presizhai / llm-elasticrag

LLM RAG

Overview of LLMs and RAG

Large Language Models (LLMs)

LLM refers to advanced natural language processing (NLP) models that are trained on vast amounts of text data to understand and generate human-like language. Some examples of LLMs include GPT-3, BERT, T5, and the model that powers me, Claude.

• Purpose: Generate and understand text in a human-like manner.
• Structure: Built using deep learning techniques, especially Transformer architectures.
• Size: Characterized by having a vast number of parameters (billions to trillions), enabling nuanced understanding and generation.
• Training: Pre-trained on large datasets of text to learn a broad understanding of language, then fine-tuned for specific tasks.

Retrieval-Augmented Generation (RAG)

RAG is an approach that combines the capabilities of an LLM with a retrieval system that can look up relevant information from a knowledge base or corpus of documents. The retrieved information is then provided as additional context to the LLM, enhancing its ability to understand and respond to queries that require access to specific facts or knowledge beyond what is contained in its training data alone.

The RAG architecture aims to combine the strong language understanding and generation abilities of LLMs with the ability to access and reason over a broad knowledge base, potentially improving performance on question-answering, analysis, and other tasks that require accessing external information sources. It can be particularly useful for handling queries related to current events, specific domains, or rapidly changing information landscapes.

• Purpose: Enhance language model responses with information retrieved from external sources.
• How It Works: Combines a language model with a retrieval system, typically a document database or search engine.
• Process:
  • Queries an external knowledge source based on input.
  • Integrates retrieved information into the generation process to provide contextually rich and accurate responses.
• Advantages: Improves the factual accuracy and relevance of generated text.
• Use Cases: Fact-checking, knowledge-intensive tasks like medical diagnosis assistance, and detailed content creation where accuracy is crucial.

Chat with Your Own Data: A Retrieval-Augmented Generation (RAG) System

This project is a Retrieval-Augmented Generation (RAG) system that allows you to chat with your own data using natural language queries. It combines the power of large language models (LLMs) with a retrieval system to provide contextually rich and accurate responses based on your data.

Tech Stack:

• Docker: Containerization for efficient development and deployment.
• Python: Scripting language for core application logic and data processing.
• Elastic Search: Search and analytics engine for indexing and retrieving documents from the data corpus.
• OpenAI: Provides access to advanced large language models ( LLM component) for generating human-like responses and chatbot functionality.
• GitHub: Version control and collaboration platform for code management.

Project Overview:

This project leverages the power of OpenAI's large language models to create a conversational AI that users can train on their own data. By utilizing Elastic Search for data indexing and retrieval, the system can effectively learn from user-provided information and respond to prompts and questions in a comprehensive manner.

Project Structure:

• docker: Contains Dockerfiles for building and running the application containers.
• data: Stores user-provided data for training the chatbot.
• elasticsearch: Configuration files and scripts for interacting with Elastic Search.
• openai: Scripts for handling OpenAI API interactions.
• python: Core application logic written in Python.