + color: var(--cloud2-purple); + padding: 6px 12px; + border-radius: 4px; + font-size: 13px; + font-weight: 600; +} + +/* Markdown Dark Theme Overrides */ +.markdown-body { + box-sizing: border-box; + width: 100%; + max-width: 100%; + margin: 0; + padding: 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Helvetica Neue', sans-serif; + font-size: 16px; + line-height: 1.6; + color: var(--text-primary) !important; + background-color: transparent !important; + word-wrap: break-word; + overflow-wrap: break-word; +} + +.markdown-body h1, +.markdown-body h2, +.markdown-body h3, +.markdown-body h4, +.markdown-body h5, +.markdown-body h6 { + color: var(--text-primary) !important; + font-weight: 600 !important; + margin-top: 24px !important; + margin-bottom: 16px !important; + border-bottom: 1px solid var(--border-primary) !important; + padding-bottom: 8px !important; +} + +.markdown-body p, +.markdown-body li, +.markdown-body span, +.markdown-body div { + color: var(--text-primary) !important; + max-width: 100%; + word-wrap: break-word; + overflow-wrap: break-word; +} + +.markdown-body ul, +.markdown-body ol { + max-width: 100%; + word-wrap: break-word; + overflow-wrap: break-word; + padding-left: 2em; + color: var(--text-primary) !important; +} + +.markdown-body a { + color: var(--accent-cyan) !important; + text-decoration: none; +} + +.markdown-body a:hover { + text-decoration: underline; + color: var(--accent-violet) !important; +} + +.markdown-body pre { + background-color: var(--bg-secondary) !important; + padding: 16px !important; + border-radius: 8px !important; + overflow-x: auto !important; + margin: 16px 0 !important; + max-width: 100%; + white-space: pre-wrap; + word-wrap: break-word; + border: 1px solid var(--border-primary) !important; +} + +.markdown-body pre code { + background-color: transparent !important; + padding: 0 !important; + color: var(--text-primary) !important; + white-space: pre-wrap; + word-wrap: break-word; +} + +.markdown-body code { + background-color: var(--bg-secondary) !important; + padding: 0.2em 0.4em !important; + border-radius: 6px !important; + font-family: 'SFMono-Regular', 'Consolas', 'Liberation Mono', 'Menlo', monospace !important; + font-size: 85% !important; + color: var(--accent-cyan) !important; + word-wrap: break-word; + overflow-wrap: break-word; + border: 1px solid var(--border-primary) !important; +} + +.markdown-body blockquote { + padding: 0 1em !important; + color: var(--text-secondary) !important; + border-left: 0.25em solid var(--accent-violet) !important; + margin: 16px 0 !important; + background-color: rgba(124, 58, 237, 0.05) !important; +} + +.markdown-body table { + border-collapse: collapse; + width: 100%; + margin: 16px 0; + background-color: var(--bg-secondary); + border-radius: 8px; + overflow: hidden; +} + +.markdown-body th, +.markdown-body td { + padding: 12px 16px; + border: 1px solid var(--border-primary); + color: var(--text-primary) !important; +} + +.markdown-body th { + background-color: var(--bg-tertiary); + font-weight: 600; + color: var(--accent-cyan) !important; +} + +.markdown-body hr { + height: 2px; + padding: 0; + margin: 24px 0; + background: linear-gradient(90deg, var(--accent-cyan) 0%, var(--accent-violet) 100%); + border: 0; + border-radius: 2px; +} + +.markdown-body input[type="checkbox"] { + margin-right: 0.5em; + accent-color: var(--accent-cyan); +} + +/* Professional Loading Animation */ +@keyframes pulse-professional { + 0%, 100% { + opacity: 1; + transform: scale(1); + } + 50% { + opacity: 0.7; + transform: