Week 10: Building LLM-based Q&A Systems

Week 10: Building LLM-based Q&A Systems#

Overview#

This week focuses on building Question-Answering (Q&A) systems using Large Language Models. Students will learn about vector databases for efficient information retrieval, document parsing techniques, and the integration of these components with LLMs. This knowledge forms the foundation for more advanced applications like RAG systems that will be covered in Week 13 and prepares students for practical implementation in their final projects.

Learning Objectives#

By the end of this week, you will be able to:

  1. Understand the architecture and components of LLM-based Q&A systems

  2. Implement document parsing and preprocessing for Q&A applications

  3. Work with vector databases for efficient information storage and retrieval

  4. Create a basic Q&A system by integrating document processing with LLMs

  5. Evaluate and optimize Q&A system performance

Key Topics#

1. Introduction to LLM-based Q&A Systems#

  • Architecture overview of modern Q&A systems

  • Comparison with traditional Q&A approaches

  • Components and their interactions:

    • Document processing pipeline

    • Vector storage and retrieval

    • LLM integration

    • Response generation

2. Document Processing and Parsing#

  • Document preprocessing techniques

    • Text extraction and cleaning

    • Chunking strategies

    • Metadata extraction

  • Handling different document formats

    • Plain text

    • PDF documents

    • HTML content

    • Structured data (JSON, CSV)

  • Information extraction methods

    • Regular expressions

    • Rule-based parsing

    • LLM-assisted extraction

3. Vector Databases and Embeddings#

  • Understanding vector databases

    • Purpose and advantages

    • Popular solutions (Pinecone, Weaviate, Milvus)

  • Document embeddings

    • Embedding models and techniques

    • Dimension reduction strategies

    • Similarity search methods

  • Vector database operations

    • Indexing documents

    • Querying and retrieval

    • Updating and maintenance

4. System Integration and Implementation#

  • Connecting components

    • Document processor integration

    • Vector database connectivity

    • LLM API integration

  • Query processing pipeline

    • Query understanding

    • Context retrieval

    • Response generation

  • Error handling and edge cases

    • Invalid queries

    • Missing information

    • Confidence scoring

Practical Component#

In this week’s practical session, you will:

  • Set up a vector database using Pinecone or similar service

  • Implement document parsing and embedding generation

  • Create a simple Q&A system using OpenAI’s API

  • Test and evaluate system performance with various query types

Assignment#

Design and implement a basic Q&A system that can:

  1. Process and store a collection of provided documents

  2. Accept natural language questions

  3. Retrieve relevant information from the document collection

  4. Generate accurate answers using an LLM

Deliverables:

  • Working Q&A system implementation

  • Documentation of design choices and implementation details

  • Performance evaluation results

  • Brief report on challenges faced and solutions implemented

Looking Ahead#

Next week, we’ll explore web application development basics, learning how to create user interfaces for our Q&A systems using Flask or Streamlit. This will enable us to build complete, user-friendly applications that showcase the power of LLM-based systems.

References and Additional Resources#

  • Vector Database Documentation:

    • Pinecone Documentation

    • Weaviate Documentation

  • Document Processing Libraries:

    • PyPDF2 for PDF processing

    • Beautiful Soup for HTML parsing

  • LLM Integration:

    • OpenAI API Documentation

    • LangChain Documentation

  • Academic Papers and Articles on Modern Q&A Systems

This outline provides a comprehensive foundation for building LLM-based Q&A systems while preparing students for the more advanced topics in subsequent weeks. The practical components and assignments help reinforce theoretical concepts through hands-on experience.