Vector Store

Vector database implementations for semantic search, embeddings storage, and AI-powered features in Bifrost.

Overview

The VectorStore is a core component of Bifrost’s framework package that provides a unified interface for vector database operations. It enables plugins to store embeddings, perform similarity searches, and build AI-powered features like semantic caching, content recommendations, and knowledge retrieval.

Key Capabilities:

• Vector Similarity Search: Find semantically similar content using embeddings
• Namespace Management: Organize data into separate collections with custom schemas
• Flexible Filtering: Query data with complex filters and pagination
• Multiple Backends: Support for Weaviate, Redis/Valkey-compatible, Qdrant, and Pinecone vector stores
• High Performance: Optimized for production workloads
• Scalable Storage: Handle millions of vectors with efficient indexing

VectorStore Interface Usage

Creating Namespaces

Create collections (namespaces) with custom schemas:

// Define properties for your data
properties := map[string]vectorstore.VectorStoreProperties{
    "content": {
        DataType:    vectorstore.VectorStorePropertyTypeString,
        Description: "The main content text",
    },
    "category": {
        DataType:    vectorstore.VectorStorePropertyTypeString,
        Description: "Content category",
    },
    "tags": {
        DataType:    vectorstore.VectorStorePropertyTypeStringArray,
        Description: "Content tags",
    },
}

// Create namespace
err := store.CreateNamespace(ctx, "my_content", 1536, properties)
if err != nil {
    log.Fatal("Failed to create namespace:", err)
}

Storing Data with Embeddings

Add data with vector embeddings for similarity search:

// Your embedding data (typically from an embedding model)
embedding := []float32{0.1, 0.2, 0.3 } // example 3-dimensional vector

// Metadata associated with this vector
metadata := map[string]interface{}{
    "content":  "This is my content text",
    "category": "documentation",
    "tags":     []string{"guide", "tutorial"},
}

// Store in vector database
err := store.Add(ctx, "my_content", "unique-id-123", embedding, metadata)
if err != nil {
    log.Fatal("Failed to add data:", err)
}

Similarity Search

Find similar content using vector similarity:

// Query embedding (from user query)
queryEmbedding := []float32{0.15, 0.25, 0.35, ...}

// Optional filters
filters := []vectorstore.Query{
    {
        Field:    "category",
        Operator: vectorstore.QueryOperatorEqual,
        Value:    "documentation",
    },
}

// Perform similarity search
results, err := store.GetNearest(
    ctx,
    "my_content",        // namespace
    queryEmbedding,      // query vector
    filters,             // optional filters
    []string{"content", "category"}, // fields to return
    0.7,                 // similarity threshold (0-1)
    10,                  // limit
)

for _, result := range results {
    fmt.Printf("Score: %.3f, Content: %s\n", *result.Score, result.Properties["content"])
}

Data Retrieval and Management

Query and manage stored data:

// Get specific item by ID
item, err := store.GetChunk(ctx, "my_content", "unique-id-123")
if err != nil {
    log.Fatal("Failed to get item:", err)
}

// Get all items with filtering and pagination
allResults, cursor, err := store.GetAll(
    ctx,
    "my_content",
    []vectorstore.Query{
        {Field: "category", Operator: vectorstore.QueryOperatorEqual, Value: "documentation"},
    },
    []string{"content", "tags"}, // select fields
    nil,  // cursor for pagination
    50,   // limit
)

// Delete items
err = store.Delete(ctx, "my_content", "unique-id-123")

Supported Vector Stores

Weaviate

Production-ready vector database with gRPC support.

Redis / Valkey

High-performance in-memory vector store.

Qdrant

Rust-based vector search engine with advanced filtering.

Pinecone

Managed vector database with serverless options.

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Use Cases

Semantic Caching

Build intelligent caching systems that understand query intent rather than just exact matches.

Applications:

• Customer support systems with FAQ matching
• Code completion and documentation search
• Content management with semantic deduplication

Knowledge Base & Search

Create intelligent search systems that understand user queries contextually.

Applications:

• Document search and retrieval systems
• Product recommendation engines
• Research paper and knowledge discovery platforms

Content Classification

Automatically categorize and tag content based on semantic similarity.

Applications:

• Email classification and routing
• Content moderation and filtering
• News article categorization and clustering

Recommendation Systems

Build personalized recommendation engines using vector similarity.

Applications:

• Product recommendations based on user preferences
• Content suggestions for media platforms
• Similar document or article recommendations

Related Documentation

Topic	Documentation	Description
Framework Overview	What is Framework	Understanding the framework package and VectorStore interface
Semantic Caching	Semantic Caching	Using VectorStore for AI response caching