scale(0.98); + } +} + +.pulse-professional { + animation: pulse-professional 2s ease-in-out infinite; +} + +/* Legacy support */ +.pulse-glow { + @apply pulse-professional; +} + +/* Slide-in Animation */ +@keyframes slideIn { + from { + transform: translateX(100%); + opacity: 0; + } + to { + transform: translateX(0); + opacity: 1; + } +} + +.animate-slideIn { + animation: slideIn 0.3s ease-out; +} + +/* Slide-in from Bottom Animation (for Floating Panel) */ +@keyframes slideInFromBottom { + from { + transform: translateY(100%); + opacity: 0; + } + to { + transform: translateY(0); + opacity: 1; + } +} + +.animate-slideInFromBottom { + animation: slideInFromBottom 0.4s cubic-bezier(0.4, 0, 0.2, 1); +} + +/* Fade-in Animation */ +@keyframes fadeIn { + from { + opacity: 0; + transform: translateY(20px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +.animate-fadeIn { + animation: fadeIn 0.5s ease-out; +} + +/* Professional Status Indicators */ +.status-running { + background: rgba(46, 49, 146, 0.1); + border: 1px solid var(--cloud2-primary); + box-shadow: var(--shadow-sm); +} + +.status-completed { + background: rgba(40, 167, 69, 0.1); + border: 1px solid var(--color-success); + box-shadow: var(--shadow-sm); +} + +.status-failed { + background: rgba(220, 53, 69, 0.1); + border: 1px solid var(--color-error); + box-shadow: var(--shadow-sm); +} + +.status-info { + background: rgba(23, 162, 184, 0.1); + border: 1px solid var(--color-info); + box-shadow: var(--shadow-sm); +} + +.status-warning { + background: rgba(255, 193, 7, 0.1); + border: 1px solid var(--color-warning); + box-shadow: var(--shadow-sm); +} + +/* ══════════════════════════════════════════════════ + COSMIC BACKGROUND (Subtle for Light Mode) +══════════════════════════════════════════════════ */ +.cosmic-bg { + position: fixed; + inset: 0; + pointer-events: none; + z-index: 0; + overflow: hidden; +} + +.cosmic-orb { + position: absolute; + border-radius: 50%; + filter: blur(90px); + opacity: 0.04; + transition: opacity 0.3s ease; +} + +[data-theme="dark"] .cosmic-orb { + opacity: 0.12; +} + +.orb-purple-1 { + width: 640px; + height: 640px; + top: -200px; + left: -120px; + background: radial-gradient(circle, #A335FC, transparent 70%); +} + +.orb-purple-2 { + width: 480px; + height: 480px; + bottom: -80px; + right: -100px; + background: radial-gradient(circle, #8B2BD9, transparent 70%); +} + +.orb-purple-3 { + width: 320px; + height: 320px; + top: 42%; + left: 50%; + transform: translate(-50%, -50%); + background: radial-gradient(circle, #BB5DFF, transparent 70%); + opacity: 0.03; +} + +.stars { + position: absolute; + inset: 0; + opacity: 0.25; + transition: opacity 0.3s ease; +} + +[data-theme="dark"] .stars { + opacity: 0.5; +} + +.stars::before, +.stars::after { + content: ''; + position: absolute; + inset: 0; + background-image: + radial-gradient(1px 1px at 15% 22%, rgba(163, 53, 252, 0.3) 0%, transparent 100%), + radial-gradient(1px 1px at 38% 9%, rgba(163, 53, 252, 0.25) 0%, transparent 100%), + radial-gradient(1.5px 1.5px at 62% 31%, rgba(163, 53, 252, 0.2) 0%, transparent 100%), + radial-gradient(1px 1px at 84% 15%, rgba(163, 53, 252, 0.3) 0%, transparent 100%), + radial-gradient(1px 1px at 7% 65%, rgba(163, 53, 252, 0.15) 0%, transparent 100%), + radial-gradient(1.5px 1.5px at 55% 78%, rgba(163, 53, 252, 0.25) 0%, transparent 100%), + radial-gradient(1px 1px at 91% 55%, rgba(163, 53, 252, 0.15) 0%, transparent 100%), + radial-gradient(1px 1px at 28% 88%, rgba(163, 53, 252, 0.25) 0%, transparent 100%); +} + +.stars::after { + background-image: + radial-gradient(1px 