benitomartin/agentic-rag-langchain-pinecone
Hybrid Knowledge Agentic RAG System
This project helps you build a smart Q&A system that can answer questions based on a large collection of documents, like TEDx Talk transcripts. You feed it your documents, and it allows users to ask natural language questions, providing relevant answers and even remembering past conversations. It's designed for anyone who needs to quickly find specific information within extensive text archives.
No commits in the last 6 months.
Use this if you need an intelligent system to answer questions directly from your own documents, remembering previous interactions for a more fluid experience.
Not ideal if you're looking for a simple keyword search or don't need the advanced conversational and information retrieval capabilities.
Stars
9
Forks
6
Language
Jupyter Notebook
License
—
Category
Last pushed
May 19, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/benitomartin/agentic-rag-langchain-pinecone"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
run-llama/llama_index
LlamaIndex is the leading document agent and OCR platform
emarco177/documentation-helper
Reference implementation of a RAG-based documentation helper using LangChain, Pinecone, and Tavily..
janus-llm/janus-llm
Leveraging LLMs for modernization through intelligent chunking, iterative prompting and...
JetXu-LLM/llama-github
Llama-github is an open-source Python library that empowers LLM Chatbots, AI Agents, and...
Vasallo94/ObsidianRAG
RAG system to query your Obsidian notes using LangGraph and local LLMs (Ollama)