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Vector Stores (maticlib.vectorstores)

Manage and query high-dimensional embeddings across different backend engines.

Supported Engines

  • InMemoryVectorIndex: Great for quick, lightweight local testing via Numpy.
  • ChromaVectorIndex: Ephemeral or persistent ChromaDB instances.
  • MilvusVectorIndex: Scalable PyMilvus (Lite or Server modes).
  • PineconeVectorIndex: Connects directly to Pinecone Cloud.
  • QdrantVectorIndex: Uses Qdrant Cloud or local instances.
  • SchemaVectorIndex: A wrapper explicitly for storing database schemas (DDLs) for Text2SQL workflows.
from maticlib.vectorstores.chroma import ChromaVectorIndex
from maticlib.embeddings.openai import OpenAIEmbeddings

vector_index = ChromaVectorIndex(
    embeddings=OpenAIEmbeddings(),
    persist_directory="./chroma_db"
)
results = vector_index.similarity_search("How do I fix this error?", k=3)