Agentic RAG: Step-by-Step Tutorial With Demo Project

This article demonstrates a practical implementation of Agentic RAG, a powerful technique combining AI agents with Retrieval-Augmented Generation (RAG) to create a more adaptable and intelligent AI system. Unlike traditional models limited by their training data, Agentic RAG can independently access and reason with information from various sources.

This hands-on tutorial focuses on building a RAG pipeline using LangChain, capable of answering user queries using both local documents and internet searches.

Architecture and Workflow:

The pipeline follows these steps:

1. User Query: The process begins with a user's question.

2. Query Routing: The system determines if it can answer the query using existing knowledge. If yes, it provides an immediate response. If not, the query proceeds to data retrieval.

3. Data Retrieval: The system accesses two potential sources:

   • Local Documents: A pre-processed PDF (in this case, "Generative AI Principles") serves as a knowledge base. Relevant information is extracted via similarity search.
   • Internet Search: If necessary, the system uses Serper.dev to search the web for up-to-date information.

4. Context Building: Retrieved data (from the PDF or the web) is compiled into a coherent context.

5. Answer Generation: This context is fed to a Large Language Model (LLM) – specifically, Llama-3.3-70b-specdec for general tasks and gemini/gemini-1.5-flash for web scraping – to generate a comprehensive and accurate answer.

Implementation Details:

The tutorial provides a step-by-step guide, including:

• Prerequisites: Groq API key, Gemini API key, and Serper.dev API key are required.
• Package Installation: Necessary Python packages are installed using pip.
• Environment Setup: API keys are managed securely using a .env file.
• LLM Initialization: Two LLMs are initialized: one for general tasks and another specifically for web scraping.
• Decision-Making Function (check_local_knowledge): This function determines whether the local PDF contains sufficient information to answer the query.
• Web Scraping Agent: A crewai agent uses Serper.dev to search and summarize relevant web content.
• Vector Database Creation: A FAISS vector database is created from the PDF using sentence embeddings.
• Final Answer Generation: The LLM generates the final answer based on the retrieved context and the user's query.

The code demonstrates how to load the PDF, split it into chunks, create embeddings, and perform similarity searches. The web scraping component uses a dedicated agent to efficiently retrieve and summarize relevant web pages. The final answer is generated using a conversational prompt that includes both the user's query and the compiled context.

Example and Results:

The example query, "What is Agentic RAG?", effectively showcases the system's ability to leverage both local and external knowledge. The output provides a detailed explanation of Agentic RAG, including its components, advantages, and limitations. This demonstrates the system's adaptability and ability to provide comprehensive answers even when the information isn't directly present in the local document.

Conclusion:

This tutorial provides a clear and practical guide to building an Agentic RAG pipeline. The resulting system is highly adaptable, capable of handling diverse queries by intelligently combining local and online information sources. This approach offers a significant improvement over traditional AI models, demonstrating the potential of Agentic RAG for creating more robust and informative AI applications.

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