AstraDBVectorStore#

class langchain_astradb.vectorstores.AstraDBVectorStore(*, collection_name: str, embedding: Embeddings | None = None, token: str | TokenProvider | None = None, api_endpoint: str | None = None, environment: str | None = None, astra_db_client: AstraDB | None = None, async_astra_db_client: AsyncAstraDB | None = None, namespace: str | None = None, metric: str | None = None, batch_size: int | None = None, bulk_insert_batch_concurrency: int | None = None, bulk_insert_overwrite_concurrency: int | None = None, bulk_delete_concurrency: int | None = None, setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False, metadata_indexing_include: Iterable[str] | None = None, metadata_indexing_exclude: Iterable[str] | None = None, collection_indexing_policy: Dict[str, Any] | None = None, collection_vector_service_options: CollectionVectorServiceOptions | None = None, collection_embedding_api_key: str | EmbeddingHeadersProvider | None = None)[source]#

AstraDB vector store integration.

Setup:

Install langchain-astradb and head to the [AstraDB website](https://astra.datastax.com), create an account, create a new database and [create an application token](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#generate-application-token).

pip install -qU langchain-astradb

Key init args — indexing params:

collection_name: str: Name of the collection.
embedding: Embeddings: Embedding function to use.

Key init args — client params:

api_endpoint: str: AstraDB API endpoint.
token: str: API token for Astra DB usage.
namespace: Optional[str]: Namespace (aka keyspace) where the collection is created

# TODO: Replace with relevant init params. Instantiate:

Get your API endpoint and application token from the dashboard of your database.

import getpass
from langchain_astradb import AstraDBVectorStore
from langchain_openai import OpenAIEmbeddings

ASTRA_DB_API_ENDPOINT = getpass.getpass("ASTRA_DB_API_ENDPOINT = ")
ASTRA_DB_APPLICATION_TOKEN = getpass.getpass("ASTRA_DB_APPLICATION_TOKEN = ")

vector_store = AstraDBVectorStore(
    collection_name="astra_vector_langchain",
    embedding=OpenAIEmbeddings(),
    api_endpoint=ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

Add Documents:

from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")

documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)

Delete Documents:

vector_store.delete(ids=["3"])

Search:

results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* thud [{'bar': 'baz'}]

Search with filter:

results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* thud [{'bar': 'baz'}]

Search with score:

results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

* [SIM=0.916135] foo [{'baz': 'bar'}]

Async:

# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)

# delete documents
# await vector_store.adelete(ids=["3"])

# search
# results = vector_store.asimilarity_search(query="thud",k=1)

# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

* [SIM=0.916135] foo [{'baz': 'bar'}]

Use as Retriever:

retriever = vector_store.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 1, "score_threshold": 0.5},
)
retriever.invoke("thud")

[Document(metadata={'bar': 'baz'}, page_content='thud')]

Wrapper around DataStax Astra DB for vector-store workloads.

For quickstart and details, visit https://docs.datastax.com/en/astra/astra-db-vector/

Parameters:

