# RAG System Fixes Summary

## Issues Identified and Fixed

### 1. OpenAI API Key Not Loading
**Problem**: The API key was configured in `.env` but not being loaded properly in some contexts.
**Solution**: Ensure `load_dotenv()` is called at the start of the application.
- ✅ Fixed in `main.py` (line 48)
- ✅ Added to test scripts

### 2. Score Threshold Too High (0.7)
**Problem**: The similarity score threshold of 0.7 was too restrictive, causing no documents to be retrieved.
**Solution**: Lowered the threshold to 0.3 for better retrieval.
- ✅ Fixed in `rag/chat.py` (lines 101, 233)
- ✅ Fixed in `rag/retrieval.py` (line 25)

### 3. Invalid Model Name (gpt-5-nano)
**Problem**: The model name `gpt-5-nano` doesn't exist in OpenAI's API.
**Solution**: Changed to `gpt-4-turbo-preview` which is a valid model.
- ✅ Fixed in `.env` (line 3)

### 4. Cloud Qdrant Configuration
**Problem**: Tests were failing because they expected localhost Qdrant but the system uses cloud Qdrant.
**Solution**: The system is correctly configured to use cloud Qdrant at `https://9811842d-bebd-4da5-85c2-c25fc5e59d7d.us-west-1-0.aws.cloud.qdrant.io`

## Current System Status

- ✅ **Embedding Generation**: Working correctly with OpenAI API
- ✅ **Qdrant Connection**: Connected to cloud instance with 1407 vectors indexed
- ✅ **Document Retrieval**: Successfully retrieving documents with scores ~0.72
- ✅ **Chat Functionality**: End-to-end chat working with proper citations

## Test Results

The test suite confirmed:
1. Single and batch embedding generation works
2. Qdrant cloud connection is functional
3. Document search and retrieval works with proper scoring
4. Chat generates responses with proper citations

## Recommendations

1. **Monitor Performance**: With 1407 documents indexed, the system should perform well for queries
2. **Score Threshold**: The 0.3 threshold is working well, but can be fine-tuned based on user feedback
3. **Regular Updates**: Re-index documents when content changes using the `/ingest` endpoint
4. **Monitoring**: Check the `/health` endpoint periodically for system status

## API Endpoints

- **Chat**: `POST /chat` - Query the RAG system
- **Health**: `GET /health` - Check system status
- **Ingest**: `POST /ingest` - Trigger document ingestion
- **Collections**: `GET /collections` - List Qdrant collections

## Environment Variables

Key environment variables in `.env`:
- `OPENAI_API_KEY`: Your OpenAI API key
- `OPENAI_MODEL`: Chat completion model (gpt-4-turbo-preview)
- `OPENAI_EMBEDDING_MODEL`: Embedding model (text-embedding-3-small)
- `QDRANT_URL`: Cloud Qdrant endpoint
- `QDRANT_API_KEY`: Qdrant API key
- `BOOK_CONTENT_PATH`: Path to markdown documents