Multi-Agent RAG (Retrieval-Augmented Generation) System
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https://mistral.ai/news/agents-api
I've got some news regarding the AIFAQ Multi Agents development! I've prepared a new README that should significantly streamline our development process. The previous one wasn't quite right, but this updated version will make things much easier to get started.One of the key improvements is that I've made some minor changes to skip authentication during development. This means you won't need to add Google or OpenAI credentials to run the application locally. It now works seamlessly with the free tier of Mistral API and an HF access token.You can check out the updated branch with the new README here: GitHub - RAWx18/aifaq at agents Happy developing!
GitHub
GitHub - RAWx18/aifaq at agents
AI FAQ Proof-of-Concept project: it provides a chatbot that replies to the questions on Hyperledger Ecosystem - GitHub - RAWx18/aifaq at agents
Multi-Agent RAG (Retrieval-Augmented Generation) System
Technical Goals:
Multi-Agent Architecture: Build a working prototype of modular agents (retriever, router, summarizer, responder) communicating through an orchestrator.
Plugin Support: Enable pluggable data sources (e.g., Confluence, GitHub Wikis, custom PDFs).
Scalable RAG Pipeline: Implement efficient document chunking, embedding caching, and vector search optimizations.
Agent Collaboration Logic: Develop logic for agents to delegate subtasks among themselves dynamically (example: a retriever agent passes relevant docs to a summarizer agent).
Baseline Evaluation Metrics: Create benchmark tests to measure precision, latency, and accuracy improvements vs. a simple single-agent model.
Community Deliverables:
Public Demo Repository with a clean README and deployment instructions (Docker optional).
Technical Blog Post: Write a blog post showcasing how multi-agent RAG outperforms single-agent baseline.
Linux Foundation Network Event