Summary of "2-Build RAG Pipeline From Scratch-Data Ingestion to Vector DB Pipeline-Part 1"

Summary of “2-Build RAG Pipeline From Scratch-Data Ingestion to Vector DB Pipeline-Part 1”

This video provides a comprehensive tutorial on building a Retrieval-Augmented Generation (RAG) pipeline from scratch, focusing on the data ingestion pipeline and vector database pipeline components. It emphasizes practical implementation with modular coding, starting from basics in a Jupyter notebook and gradually increasing complexity.

Key Technological Concepts and Product Features Covered

  1. RAG Pipeline Overview

    • Two main pipelines:
      • Data Injection Pipeline: Ingesting and parsing data from various file formats (PDF, HTML, Excel, DB files, etc.) into a structured document format.
      • Query Retrieval Pipeline: Retrieving relevant documents from a vector database based on user queries.

  2. Document Data Structure

    • Central to the pipeline is the document structure which contains:
      • Page Content: The actual text extracted from files.
      • Metadata: Additional information such as filename, page count, author, timestamps, etc.
    • This structure facilitates efficient chunking, embedding, storage, and retrieval.

  3. Data Injection Pipeline Details

    • Reading various file types using LangChain loaders:
      • Text Loader for TXT files
      • Directory Loader for batch loading multiple files
      • PDF Loaders: PyPDF and PyMuPDF (with PyMuPDF preferred for richer metadata extraction)
    • The loaders convert raw data into LangChain’s document structure.

  4. Chunking

    • Large documents are split into smaller chunks to respect the fixed context size limits of embedding and LLM models.
    • Chunking enables manageable input sizes for embedding generation.

  5. Embedding Generation

    • Uses sentence-transformers library with the Hugging Face model all-miniLM-L6-v2 (embedding dimension: 384).
    • Text chunks are converted into vector embeddings for semantic search.

  6. Vector Store (Vector Database)

    • Uses ChromaDB as the vector store backend with persistence on disk.
    • Implements a class to initialize the vector store, create collections, and add documents with embeddings, metadata, and unique IDs (UUIDs).
    • Supports similarity search based on cosine similarity.

  7. Retrieval Pipeline

    • Implements a RAG Retriever class that:
      • Converts user queries into embeddings.
      • Queries the vector store for the most relevant documents based on similarity scores.
      • Applies optional filters based on metadata.
    • Returns documents with content, metadata, and similarity scores as context for downstream LLM usage.

  8. Modular Coding Approach

    • The code is structured in classes for embedding management, vector store management, and retrieval to promote reusability and scalability.
    • Initial code is demonstrated in Jupyter notebooks, with plans to refactor into a source folder for pipeline modularization.

  9. Practical Demonstrations

    • Creating sample text files programmatically.
    • Loading and parsing PDF and text files.
    • Executing chunking and embedding steps.
    • Storing embeddings in a persistent vector store.
    • Querying vector store and retrieving relevant context.

  10. Next Steps Preview - Integration of LLM with retrieved context for generation (to be covered in the next video). - Further modularization and pipeline orchestration.

Reviews, Guides, or Tutorials Provided

Main Speaker / Source

This video is a foundational resource for developers and data scientists looking to build efficient RAG pipelines involving document ingestion, chunking, embedding, vector storage, and retrieval, using open-source tools and modular code design.

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