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Vector Databases: Pinecone, Weaviate & pgvector · Lesson

Contextual Information Retrieval

Implement strategies for retrieving the most relevant context from your vector store to augment LLM prompts.

What is Context Retrieval?

In a Retrieval-Augmented Generation (RAG) system, the Large Language Model (LLM) needs relevant information to generate accurate responses.

  • Contextual Information Retrieval is the process of finding and fetching this relevant data from your vector database.
  • It's the bridge that connects the user's query to the knowledge stored in your specialized data.

The Retrieval Workflow

When a user asks a question, several steps happen to get the right context:

  1. The user's question (query) is converted into a vector embedding.
  2. This query embedding is sent to the vector database.
  3. The vector database searches for stored document embeddings that are most similar to the query embedding.
  4. The text chunks associated with these similar embeddings are retrieved and sent to the LLM.

All lessons in this course

  1. RAG System Architecture Overview
  2. Integrating with LLM Frameworks
  3. Contextual Information Retrieval
  4. Chunking Strategies for RAG
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