https://github.com/sreehari05055/chatpilot
ChatPilot is a lightweight RAG tool for individuals and small teams who don't want to maintain complex AI stacks, yet still need reliable retrieval, tool use, and streaming responses.
https://github.com/sreehari05055/chatpilot
agent ai ai-search automated-code-generation deep-learning document-parser google-cloud multi-agent openai retrieval-augmented-generation
Last synced: about 1 month ago
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ChatPilot is a lightweight RAG tool for individuals and small teams who don't want to maintain complex AI stacks, yet still need reliable retrieval, tool use, and streaming responses.
- Host: GitHub
- URL: https://github.com/sreehari05055/chatpilot
- Owner: Sreehari05055
- License: apache-2.0
- Created: 2025-12-14T12:39:36.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-01-29T21:33:19.000Z (about 2 months ago)
- Last Synced: 2026-01-30T10:46:17.800Z (about 2 months ago)
- Topics: agent, ai, ai-search, automated-code-generation, deep-learning, document-parser, google-cloud, multi-agent, openai, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 2.19 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Notice: NOTICE
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README
# ✈️ 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](https://github.com/Sreehari05055/Demo-frontend)
---
## 🚀 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
---
## 🛠️ 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
---
## 🏁 Getting Started
For detailed setup instructions including prerequisites, API key configuration, and Docker setup, see **[docs/SETUP.md](docs/SETUP.md)**.
**Quick Start:**
```bash
# 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.
---
## 📜 License
Apache-2.0