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geethac2l merged 1 commit into from
Mar 24, 2026
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update README, github workflow file and add PR template #5

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39 changes: 39 additions & 0 deletions .github/pull_request_template.md
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## Summary

<!-- What does this PR do? Keep it to 1-3 bullet points. -->

-

## Type of Change

<!-- Check the one that applies. -->

- [ ] Bug fix
- [ ] New feature / enhancement
- [ ] Documentation update
- [ ] Refactor (no behavior change)
- [ ] Chore (dependencies, CI, tooling)

## Changes Made

<!-- Briefly describe the key changes. Link to relevant issues if applicable. -->

Resolves #<!-- issue number -->

## How to Test

<!-- Steps a reviewer can follow to verify the changes. -->

1.

## Checklist

- [ ] I have read the [Contributing Guide](../CONTRIBUTING.md)
- [ ] My branch is up to date with `main`
- [ ] New environment variables (if any) are documented in `.env.example` and the README
- [ ] No secrets, API keys, or credentials are included in this PR
- [ ] I have tested my changes locally

## Screenshots (if applicable)

<!-- Add screenshots for UI changes. Delete this section if not applicable. -->
2 changes: 1 addition & 1 deletion .github/workflows/code-scans.yaml
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Expand Up @@ -37,7 +37,7 @@ jobs:
run: mkdir -p trivy-reports

- name: Run Trivy FS Scan
uses: aquasecurity/trivy-action@0.24.0
uses: aquasecurity/trivy-action@0.35.0
with:
scan-type: 'fs'
scan-ref: '.'
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156 changes: 118 additions & 38 deletions README.md
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<p align="center">
<img src="docs/assets/InnovationHub-HeaderImage.png" width="800" alt="DocuBot AI Documentation Generator">
<img src="docs/assets/InnovationHub-HeaderImage.png" width="800" alt="Company logo">
</p>

# 📚 DocuBot - AI Documentation Generator

AI-powered documentation generation using multi-provider LLMs and specialized micro-agent architecture for comprehensive README creation.

An AI-powered full-stack application that automatically generates high-quality project documentation from source code repositories. Connect a GitHub repo, let specialized micro-agents analyze the codebase, architecture, dependencies, and APIs, and get structured README documentation in minutes, powered by multi-provider LLMs, OpenAI-compatible endpoints, or locally hosted models such as Ollama.
---

## 📋 Table of Contents
Expand All @@ -18,7 +17,8 @@ AI-powered documentation generation using multi-provider LLMs and specialized mi
- [Project Structure](#project-structure)
- [Usage Guide](#usage-guide)
- [LLM Provider Configuration](#llm-provider-configuration)
- [Performance Benchmarks](#performance-benchmarks)
- [Inference Benchmarks](#inference-benchmarks)
- [Model Capabilities](#model-capabilities)
- [Environment Variables](#environment-variables)
- [Technology Stack](#technology-stack)
- [Troubleshooting](#troubleshooting)
Expand All @@ -28,7 +28,15 @@ AI-powered documentation generation using multi-provider LLMs and specialized mi

## Project Overview

**DocuBot** is an intelligent documentation generation platform that analyzes GitHub repositories using specialized micro-agents to automatically create comprehensive, well-structured README documentation with minimal human intervention.
**DocuBot** shows how agentic AI can be applied to one of the most time-consuming software tasks: documentation. The applicatoin analyzes real project evidence from a repository and uses specialized micro-agents to generate structured, context-aware README documentation that is more accurate and maintainable than traditional single-prompt generation.

The application supports a flexible inference layer, allowing it to work with OpenAI, Groq, OpenRouter, custom OpenAI-compatible APIs, and local Ollama deployments. This makes it practical for cloud-based teams, enterprise environments, and privacy-sensitive local setups alike.

