IntraIntel.ai - Multi-LLM Agent Coding Challenge
@Kush614
Overview
What is IntraIntel.ai - Multi-LLM Agent Coding Challenge?
It is a Python-based multi-agent system that answers medical questions by orchestrating multiple free LLMs from the Hugging Face Inference API. It uses a custom HTTP-based Model Context Protocol (MCP) to interact with separate tool servers for web search (DuckDuckGo) and PubMed search, and implements a Retrieval-Augmented Generation (RAG) pipeline with optional query refinement and snippet summarization.
How to use IntraIntel.ai - Multi-LLM Agent Coding Challenge?
Clone the project, create a Python virtual environment, install dependencies (FastAPI, uvicorn, httpx, duckduckgo-search, biopython, python-dotenv), and set HF_API_TOKEN and NCBI_EMAIL in a .env file. Accept the license terms for mistralai/Mistral-7B-Instruct-v0.3 on Hugging Face. Start the Web Search MCP server on port 8001 and the PubMed MCP server on port 8002 in separate terminals. Then run python agent/main_agent.py to process a predefined set of medical questions.
Key features of IntraIntel.ai - Multi-LLM Agent Coding Challenge
- Multi-step RAG pipeline using up to three distinct LLMs.
- Optional query refinement and snippet summarization steps.
- Separate synthesized answers from web search and PubMed with source links.
- Asynchronous HTTP communication via FastAPI and Uvicorn.
- API-key-free web search via DuckDuckGo and free Hugging Face models.
Use cases of IntraIntel.ai - Multi-LLM Agent Coding Challenge
- Answering medical questions by combining general web and PubMed literature.
- Comparing synthesized answers from two independent information sources.
- Automating evidence-based research for clinical or educational queries.
- Demonstrating an agentic RAG workflow with multiple free LLMs.
FAQ from IntraIntel.ai - Multi-LLM Agent Coding Challenge
Which LLMs are used?
The system uses mistralai/Mistral-7B-Instruct-v0.3 for query refinement and answer synthesis, and sshleifer/distilbart-cnn-6-6 for snippet summarization, both accessed via the free Hugging Face Inference API.
What are the main dependencies?
FastAPI, uvicorn, httpx, duckduckgo-search, biopython, and python-dotenv.
How do the MCP servers communicate?
They use a simple HTTP-based JSON protocol: a POST request to /execute with {"query": "..."} returns a JSON response containing source, status, and results (title, snippet, URL or PubMed ID).
What authentication is required?
A Hugging Face API token (HF_API_TOKEN) with read permissions, and an email address (NCBI_EMAIL) for polite access to NCBI’s Entrez system.
Are there performance considerations?
The first call to a Hugging Face model may take 20–90 seconds as the model loads; subsequent calls are faster. The script includes pauses to respect API rate limits.