embedding (Optional[Embeddings]) – the embeddings function or service to use. This enables client-side embedding functions or calls to external embedding providers. If embedding is provided, arguments collection_vector_service_options and collection_embedding_api_key cannot be provided.
collection_name (str) – name of the Astra DB collection to create/use.
token (Optional[Union[str, TokenProvider]]) – API token for Astra DB usage, either in the form of a string or a subclass of astrapy.authentication.TokenProvider. If not provided, the environment variable ASTRA_DB_APPLICATION_TOKEN is inspected.
api_endpoint (Optional[str]) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided, the environment variable ASTRA_DB_API_ENDPOINT is inspected.
environment (Optional[str]) – a string specifying the environment of the target Data API. If omitted, defaults to “prod” (Astra DB production). Other values are in astrapy.constants.Environment enum class.
astra_db_client (Optional[AstraDBClient]) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).
async_astra_db_client (Optional[AsyncAstraDBClient]) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).
namespace (Optional[str]) – namespace (aka keyspace) where the collection is created. If not provided, the environment variable ASTRA_DB_KEYSPACE is inspected. Defaults to the database’s “default namespace”.
metric (Optional[str]) – similarity function to use out of those available in Astra DB. If left out, it will use Astra DB API’s defaults (i.e. “cosine” - but, for performance reasons, “dot_product” is suggested if embeddings are normalized to one).
batch_size (Optional[int]) – Size of document chunks for each individual insertion API request. If not provided, astrapy defaults are applied.
bulk_insert_batch_concurrency (Optional[int]) – Number of threads or coroutines to insert batches concurrently.
bulk_insert_overwrite_concurrency (Optional[int]) – Number of threads or coroutines in a batch to insert pre-existing entries.
bulk_delete_concurrency (Optional[int]) – Number of threads or coroutines for multiple-entry deletes.
pre_delete_collection (bool) – whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is.
metadata_indexing_include (Optional[Iterable[str]]) – an allowlist of the specific metadata subfields that should be indexed for later filtering in searches.
metadata_indexing_exclude (Optional[Iterable[str]]) – a denylist of the specific metadata subfields that should not be indexed for later filtering in searches.
collection_indexing_policy (Optional[Dict[str, Any]]) – a full “indexing” specification for what fields should be indexed for later filtering in searches. This dict must conform to to the API specifications (see docs.datastax.com/en/astra/astra-db-vector/api-reference/ data-api-commands.html#advanced-feature-indexing-clause-on-createcollection)
collection_vector_service_options (Optional[CollectionVectorServiceOptions]) – specifies the use of server-side embeddings within Astra DB. If passing this parameter, embedding cannot be provided.
collection_embedding_api_key (Optional[Union[str, EmbeddingHeadersProvider]]) – for usage of server-side embeddings within Astra DB. With this parameter one can supply an API Key that will be passed to Astra DB with each data request. This parameter can be either a string or a subclass of astrapy.authentication.EmbeddingHeadersProvider. This is useful when the service is configured for the collection, but no corresponding secret is stored within Astra’s key management system. This parameter cannot be provided without specifying collection_vector_service_options.
setup_mode (SetupMode)

Note

For concurrency in synchronous add_texts():, as a rule of thumb, on a typical client machine it is suggested to keep the quantity bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency much below 1000 to avoid exhausting the client multithreading/networking resources. The hardcoded defaults are somewhat conservative to meet most machines’ specs, but a sensible choice to test may be:

bulk_insert_batch_concurrency = 80
bulk_insert_overwrite_concurrency = 10

A bit of experimentation is required to nail the best results here, depending on both the machine/network specs and the expected workload (specifically, how often a write is an update of an existing id). Remember you can pass concurrency settings to individual calls to add_texts() and add_documents() as well.

Attributes

embeddings

Accesses the supplied embeddings object.

Methods

`__init__`(*, collection_name[, embedding, ...])	Wrapper around DataStax Astra DB for vector-store workloads.
`aadd_documents`(documents, **kwargs)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas, ids, ...])	Run texts through the embeddings and add them to the vectorstore.
`aclear`()	Empty the collection of all its stored entries.
`add_documents`(documents, **kwargs)	Add or update documents in the vectorstore.
`add_texts`(texts[, metadatas, ids, ...])	Run texts through the embeddings and add them to the vectorstore.
`adelete`([ids, concurrency])	Delete by vector ids.
`adelete_by_document_id`(document_id)	Remove a single document from the store, given its document ID.
`adelete_collection`()	Completely delete the collection from the database (as opposed to `aclear()`, which empties it only).
`afrom_documents`(documents[, embedding])	Create an Astra DB vectorstore from a document list.
`afrom_texts`(texts[, embedding, metadatas, ids])	Create an Astra DB vectorstore from raw texts.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k, filter])	Return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k, ...])	Return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(query[, k, filter])	Return docs most similar to query with score.
`asimilarity_search_with_score_by_vector`(...)	Return docs most similar to embedding vector with score.
`asimilarity_search_with_score_id`(query[, k, ...])	Return docs most similar to the query with score and id.
`asimilarity_search_with_score_id_by_vector`(...)	Return docs most similar to embedding vector with score and id.
`astreaming_upsert`(items, /, batch_size, **kwargs)
`aupsert`(items, /, **kwargs)
`clear`()	Empty the collection of all its stored entries.
`delete`([ids, concurrency])	Delete by vector ids.
`delete_by_document_id`(document_id)	Remove a single document from the store, given its document ID.
`delete_collection`()	Completely delete the collection from the database (as opposed to `clear()`, which empties it only).
`from_documents`(documents[, embedding])	Create an Astra DB vectorstore from a document list.
`from_texts`(texts[, embedding, metadatas, ids])	Create an Astra DB vectorstore from raw texts.
`get_by_ids`(ids, /)	Get documents by their IDs.
`max_marginal_relevance_search`(query[, k, ...])	Return docs selected using the maximal marginal relevance.
`max_marginal_relevance_search_by_vector`(...)	Return docs selected using the maximal marginal relevance.
`search`(query, search_type, **kwargs)	Return docs most similar to query using a specified search type.
`similarity_search`(query[, k, filter])	Return docs most similar to query.
`similarity_search_by_vector`(embedding[, k, ...])	Return docs most similar to embedding vector.
`similarity_search_with_relevance_scores`(query)	Return docs and relevance scores in the range [0, 1].
`similarity_search_with_score`(query[, k, filter])	Return docs most similar to query with score.
`similarity_search_with_score_by_vector`(embedding)	Return docs most similar to embedding vector with score.
`similarity_search_with_score_id`(query[, k, ...])	Return docs most similar to the query with score and id.
`similarity_search_with_score_id_by_vector`(...)	Return docs most similar to embedding vector with score and id.
`streaming_upsert`(items, /, batch_size, **kwargs)
`upsert`(items, /, **kwargs)

