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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
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
To set up the environment, clone the repository and install the required dependencies:
git clone https://github.com/yourusername/agentic-rag-system.git
cd agentic-rag-system
pip install -r requirements.txt
The requirements.txt includes dependencies such as:
faiss-cpu: For efficient similarity search.
sentence-transformers: For embedding models.
ollama: For LLM interactions.
langchain: For chaining models and agents.
chromadb: For Chroma-based document retrieval.
Install the additional dependencies for web browsing:
pip install requests beautifulsoup4
Usage
Run the following command to execute the main script:
python main.py
The script processes predefined queries using the RAG system and generates answers based on documents and/or live web data.
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.
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.
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.
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:
سوال 1 (1):
شاه عباس صفوی کیست؟
پاسخ:
The system demonstrates effective integration of multiple techniques to solve complex QA tasks.
## License
This project is licensed under the MIT License.