VantaDB × LlamaIndex

LlamaIndex BasePydanticVectorStore adapter for VantaDB.

Install

Not on PyPI yet. The vantadb-llamaindex package builds and passes twine check locally; it will go live with the first adapters-v* tag release. Until then, install from source.

# Today, from a repo checkout
cd integrations/llamaindex && pip install .

Install from PyPI (after first release)

pip install vantadb-llamaindex

Quickstart

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.openai import OpenAIEmbedding
from vantadb_llamaindex import VantaDBVectorStore

# Create vector store
vector_store = VantaDBVectorStore(
    db_path="./my_data",
    namespace="docs",
)

# Create index from documents
documents = SimpleDirectoryReader("./data").load_data()
embed_model = OpenAIEmbedding(model="text-embedding-3-small")

index = VectorStoreIndex.from_documents(
    documents,
    embed_model=embed_model,
    vector_store=vector_store,
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is VantaDB?")
print(response)

API

• add(nodes) — add pre-embedded nodes to the store
• delete(ref_doc_id) — delete all nodes from a document
• query(query) — vector + hybrid search
• get_nodes(node_ids) — retrieve nodes by ID
• clear() — remove all records

Why VantaDB?

• Embedded & local-first: the storage engine is a Rust library embedded in your process — no server to deploy, no network hop; data lives in your filesystem.
• Persistent hybrid search: vectors + BM25 text search out of the box, where LlamaIndex's SimpleVectorStore covers only small single-process sessions.
• Zero-setup alternative to hosted stacks: unlike Zep (requires a server) or Cognee (spins up its own knowledge-graph runtime), VantaDB is a plain library you import.

Development

pip install -e .