This makes DocuBot suitable for:

- **Enterprise teams** — integrate with internal gateways, hosted APIs, or private inference infrastructure
- **Local experimentation** — run documentation generation with self-hosted models through Ollama
- **Hardware benchmarking** — measure SLM throughput on Apple Silicon, CUDA, or Intel Gaudi hardware

### How It Works

Expand Down Expand Up @@ -442,20 +450,21 @@ DocuBot/

### Performance Tips

- **Model Selection**: For faster processing, use `gpt-4o-mini` or Groq's `llama-3.2-90b-text-preview`
- **Local Development**: Use Ollama with `qwen2.5:7b` for private, offline documentation generation
- **Monorepo**: Select specific subprojects for focused documentation
- **PR Creation**: Requires `GITHUB_TOKEN` with `repo` scope in `api/.env`
- **Use the largest model your hardware can sustain.** `qwen3:14b` produces the best documentation quality; `qwen3:4b` is faster and good for benchmarking.
- **Lower `LLM_TEMPERATURE`** (e.g., `0.1`) for more factual, evidence-grounded documentation. Raise it slightly (e.g., `0.3–0.5`) for more descriptive, narrative-style README prose.
- **Keep repositories focused.** The agents analyze up to `MAX_FILES_TO_SCAN` files (default: 500). For large monorepos, use the built-in project selector to target a specific subproject rather than letting agents scan the entire repo.
- **On Apple Silicon**, always run Ollama natively — never inside Docker. The Metal GPU backend delivers significantly higher throughput for sequential multi-agent workloads compared to CPU-only inference.
- **On Linux with an NVIDIA GPU**, set `CUDA_VISIBLE_DEVICES` before starting Ollama to target a specific GPU.
- **For enterprise remote APIs**, choose a model with a large context window (≥16k tokens) to avoid truncation on longer inputs.

---

## LLM Provider Configuration

DocuBot supports multiple LLM providers. Choose the one that best fits your needs:
DocuBot supports multiple LLM providers. All providers are configured via the `.env` file. Set `INFERENCE_PROVIDER=ollama` for local inference.

### OpenAI (Recommended for Production)

**Best for**: Highest quality outputs, production deployments
### OpenAI

- **Get API Key**: https://platform.openai.com/account/api-keys
- **Models**: `gpt-4o`, `gpt-4-turbo`, `gpt-4o-mini`
Expand All @@ -468,9 +477,9 @@ DocuBot supports multiple LLM providers. Choose the one that best fits your need
LLM_MODEL=gpt-4o
```

### Groq (Fast & Free Tier)
### Groq

**Best for**: Fast inference, development, free tier testing
Groq provides OpenAI-compatible endpoints with extremely fast inference (LPU hardware).

- **Get API Key**: https://console.groq.com/keys
- **Models**: `llama-3.2-90b-text-preview`, `llama-3.1-70b-versatile`
Expand All @@ -484,14 +493,13 @@ DocuBot supports multiple LLM providers. Choose the one that best fits your need
LLM_MODEL=llama-3.2-90b-text-preview
```

### Ollama (Local & Private)
### Ollama

**Best for**: Local deployment, privacy, no API costs, offline operation
Runs inference locally on the host machine with full GPU acceleration.

- **Install**: https://ollama.com/download
- **Pull Model**: `ollama pull qwen2.5:7b`
- **Models**: `qwen2.5:7b`, `llama3.1:8b`, `llama3.2:3b`
- **Pricing**: Free (local hardware costs only)
- **Install Ollama**: https://ollama.com/download
- **Pull Model**: `ollama pull qwen3:14b`
- **Models**: `qwen3:4b`, `llama3.1:8b`, `llama3.2:3b`
- **Configuration**:
```bash
LLM_PROVIDER=ollama
Expand All @@ -505,15 +513,15 @@ DocuBot supports multiple LLM providers. Choose the one that best fits your need
curl -fsSL https://ollama.com/install.sh | sh

# Pull model
ollama pull qwen2.5:7b
ollama pull qwen3:14b

# Verify it's running
# Verify Ollama is running:
curl http://localhost:11434/api/tags
```

### OpenRouter (Multi-Model Access)
### OpenRouter

**Best for**: Access to multiple models through one API, model flexibility
OpenRouter provides a unified API across hundreds of models from different providers.

