Vector Store Implementation

When to Use Vector Stores

Vector Stores Shine When

• You have large collections of text that won't fit in a single prompt
• You need fast semantic search across documents
• Your application uses RAG (Retrieval Augmented Generation)
• You want metadata-driven filtering for targeted queries
• You're building a knowledge base or document search system

Skip Vector Stores If

• Your entire dataset fits comfortably in one prompt (< 100K tokens)
• You're not doing similarity search or retrieval
• Your use case requires exact matching (use a traditional database)
• Approximate nearest neighbor search adds no value

Core Methods

Methods	Description
upsert()	Insert new vectors or update existing ones
fetch()	Retrieve specific vectors by their IDs
search()	Search for vectors similar to the query
delete()	Remove vectors
count()	Count total vectors in the store