langchain-actian-vectorai 1.0.0

An integration package connecting Actian VectorAI DB and LangChain

langchain-actian-vectorai

LangChain VectorStore integration for Actian VectorAI DB.

Installation

pip install langchain-actian-vectorai

Quick Start

from actian_vectorai import VectorAIClient, VectorParams, Distance
from langchain_actian_vectorai import ActianVectorAIVectorStore
from langchain_openai import OpenAIEmbeddings

client = VectorAIClient("localhost:6574")
client.connect()

if client.collections.exists(collection_name):
    client.collections.delete(collection_name)
    
client.collections.create(
    "my_collection",
    vectors_config=VectorParams(size=4096, distance=Distance.Cosine),
)

store = ActianVectorAIVectorStore(
    client=client,
    collection_name="my_collection",
    embedding=OpenAIEmbeddings(),
)

ids = store.add_texts(["hello world", "goodbye world"])
results = store.similarity_search("hello", k=1)

Note: Document IDs can be provided as UUID strings. If not provided, a UUID will be automatically generated for each document.

Create from Texts

Use from_texts to create a vector store, set up a collection, and add texts in a single call. IDs can be provided as UUID strings; if omitted, UUIDs are automatically generated:

from uuid import uuid4

store = ActianVectorAIVectorStore.from_texts(
    texts=["the cat sat on the mat", "the dog played in the park"],
    embedding=OpenAIEmbeddings(),
    metadatas=[{"source": "book"}, {"source": "article"}],
    ids=[uuid4().hex, uuid4().hex],  # optional — UUIDs auto-generated if omitted
    collection_name="my_collection",
    url="localhost:6574",
)

Create from Documents

Use from_documents to create a vector store from LangChain Document objects. Document IDs can be provided as UUID strings via Document.id; if not provided, UUIDs are automatically generated:

from langchain_core.documents import Document
from uuid import uuid4

docs = [
    Document(page_content="foo", metadata={"baz": "bar"}, id=uuid4().hex),
    Document(page_content="thud", metadata={"bar": "baz"}),  # UUID auto-generated
]
store = ActianVectorAIVectorStore.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    collection_name="my_collection",
    url="localhost:6574",
)

Connect to Existing Collection

Use from_existing_collection to connect to a collection that already exists on the server, without adding any data. The collection is verified to exist before returning:

store = ActianVectorAIVectorStore.from_existing_collection(
    collection_name="my_collection",
    embedding=OpenAIEmbeddings(),
    url="localhost:6574",
)

# The store is ready for search, retrieval, and mutation
results = store.similarity_search("hello", k=3)
ids = store.add_texts(["new document"])

Custom Payload Keys

By default, document content is stored under "page_content" and metadata under "metadata" in the VectorAI point payload:

{
    "page_content": "Lorem ipsum dolor sit amet",
    "metadata": {
        "foo": "bar"
    }
}

You can override these keys with content_payload_key and metadata_payload_key to work with collections that use a different payload schema — for example, a legacy collection or one shared with another system:

# Connect with non-standard payload keys
store = ActianVectorAIVectorStore.from_existing_collection(
    collection_name="legacy_archive",
    embedding=OpenAIEmbeddings(),
    url="localhost:6574",
    content_payload_key="blog_text",
    metadata_payload_key="extra_info",
)

# All operations honour the custom keys automatically
results = store.similarity_search("hello", k=3)
ids = store.add_texts(
    ["new post"],
    metadatas=[{"author": "Alice"}],
)
docs = store.get_by_ids(ids)

The custom keys are supported across all class methods (from_texts, from_documents, afrom_texts, afrom_documents, construct_instance, from_existing_collection) and all search / retrieval operations.

Note: content_payload_key and metadata_payload_key must be different. Passing the same value for both raises ValueError.

