Summary
Highlights
The video continues the discussion on RAG, explaining its purpose in fixing drawbacks of LLMs and outlining the two main pipelines: data injection and retrieval. The session will focus on a practical, step-by-step implementation, moving from basic Jupyter Notebook code to modular, class-based coding. Key initial topics include understanding document structure for external knowledge bases.
The first critical step in the data injection pipeline is reading various file types (PDF, HTML, DB, Excel) and converting their content into a structured format, specifically the document structure. This structure is essential for applying strategies like chunking and embedding before storing data in a vector database. The document structure typically consists of 'page content' and 'metadata', with metadata providing additional file information like source, page numbers, or author.
After data parsing, chunking is applied to divide the document content into smaller, manageable parts. This is crucial because LLM or embedding models have fixed context sizes, preventing them from processing very large documents at once. Each chunk is then converted into numerical vectors (embeddings), which are subsequently stored in a vector database. This process enables efficient similarity searches and accurate retrieval later on.
The practical implementation begins by setting up a VS Code environment, initializing a UV repository, and installing necessary Python packages like `langchain`, `langchain-core`, `langchain-community`, `pypdf`, and `pymupdf`. The video also demonstrates how to create data and notebook folders and set up an IPython kernel for Jupyter Notebook development.
The video introduces the `Document` class from `langchain_core.documents`, explaining how to manually create a document with `page_content` and `metadata`. It then demonstrates how to use Langchain's `TextLoader` to read a `.txt` file—created programmatically for the example—and how `DirectoryLoader` can be used to load multiple files from a directory, converting them all into the document structure.
The process of loading PDF files using `PyMuPDFLoader` (or `PyPDFLoader`) is shown, highlighting its ability to extract rich metadata alongside page content. The benefit of using document loaders is their automatic conversion of various data formats into the standardized document structure, which is vital for subsequent processing like chunking and embedding, especially due to the valuable metadata they provide.
The next step focuses on embeddings and vector store databases. The video outlines the installation of `sentence-transformers` and `chromadb` (or `faiss-cpu`) libraries. It then designs an `EmbeddingManager` class to handle document embedding generation using a pre-trained model (all-MiniLM-L6-v2) from Hugging Face, which converts text into 384-dimensional vectors.
A `VectorStore` class is developed to manage the vector database using ChromaDB. This class initializes a persistent ChromaDB client, creates a collection (e.g., 'PDF Documents'), and defines a method (`add_documents`) to add documents and their generated embeddings. This involves preparing structured data with IDs, metadata, document text, and their corresponding embedding vectors for storage.
The chunks obtained from loaded documents are converted into embeddings using the `EmbeddingManager`. These embeddings, along with their corresponding document chunks, are then added to the ChromaDB vector store. The video shows that 359 documents were successfully added, and a persistent directory was created to store the vector database on disk.
Finally, a `RAGRetriever` class is created to handle query-based retrieval from the vector store. This class takes instances of `VectorStore` and `EmbeddingManager`. The `retrieve` function within this class converts a user query into an embedding, queries the vector database, and returns relevant documents with their similarity scores, enabling the retrieval of context based on user input.