67 lines
3.3 KiB
Markdown
67 lines
3.3 KiB
Markdown
# Deploying Agentic RAG Systems to Perform Various Tasks Using LLMs
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This repository showcases the implementation of a Retrieval-Augmented Generation (RAG) system for answering questions using large language models (LLMs) and document retrieval. The system integrates document indexing, chunking, and similarity search with advanced language models like `gemma2:9b` to provide context-aware responses. Additionally, it incorporates a web-browsing agent for retrieving live data.
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## Table of Contents
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- [Overview](#overview)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Components](#components)
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- [RAG System](#rag-system)
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- [Answer Generator](#answer-generator)
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- [Web Browsing Agent](#web-browsing-agent)
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- [Chroma-based RAG](#chroma-based-rag)
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- [Results](#results)
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- [License](#license)
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## Overview
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The project is designed to perform tasks like document-based question answering, real-time information retrieval via web scraping, and context-aware response generation. It leverages multiple techniques:
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- **RAG (Retrieval-Augmented Generation)**: Uses document indexing and retrieval for question answering.
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- **Web Browsing**: Fetches live data to answer real-time queries.
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- **Chroma and FAISS**: Index and retrieve relevant document chunks efficiently.
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The system is multilingual and supports Persian language queries.
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## Installation
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Steps Performed:
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Document Processing: The documents are chunked into smaller segments for efficient retrieval.
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Index Creation or Loading: An FAISS index or Chroma-based vector store is created or loaded for similarity search.
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Query Answering: A set of queries is processed, and answers are generated using LLMs, based on the retrieved document chunks or web content.
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Results are saved in an output file (response.txt or agent_results.txt).
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## Components
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### RAG System
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The RAG system includes:
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Document Chunking: Splitting large documents into smaller chunks to improve retrieval performance.
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Index Creation: Using FAISS (or Chroma) for indexing the document chunks based on their embeddings.
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Similarity Search: Utilizing cosine similarity for retrieving relevant chunks during query processing.
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### Answer Generator
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The Answer Generator class interacts with the RAG system to fetch the most relevant document chunks based on a given question. It then uses the LLM to generate a context-aware response.
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### Chroma-based RAG
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An alternative RAG implementation using Chroma for storing and querying document embeddings is also included. This utilizes LangChain's Chroma integration for efficient vector store management and querying.
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### Web Browsing Agent
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The Web Browsing Agent fetches real-time information from the web by scraping web pages. The agent can be used to get live data on current events, statistics, and more.
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### Doc Search Agent
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### Deep Search Agent
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### The Power of Agentic Search
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## Results
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The system successfully processes predefined questions and generates responses based on the relevant document context. Additionally, the web-browsing agent retrieves live data for real-time questions, providing a comprehensive, multi-source approach to answering queries.
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Example:
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The system demonstrates effective integration of multiple techniques to solve complex QA tasks.
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