Async: Create from Texts

Use afrom_texts to asynchronously create a vector store, set up a collection, and add texts. This uses AsyncVectorAIClient under the hood for non-blocking operations:

store = await ActianVectorAIVectorStore.afrom_texts(
    texts=["the cat sat on the mat", "the dog played in the park"],
    embedding=OpenAIEmbeddings(),
    metadatas=[{"source": "book"}, {"source": "article"}],
    collection_name="my_collection",
    url="localhost:6574",
)

# The returned store supports all async operations
results = await store.asimilarity_search("cat", k=2)

Async: Create from Documents

Use afrom_documents to asynchronously create a vector store from LangChain Document objects. Document IDs are preserved when set:

from langchain_core.documents import Document

docs = [
    Document(page_content="foo", metadata={"baz": "bar"}, id="a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"),
    Document(page_content="thud", metadata={"bar": "baz"}, id="b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5"),
]
store = await ActianVectorAIVectorStore.afrom_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    collection_name="my_collection",
    url="localhost:6574",
)

# Search, delete, and retrieve all work asynchronously
results = await store.asimilarity_search("foo", k=1)
await store.adelete(ids=["a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4"])

Get by IDs

Retrieve documents by their IDs:

# Retrieve documents by ID
docs = store.get_by_ids(["a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4", "b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5"])
for doc in docs:
    print(doc.page_content, doc.metadata)

# Returns an empty list if no IDs are provided or none found
docs = store.get_by_ids([])

# Async variant
docs = await store.aget_by_ids(["a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4", "b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5"])

Delete by Filter

Remove documents matching a metadata filter without knowing their IDs. Accepts the same dict-style filters used by search methods, or a native Filter object from actian_vectorai.FilterBuilder:

# Dict-style filter — delete all documents with category "obsolete"
store.delete_by_filter({"category": "obsolete"})

# Comparison operators work too
store.delete_by_filter({"year": {"$lt": 2020}})

# Native FilterBuilder for advanced conditions
from actian_vectorai import Field, FilterBuilder

f = FilterBuilder().must(Field("metadata.category").eq("C")).build()
store.delete_by_filter(f)

# Async variant
await store.adelete_by_filter({"status": "archived"})

Note: Passing None as the filter raises ValueError to prevent accidental deletion of all documents.

Similarity Search

# Basic search (returns list of Document)
results = store.similarity_search("hello", k=4)

# General search (returns list of Document, supports all search types)
results = store.search("hello", k=4)

# Search with scores
results = store.similarity_search_with_score("hello", k=4)
for doc, score in results:
    print(f"[{score:.3f}] {doc.page_content}")

# Search with relevance scores (normalized to [0, 1])
results = store.similarity_search_with_relevance_scores("hello", k=4)

# Search by embedding vector with scores
vec = embeddings.embed_query("hello")
results = store.similarity_search_with_score_by_vector(vec, k=4)
for doc, score in results:
    print(f"[{score:.3f}] {doc.page_content}")

# Async search
results = await store.asimilarity_search("hello", k=4)
results = await store.asearch("hello", k=4)
results = await store.asimilarity_search_with_score("hello", k=4)
results = await store.asimilarity_search_with_relevance_scores("hello", k=4)
results = await store.asimilarity_search_with_score_by_vector(vec, k=4)

Score Threshold

All search methods accept an optional score_threshold parameter to filter out results below a minimum similarity score:

# Only return results with score >= 0.8
results = store.similarity_search("hello", k=10, score_threshold=0.8)

# Works with all search variants
results = store.similarity_search_with_score("hello", k=10, score_threshold=0.8)
results = store.max_marginal_relevance_search("hello", k=4, score_threshold=0.7)

# Async variants too
results = await store.asimilarity_search("hello", k=10, score_threshold=0.8)

Search Parameters (HNSW Tuning & Exact Search)

All search methods accept an optional search_params parameter to control low-level search behaviour. Pass a SearchParams object from the actian_vectorai package:

from actian_vectorai import SearchParams

# Search with custom HNSW ef (higher = more accurate, slower)
results = store.similarity_search(
    "hello", k=5,
    search_params=SearchParams(hnsw_ef=256),
)

# Exact (brute-force) search — bypasses HNSW index for 100% recall
results = store.similarity_search(
    "hello", k=5,
    search_params=SearchParams(exact=True),
)

