Docs/add claude md

CLAUDE.md

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+# TAP AI — AI Agent Context
+
+Python conversational AI engine built on Frappe. Intelligently routes student queries
+to either a Text-to-SQL engine or a Vector RAG engine, with async processing via RabbitMQ.
+
+## Architecture at a Glance
+```
+Student query (text or voice)
+        │
+        ▼
+tap_ai/api/query.py         ← single entry-point API (Frappe whitelisted)
+        │
+        ▼
+LLM router (llm_worker.py)  ← decides: SQL or RAG?
+        │
+   ┌────┴─────┐
+   │ SQL      │ RAG
+   ▼          ▼
+remote_db   Pinecone
+(PostgreSQL) vector store
+   │          │
+   └────┬─────┘
+        ▼
+   result.py / voice_result.py  ← format + return response
+```
+
+## Key Files
+| File | Purpose |
+|---|---|
+| `tap_ai/api/query.py` | Main entry point — receives the query, publishes to RabbitMQ |
+| `tap_ai/api/result.py` | Polls for result after worker finishes |
+| `tap_ai/api/voice_query.py` | Voice input variant — receives audio, calls STT first |
+| `tap_ai/api/voice_result.py` | Voice output — calls TTS on the answer |
+| `tap_ai/workers/llm_worker.py` | RabbitMQ consumer — routes query to SQL or RAG |
+| `tap_ai/workers/stt_worker.py` | Speech-to-Text via Whisper |
+| `tap_ai/workers/tts_worker.py` | Text-to-Speech for voice output |
+| `tap_ai/infra/config.py` | `TAPConfig` — loads from Frappe `site_config.json` first |
+| `tap_ai/infra/llm_client.py` | OpenAI client wrapper |
+| `tap_ai/infra/sql_catalog.py` | DB schema catalog for Text-to-SQL |
+| `tap_ai/utils/remote_db.py` | PostgreSQL connection to `data.evalix.xyz` |
+| `tap_ai/utils/mq.py` | RabbitMQ publish/consume helpers |
+| `tap_ai/utils/dynamic_config.py` | Runtime config updates from TAP LMS |
+| `tap_ai/schema/generate_schema.py` | Generates SQL catalog from live DB schema |
+
+## Configuration
+All secrets live in Frappe's `site_config.json` (never hardcoded). `TAPConfig` reads them:
+```python
+from tap_ai.infra.config import TAPConfig
+config = TAPConfig()
+openai_key = config.get("openai_api_key")
+pinecone_key = config.get("pinecone_api_key")
+```
+Keys expected in `site_config.json`:
+- `openai_api_key`
+- `pinecone_api_key`, `pinecone_index`
+- `remote_db_host`, `remote_db_name`, `remote_db_user`, `remote_db_password`
+- `rabbitmq_host`, `rabbitmq_port`
+- `enable_voice` (boolean)
+
+**Never add API keys to source code or commit them to git.**
+
+## Tech Stack
+- Python **3.10+**
+- Frappe **v15** (framework + API layer)
+- OpenAI GPT (LLM routing + SQL generation + answer synthesis)
+- Pinecone (vector store for RAG)
+- PostgreSQL on `data.evalix.xyz` (remote LMS data)
+- RabbitMQ + Pika (async processing)
+- Redis (caching, conversation history)
+- Flask (Telegram webhook — `telegram_webhook.py`, runs separately from Frappe)
+
+## Running Workers
+Workers must be started separately from `bench start`:
+```bash
+# In a separate terminal in your bench directory:
+python tap_ai/workers/llm_worker.py
+python tap_ai/workers/stt_worker.py   # only if voice is enabled
+python tap_ai/workers/tts_worker.py   # only if voice is enabled
+```
+Without workers running, API calls will enqueue but never return a result.
+
+## Code Style
+- **Ruff** for linting (`ruff check .`) and formatting (`ruff format .`)
+- All new functions must have type annotations
+- Config values always go through `TAPConfig.get()` — never `os.environ` directly
+- RabbitMQ messages are JSON-serialised dicts; always document the expected shape in a comment
+
+## Common Pitfalls
+- **Workers not running** → API enqueues but hangs. Check RabbitMQ management UI.
+- **Pinecone index mismatch** → ensure `pinecone_index` in `site_config.json` matches the deployed index name
+- **Remote DB connection** → `data.evalix.xyz` requires VPN or IP allowlist in production
+- **Schema cache stale** → re-run `generate_schema.py` after DB schema changes
+
+## Testing
+```bash
+# Sanity-check remote DB connectivity
+python test_remote_connection.py
+
+# Frappe-context tests
+bench run-tests --app tap_ai
+```