Vector Stores in Railtracks

Vector stores are specialized databases that enable fast, semantic search across your documents. They let your AI agents efficiently find and retrieve relevant information based on meaning rather than exact keyword matches.

What Is a Vector Store?

A vector store is a specialized database designed to store and search embeddings — numerical representations of text, code, or other content that capture semantic meaning.

Whenever you insert a document into a vector store, it goes through:

Chunking — Breaking content into manageable pieces
Embedding — Converting chunks into numerical vectors
Storing — Indexing vectors for efficient retrieval

The vector store handles that last step, creating an index optimized for similarity search.

Why Vector Stores Matter

Traditional databases search for exact matches. Vector stores search by semantic similarity:

They index high-dimensional vector representations
They search using distance metrics (cosine similarity, euclidean distance, etc.)
They return ranked results based on how close vectors are in embedding space

This enables semantic search, recommendation systems, and retrieval workflows like RAG.

How Vector Stores Work (Conceptually)

sequenceDiagram
    participant U as Text
    participant E as Embed
    participant S as Vector Store

    U->>E: Convert to embedding
    E->>S: Insert vector + metadata
    U->>S: Query with search embedding
    S->>U: Return most similar vectors

Key Operations

Insert: Add vectors with optional metadata (IDs, text, tags)
Query: Find nearest neighbors to a query vector
Update: Modify existing vectors or metadata
Delete: Remove vectors from the index

Common Use Cases

Vector stores power:

Semantic search — Find documents by meaning, not keywords
Recommendation engines — Suggest similar items or content
Retrieval-Augmented Generation (RAG) — Provide relevant context to LLMs
Deduplication — Identify similar or duplicate content
Classification — Group content by semantic similarity