68 lines
3.4 KiB
Markdown
68 lines
3.4 KiB
Markdown
# Deploying Agentic RAG Systems to Perform Various Tasks Using LLMs
|
|
|
|
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.
|
|
|
|
## Table of Contents
|
|
- [Overview](#overview)
|
|
- [Installation](#installation)
|
|
- [Usage](#usage)
|
|
- [Components](#components)
|
|
- [RAG System](#rag-system)
|
|
- [Answer Generator](#answer-generator)
|
|
- [Web Browsing Agent](#web-browsing-agent)
|
|
- [Chroma-based RAG](#chroma-based-rag)
|
|
- [Results](#results)
|
|
- [License](#license)
|
|
|
|
## Overview
|
|
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:
|
|
- **RAG (Retrieval-Augmented Generation)**: Uses document indexing and retrieval for question answering.
|
|
- **Web Browsing**: Fetches live data to answer real-time queries.
|
|
- **Chroma and FAISS**: Index and retrieve relevant document chunks efficiently.
|
|
|
|
The system is multilingual and supports Persian language queries.
|
|
|
|
## Installation
|
|
|
|
Steps Performed:
|
|
Document Processing: The documents are chunked into smaller segments for efficient retrieval.
|
|
Index Creation or Loading: An FAISS index or Chroma-based vector store is created or loaded for similarity search.
|
|
Query Answering: A set of queries is processed, and answers are generated using LLMs, based on the retrieved document chunks or web content.
|
|
Results are saved in an output file (response.txt or agent_results.txt).
|
|
|
|
## Components
|
|
### RAG System
|
|
The RAG system includes:
|
|
|
|
Document Chunking: Splitting large documents into smaller chunks to improve retrieval performance.
|
|
Index Creation: Using FAISS (or Chroma) for indexing the document chunks based on their embeddings.
|
|
Similarity Search: Utilizing cosine similarity for retrieving relevant chunks during query processing.
|
|
### Answer Generator
|
|
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.
|
|
|
|
### Chroma-based RAG
|
|
|
|
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.
|
|
|
|
### Web Browsing Agent
|
|
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.
|
|
|
|
### Doc Search Agent
|
|
|
|
### Deep Search Agent
|
|
|
|
### The Power of Agentic Search
|
|
|
|
|
|
## Results
|
|
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.
|
|
|
|
Example:
|
|
|
|
|
|
|
|
The system demonstrates effective integration of multiple techniques to solve complex QA tasks.
|
|
|
|
## License
|
|
This project is licensed under the MIT License.
|