# Combine with filter and score threshold
results = store.similarity_search(
    "hello", k=5,
    filter={"topic": "ml"},
    score_threshold=0.7,
    search_params=SearchParams(hnsw_ef=128),
)

# Works with all search variants
results = store.similarity_search_with_score(
    "hello", k=5, search_params=SearchParams(exact=True),
)
results = store.max_marginal_relevance_search(
    "hello", k=4, fetch_k=20,
    search_params=SearchParams(hnsw_ef=256),
)

# Async variants
results = await store.asimilarity_search(
    "hello", k=5, search_params=SearchParams(exact=True),
)

Parameter	Type	Default	Description
hnsw_ef	int | None	None	Search-time ef for HNSW (higher = more accurate)
exact	bool | None	None	Force exact brute-force search (bypasses index)
quantization	QuantizationSearchParams | None	None	Quantization search config
indexed_only	bool | None	None	Only search indexed segments

Metadata Filtering

Filter search results by document metadata using a simple dict syntax. Each key-value pair becomes an AND condition that matches against the stored metadata fields:

# Add documents with metadata (e.g., topic and year fields)
store.add_texts(
    [
        "Python is a popular programming language",
        "Machine learning transforms data into insights",
        "Vector databases enable semantic search",
        "Neural networks learn hierarchical features",
        "SQL is the language of relational databases",
    ],
    metadatas=[
        {"topic": "programming", "year": 2024},
        {"topic": "ml", "year": 2024},
        {"topic": "databases", "year": 2024},
        {"topic": "ml", "year": 2023},
        {"topic": "databases", "year": 2020},
    ],
)

# Filter by a single metadata field
results = store.similarity_search(
    "data science",
    k=3,
    filter={"topic": "ml"},
)

# Filter by multiple fields (AND semantics)
results = store.similarity_search(
    "data science",
    k=3,
    filter={"topic": "ml", "year": 2024},
)

# Works with all search methods
results = store.similarity_search_with_score(
    "learning", k=3, filter={"topic": "ml"}
)
results = store.max_marginal_relevance_search(
    "learning", k=3, fetch_k=10, filter={"topic": "ml"}
)

# Async variants also support dict filters
results = await store.asimilarity_search("learning", k=3, filter={"topic": "ml"})

Supported value types: strings, integers, booleans. Nested dicts and lists are also supported for complex metadata structures.

Comparison Operators

Use operator dicts to express richer conditions on a single field:

Operator	Meaning	Example
$eq	Equal	{"topic": {"$eq": "ml"}}
$ne	Not equal	{"topic": {"$ne": "databases"}}
$gt	Greater than	{"year": {"$gt": 2020}}
$gte	Greater than or equal	{"year": {"$gte": 2023}}
$lt	Less than	{"year": {"$lt": 2024}}
$lte	Less than or equal	{"year": {"$lte": 2023}}
$in	In list	{"topic": {"$in": ["ml", "db"]}}
$nin	Not in list	{"topic": {"$nin": ["sql", "html"]}}
$text	Full-text substring	{"desc": {"$text": "wireless"}}
$between	Inclusive range	{"price": {"$between": [10, 100]}}
$range	Range with keyword bounds	{"price": {"$range": {"gte": 10, "lt": 100}}}
$values_count	Array length bounds	{"tags": {"$values_count": {"gte": 1, "lte": 5}}}
$datetime_gt	Datetime >	{"created": {"$datetime_gt": dt}}
$datetime_gte	Datetime >=	{"created": {"$datetime_gte": dt}}
$datetime_lt	Datetime <	{"created": {"$datetime_lt": dt}}
$datetime_lte	Datetime <=	{"created": {"$datetime_lte": dt}}
$datetime_between	Datetime range	{"created": {"$datetime_between": [start, end]}}

Operators can be combined on the same field for range queries:

# Documents with year between 2022 and 2024 (inclusive)
results = store.similarity_search(
    "learning", k=3, filter={"year": {"$gte": 2022, "$lte": 2024}}
)

