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This article explores Agentic RAG, a powerful technique that enhances Large Language Models (LLMs) by combining the decision-making capabilities of agentic AI with the adaptability of Retrieval-Augmented Generation (RAG). Unlike traditional models limited by their training data, Agentic RAG allows LLMs to independently access and reason with information from various sources. This practical guide focuses on building a hands-on RAG pipeline using LangChain.

Building an Agentic RAG Pipeline with LangChain

The following steps detail the creation of a RAG pipeline, illustrated by the architecture diagram below:

1. User Query: The process begins with a user's question, initiating the pipeline.

2. Query Routing: The system determines if it can answer the query using existing knowledge. A positive response yields an immediate answer; otherwise, the query proceeds to data retrieval.

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

   • Local Documents: A pre-processed PDF (e.g., Generative AI Principles) serves as a knowledge base, with the system searching for relevant information within.
   • Internet Search: For broader context, the pipeline uses external sources to gather up-to-date information.

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

5. Answer Generation: This compiled context is fed to a Large Language Model (LLM) to generate a precise and informative answer.

Setting Up the Environment

Before starting, ensure you have the following:

• Groq API key: Groq API Console
• Gemini API key: Gemini API Console
• Serper.dev API key: Serper.dev API Key

Install necessary Python packages:

pip install langchain-groq faiss-cpu crewai serper pypdf2 python-dotenv setuptools sentence-transformers huggingface distutils

Save API keys securely in a .env file (example):

pip install langchain-groq faiss-cpu crewai serper pypdf2 python-dotenv setuptools sentence-transformers huggingface distutils

The code utilizes various libraries for: operating system interaction (os), environment variable loading (dotenv), vector database management (FAISS), PDF processing (PyPDFLoader), text splitting (RecursiveCharacterTextSplitter), embedding generation (HuggingFaceEmbeddings), LLM interaction (ChatGroq, LLM), web searching (SerperDevTool, ScrapeWebsiteTool), and agent orchestration (Agent, Task, Crew).

LLM Initialization and Decision-Making

Two LLMs are initialized: llm (using llama-3.3-70b-specdec) for general tasks and crew_llm (using gemini/gemini-1.5-flash) for web scraping. A check_local_knowledge() function acts as a router, determining if a local answer is sufficient based on the provided context.

Web Scraping Agent and Vector Database

A web scraping agent, built using the crewai library, retrieves and summarizes relevant web content. The setup_vector_db() function creates a FAISS vector database from the PDF, enabling efficient similarity searches. get_local_content() retrieves the top 5 most relevant chunks from the database.

Answer Generation and Main Function

The generate_final_answer() function uses the LLM to create the final response based on the gathered context. The main() function orchestrates the entire process, handling query routing, context retrieval, and answer generation. An example query ("What is Agentic RAG?") demonstrates the system's ability to integrate local and web-based information for a comprehensive response. The output showcases the system's capability to provide a detailed explanation of Agentic RAG, even when the information isn't directly present in the local PDF.

This revised response provides a more concise and organized explanation of the Agentic RAG pipeline, focusing on the key steps and functionalities involved. It also clarifies the purpose and usage of each code segment and library.

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