✈️ ChatPilot: Intelligent Agentic RAG & Autonomous Research Engine

ChatPilot is a tool-augmented Agentic Copilot that goes beyond simple chat. It utilizes a single AI agent to interact with your local files, conduct deep web research, and perform autonomous data analysis through Python code execution. Demo Frontend: ChatPilot Frontend

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🚀 Key Capabilities

🧠 Agentic Context Reasoning & Document Intelligence

Powered by Docling, ChatPilot supports complex document understanding, including both scanned and digital formats. It utilizes a sophisticated Agentic Intelligence workflow:

• Multi-Keyword Search: The agent identifies and searches for multiple keywords to ensure comprehensive retrieval.
• Context Re-evaluation: Linked content and retrieved snippets are dynamically re-evaluated by the agent for relevance.
• Sub-clause Retrieval: The LLM can explicitly call for sub-clauses to retrieve deep context when simpler retrieval isn't enough.
• Scanned Document Support: Handles OCR and layout analysis for scanned PDFs and images using Docling.
• 🎯 PDF Highlighting: Precise coordinate-based highlighting in the frontend - retrieved chunks are highlighted directly in the PDF viewer with element-level bounding boxes.

📊 Autonomous Data Analysis

Upload CSV or Excel files and ask for insights. ChatPilot functions as a Data Analyst Agent:

• Planning: Formulates a step-by-step analysis strategy.
• Execution: Writes and runs Python code in a secure sandbox.
• Self-Correction: If the code fails, the agent analyzes the error and automatically retries until it gets the result.

🌐 Agentic Web Research

Integration with Tavily or Google Custom Search enables high-fidelity information gathering:

• Deep Research: Conducts multi-step, agentic research loops to generate thorough and structured reports—ideal for deep competitor analysis and market trends.
• Real-Time Knowledge: Accesses the latest news and specialized finance/news topics.
• Web Fetch: Extracts and cleans content from URLs for use as context in analysis tasks.

⚡ Parallel Tool Execution

ChatPilot supports parallel tool calls within a single agent step, allowing multiple retrieval or research tools to run concurrently. This reduces latency, enables multi-source context gathering, and improves efficiency while preserving a single-agent architecture.

🔓 Multi-LLM Flexibility

Built on LangChain, ChatPilot supports switching between top providers. Use the same agent with your preferred model provider:

• OpenAI
• Anthropic
• DeepSeek
• Google (Gemini)
• Local models via Ollama

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🛠️ Tech Stack

• Backend: FastAPI (Python 3.10+)
• Server: Uvicorn (ASGI server with multi-worker support)
• Agent Framework: LangChain (Single-agent architecture with parallel tool execution)
• Data Agent: LangGraph (For iterative code execution and self-correction)
• Vector Store: ChromaDB
• Embeddings & Reranking:
  • Local Option: HuggingFace embeddings (BAAI BGE-small-en-v1.5) + BGE reranker-v2-m3 (fully offline)
  • Cloud Option: Cohere API (1024D embeddings with reranking)
• Containerization: Docker (Secure code execution sandbox)
• Document Processing: Docling (Advanced document intelligence with OCR and bbox extraction)
• Web Search: Tavily API, Google Custom Search
• Code Execution: Isolated Docker sandbox with resource limits

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🏁 Getting Started

For detailed setup instructions including prerequisites, API key configuration, and Docker setup, see docs/SETUP.md.

Quick Start:

# 1. Clone and install dependencies
git clone https://github.com/Sreehari05055/ChatPilot.git
cd ChatPilot
pip install -r requirements.txt

# 2. Set up your .env file with API keys (see docs/SETUP.md)

# 3. Build Docker sandbox
docker build -t python-sandbox:latest -f Dockerfile .

# 4. Run the server
python main.py

Server runs on http://localhost:8000 with 4 worker processes.

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📜 License

Apache-2.0