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It leverages Llama 3.1 for natural language processing and \"all-MiniLM-L6-v2\" for generating embeddings.\n\n## Overview\n\nThis project, implemented in a Jupyter Notebook, showcases how to:\n\n*   Connect to Cassandra.\n*   Load and process data from web pages using `WebBaseLoader`.\n*   Convert text to vectors using the \"all-MiniLM-L6-v2\" Hugging Face embedding model.\n*   Perform document retrieval and question routing using LangChain and related libraries.\n*   Generate human-like responses using the Llama 3.1 LLM via GROQ.\n\n## Features\n\n*   **Cassandra Integration:** Connects to Cassandra for storing and retrieving document embeddings.\n*   **LangChain:** Utilizes LangChain for web page loading, text splitting, embedding, and vector store operations.\n*   **WebBaseLoader:** Uses `WebBaseLoader` to efficiently load content from web pages.\n*   **Hugging Face Embeddings:** Employs the \"all-MiniLM-L6-v2\" model for generating high-quality text embeddings.\n*   **Llama 3.1 with GROQ:** Integrates the Llama 3.1 LLM using GROQ for refined, human-like responses.\n*   **WikiSearch:** Integrates the Wikipedia API for answering questions not related to stored documents.\n*   **Query Routing:** Routes user queries to either the Cassandra vector store or WikiSearch based on relevance.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarhaj499%2Fmulti_ai_agent_with_rag_and_wikisearch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarhaj499%2Fmulti_ai_agent_with_rag_and_wikisearch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarhaj499%2Fmulti_ai_agent_with_rag_and_wikisearch/lists"}