Integrating with a Vector Database
Learn to connect your RAG application to a vector database like Pinecone, Weaviate, or Chroma, and perform indexing and queries.
Connect RAG & Vector DBs
Welcome to the final lesson in our 'Deep Dive into Vector Databases' course! You've learned why vector databases are essential for RAG systems and what vector embeddings are.
Now, let's bring it all together. This lesson focuses on the practical steps of integrating a vector database into your RAG application.
We'll cover:
- Connecting to a vector database client.
- Preparing your data for storage.
- Indexing (adding) your data.
- Querying (searching) for relevant information.
Vector DBs in RAG: A Quick Recap
Before we dive into integration, let's quickly recall the role of vector databases in RAG.
They are specialized databases designed to store and efficiently search vector embeddings. These embeddings are numerical representations of text, images, or other data, capturing their semantic meaning.
When a user asks a question, we convert it into an embedding, search the vector database for similar document embeddings, and retrieve the most relevant chunks of information. This context is then fed to the LLM.
All lessons in this course
- The Necessity of Vector Databases
- Vector Embeddings and Similarity Search
- Integrating with a Vector Database
- Indexing, Filtering & Hybrid Search