# Same range, using $between shorthand
results = store.similarity_search(
    "learning", k=3, filter={"year": {"$between": [2022, 2024]}}
)

# Arbitrary range with keyword bounds (half-open interval)
results = store.similarity_search(
    "item", k=3, filter={"price": {"$range": {"gte": 50, "lt": 200}}}
)

# Full-text substring match on a metadata field
results = store.similarity_search(
    "audio", k=3, filter={"description": {"$text": "wireless"}}
)

# Documents where topic is NOT "databases"
results = store.similarity_search(
    "learning", k=3, filter={"topic": {"$ne": "databases"}}
)

# Documents where topic is one of "ml" or "programming"
results = store.similarity_search(
    "learning", k=3, filter={"topic": {"$in": ["ml", "programming"]}}
)

Datetime Operators

Filter by datetime fields using datetime objects:

from datetime import datetime, timezone

# Documents created after a specific date
results = store.similarity_search(
    "recent", k=3,
    filter={"created": {"$datetime_gte": datetime(2024, 1, 1, tzinfo=timezone.utc)}},
)

# Documents created within a date range
results = store.similarity_search(
    "recent", k=3,
    filter={"created": {"$datetime_between": [
        datetime(2024, 1, 1, tzinfo=timezone.utc),
        datetime(2025, 1, 1, tzinfo=timezone.utc),
    ]}},
)

Array Operators

# Filter by number of values in an array field
results = store.similarity_search(
    "tagged", k=3,
    filter={"tags": {"$values_count": {"gte": 2, "lte": 5}}},
)

Logical Operators

Combine conditions with $and, $or, and $not:

# OR — match documents where topic is "ml" OR topic is "databases"
results = store.similarity_search(
    "data science", k=5,
    filter={"$or": [{"topic": "ml"}, {"topic": "databases"}]},
)

# AND — explicitly combine conditions (equivalent to multi-key dict)
results = store.similarity_search(
    "learning", k=3,
    filter={"$and": [{"topic": "ml"}, {"year": {"$gte": 2023}}]},
)

# NOT — exclude documents matching a condition
results = store.similarity_search(
    "learning", k=3,
    filter={"$not": {"topic": "databases"}},
)

# Nested logical operators
results = store.similarity_search(
    "data", k=5,
    filter={
        "$and": [
            {"$or": [{"topic": "ml"}, {"topic": "programming"}]},
            {"year": {"$gte": 2023}},
        ]
    },
)

# $min_should — at least N of the listed conditions must match
results = store.similarity_search(
    "data", k=5,
    filter={
        "$min_should": {
            "conditions": [
                {"topic": "ml"},
                {"topic": "databases"},
                {"topic": "programming"},
            ],
            "min_count": 2,
        }
    },
)

Standalone Conditions

Top-level filter conditions that operate on point-level properties rather than individual metadata fields:

Operator	Meaning	Example
$has_id	Point ID in list	{"$has_id": ["a1b2c3d4...", "b2c3d4e5..."]}
$has_vector	Point has named vector	{"$has_vector": "text"}
$is_empty	Metadata field is empty	{"$is_empty": "tags"}
$nested	Nested object match	{"$nested": {"key": "addr", "filter": {...}}}

# Filter by specific point IDs
results = store.similarity_search(
    "doc", k=3,
    filter={"$has_id": ["a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4", "b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5", "c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6"]},
)

# Filter for points that have a specific named vector
results = store.similarity_search(
    "doc", k=3,
    filter={"$has_vector": "text"},
)

# Filter for points where a metadata field is empty or missing
results = store.similarity_search(
    "doc", k=3,
    filter={"$is_empty": "tags"},
)

# Nested object match — filter inside a nested metadata structure
results = store.similarity_search(
    "doc", k=3,
    filter={"$nested": {"key": "address", "filter": {"city": "Paris"}}},
)

# Combine standalone conditions with field operators
results = store.similarity_search(
    "item", k=5,
    filter={
        "$has_id": ["a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4", "b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5"],
        "price": {"$between": [50, 200]},
    },
)

