Building a Production-Ready RAG-based LLM Python Application

Introduction

This project is dedicated to creating a scalable, high-performance Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) application using Python. The goal is to process an extensive collection of documents efficiently, utilizing the combined power of Ray for distributed computing, ChromaDB for vector storage, and LangChain. This application is designed to go beyond basic chatbot functionalities, aiming to handle complex, large-scale data processing tasks with the best (and last) discoveries for evaluating the outputs and mitigate halluccination.

Papers

Query Expansion by Prompting Large Language Models

Project Components

1. Ray (Distributed Computing):

• Manage and scale workloads across multiple nodes.
• Distribute tasks like loading, chunking, embedding, indexing, and serving efficiently.

2. ChromaDB (Vector Store):

• Store and retrieve large-scale vector data.
• Optimize embedding and indexing processes for performance.

3. LangChain:

• Utilize for building generating embeddings and chunking.

Overview

documents_loader.py

This Python script is designed to automate the process of fetching, downloading, and ingesting academic papers from the arXiv database.

• Fetch Papers: Retrieve a list of paper URLs from arXiv based on a search query.
• Download Papers: Concurrently download papers from the fetched URLs.
• Ingest Documents: Extract text and metadata from the downloaded PDF documents.

splitter.py

This script is designed to chunk large text sections into smaller, more manageable pieces.

• Chunking Text Sections: Breaks down larger text sections into smaller chunks based on specified size and overlap parameters.

embeddings.py

• Text Embedding: Converts text chunks into embeddings using selected models from OpenAI or HuggingFace.

storage.py

This script provides functionality for storing and querying documents in a ChromaDB collection, which is beneficial for managing large datasets with embedded textual data.

• Document Storage in ChromaDB: Manages the addition of batches of documents to a ChromaDB collection.
• Singleton Pattern: Ensures a single, consistent instance manages the database connection.

retrieval.py

Query expansion is a widely used technique to improve the recall of retrieved documents. This script is designed for augmenting and generating multiple related queries using an OpenAI language model.

• Query Augmentation: Enhances a given query by generating an augmented version using a language model.
• Multiple Related Query Generation: Creates several related queries based on the input query to cover different aspects of a given topic.

generation.py

This script is designed to act as a virtual assistant for research purposes, capable of augmenting queries, retrieving relevant documents, and generating informed responses.

• Comprehensive Query Processing: Enhances, retrieves, and generates responses to user queries using advanced NLP models and methods.
• Document Retrieval and Re-ranking: Retrieves relevant documents and re-ranks them based on their relevance to the query.

trulens_evaluation.py

This script establishes a feedback and evaluation system for a ResearchAssistant class using TruLens, an evaluation library.

• Feedback and Evaluation System: Implements a comprehensive system to evaluate the performance of a research assistant model on specific NLP tasks.
• Integration with TruLens: Utilizes TruLens for monitoring and assessing the performance of the model.

Key Features

• Scalable Document Processing:

  • Efficiently handle large volumes of documents.
  • Utilize distributed computing to manage heavy workloads.

• Performance Optimization:

  • Benchmark and profile different components to improve retrieval scores and overall quality.
  • Fine-tune the system for optimal performance under various loads.

• Advanced Data Processing Techniques:

  • Employ methods like prompt engineering, lexical search, reranking, and data flywheel to enhance the application's efficiency and accuracy.

• High Availability and Scalability:

  • Ensure the application can handle high demand and remain operational under diverse conditions.
  • Implement strategies for load balancing, failover, and redundancy.

System Architecture

1. Data Processing Pipeline:

• Automated loading and processing of documents.
• Efficient chunking and embedding of text for analysis.

2. Distributed Task Management:

• Utilize Ray to distribute tasks across multiple servers or nodes.
• Handle large-scale computations in a parallel and distributed manner.

3. Vector Storage and Retrieval:

• Use ChromaDB for storing and accessing large vector datasets.
• Optimize retrieval processes for speed and accuracy.

4. Application Serving Layer:

• Design a robust API layer for handling user requests.
• Scale the serving infrastructure to manage varying loads.

5. Monitoring and Logging:

• Implement comprehensive monitoring for performance tracking.
• Maintain detailed logs for troubleshooting and optimization.

Conclusion

This Python application aims to be a benchmark in handling and processing large-scale document datasets with high efficiency. By leveraging cutting-edge technologies and methodologies, this project stands at the forefront of scalable, high-performance data processing solutions.