__init__(*, collection_name: str, embedding: Embeddings | None = None, token: str | TokenProvider | None = None, api_endpoint: str | None = None, environment: str | None = None, astra_db_client: AstraDB | None = None, async_astra_db_client: AsyncAstraDB | None = None, namespace: str | None = None, metric: str | None = None, batch_size: int | None = None, bulk_insert_batch_concurrency: int | None = None, bulk_insert_overwrite_concurrency: int | None = None, bulk_delete_concurrency: int | None = None, setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False, metadata_indexing_include: Iterable[str] | None = None, metadata_indexing_exclude: Iterable[str] | None = None, collection_indexing_policy: Dict[str, Any] | None = None, collection_vector_service_options: CollectionVectorServiceOptions | None = None, collection_embedding_api_key: str | EmbeddingHeadersProvider | None = None) → None[source]#

Wrapper around DataStax Astra DB for vector-store workloads.

For quickstart and details, visit https://docs.datastax.com/en/astra/astra-db-vector/

Parameters:

embedding (Embeddings | None) – the embeddings function or service to use. This enables client-side embedding functions or calls to external embedding providers. If embedding is provided, arguments collection_vector_service_options and collection_embedding_api_key cannot be provided.
collection_name (str) – name of the Astra DB collection to create/use.
token (str | TokenProvider | None) – API token for Astra DB usage, either in the form of a string or a subclass of astrapy.authentication.TokenProvider. If not provided, the environment variable ASTRA_DB_APPLICATION_TOKEN is inspected.
api_endpoint (str | None) – full URL to the API endpoint, such as https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided, the environment variable ASTRA_DB_API_ENDPOINT is inspected.
environment (str | None) – a string specifying the environment of the target Data API. If omitted, defaults to “prod” (Astra DB production). Other values are in astrapy.constants.Environment enum class.
astra_db_client (AstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).
async_astra_db_client (AsyncAstraDB | None) – DEPRECATED starting from version 0.3.5. Please use ‘token’, ‘api_endpoint’ and optionally ‘environment’. you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance (alternatively to ‘token’, ‘api_endpoint’ and ‘environment’).
namespace (str | None) – namespace (aka keyspace) where the collection is created. If not provided, the environment variable ASTRA_DB_KEYSPACE is inspected. Defaults to the database’s “default namespace”.
metric (str | None) – similarity function to use out of those available in Astra DB. If left out, it will use Astra DB API’s defaults (i.e. “cosine” - but, for performance reasons, “dot_product” is suggested if embeddings are normalized to one).
batch_size (int | None) – Size of document chunks for each individual insertion API request. If not provided, astrapy defaults are applied.
bulk_insert_batch_concurrency (int | None) – Number of threads or coroutines to insert batches concurrently.
bulk_insert_overwrite_concurrency (int | None) – Number of threads or coroutines in a batch to insert pre-existing entries.
bulk_delete_concurrency (int | None) – Number of threads or coroutines for multiple-entry deletes.
pre_delete_collection (bool) – whether to delete the collection before creating it. If False and the collection already exists, the collection will be used as is.
metadata_indexing_include (Iterable[str] | None) – an allowlist of the specific metadata subfields that should be indexed for later filtering in searches.
metadata_indexing_exclude (Iterable[str] | None) – a denylist of the specific metadata subfields that should not be indexed for later filtering in searches.
collection_indexing_policy (Dict[str, Any] | None) – a full “indexing” specification for what fields should be indexed for later filtering in searches. This dict must conform to to the API specifications (see docs.datastax.com/en/astra/astra-db-vector/api-reference/ data-api-commands.html#advanced-feature-indexing-clause-on-createcollection)
collection_vector_service_options (CollectionVectorServiceOptions | None) – specifies the use of server-side embeddings within Astra DB. If passing this parameter, embedding cannot be provided.
collection_embedding_api_key (str | EmbeddingHeadersProvider | None) – for usage of server-side embeddings within Astra DB. With this parameter one can supply an API Key that will be passed to Astra DB with each data request. This parameter can be either a string or a subclass of astrapy.authentication.EmbeddingHeadersProvider. This is useful when the service is configured for the collection, but no corresponding secret is stored within Astra’s key management system. This parameter cannot be provided without specifying collection_vector_service_options.
setup_mode (SetupMode)