Native FilterBuilder

You can also pass a pre-built VectorAI Filter object (from actian_vectorai.FilterBuilder) directly for full access to the filter DSL — including should (OR), must_not (NOT), range, and other advanced operators:

from actian_vectorai import Field, FilterBuilder

# Range filter: topic is "ml" AND year >= 2023
f = (
    FilterBuilder()
    .must(Field("metadata.topic").eq("ml"))
    .must(Field("metadata.year").gte(2023))
    .build()
)
results = store.similarity_search("learning", k=3, filter=f)

# OR via should: match "ml" or "databases"
f = (
    FilterBuilder()
    .should(Field("metadata.topic").eq("ml"))
    .should(Field("metadata.topic").eq("databases"))
    .build()
)
results = store.similarity_search("data science", k=3, filter=f)

# NOT via must_not: exclude "programming"
f = (
    FilterBuilder()
    .must_not(Field("metadata.topic").eq("programming"))
    .build()
)
results = store.similarity_search("learning", k=3, filter=f)

# Combined must + must_not
f = (
    FilterBuilder()
    .must(Field("metadata.topic").eq("ml"))
    .must_not(Field("metadata.year").lt(2023))
    .build()
)
results = store.similarity_search("learning", k=3, filter=f)

HNSW and Optimizer Configuration

Tune the HNSW index and optimizer parameters at collection creation time or update them on an existing collection.

At creation time

Pass hnsw_config and/or optimizers_config to any factory method:

from actian_vectorai import HnswConfigDiff, OptimizersConfigDiff

store = ActianVectorAIVectorStore.from_texts(
    ["hello world", "goodbye world"],
    embedding=my_embeddings,
    collection_name="products",
    url="localhost:6574",
    hnsw_config=HnswConfigDiff(ef_construct=200, m=32),
    optimizers_config=OptimizersConfigDiff(indexing_threshold=10000),
)

Also supported in from_documents, afrom_texts, afrom_documents, construct_instance, and aconstruct_instance.

Updating an existing collection

Use update_collection_config() to adjust parameters after creation:

from actian_vectorai import HnswConfigDiff, OptimizersConfigDiff

store.update_collection_config(
    hnsw_config=HnswConfigDiff(ef_construct=200, m=32),
    optimizers_config=OptimizersConfigDiff(indexing_threshold=10000),
)

Parameter	Type	Description
hnsw_config	HnswConfigDiff	HNSW index parameters: ef_construct (build-time search depth), m (graph links per node)
optimizers_config	OptimizersConfigDiff	Optimizer parameters: indexing_threshold (min vectors before HNSW index is built)

Index Type Selection

Choose the index algorithm at collection creation time with index_type. This is a VDE extension — the default is INDEX_TYPE_AUTO.

Available values (actian_vectorai.IndexType):

Value	Description
INDEX_TYPE_AUTO	Server picks the best algorithm (default)
INDEX_TYPE_FLAT	Brute-force exact search — best for small collections
INDEX_TYPE_HNSW	HNSW approximate search — best for large collections

from actian_vectorai import IndexType

# FLAT index for a small collection
store = ActianVectorAIVectorStore.from_texts(
    ["hello world", "goodbye world"],
    embedding=my_embeddings,
    collection_name="small_lookup",
    url="localhost:6574",
    index_type=IndexType.INDEX_TYPE_FLAT,
)

Combine with hnsw_config for fine-tuned HNSW indexing:

from actian_vectorai import HnswConfigDiff, IndexType

store = ActianVectorAIVectorStore.from_texts(
    ["hello world", "goodbye world"],
    embedding=my_embeddings,
    collection_name="products",
    url="localhost:6574",
    index_type=IndexType.INDEX_TYPE_HNSW,
    hnsw_config=HnswConfigDiff(ef_construct=200, m=32),
)

Also supported in from_documents, afrom_texts, afrom_documents, construct_instance, and aconstruct_instance.

Named Vectors

Named vectors allow a single collection to hold multiple vector spaces (e.g. text embeddings and image embeddings) with independent dimensionality and distance metrics. Each ActianVectorAIVectorStore targets one named vector at a time via the vector_name parameter.

