การจัดเก็บและเรียกคืนเวกเตอร์แทนความหมาย

Welcome to Lesson 3! In this lesson, we'll connect the dots between text chunks and vector databases.

You'll learn how to generate numerical representations (embeddings) from your document chunks and then store them efficiently in a vector database for quick and accurate retrieval.

Before we dive in, let's quickly recap. From previous lessons, you know:

• Document Chunks: Large documents are split into smaller, manageable pieces to fit LLM context windows and improve retrieval granularity.
• Text Embeddings: These are numerical vectors that capture the semantic meaning of text. Similar texts have similar embeddings.

Our goal now is to turn those chunks into embeddings and make them searchable!

Here's the typical workflow for getting your data ready for RAG:

1. Load & Split: Ingest raw documents and break them into chunks.
2. Embed: Convert each text chunk into an embedding vector using an embedding model.
3. Store: Save these embedding vectors (along with their original text chunks and metadata) in a vector database.
4. Retrieve: When a user asks a question, embed the query, search the vector database for similar embeddings, and retrieve the most relevant chunks.

First, we need an embedding model. LangChain provides interfaces for many models, including those from OpenAI, Cohere, and local models like those from Hugging Face.

For this example, we'll use a local Hugging Face model to avoid needing an API key. This model turns text into a vector of numbers.

Once our embedding model is ready, we can feed it text chunks. The model will then output a list of numbers (our embedding vector) for each chunk.

These vectors are what the vector database will use to find similar pieces of information.

A vector store is a specialized database designed to efficiently store and query high-dimensional vectors. It's built to quickly find vectors that are 'close' to a given query vector.

Think of it as a super-fast index for semantic meaning. When you search, it doesn't look for keywords; it looks for meaning.

LangChain supports many vector stores, from simple in-memory ones like FAISS to robust cloud services like Pinecone or Chroma.

Let's use FAISS, an in-memory vector store, to demonstrate storage. We'll take our text chunks and their embeddings and add them to FAISS. FAISS handles the indexing for fast search.

In a real application, you'd load chunks from documents first, then embed them, and finally store them. Here, we'll create simple documents directly.

Now that our documents are embedded and stored, we can query the vector store to find the most semantically similar documents to our question.

The vector store will embed your query, compare its vector to all stored vectors, and return the top 'k' (e.g., top 4) most similar documents.

Notice in the previous example how we included metadata with our documents? This is incredibly powerful!

• Filtering: You can filter searches based on metadata (e.g., only search documents from a specific author or date).
• Context: Metadata helps the LLM understand the source and relevance of the retrieved chunk, improving answer quality.
• Debugging: It's easier to trace where information came from.

Always consider what metadata is useful to store alongside your text chunks.

Great job! In this lesson, you've mastered the critical steps of preparing your data for a RAG system:

• Initializing an embedding model to convert text into numerical vectors.
• Understanding the role of a vector store for efficient storage and similarity search.
• Implementing the process of generating embeddings and storing them (e.g., using FAISS).
• Performing similarity searches to retrieve relevant document chunks based on a query.
• Recognizing the importance of metadata for richer context and filtering.

These skills are fundamental to building effective RAG applications!