Building a Personalized AI Chatbot with LLM-RAG-Modeling-Q&A

In the fast-evolving world of AI, the ability to process user queries and provide precise, informed actions is invaluable. Enter the LLM-RAG-Modeling-System — an advanced AI agent that leverages large language models (LLMs) to offer a personalized and interactive user experience. This article breaks down the system’s core components, architecture, and workflow, guiding you through the setup and execution of Basic Rag-LLM application

Key Components of the System

The heart of the LLM-RAG-Modeling-Basic lies in its integration of several terminology :

• Semantic Search & Retrieval: Ensures accurate and efficient information retrieval.
• LangChain Tools: Facilitates seamless task integration.
• Vector Database: Stores and retrieves data efficiently.
• LLM: Generates responses by analyzing user queries and the retrieved information, enriched with additional context from external sources.

These components work together to form a robust Personalized Agent that processes user queries and determines appropriate actions

System Architecture

At its core, the LLM-RAG Modeling System utilizes Retrieval-Augmented Generation (RAG) to process user queries. This involves converting text data (queries) and documents into numerical representations for efficient retrieval using semantic search, supported by a vector database.

LLM Respond Pipeline Schema

The LLM Respond schema is designed to process user queries and determine actions using LLMs. It includes:

• Core Module
• Semantic Search & Retrieval
• LangChain Tools
• Vector Database

The LLM generates responses by analyzing user queries and retrieved information about document analysis, enriched with additional context from external sources.

Workflow Pipeline Project

The following diagram illustrates a Retrieval-Augmented Generation (RAG) sequence, outlining the steps to build an AI system for dynamic analysis and decision-making in Data Domain.

Step 1: Define the Most Capable LLM and Run a Quantized Model Locally

• Goal: Identify and configure the most suitable LLM for local hardware.
• Explanation: LLMs, trained on vast text datasets, can generate text, translate languages, and answer questions. Choosing an LLM model that balances accuracy and efficiency is crucial for local deployment. Quantization reduces the size and computational demands of the LLM without significantly affecting accuracy.

Step 2: Fine-Tune the Model on a Specific Data Domain

• Goal: Adapt the LLM to the Projects Colab Platform’s specific terminology and domain.
• Explanation: Fine-tuning involves training the LLM on a focused dataset to enhance its ability to identify relevant patterns and trends specific to the domain.

Step 3: Define the RAG System Pipeline and Select a Vector Database

• Goal: Design the RAG system pipeline and choose a suitable vector database.
• Explanation: RAG systems combine LLMs with information retrieval. This involves:

1. User Query: Submission of a query.
2. Retrieval: Retrieving relevant documents from the vector database.
3. Prompt + Retrieved Enhanced Context: Creating a prompt for the LLM using the retrieved documents.
4. LLM Response Generation: Generating a response based on the prompt.
5. vector Database Selection: Options include LlamaIndex, LangChain, and standalone databases like Chroma, FAISS, Pinecone, and Milvus.

Step 4: Build a UI for User Interaction

• Goal: Develop a user interface for interaction with the LLM.
• Explanation: Using Streamlit, create a user-friendly interface for users to submit queries and receive responses.

LLM Building Flow

Here is an overview of the chosen technology stack for each development step:

Step 1: LLM Selection and Optimization

• Technology: Hugging Face Ecosystem with PyTorch
• Explanation: Leverage Hugging Face for pre-trained LLMs and tools, and PyTorch for model manipulation and fine-tuning. Options include:
• Ollama: For efficient inference and deployment of LLMs.
• LlamaCPP (Optional): For performance improvements on compatible hardware.

Step 2: RAG System Pipeline and Vector Database Selection

• Technology: Retrieval System (LlamaIndex, LangChain, or Vector Database) with Python
• Explanation: Design the pipeline for document retrieval and LLM response generation. Vector database options include LlamaIndex, LangChain, Chroma, FAISS, Pinecone, and Milvus.

Step 3: Build the UI

• Technology: Streamlit
• Explanation: Streamlit allows rapid development of interactive interfaces for users to interact with the LLM model.

By following these steps and leveraging the right technologies, you can build a powerful AI system that enhances decision-making and provides valuable insights in real-time.

Project Reference Github : https://github.com/youness-elbrag/Rag-Ollama