Creating a collection with a named vector

When you pass vector_name, the collection is automatically created with a named-vector configuration:

store = ActianVectorAIVectorStore.from_texts(
    ["hello world", "machine learning"],
    embedding=my_embeddings,
    collection_name="products",
    url="localhost:6574",
    vector_name="text",          # store embeddings under the "text" name
)

Creating a multi-vector collection

Use vectors_config to define multiple independent vector spaces upfront:

from actian_vectorai import Distance, VectorAIClient, VectorParams
from langchain_actian_vectorai import ActianVectorAIVectorStore

client = VectorAIClient("localhost:6574")
client.connect()

# Create a collection with two named vector spaces
client.collections.create(
    "multimodal",
    vectors_config={
        "text": VectorParams(size=384, distance=Distance.Cosine),
        "image": VectorParams(size=512, distance=Distance.Euclid),
    },
)

# One store for text embeddings
text_store = ActianVectorAIVectorStore(
    client=client,
    collection_name="multimodal",
    embedding=text_embeddings,
    vector_name="text",
)

# Another store for image embeddings
image_store = ActianVectorAIVectorStore(
    client=client,
    collection_name="multimodal",
    embedding=image_embeddings,
    vector_name="image",
)

Or use from_texts / from_documents with both vector_name and vectors_config:

from actian_vectorai import Distance, VectorParams

store = ActianVectorAIVectorStore.from_documents(
    docs,
    embedding=my_embeddings,
    collection_name="multimodal",
    url="localhost:6574",
    vector_name="text",
    vectors_config={
        "text": VectorParams(size=384, distance=Distance.Cosine),
        "image": VectorParams(size=512, distance=Distance.Euclid),
    },
)

Searching a named vector

All search methods automatically target the configured named vector:

# Similarity search on the "text" vector space
results = text_store.similarity_search("hello", k=5)

# Search with scores
results = text_store.similarity_search_with_score("hello", k=5)

# MMR search
results = text_store.max_marginal_relevance_search("hello", k=4, fetch_k=20)

# Metadata filtering works as usual
results = text_store.similarity_search(
    "hello", k=5, filter={"topic": "ml"}
)

# Async search
results = await text_store.asimilarity_search("hello", k=5)

Connecting to an existing named-vector collection

store = ActianVectorAIVectorStore.from_existing_collection(
    collection_name="multimodal",
    embedding=text_embeddings,
    url="localhost:6574",
    vector_name="text",
)
results = store.similarity_search("query", k=3)

Max Marginal Relevance Search

MMR optimizes for both similarity to the query and diversity among results:

# Standard MMR search
results = store.max_marginal_relevance_search(
    "machine learning",
    k=4,
    fetch_k=20,
    lambda_mult=0.5,
)

# MMR by vector (provide embedding directly)
embedding = store._embed_query("machine learning")
results = store.max_marginal_relevance_search_by_vector(
    embedding,
    k=4,
    fetch_k=20,
    lambda_mult=0.5,
)

# MMR with scores — returns (Document, score) tuples
results = store.max_marginal_relevance_search_with_score_by_vector(
    embedding,
    k=4,
    fetch_k=20,
    lambda_mult=0.5,
)
for doc, score in results:
    print(f"[{score:.3f}] {doc.page_content}")

# Async MMR
results = await store.amax_marginal_relevance_search(
    "machine learning", k=4, fetch_k=20,
)
# Async MMR by vector
embedding = await store._aembed_query("machine learning")
results = await store.amax_marginal_relevance_search_by_vector(
    embedding,
    k=4,
    fetch_k=20,
    lambda_mult=0.5,
)
# Async MMR with scores
results = await store.amax_marginal_relevance_search_with_score_by_vector(
    embedding,
    k=4,
    fetch_k=20,
    lambda_mult=0.5,
)

Use as Retriever

# Create a retriever from the vector store
retriever = store.as_retriever(
    search_type="mmr",
    search_kwargs={"k": 4, "fetch_k": 20, "lambda_mult": 0.5},
)
docs = retriever.invoke("machine learning")