Return type:

None

Note

bulk_insert_batch_concurrency = 80
bulk_insert_overwrite_concurrency = 10

async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]#

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, ids: List[str] | None = None, *, batch_size: int | None = None, batch_concurrency: int | None = None, overwrite_concurrency: int | None = None, **kwargs: Any) → List[str][source]#

Run texts through the embeddings and add them to the vectorstore.

If passing explicit ids, those entries whose id is in the store already will be replaced.

Parameters:

texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas.
ids (List[str] | None) – Optional list of ids.
batch_size (int | None) – Size of document chunks for each individual insertion API request. If not provided, defaults to the vector-store overall defaults (which in turn falls to astrapy defaults).
batch_concurrency (int | None) – number of simultaneous coroutines to process insertion batches concurrently. Defaults to the vector-store overall setting if not provided.
overwrite_concurrency (int | None) – number of simultaneous coroutines to process pre-existing documents in each batch. Defaults to the vector-store overall setting if not provided.
kwargs (Any)

Return type:

List[str]

Note

There are constraints on the allowed field names in the metadata dictionaries, coming from the underlying Astra DB API. For instance, the $ (dollar sign) cannot be used in the dict keys. See this document for details: https://docs.datastax.com/en/astra/astra-db-vector/api-reference/data-api.html

Returns:

The list of ids of the added texts.

Parameters:

texts (Iterable[str])
metadatas (List[dict] | None)
ids (List[str] | None)
batch_size (int | None)
batch_concurrency (int | None)
overwrite_concurrency (int | None)
kwargs (Any)

Return type:

List[str]

async aclear() → None[source]#

Empty the collection of all its stored entries.

Return type:: None

add_documents(documents: List[Document], **kwargs: Any) → List[str]#

Add or update documents in the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

Run texts through the embeddings and add them to the vectorstore.

If passing explicit ids, those entries whose id is in the store already will be replaced.

Parameters:

texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas.
ids (List[str] | None) – Optional list of ids.
batch_size (int | None) – Size of document chunks for each individual insertion API request. If not provided, defaults to the vector-store overall defaults (which in turn falls to astrapy defaults).
batch_concurrency (int | None) – number of threads to process insertion batches concurrently. Defaults to the vector-store overall setting if not provided.
overwrite_concurrency (int | None) – number of threads to process pre-existing documents in each batch. Defaults to the vector-store overall setting if not provided.
kwargs (Any)

Return type:

List[str]

Note

Returns:

The list of ids of the added texts.

Parameters:

texts (Iterable[str])
metadatas (List[dict] | None)
ids (List[str] | None)
batch_size (int | None)
batch_concurrency (int | None)
overwrite_concurrency (int | None)
kwargs (Any)

Return type:

List[str]

async adelete(ids: List[str] | None = None, concurrency: int | None = None, **kwargs: Any) → bool | None[source]#

Delete by vector ids.

Parameters:

ids (List[str] | None) – List of ids to delete.
concurrency (int | None) – max number of simultaneous coroutines for single-doc delete requests. Defaults to vector-store overall setting.
kwargs (Any)

Returns:

True if deletion is (entirely) successful, False otherwise.

Return type:

bool | None

async adelete_by_document_id(document_id: str) → bool[source]#

Remove a single document from the store, given its document ID.

Parameters:: document_id (str) – The document ID
Return type:: bool

Returns: True if a document has indeed been deleted, False if ID not found.

async adelete_collection() → None[source]#

Completely delete the collection from the database (as opposed to aclear(), which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution.

