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README.md
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README.md
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README for Test Dorna
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Overview
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This repository contains an implementation of a Question-Answering (QA) system leveraging Retrieval-Augmented Generation (RAG) and other advanced techniques like Chroma and LangChain. The system uses a combination of pre-trained models and custom indexing to retrieve context from a set of documents and generate context-aware answers to questions. It also includes a web-browsing agent to fetch real-time information from the web.
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# Deploying Agentic RAG Systems to Perform Various Tasks Using LLMs
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Key Features
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RAG-based Search: Use FAISS or Chroma to index and retrieve document chunks based on cosine similarity.
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Answer Generation: Combines document retrieval with large language models (LLM) like llama3.2 to generate coherent answers.
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Web Browsing Tool: An agent that can scrape the web for up-to-date information.
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Multilingual: Supports multi-language queries (e.g., Persian).
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Installation
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Prerequisites
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Ensure you have the following libraries installed:
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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 `llama3.2` to provide context-aware responses. Additionally, it incorporates a web-browsing agent for retrieving live data.
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bash
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Copy
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pip install faiss-cpu sentence-transformers ollama numpy
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pip install langchain chromadb sentence-transformers ollama
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pip install -U langchain-community
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Libraries
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faiss-cpu: Used for efficient similarity search and clustering.
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sentence-transformers: To embed documents and queries for similarity search.
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ollama: A Python package for interacting with Ollama models.
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langchain: For chaining multiple LLMs and tools in complex workflows.
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chromadb: A vector store used for Chroma-based RAG.
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numpy: For array manipulation.
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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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To set up the environment, clone the repository and install the required dependencies:
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```
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git clone https://github.com/yourusername/agentic-rag-system.git
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cd agentic-rag-system
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pip install -r requirements.txt
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The requirements.txt includes dependencies such as:
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faiss-cpu: For efficient similarity search.
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sentence-transformers: For embedding models.
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ollama: For LLM interactions.
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langchain: For chaining models and agents.
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chromadb: For Chroma-based document retrieval.
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Install the additional dependencies for web browsing:
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pip install requests beautifulsoup4
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Usage
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Initialization
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RAG System: The RAG system creates an index or loads an existing one. The system works with multiple document sources (defined in DOCUMENT_PATHS) and splits them into chunks for efficient retrieval.
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Answer Generator: Uses the RAG system to fetch relevant document chunks based on the question and generates a response using the LLM.
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Web Browsing Tool: Fetches real-time information from the web using a specific URL.
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Example Flow
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Document Processing:
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Run the following command to execute the main script:
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Chunks are created from documents (e.g., .txt files).
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The system creates or loads an index to facilitate similarity-based search.
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Query Execution:
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A list of predefined questions is processed.
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For each query, the system searches for relevant context and generates a response using the LLM.
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Running the Main Script
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To run the script, execute the following:
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bash
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Copy
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python main.py
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This will process a set of predefined queries, generate responses using the RAG system, and save the results to a file (e.g., response.txt).
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The script processes predefined queries using the RAG system and generates answers based on documents and/or live web data.
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```
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Agent with Web Browsing
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The system can use a web browsing agent to fetch live web data. The agent uses the requests and BeautifulSoup libraries to scrape web pages and return content up to 5000 characters.
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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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Example of using the Agent:
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python
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Copy
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from langchain.agents import Tool, initialize_agent, AgentType
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## Components
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RAG System
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The RAG system includes:
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# Define tools
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tools = [
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Tool(
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name="Document_Search",
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func=document_search_function, # Function for document search
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description="For questions about documents"
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),
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Tool(
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name="Web_Browser",
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func=web_browser_tool, # Web browsing function
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description="For fetching live web data"
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)
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]
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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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# Initialize agent
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agent = initialize_agent(
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tools=tools,
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llm=llama_model, # Use your pre-trained LLM here
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agent_type=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
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memory=memory_buffer, # Optional: To use conversation memory
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verbose=True
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)
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Results
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Queries will be processed sequentially, and the results will be saved to the specified output file.
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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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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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## 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 output:
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@ -86,13 +80,8 @@ Copy
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چرا اینترنت همراه اول گوشی وصل نمیشود؟
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پاسخ:
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برای حل این مشکل، اول باید اطمینان حاصل کنید که شبکه موبایل شما به درستی فعال است...
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Customization
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Document Paths: Modify the DOCUMENT_PATHS list to point to your own documents.
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Embedding Models: You can change the EMBEDDING_MODEL and LLM_MODEL to other pre-trained models.
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Chunking Parameters: Modify CHUNK_SIZE and OVERLAP to control the document chunking behavior.
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License
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This project is licensed under the MIT License - see the LICENSE file for details.
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برای حل این مشکل، ابتدا باید بررسی کنید که شبکه موبایل شما در دسترس است و ...
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The system demonstrates effective integration of multiple techniques to solve complex QA tasks.
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Acknowledgements
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This project leverages LangChain, Ollama, FAISS, and Chroma for building efficient and scalable RAG systems.
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## License
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This project is licensed under the MIT License.
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