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LangChain / RAG / Vector DBs · Lesson

Storing and Retrieving Embeddings

Implement the process of generating embeddings from document chunks and storing them in a vector database for later retrieval.

Storing & Retrieving Embeddings

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.

Recap: Chunks & Embeddings

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!

All lessons in this course

  1. Understanding Text Embeddings
  2. Introduction to Vector Databases
  3. Storing and Retrieving Embeddings
  4. Measuring Embedding Similarity
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