Return type:: None

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings | None = None, **kwargs: Any) → AstraDBVectorStore[source]#

Create an Astra DB vectorstore from a document list.

Utility method that defers to ‘afrom_texts’ (see that one).

Args: see ‘afrom_texts’, except here you have to supply ‘documents’: in place of ‘texts’ and ‘metadatas’.

Returns:

an AstraDBVectorStore vectorstore.

Parameters:

documents (List[Document])
embedding (Embeddings | None)
kwargs (Any)

Return type:

AstraDBVectorStore

async classmethod afrom_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, ids: List[str] | None = None, **kwargs: Any) → AstraDBVectorStore[source]#

Create an Astra DB vectorstore from raw texts.

Parameters:

texts (List[str]) – the texts to insert.
metadatas (List[dict] | None) – metadata dicts for the texts.
ids (List[str] | None) – ids to associate to the texts.
**kwargs (Any) – you can pass any argument that you would to aadd_texts() and/or to the ‘AstraDBVectorStore’ constructor (see these methods for details). These arguments will be routed to the respective methods as they are.
embedding (Embeddings | None)

Returns:

an AstraDBVectorStore vectorstore.

Return type:

AstraDBVectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

Added in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents selected by maximal marginal relevance.

Return type:

List[Document]

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents selected by maximal marginal relevance.

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: search_type (Optional[str]): Defines the type of search that

the Retriever should perform. Can be “similarity” (default), “mmr”, or “similarity_score_threshold”.

search_kwargs (Optional[Dict]): Keyword arguments to pass to the

search function. Can include things like:

k: Amount of documents to return (Default: 4) score_threshold: Minimum relevance threshold

for similarity_score_threshold

fetch_k: Amount of documents to pass to MMR algorithm: (Default: 20)
lambda_mult: Diversity of results returned by MMR;: 1 for minimum diversity and 0 for maximum. (Default: 0.5)

filter: Filter by document metadata

Returns:

Retriever class for VectorStore.

Return type:

VectorStoreRetriever

Examples:

# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)

# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)

# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)

# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})

# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]#

Async return docs most similar to query using a specified search type.

Parameters:

query (str) – Input text.
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

async asimilarity_search(query: str, k: int = 4, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to query.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents most similar to the query.

Return type:

List[Document]

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to embedding vector.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents most similar to the query vector.

Return type:

List[Document]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]#

Async return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

filter the resulting set of retrieved docs

Returns:

List of Tuples of (doc, similarity_score)

Return type:

List[Tuple[Document, float]]

async asimilarity_search_with_score(query: str, k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to query with score.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score), the most similar to the query vector.

Return type:

List[Tuple[Document, float]]

async asimilarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to embedding vector with score.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score), the most similar to the query vector.

Return type:

List[Tuple[Document, float]]

async asimilarity_search_with_score_id(query: str, k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float, str]][source]#

Return docs most similar to the query with score and id.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score, id), the most similar to the query.

Return type:

List[Tuple[Document, float, str]]

async asimilarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float, str]][source]#

Return docs most similar to embedding vector with score and id.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score, id), the most similar to the query vector.

Return type:

List[Tuple[Document, float, str]]

astreaming_upsert(items: AsyncIterable[Document], /, batch_size: int, **kwargs: Any) → AsyncIterator[UpsertResponse]#

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion. Async version of streaming_upsert.

Parameters:

items (AsyncIterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead the ID should be provided as part of the Document object.

Yields:

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type:

AsyncIterator[UpsertResponse]

Added in version 0.2.11.

async aupsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse#

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore. Async version of upsert.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

When an ID is specified and the document already exists in the vectorstore, the upsert method should update the document with the new data. If the document does not exist, the upsert method should add the document to the vectorstore.

Parameters:

items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type:

UpsertResponse

Added in version 0.2.11.

clear() → None[source]#

Empty the collection of all its stored entries.

Return type:: None

delete(ids: List[str] | None = None, concurrency: int | None = None, **kwargs: Any) → bool | None[source]#

Delete by vector ids.

Parameters:

ids (List[str] | None) – List of ids to delete.
concurrency (int | None) – max number of threads issuing single-doc delete requests. Defaults to vector-store overall setting.
kwargs (Any)

Returns:

True if deletion is (entirely) successful, False otherwise.

