haive.core.models.vectorstore

Haive Vector Store Module.

This module provides comprehensive abstractions and implementations for working with vector stores in the Haive framework. Vector stores are specialized databases optimized for storing and retrieving high-dimensional vectors, typically used for similarity search in RAG (Retrieval-Augmented Generation) applications.

Vector stores enable efficient semantic search by storing document embeddings and providing fast similarity-based retrieval. They are essential components for building RAG systems, recommendation engines, and other applications that require similarity search over large document collections.

Supported Vector Store Providers:

• Chroma: Open-source vector database with local and server modes

• Pinecone: Managed vector database service

• Weaviate: Open-source vector search engine

• Qdrant: Vector similarity search engine

• FAISS: Facebook AI Similarity Search (local/in-memory)

• Milvus: Open-source vector database

• OpenSearch: Elasticsearch-based vector search

• Redis: Redis with vector search capabilities

• Supabase: PostgreSQL with vector extensions (pgvector)

• MongoDB Atlas: MongoDB with vector search

• LanceDB: Serverless vector database

• Marqo: Tensor search engine

Key Components:

• Base Classes: Abstract base classes for vector store configurations

• Provider Types: Enumeration of supported vector store providers

• Configuration: Provider-specific configuration classes with validation

• Factory Functions: Simplified creation of vector store instances

• Security: Secure handling of connection strings and API keys

• Performance: Connection pooling and caching optimizations

Typical usage example:

from haive.core.models.vectorstore import VectorStoreConfig, VectorStoreProvider

# Configure a vector store
config = VectorStoreConfig(
    provider=VectorStoreProvider.CHROMA,
    collection_name="documents",
    persist_directory="./chroma_db",
    embedding_function=embedding_config
)

# Create the vector store
vectorstore = config.instantiate()

# Add documents
vectorstore.add_texts(["Document content"], metadatas=[{"source": "doc1"}])

# Search for similar documents
results = vectorstore.similarity_search("query text", k=5)

Architecture:

Vector stores in Haive follow a consistent configuration pattern:

1. Provider Selection: Choose from supported vector store providers

2. Configuration: Set provider-specific parameters (endpoints, credentials, etc.)

3. Instantiation: Create the actual vector store instance

4. Operations: Add documents, search, update, delete operations

All vector stores support the same core operations for consistency, while provider-specific features are available through configuration.

Performance Considerations:

• Index Type: Different providers support different index types (HNSW, IVF, etc.)

• Batch Operations: Use batch operations for better performance when adding many documents

• Connection Pooling: Configured automatically for cloud providers

• Caching: In-memory caching for frequently accessed embeddings

See provider-specific documentation for optimization guidelines.

Examples

Local vector store for development:

config = VectorStoreConfig(
    provider=VectorStoreProvider.CHROMA,
    persist_directory="./local_db"
)

Cloud vector store for production:

config = VectorStoreConfig(
    provider=VectorStoreProvider.PINECONE,
    api_key_env_var="PINECONE_API_KEY",
    environment="us-west1-gcp",
    index_name="production-index"
)

Vector store with custom embeddings:

from haive.core.models.embeddings import OpenAIEmbeddingConfig

embedding_config = OpenAIEmbeddingConfig(model="text-embedding-3-small")

config = VectorStoreConfig(
    provider=VectorStoreProvider.WEAVIATE,
    url="http://localhost:8080",
    embedding_config=embedding_config
)

VectorStoreConfig(*[, name, ...])	Configuration model for a vector store.
VectorStoreProvider(*values)	Enumeration of supported vector store providers.

Functions

add_document(*args, **kwargs)

Placeholder function.

Package Contents

haive.core.models.vectorstore.add_document(*args, **kwargs)

Placeholder function.