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This article explores Agentic RAG, a powerful approach combining agentic AI's decision-making with RAG's adaptability for dynamic information retrieval and generation. Unlike traditional models limited by training data, Agentic RAG independently accesses and reasons with information from various sources. This practical guide focuses on building a LangChain-based RAG pipeline.

Agentic RAG Project: A Step-by-Step Guide

The project constructs a RAG pipeline following this architecture:

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

2. Query Routing: The system determines if it can answer using existing knowledge. If yes, it responds directly; otherwise, it proceeds to data retrieval.

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

   • Local Documents: A pre-processed PDF (Generative AI Principles) serves as the knowledge base.
   • Internet Search: For broader context, the system uses external sources via web scraping.

4. Context Building: Retrieved data is compiled into a coherent context.

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

Setting Up the Environment

Prerequisites:

1. Groq API key (Groq API Console)
2. Gemini API key (Gemini API Console)
3. Serper.dev API key (Serper.dev API Key)

Installation:

Install necessary Python packages:

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

API Key Management: Store API keys securely in a .env file (example below):

import os
from dotenv import load_dotenv
# ... other imports ...

load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
SERPER_API_KEY = os.getenv("SERPER_API_KEY")
GEMINI = os.getenv("GEMINI")

Code Overview:

The code utilizes several LangChain components: FAISS for vector database, PyPDFLoader for PDF processing, RecursiveCharacterTextSplitter for text chunking, HuggingFaceEmbeddings for embedding generation, ChatGroq and LLM for LLMs, SerperDevTool for web search, and crewai for agent orchestration.

Two LLMs are initialized: llm (llama-3.3-70b-specdec) for general tasks and crew_llm (gemini/gemini-1.5-flash) for web scraping. A check_local_knowledge() function routes queries based on local context availability. A web scraping agent, built using crewai, retrieves and summarizes web content. A vector database is created from the PDF using FAISS. Finally, generate_final_answer() combines context and query to produce the final response.

Example Usage and Output:

The main() function demonstrates querying the system. For example, the query "What is Agentic RAG?" triggers web scraping, resulting in a comprehensive explanation of Agentic RAG, its components, benefits, and limitations. The output showcases the system's ability to dynamically access and synthesize information from diverse sources. The detailed output is omitted here for brevity but is available in the original input.

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