- **Get API Key**: https://openrouter.ai/keys
- **Models**: Claude, Gemini, GPT-4, Llama, and 100+ others
Expand All @@ -539,6 +547,13 @@ LLM_BASE_URL=https://your-custom-endpoint.com/v1
LLM_MODEL=your-model-name
```

If the endpoint uses a private domain mapped in `/etc/hosts`, also set:

```bash
LOCAL_URL_ENDPOINT=your-private-domain.internal
```


### Switching Providers

To switch providers, simply update `api/.env` and restart:
Expand All @@ -557,27 +572,92 @@ docker compose up -d

---

## Performance Benchmarks

The following benchmarks were collected by running DocuBot's full 9-agent documentation pipeline across three inference environments. Use these results to choose the right deployment profile for your needs.
## Inference Benchmarks

> **Note:** Intel Enterprise Inference was tested on Intel Xeon hardware to demonstrate on-premises SLM deployment for enterprise codebases.
The table below compares inference performance across different providers, deployment modes, and hardware profiles using a standardized DocuBot's full 9-agent documentation pipeline.

### Results

| Model Type / Inference Provider | Model Name | Deployment | Context Window | Avg Input Tokens | Avg Output Tokens | Avg Total Tokens / Request | P50 Latency (ms) | P95 Latency (ms) | Throughput (req/sec) | Hardware Profile |
| Provider | Model | Deployment | Context Window | Avg Input Tokens | Avg Output Tokens | Avg Total Tokens / Request | P50 Latency (ms) | P95 Latency (ms) | Throughput (req/sec) | Hardware |
|---|---|---|---|---|---|---|---|---|---|---|
| vLLM | Qwen3-4B-Instruct-2507 | Local | 262.1K | 3,040 | 307.7 | 5809 | 15,864 | 40,809 | 0.0580 | Apple Silicon (Metal) |
| Enterprise Inference / SLM · [Intel OPEA EI](https://opea.dev) | Qwen3-4B-Instruct-2507 | CPU (Xeon) | 8.1K | 4,211.9 | 270 | 4481 | 10,540 | 32,205 | 0.076 | CPU-only |
| vLLM | Qwen3-4B-Instruct-2507 | Local | 262.1K | 3,040 | 307.7 | 5809 | 15,864 | 40,809 | 0.0580 | Apple Silicon (Metal)(Macbook Pro M4) |
| [Intel OPEA EI](https://github.com/opea-project/Enterprise-Inference) | Qwen3-4B-Instruct-2507 | CPU (Xeon) | 8.1K | 4,211.9 | 270 | 4481 | 10,540 | 32,205 | 0.076 | CPU-only |
| OpenAI (Cloud) | gpt-4o-mini | API (Cloud) | 128K | 3,820.11 | 316.41 | 4136.52 | 7,760 | 23,535 | 0.108 | N/A |

> **Notes:**
>
> - All benchmarks use the same Documentation generation workflow. Token counts may vary slightly per run due to non-deterministic model output.
> - vLLM on Apple Silicon uses Metal (MPS) GPU acceleration.
> - [Intel OPEA Enterprise Inference](https://github.com/opea-project/Enterprise-Inference) runs on Intel Xeon CPUs without GPU acceleration.

---

## Model Capabilities

### Qwen3-4B-Instruct-2507

### Model Capabilities
A 4-billion-parameter open-weight code model from Alibaba's Qwen team (July 2025 release), designed for on-prem and edge deployment.

| Model | Highlights |
|---|---|
| **Qwen3-4B-Instruct-2507** | 4B-parameter code-specialized model with 262.1K native context (deployment-limited to 8.1K on Xeon CPU). Supports multi-agent documentation generation, code analysis, and structured JSON output. Enables full on-premises deployment with data sovereignty for enterprise codebases. |
| **gpt-4o-mini** | Cloud-native multimodal model with 128K context, optimized for code understanding and technical documentation. Delivers 42% higher throughput and 26% lower latency versus CPU-based alternatives while supporting concurrent multi-agent orchestration at cloud scale. |

| Attribute | Details |
| --------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| **Parameters** | 4.0B total (3.6B non-embedding) |
