> ## Documentation Index > Fetch the complete documentation index at: https://actianvectorai-docs-low-effort-fixes.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # LlamaIndex > Use Actian VectorAI DB as a vector store in LlamaIndex for building RAG pipelines, semantic search, and AI-powered applications. [LlamaIndex](https://www.llamaindex.ai/) is a data framework for building LLM applications over external data. It provides tools for ingesting, structuring, and querying data, making it straightforward to connect large language models with diverse data sources. Actian VectorAI DB integrates with LlamaIndex as a vector store through the `llama-index-vector-stores-actian-vectorai` package. This integration supports all standard LlamaIndex vector store operations, including adding nodes, similarity search, metadata filtering, and both synchronous and asynchronous workflows. ## Installation Install the VectorAI DB vector store integration for LlamaIndex: ```bash theme={null} pip install llama-index-vector-stores-actian-vectorai ``` You also need LlamaIndex core and an embedding provider: ```bash theme={null} pip install llama-index-core llama-index-embeddings-openai ``` ## Requirements Before using this integration, make sure your environment meets the following prerequisites: * Python 3.10–3.12 * A running Actian VectorAI DB instance (default endpoint: `localhost:6574`) ## Quickstart The `ActianVectorAIVectorStore` uses a context manager to handle connection lifecycle automatically. Vector configuration is inferred from the first inserted embedding if not specified. The following example creates text nodes with embeddings, stores them in VectorAI DB, and performs a similarity search: ```python theme={null} from llama_index.vector_stores.actian_vectorai import ActianVectorAIVectorStore from llama_index.core.schema import TextNode from llama_index.core.vector_stores.types import VectorStoreQuery # Create nodes with embeddings. nodes = [ TextNode(text="VectorAI DB supports semantic search.", embedding=[0.1, 0.2, 0.3]), TextNode(text="LlamaIndex builds RAG pipelines.", embedding=[0.4, 0.5, 0.6]), ] with ActianVectorAIVectorStore() as vector_store: # Add nodes to the vector store. vector_store.add(nodes) # Query the vector store with an embedding. query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=5) result = vector_store.query(query) ``` ## Connection management The integration supports several connection patterns for managing client lifecycle. The examples in this section assume you have `nodes` and `query` objects as shown in the [Quickstart](#quickstart) above. ### Context manager (recommended) Use a context manager for automatic connection handling: ```python theme={null} with ActianVectorAIVectorStore(url="localhost:6574") as vector_store: vector_store.add(nodes) result = vector_store.query(query) ``` ### Manual connection For fine-grained control over connection lifecycle: ```python theme={null} vector_store = ActianVectorAIVectorStore() vector_store.connect() vector_store.add(nodes) result = vector_store.query(query) vector_store.close() ``` ### External client Pass a pre-configured `VectorAIClient` when you need to share a connection or supply custom client configuration: ```python theme={null} from actian_vectorai import VectorAIClient with VectorAIClient("localhost:6574") as client: vector_store = ActianVectorAIVectorStore(client=client) vector_store.add(nodes) result = vector_store.query(query) ``` When an external client is provided, `url` and `client_kwargs` are ignored. The caller is responsible for managing the client's lifecycle. ## Async operations All operations have async counterparts for non-blocking workflows. Async methods use `AsyncVectorAIClient` under the hood. The examples in this section use the same `nodes` setup as the [Quickstart](#quickstart). ### Async context manager Use an async context manager for automatic connection handling: ```python theme={null} from llama_index.vector_stores.actian_vectorai import ActianVectorAIVectorStore from llama_index.core.vector_stores.types import VectorStoreQuery async with ActianVectorAIVectorStore() as vector_store: await vector_store.async_add(nodes) query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=5) result = await vector_store.aquery(query) await vector_store.adelete("doc_01") await vector_store.aclear() ``` ### Async manual connection For fine-grained control over async connection lifecycle: ```python theme={null} vector_store = ActianVectorAIVectorStore() await vector_store.aconnect() await vector_store.async_add(nodes) query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=5) result = await vector_store.aquery(query) await vector_store.aclose() ``` ### Async external client Pass a pre-configured `AsyncVectorAIClient` when you need to share an async connection: ```python theme={null} from actian_vectorai import AsyncVectorAIClient async with AsyncVectorAIClient("localhost:6574") as async_client: vector_store = ActianVectorAIVectorStore(async_client=async_client) await vector_store.async_add(nodes) query = VectorStoreQuery(query_embedding=[0.1, 0.2, 0.3], similarity_top_k=5) result = await vector_store.aquery(query) ``` The `async_client` must be a different instance from the internal async client of a provided sync `client`. ## Deleting data Remove nodes from the vector store using document IDs, metadata filters, or by clearing the entire collection. ### Delete by source document ID Remove all nodes associated with a source document: ```python theme={null} with ActianVectorAIVectorStore() as vector_store: vector_store.delete("doc_01") ``` ### Delete with metadata filters Remove nodes matching specific metadata conditions: ```python theme={null} from llama_index.core.vector_stores.types import ( FilterOperator, MetadataFilter, MetadataFilters, ) vector_store.delete_nodes( filters=MetadataFilters( filters=[ MetadataFilter( key="category", operator=FilterOperator.IN, value=["ai", "energy"], ), ] ) ) ``` ### Clear collection Delete the entire collection: ```python theme={null} with ActianVectorAIVectorStore() as vector_store: vector_store.clear() ``` ## Custom vector configuration Specify explicit vector parameters instead of relying on auto-detection: ```python theme={null} 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) ``` When `dense_vector_params` is omitted, vector configuration is inferred from the first inserted embedding and defaults to cosine distance. ## Metadata filtering Metadata filters can be used with `query`, `delete_nodes`, and `adelete_nodes` to narrow results based on payload fields. ### Supported filter operators The following operators are supported for metadata filtering: | Operator | Description | | ------------- | --------------------------------------------------------------- | | `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 through `FilterCondition`: ```python theme={null} from llama_index.core.vector_stores.types import ( FilterCondition, FilterOperator, MetadataFilter, MetadataFilters, VectorStoreQuery, ) # AND condition query = VectorStoreQuery( query_embedding=[0.1, 0.2, 0.3], 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, ), ) result = vector_store.query(query) ``` ## Configuration The following table lists the parameters you can pass when creating an `ActianVectorAIVectorStore` instance. | 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`. | ## API reference The following table lists all available methods and their async counterparts. | Method | Async variant | Description | | ---------------- | ----------------- | ----------------------------------------- | | `add()` | `async_add()` | Add nodes to the vector store. | | `query()` | `aquery()` | Query the vector store with an embedding. | | `delete()` | `adelete()` | Delete nodes by source document ID. | | `delete_nodes()` | `adelete_nodes()` | Delete nodes matching metadata filters. | | `clear()` | `aclear()` | Delete the entire collection. | | `connect()` | `aconnect()` | Manually open a connection. | | `close()` | `aclose()` | Manually close a connection. | ## Limitations The following limitations apply to the current version of this integration: * `get_nodes()` and `aget_nodes()` are not implemented (pending scroll API support in the Actian VectorAI client). * Only `VectorStoreQueryMode.DEFAULT` (dense vector search) is supported. *** ## Next steps Explore related topics to get the most out of VectorAI DB with LlamaIndex: * [LangChain](/docs/integrations/langchain) — Alternative framework for RAG pipelines. * [Search](/docs/fundamentals/search/search) — Understand the underlying vector search operations. * [Filtering](/docs/fundamentals/filtering/filtering) — Apply metadata conditions to narrow search results.