# Async retriever
docs = await retriever.ainvoke("machine learning")

API Reference

Method	Async	Description
construct_instance()	aconstruct_instance()	Low-level factory: connect, create collection, return store
from_texts()	afrom_texts()	Create store, collection, and add texts
from_documents()	afrom_documents()	Create store, collection, and add documents
from_existing_collection()		Connect to an existing collection (no data added)
add_texts()	aadd_texts()	Add texts to existing store
add_documents()	aadd_documents()	Add documents to existing store
delete()	adelete()	Delete by IDs
delete_by_filter()	adelete_by_filter()	Delete by metadata filter
get_by_ids()	aget_by_ids()	Retrieve documents by IDs
search()	asearch()	General search (all types, returns Document)
similarity_search()	asimilarity_search()	Search by query text
similarity_search_with_score()	asimilarity_search_with_score()	Search with scores
similarity_search_by_vector()	asimilarity_search_by_vector()	Search by embedding
similarity_search_with_score_by_vector()	asimilarity_search_with_score_by_vector()	Search by embedding with scores
similarity_search_with_relevance_scores()	asimilarity_search_with_relevance_scores()	Search with normalized relevance scores
max_marginal_relevance_search()	amax_marginal_relevance_search()	MMR search (by text)
max_marginal_relevance_search_by_vector()	amax_marginal_relevance_search_by_vector()	MMR search by embedding
max_marginal_relevance_search_with_score_by_vector()	amax_marginal_relevance_search_with_score_by_vector()	MMR search by embedding with scores
as_retriever()		Convert store to Retriever interface
update_collection_config()		Update HNSW / optimizer config on the collection
close()		Close client connections and release resources

Resource Management

The vector store supports Python's context manager protocol for automatic resource cleanup:

# Context manager — connections are closed automatically on exit
with ActianVectorAIVectorStore(
    client=client,
    collection_name="my_collection",
    embedding=my_embeddings,
) as store:
    ids = store.add_texts(["hello world"])
    results = store.similarity_search("hello", k=1)
# client connections are closed here

# Manual cleanup
store = ActianVectorAIVectorStore(
    client=client,
    collection_name="my_collection",
    embedding=my_embeddings,
)
try:
    results = store.similarity_search("hello", k=1)
finally:
    store.close()

Low-Level Factory: construct_instance

construct_instance (and its async counterpart aconstruct_instance) handle client connection and collection setup without adding any data. from_texts and from_documents use this internally:

# Sync
store = ActianVectorAIVectorStore.construct_instance(
    embedding=my_embeddings,
    url="localhost:6574",
    collection_name="my_collection",
    distance="COSINE",
    force_recreate=True,
)

# Async
store = await ActianVectorAIVectorStore.aconstruct_instance(
    embedding=my_embeddings,
    url="localhost:6574",
    collection_name="my_collection",
    distance="COSINE",
)

Error Handling

All VectorAI client errors are wrapped in ActianVectorAIException, which can be imported from the package:

from langchain_actian_vectorai import ActianVectorAIException

try:
    results = store.similarity_search("hello", k=4)
except ActianVectorAIException as exc:
    print(f"VectorAI error: {exc}")
    if exc.original:
        print(f"Caused by: {exc.original}")

Configuration

Parameter	Default	Description
url	"localhost:6574"	VectorAI server gRPC address
collection_name	Auto-generated UUID	Collection name
distance	"COSINE"	Distance metric: COSINE, EUCLID (or EUCLIDEAN), DOT
content_payload_key	"page_content"	Payload key for document content
metadata_payload_key	"metadata"	Payload key for document metadata
batch_size	64	Batch size for upsert operations
force_recreate	False	Recreate collection if it exists
vector_name	None	Named vector to target for upserts and searches
vectors_config	None	Pre-built named-vectors config dict ({name: VectorParams})
hnsw_config	None	HnswConfigDiff for HNSW index tuning at creation time
optimizers_config	None	OptimizersConfigDiff for optimizer tuning at creation time
index_type	None	IndexType enum for index algorithm selection at creation time (VDE extension)