| **Architecture** | Transformer with Grouped Query Attention (GQA) — 36 layers, 32 Q-heads / 8 KV-heads |
| **Context Window** | 262,144 tokens (256K) native |
| **Reasoning Mode** | Non-thinking only (Instruct-2507 variant). Separate Thinking-2507 variant available with always-on chain-of-thought |
| **Tool / Function Calling** | Supported; MCP (Model Context Protocol) compatible |
| **Structured Output** | JSON-structured responses supported |
| **Multilingual** | 100+ languages and dialects |
| **Code Benchmarks** | MultiPL-E: 76.8%, LiveCodeBench v6: 35.1%, BFCL-v3 (tool use): 61.9 |
| **Quantization Formats** | GGUF (Q4_K_M ~2.5 GB, Q8_0 ~4.3 GB), AWQ (int4), GPTQ (int4), MLX (4-bit ~2.3 GB) |
| **Inference Runtimes** | Ollama, vLLM, llama.cpp, LMStudio, SGLang, KTransformers |
| **Fine-Tuning** | Full fine-tuning and adapter-based (LoRA); 5,000+ community adapters on HuggingFace |
| **License** | Apache 2.0 |
| **Deployment** | Local, on-prem, air-gapped, cloud — full data sovereignty |


### GPT-4o-mini

OpenAI's cost-efficient multimodal model, accessible exclusively via cloud API.


| Attribute | Details |
| --------------------------- | --------------------------------------------------------------------------------- |
| **Parameters** | Not publicly disclosed |
| **Architecture** | Multimodal Transformer (text + image input, text output) |
| **Context Window** | 128,000 tokens input / 16,384 tokens max output |
| **Reasoning Mode** | Standard inference (no explicit chain-of-thought toggle) |
| **Tool / Function Calling** | Supported; parallel function calling |
| **Structured Output** | JSON mode and strict JSON schema adherence supported |
| **Multilingual** | Broad multilingual support |
| **Code Benchmarks** | MMMLU: ~87%, strong HumanEval and MBPP scores |
| **Pricing** | $0.15 / 1M input tokens, $0.60 / 1M output tokens (Batch API: 50% discount) |
| **Fine-Tuning** | Supervised fine-tuning via OpenAI API |
| **License** | Proprietary (OpenAI Terms of Use) |
| **Deployment** | Cloud-only — OpenAI API or Azure OpenAI Service. No self-hosted or on-prem option |
| **Knowledge Cutoff** | October 2023 |


### Comparison Summary


| Capability | Qwen3-4B-Instruct-2507 | GPT-4o-mini |
| ------------------------------- | -------------------------------- | --------------------------------- |
| Code Analysis & Documentation Generation | Yes | Yes |
| Multi-agent / agentic task execution | Yes | Yes |
| Mermaid / architecture diagram Generation | Yes | Yes |
| Function / tool calling | Yes | Yes |
| JSON structured output | Yes | Yes |
| On-prem / air-gapped deployment | Yes | No |
| Data sovereignty | Full (weights run locally) | No (data sent to cloud API) |
| Open weights | Yes (Apache 2.0) | No (proprietary) |
| Custom fine-tuning | Full fine-tuning + LoRA adapters | Supervised fine-tuning (API only) |
| Quantization for edge devices | GGUF / AWQ / GPTQ / MLX | N/A |
| Multimodal (image input) | No | Yes |
| Native context window | 256K | 128K |


> Both models support Code Analysis & Documentation Generation, Multi-agent / agentic task execution, Mermaid diagram generation, function calling, and JSON-structured output. However, only Qwen3-4B offers open weights, data sovereignty, and local deployment flexibility — making it suitable for air-gapped, regulated, or cost-sensitive environments. GPT-4o-mini offers lower latency and higher throughput via OpenAI's cloud infrastructure, with added multimodal capabilities.

---

## Environment Variables

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