1px at 20% 40%, rgba(163, 53, 252, 0.2) 0%, transparent 100%), + radial-gradient(1.5px 1.5px at 50% 20%, rgba(163, 53, 252, 0.25) 0%, transparent 100%), + radial-gradient(1px 1px at 78% 72%, rgba(163, 53, 252, 0.2) 0%, transparent 100%), + radial-gradient(1px 1px at 5% 90%, rgba(163, 53, 252, 0.15) 0%, transparent 100%); +} + +/* Glass morphism effect */ +.glass-card { + background: rgba(255, 255, 255, 0.85); + border: 1px solid rgba(163, 53, 252, 0.15); + border-radius: 20px; + backdrop-filter: blur(12px); + box-shadow: var(--shadow-card); + position: relative; + overflow: hidden; +} + +.glass-card::before { + content: ''; + position: absolute; + top: 0; + left: 0; + right: 0; + height: 1px; + background: linear-gradient(90deg, transparent 5%, rgba(163, 53, 252, 0.4) 40%, rgba(187, 93, 255, 0.4) 60%, transparent 95%); +} diff --git a/ui/src/main.jsx b/ui/src/main.jsx new file mode 100644 index 0000000..54b39dd --- /dev/null +++ b/ui/src/main.jsx @@ -0,0 +1,10 @@ +import React from 'react' +import ReactDOM from 'react-dom/client' +import App from './App.jsx' +import './index.css' + +ReactDOM.createRoot(document.getElementById('root')).render( + + + , +) diff --git a/ui/src/services/api.js b/ui/src/services/api.js new file mode 100644 index 0000000..a339f53 --- /dev/null +++ b/ui/src/services/api.js @@ -0,0 +1,112 @@ +import axios from 'axios' + +// Use environment variable or default to /api for production (nginx proxy) +// In development: http://localhost:5001/api +// In production Docker: /api (proxied by nginx) +const API_BASE_URL = import.meta.env.VITE_API_URL || '/api' + +export const api = { + /** + * Start documentation generation workflow + * @param {string} repoUrl - GitHub repository URL + * @returns {Promise<{job_id: string, status: string}>} + */ + generateDocs: async (repoUrl) => { + const response = await axios.post(`${API_BASE_URL}/generate-docs`, { + repo_url: repoUrl + }) + return response.data + }, + + /** + * Get job status + * @param {string} jobId - Job ID + * @returns {Promise} + */ + getJobStatus: async (jobId) => { + const response = await axios.get(`${API_BASE_URL}/status/${jobId}`) + return response.data + }, + + /** + * Connect to SSE stream for real-time logs + * @param {string} jobId - Job ID + * @param {function} onMessage - Callback for log messages + * @param {function} onError - Callback for errors + * @returns {EventSource} SSE connection + */ + connectToLogs: (jobId, onMessage, onError) => { + const eventSource = new EventSource(`${API_BASE_URL}/logs/${jobId}`) + + eventSource.onmessage = (event) => { + try { + const data = JSON.parse(event.data) + onMessage(data) + } catch (error) { + console.error('Failed to parse SSE message:', error) + } + } + + eventSource.onerror = (error) => { + console.error('SSE connection error:', error) + if (onError) onError(error) + eventSource.close() + } + + return eventSource + }, + + /** + * Approve agent output and continue workflow + * @param {string} jobId - Job ID + */ + approveStep: async (jobId) => { + const response = await axios.post(`${API_BASE_URL}/approve/${jobId}`) + return response.data + }, + + /** + * Reject agent output and retry with feedback + * @param {string} jobId - Job ID + * @param {string} feedback - Feedback for retry + */ + rejectStep: async (jobId, feedback) => { + const response = await axios.post(`${API_BASE_URL}/reject/${jobId}`, { + feedback + }) + return response.data + }, + + /** + * Download generated README + * @param {string} jobId - Job ID + */ + downloadReadme: async (jobId) => { + const response = await axios.get(`${API_BASE_URL}/download/${jobId}`, { + responseType: 'blob' + }) + return response.data + }, + + /** + * Submit project selection + * @param {string} jobId - Job ID + * @param {string[]} selectedProjectPaths - Array of selected project paths + */ + selectProjects: async (jobId, selectedProjectPaths) => { + const response = await axios.post(`${API_BASE_URL}/select-projects/${jobId}`, { + selected_project_paths: selectedProjectPaths + }) + return response.data + }, + + /** + * Create GitHub Pull Request with generated README + * @param {string} jobId - Job ID + * @returns {Promise<{status: string, message: string, pr_url?: string}>} + */ + createPullRequest: async (jobId) => { + const response = await axios.post(`${API_BASE_URL}/create-pr/${jobId}`) + return response.data + } +} diff --git a/ui/tailwind.config.js b/ui/tailwind.config.js new file mode 100644 index 0000000..ed11881 --- /dev/null +++ b/ui/tailwind.config.js @@ -0,0 +1,94 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: { + colors: { + // Cloud2 Labs Innovation Hub Color Scheme + cloud2: { + // Primary - Vibrant Purple (from Innovation Hub) + purple: { + DEFAULT: '#A335FC', + 50: '#F5E6FF', + 100: '#E6CCFF', + 200: '#D9B3FF', + 300: '#CC99FF', + 400: '#B866FF', + 500: '#A335FC', // Primary + 600: '#8B2BD9', + 700: '#7321B6', + 800: '#5B1893', + 900: '#430F70', + }, + // Neutral Colors + white: '#FFFFFF', + black: { + DEFAULT: '#000000', + soft: '#07000E', + }, + gray: { + 50: '#F8F9FA', + 100: '#F1F3F5', + 200: '#E9ECEF', + 300: '#DEE2E6', + 400: '#CED4DA', + 500: '#ADB5BD', + 600: '#6C757D', + 700: '#495057', + 800: '#343A40', + 900: '#212529', + }, + // Status Colors + success: '#28A745', + warning: '#FFC107', + error: '#DC3545', + info: '#17A2B8', + }, + }, + fontFamily: { + sans: ['"Inter"', 'system-ui', '-apple-system', 'sans-serif'], + mono: ['"JetBrains Mono"', '"SF Mono"', 'Consolas', 'monospace'], + }, + fontSize: { + 'hero': ['48px', { lineHeight: '1.1', letterSpacing: '-0.48px' }], + '2xl': ['24px', { lineHeight: '1.2' }], + 'xl': ['20px', { lineHeight: '1.3' }], + 'lg': ['16px', { lineHeight: '1.5' }], + 'base': ['14px', { lineHeight: '1.6' }], + 'sm': ['13px', { lineHeight: '1.5' }], + 'xs': ['12px', { lineHeight: '1.4' }], + }, + borderRadius: { + 'card': '20px', + 'button': '4px', + }, + boxShadow: { + 'card': '0 2px 8px rgba(0, 0, 0, 0.08)', + 'card-hover': '0 4px 16px rgba(163, 53, 252, 0.15)', + 'purple': '0 4px 16px rgba(163, 53, 252, 0.2)', + }, + backgroundImage: { + 'purple-gradient': 'linear-gradient(135deg, #A335FC 0%, #8B2BD9 100%)', + 'purple-gradient-soft': 'linear-gradient(180deg, #F5E6FF 0%, #FFFFFF 100%)', + }, + animation: { + 'fade-in': 'fadeIn 0.5s ease-out', + 'slide-up': 'slideUp 0.4s ease-out', + }, + keyframes: { + fadeIn: { + '0%': { opacity: '0', transform: 'translateY(10px)' }, + '100%': { opacity: '1', transform: 'translateY(0)' }, + }, + slideUp: { + '0%': { opacity: '0', transform: 'translateY(20px)' }, + '100%': { opacity: '1', transform: 'translateY(0)' }, + }, + }, + }, + }, + plugins: [], +} diff --git a/ui/vite.config.js b/ui/vite.config.js new file mode 100644 index 0000000..a36a5e7 --- /dev/null +++ b/ui/vite.config.js @@ -0,0 +1,17 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +export default defineConfig({ + plugins: [react()], + server: { + host: true, + port: 3000, + proxy: { + '/api': { + // Use 'backend' hostname in Docker, 'localhost' when running locally + target: process.env.VITE_API_TARGET || 'http://localhost:5001', + changeOrigin: true + } + } + } +})