MCP-based AI Research Assistant (RAG + LangChain + Claude)

What it does

AI agent that retrieves documents, processes context, and answers queries using an MCP architecture with RAG (Retrieval-Augmented Generation).

Tech stack

- LangChain
- Claude / Ollama-compatible models
- Vector DB: Chroma (example; configurable to Pinecone, Milvus, etc.)
- MCP (Model Context Protocol) for multi-tool orchestration

Features

- RAG-based retrieval pipeline
- Multi-tool agent (indexing, retrieval, LLM reasoning, tool calls)
- API integrations for internal data sources

Demo

See `/app/demo_output.md` for an example run showing Input → Retrieved documents → Final AI response. Include screenshots or short GIFs in the `presentation/` folder if available.

How to run (quick)

1. Create a virtual environment and install requirements.

```bash
python -m venv .venv
.venv\Scripts\activate    # Windows
pip install -r requirements.txt
```

2. Configure environment variables for your model and vector DB (examples):

```bash
export OPENAI_API_KEY=...
export CLAUDE_API_KEY=...
# For Windows PowerShell:
$env:CLAUDE_API_KEY = '...'
```

3. Run the RAG pipeline or the MCP server components (examples):

```bash
python -m rag_pipeline.run         # pipeline entry (if present)
python -m mcp_server.server        # MCP server (if present)
```

Notes

- This repo has been reorganized to focus on a single concrete use-case: a Company Knowledgebase AI. Legacy course material was archived under `/legacy_course`.
- If you want the legacy numbered course folders removed or migrated into `/legacy_course`, confirm and I will move them.