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README for Test Dorna # Deploying Agentic RAG Systems to Perform Various Tasks Using LLMs
Overview
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.
Key Features 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.
RAG-based Search: Use FAISS or Chroma to index and retrieve document chunks based on cosine similarity.
Answer Generation: Combines document retrieval with large language models (LLM) like llama3.2 to generate coherent answers.
Web Browsing Tool: An agent that can scrape the web for up-to-date information.
Multilingual: Supports multi-language queries (e.g., Persian).
Installation
Prerequisites
Ensure you have the following libraries installed:
bash ## Table of Contents
Copy - [Overview](#overview)
pip install faiss-cpu sentence-transformers ollama numpy - [Installation](#installation)
pip install langchain chromadb sentence-transformers ollama - [Usage](#usage)
pip install -U langchain-community - [Components](#components)
Libraries - [RAG System](#rag-system)
faiss-cpu: Used for efficient similarity search and clustering. - [Answer Generator](#answer-generator)
sentence-transformers: To embed documents and queries for similarity search. - [Web Browsing Agent](#web-browsing-agent)
ollama: A Python package for interacting with Ollama models. - [Chroma-based RAG](#chroma-based-rag)
langchain: For chaining multiple LLMs and tools in complex workflows. - [Results](#results)
chromadb: A vector store used for Chroma-based RAG. - [License](#license)
numpy: For array manipulation.
## 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 Usage
Initialization Run the following command to execute the main script:
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.
Answer Generator: Uses the RAG system to fetch relevant document chunks based on the question and generates a response using the LLM.
Web Browsing Tool: Fetches real-time information from the web using a specific URL.
Example Flow
Document Processing:
Chunks are created from documents (e.g., .txt files).
The system creates or loads an index to facilitate similarity-based search.
Query Execution:
A list of predefined questions is processed.
For each query, the system searches for relevant context and generates a response using the LLM.
Running the Main Script
To run the script, execute the following:
bash
Copy
python main.py python main.py
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). The script processes predefined queries using the RAG system and generates answers based on documents and/or live web data.
```
Agent with Web Browsing # Steps Performed:
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. 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).
Example of using the Agent: ## Components
python RAG System
Copy The RAG system includes:
from langchain.agents import Tool, initialize_agent, AgentType
# Define tools Document Chunking: Splitting large documents into smaller chunks to improve retrieval performance.
tools = [ Index Creation: Using FAISS (or Chroma) for indexing the document chunks based on their embeddings.
Tool( Similarity Search: Utilizing cosine similarity for retrieving relevant chunks during query processing.
name="Document_Search", Answer Generator
func=document_search_function, # Function for document search 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.
description="For questions about documents"
),
Tool(
name="Web_Browser",
func=web_browser_tool, # Web browsing function
description="For fetching live web data"
)
]
# Initialize agent Web Browsing Agent
agent = initialize_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.
tools=tools,
llm=llama_model, # Use your pre-trained LLM here Chroma-based RAG
agent_type=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, 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.
memory=memory_buffer, # Optional: To use conversation memory
verbose=True ## 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.
Results
Queries will be processed sequentially, and the results will be saved to the specified output file.
Example output: Example output:
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Customization The system demonstrates effective integration of multiple techniques to solve complex QA tasks.
Document Paths: Modify the DOCUMENT_PATHS list to point to your own documents.
Embedding Models: You can change the EMBEDDING_MODEL and LLM_MODEL to other pre-trained models.
Chunking Parameters: Modify CHUNK_SIZE and OVERLAP to control the document chunking behavior.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgements ## License
This project leverages LangChain, Ollama, FAISS, and Chroma for building efficient and scalable RAG systems. This project is licensed under the MIT License.