llama-index-vector-stores-actian-vectorai 0.1.0

llama-index vector_stores actianvectorai integration

LlamaIndex Vector Store Integration: Actian VectorAI

A LlamaIndex vector store backend powered by Actian VectorAI DB.

Installation

pip install llama-index-vector-stores-actian-vectorai

Requirements

• Python 3.10+
• A running Actian VectorAI DB instance (default endpoint: localhost:6574)

Usage

Basic (synchronous, context manager)

from llama_index.vector_stores.actian_vectorai import ActianVectorAIVectorStore
from llama_index.core.vector_stores.types import VectorStoreQuery

with ActianVectorAIVectorStore() as vector_store:
    # Add nodes
    vector_store.add(nodes)

    # Query
    query = VectorStoreQuery(query_embedding=embedding, similarity_top_k=5)
    result = vector_store.query(query)

    # Delete by source document ID
    vector_store.delete("doc_01")

    # Delete the entire collection
    vector_store.clear()

Async (context manager)

async with ActianVectorAIVectorStore() as vector_store:
    await vector_store.async_add(nodes)

    query = VectorStoreQuery(query_embedding=embedding, similarity_top_k=5)
    result = await vector_store.aquery(query)

    await vector_store.adelete("doc_01")
    await vector_store.aclear()

Manual connection management

vector_store = ActianVectorAIVectorStore()

vector_store.connect()
vector_store.add(nodes)
result = vector_store.query(query)
vector_store.close()

Async equivalent:

await vector_store.aconnect()
await vector_store.async_add(nodes)
result = await vector_store.aquery(query)
await vector_store.aclose()

External client

Pass a pre-configured VectorAIClient (or AsyncVectorAIClient) when you need to share a connection or supply custom client configuration:

from actian_vectorai import VectorAIClient, AsyncVectorAIClient

# Sync
with VectorAIClient("localhost:6574") as client:
    vector_store = ActianVectorAIVectorStore(client=client)
    vector_store.add(nodes)
    result = vector_store.query(query)

# Async
async with AsyncVectorAIClient("localhost:6574") as async_client:
    vector_store = ActianVectorAIVectorStore(async_client=async_client)
    await vector_store.async_add(nodes)
    result = await vector_store.aquery(query)

Constructor Parameters

Parameter	Type	Default	Description
url	str	"localhost:6574"	Actian VectorAI DB endpoint (host:port). Ignored when explicit clients are provided.
collection_name	str	"llama_index_collection"	Collection to use for storing vectors and metadata.
dense_vector_name	str	"llama_index_dense_vector"	Name of the dense vector field inside the collection.
dense_vector_params	VectorParams | None	None	Vector configuration (size, distance metric). Inferred from the first inserted embedding if omitted (defaults to cosine distance).
stores_text	bool	False	Store node text in the point payload in addition to metadata.
clear_existing_collection	bool	False	Delete any existing collection with the same name before the first operation.
client_kwargs	dict | None	None	Extra keyword arguments forwarded to internally created sync/async clients.
collection_kwargs	dict | None	None	Extra keyword arguments passed to collection creation. Do not include vectors_config; it is derived from dense_vector_name and dense_vector_params.
client	VectorAIClient | None	None	Pre-configured synchronous client. When provided, url and client_kwargs are ignored.
async_client	AsyncVectorAIClient | None	None	Pre-configured asynchronous client. Must be a different instance from the internal async client of a provided client.

Custom Vector Configuration

from actian_vectorai import VectorParams, Distance

with ActianVectorAIVectorStore(
    url="localhost:6574",
    collection_name="my_collection",
    dense_vector_name="dense_vector",
    dense_vector_params=VectorParams(size=1536, distance=Distance.Cosine),
) as vector_store:
    vector_store.add(nodes)

Metadata Filtering

Metadata filters can be used with query, delete_nodes, and adelete_nodes.

Supported filter operators

Operator	Notes
EQ	Exact match (string or numeric)
NE	Not equal
GT / LT	Numeric greater/less than
GTE / LTE	Numeric greater/less than or equal
IN	Match any value in a list (or comma-separated string)
NIN	Match none of the values in a list (or comma-separated string)
TEXT_MATCH	Case-sensitive substring/token match
IS_EMPTY	Field is absent or null

Unsupported operators (ANY, ALL, TEXT_MATCH_INSENSITIVE, CONTAINS) raise NotImplementedError.

Filter conditions

AND, OR, and NOT conditions are supported via FilterCondition.

from llama_index.core.vector_stores.types import (
    FilterCondition,
    FilterOperator,
    MetadataFilter,
    MetadataFilters,
    VectorStoreQuery,
)

# AND condition
query = VectorStoreQuery(
    query_embedding=embedding,
    similarity_top_k=5,
    filters=MetadataFilters(
        filters=[
            MetadataFilter(
                key="category", operator=FilterOperator.EQ, value="ai"
            ),
            MetadataFilter(
                key="score", operator=FilterOperator.GTE, value=0.7
            ),
        ],
        condition=FilterCondition.AND,
    ),
)

# Delete nodes matching a filter
vector_store.delete_nodes(
    filters=MetadataFilters(
        filters=[
            MetadataFilter(
                key="category",
                operator=FilterOperator.IN,
                value=["ai", "energy"],
            ),
        ]
    )
)

Limitations

• Only VectorStoreQueryMode.DEFAULT (dense vector search) is supported.