https://github.com/farhaj499/multi_ai_agent_with_rag_and_wikisearch
This project demonstrates the integration of multiple AI agents using Retrieval-Augmented Generation (RAG) and WikiSearch functionalities, along with Cassandra for data storage and retrieval.
https://github.com/farhaj499/multi_ai_agent_with_rag_and_wikisearch
cassandra embeddings groq huggingface langchain large-language-model llama3-1 multi-agent-system query-routing rag retrieval-augmented-generation vector-database wikisearch
Last synced: 4 months ago
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This project demonstrates the integration of multiple AI agents using Retrieval-Augmented Generation (RAG) and WikiSearch functionalities, along with Cassandra for data storage and retrieval.
- Host: GitHub
- URL: https://github.com/farhaj499/multi_ai_agent_with_rag_and_wikisearch
- Owner: Farhaj499
- License: mit
- Created: 2024-12-30T12:23:50.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-12T17:36:22.000Z (6 months ago)
- Last Synced: 2025-01-29T03:38:05.545Z (5 months ago)
- Topics: cassandra, embeddings, groq, huggingface, langchain, large-language-model, llama3-1, multi-agent-system, query-routing, rag, retrieval-augmented-generation, vector-database, wikisearch
- Language: Jupyter Notebook
- Homepage:
- Size: 33.2 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Multi AI Agent with RAG and WikiSearch
This project demonstrates the integration of multiple AI agents using Retrieval-Augmented Generation (RAG) and WikiSearch functionalities, along with Cassandra for data storage and retrieval. It leverages Llama 3.1 for natural language processing and "all-MiniLM-L6-v2" for generating embeddings.
## Overview
This project, implemented in a Jupyter Notebook, showcases how to:
* Connect to Cassandra.
* Load and process data from web pages using `WebBaseLoader`.
* Convert text to vectors using the "all-MiniLM-L6-v2" Hugging Face embedding model.
* Perform document retrieval and question routing using LangChain and related libraries.
* Generate human-like responses using the Llama 3.1 LLM via GROQ.
## Features
* **Cassandra Integration:** Connects to Cassandra for storing and retrieving document embeddings.
* **LangChain:** Utilizes LangChain for web page loading, text splitting, embedding, and vector store operations.
* **WebBaseLoader:** Uses `WebBaseLoader` to efficiently load content from web pages.
* **Hugging Face Embeddings:** Employs the "all-MiniLM-L6-v2" model for generating high-quality text embeddings.
* **Llama 3.1 with GROQ:** Integrates the Llama 3.1 LLM using GROQ for refined, human-like responses.
* **WikiSearch:** Integrates the Wikipedia API for answering questions not related to stored documents.
* **Query Routing:** Routes user queries to either the Cassandra vector store or WikiSearch based on relevance.