ChatPilot

# ChatPilot: Intelligent Agentic RAG & Autonomous Research Engine

**Demo video**: [Watch demo video](https://github.com/user-attachments/assets/12bcfb73-c5fa-46f7-8f48-2d235b98d3c8)

ChatPilot is a tool-augmented **Agentic DeepSeek** 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](https://github.com/Sreehari05055/Demo-frontend)

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## Key Capabilities

### Document Intelligence & Agentic Retrieval
Powered by **Docling**, ChatPilot supports both digital and scanned documents with layout-aware parsing and OCR.

- Multi-keyword and clause-level retrieval
- Dynamic relevance re-evaluation by the agent
- Deep context retrieval when shallow chunks are insufficient
- Coordinate-based highlighting of retrieved content directly in PDFs

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### Scholarly Research & Visual RAG
ChatPilot includes a **research-grade scholarly retrieval pipeline** focused on traceability.

- Integrated with **OpenAlex** (250M+ scholarly works)
- Natural language queries over academic literature
- **Query decomposition RAG** for higher recall and precision
- Retrieved results are reranked using semantic similarity

#### Source Highlighting
- All sources used by the LLM are returned with each response
- Each retrieved chunk is mapped to its exact location in the source document
- Referenced text is highlighted directly in the PDF viewer
- Multiple sources are surfaced and highlighted independently

This allows direct verification of claims without manually searching papers.

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### Autonomous Data Analysis
ChatPilot functions as a **data analyst agent** for structured datasets.

- Upload large CSV or Excel files (no file-size limits)
- The LLM never accesses row-level data
- Python code is generated using schema-level context only
- Code executes inside an isolated Docker sandbox

Agent loop:
- Plan → Execute → E