AI Research Assistant MCP Server

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.

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

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

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):

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.