context-ai / README.md
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Deploy complete Second Brain AI Assistant with custom UI
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---
title: Second Brain AI Assistant
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: "5.12.0"
app_file: app.py
pinned: false
license: mit
---
# Second Brain AI Assistant
A production-ready AI assistant that can answer questions about your documents using RAG (Retrieval-Augmented Generation).
## Features
- **Document Q&A**: Ask questions about your documents
- **Source Attribution**: See which documents were used for each answer
- **Clean UI**: Professional interface with proper formatting
- **Real-time Processing**: Get answers instantly
- **Tool Usage Display**: See which tools were used to generate responses
## Usage
1. Enter your question in the text box
2. Click "Ask" to get an AI-powered answer
3. View sources and tools used in the response
4. Use the debug section to see raw responses
## Example Queries
- "What pricing objections have been raised?"
- "What messaging is resonating with prospects?"
- "What concerns have prospects raised with regards to product?"
- "What has resonated with prospects based on the meeting transcripts?"
## Configuration
This space uses the following environment variables:
- `OPENAI_API_KEY`: Your OpenAI API key
- `MONGODB_URI`: MongoDB connection string
- `MONGODB_DATABASE_NAME`: Database name (default: second_brain_course)
- `MONGODB_COLLECTION_NAME`: Collection name (default: rag)
- `COMET_API_KEY`: Comet ML API key for tracking
- `COMET_PROJECT`: Project name (default: second_brain_course)
- `RETRIEVER_CONFIG_PATH`: Path to retriever config (default: configs/compute_rag_vector_index_openai_contextual_simple.yaml)
## Architecture
- **RAG Pipeline**: Uses MongoDB for document storage and retrieval
- **Embeddings**: OpenAI text-embedding-3-small for document embeddings
- **LLM**: GPT-4o-mini for response generation
- **UI**: Custom Gradio interface with enhanced formatting
- **Tools**: MongoDB retriever and final answer tools
## Local Development
```bash
# Install dependencies
uv sync
# Run the agent
make run_agent_app
# Or run directly
python app.py
```
## License
MIT License