Return type:

bool | None

delete_by_document_id(document_id: str) → bool[source]#

Remove a single document from the store, given its document ID.

Parameters:: document_id (str) – The document ID
Return type:: bool

Returns: True if a document has indeed been deleted, False if ID not found.

delete_collection() → None[source]#

Completely delete the collection from the database (as opposed to clear(), which empties it only). Stored data is lost and unrecoverable, resources are freed. Use with caution.

Return type:: None

classmethod from_documents(documents: List[Document], embedding: Embeddings | None = None, **kwargs: Any) → AstraDBVectorStore[source]#

Create an Astra DB vectorstore from a document list.

Utility method that defers to ‘from_texts’ (see that one).

Args: see ‘from_texts’, except here you have to supply ‘documents’: in place of ‘texts’ and ‘metadatas’.

Returns:

an AstraDBVectorStore vectorstore.

Parameters:

documents (List[Document])
embedding (Embeddings | None)
kwargs (Any)

Return type:

AstraDBVectorStore

classmethod from_texts(texts: List[str], embedding: Embeddings | None = None, metadatas: List[dict] | None = None, ids: List[str] | None = None, **kwargs: Any) → AstraDBVectorStore[source]#

Create an Astra DB vectorstore from raw texts.

Parameters:

texts (List[str]) – the texts to insert.
embedding (Embeddings | None) – the embedding function to use in the store.
metadatas (List[dict] | None) – metadata dicts for the texts.
ids (List[str] | None) – ids to associate to the texts.
**kwargs (Any) – you can pass any argument that you would to add_texts() and/or to the ‘AstraDBVectorStore’ constructor (see these methods for details). These arguments will be routed to the respective methods as they are.

Returns:

an AstraDBVectorStore vectorstore.

Return type:

AstraDBVectorStore

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

Added in version 0.2.11.

max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents selected by maximal marginal relevance.

Return type:

List[Document]

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents selected by maximal marginal relevance.

Return type:

List[Document]

search(query: str, search_type: str, **kwargs: Any) → List[Document]#

Return docs most similar to query using a specified search type.

Parameters:

query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

similarity_search(query: str, k: int = 4, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to query.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents most similar to the query.

Return type:

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

Return docs most similar to embedding vector.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.
kwargs (Any)

Returns:

The list of Documents most similar to the query vector.

Return type:

List[Document]

similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]#

Return docs and relevance scores in the range [0, 1].

0 is dissimilar, 1 is most similar.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) –
kwargs to be passed to similarity search. Should include: score_threshold: Optional, a floating point value between 0 to 1 to

filter the resulting set of retrieved docs.

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

similarity_search_with_score(query: str, k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to query with score.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score), the most similar to the query vector.

Return type:

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float]][source]#

Return docs most similar to embedding vector with score.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score), the most similar to the query vector.

Return type:

List[Tuple[Document, float]]

similarity_search_with_score_id(query: str, k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float, str]][source]#

Return docs most similar to the query with score and id.

Parameters:

query (str) – Query to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score, id), the most similar to the query.

Return type:

List[Tuple[Document, float, str]]

similarity_search_with_score_id_by_vector(embedding: List[float], k: int = 4, filter: Dict[str, Any] | None = None) → List[Tuple[Document, float, str]][source]#

Return docs most similar to embedding vector with score and id.

Parameters:

embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Dict[str, Any] | None) – Filter on the metadata to apply.

Returns:

The list of (Document, score, id), the most similar to the query vector.

Return type:

List[Tuple[Document, float, str]]

streaming_upsert(items: Iterable[Document], /, batch_size: int, **kwargs: Any) → Iterator[UpsertResponse]#

Beta

Added in 0.2.11. The API is subject to change.

Upsert documents in a streaming fashion.

Parameters:

items (Iterable[Document]) – Iterable of Documents to add to the vectorstore.
batch_size (int) – The size of each batch to upsert.
kwargs (Any) – Additional keyword arguments. kwargs should only include parameters that are common to all documents. (e.g., timeout for indexing, retry policy, etc.) kwargs should not include ids to avoid ambiguous semantics. Instead, the ID should be provided as part of the Document object.

Yields:

UpsertResponse – A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type:

Iterator[UpsertResponse]

Added in version 0.2.11.

upsert(items: Sequence[Document], /, **kwargs: Any) → UpsertResponse#

Beta

Added in 0.2.11. The API is subject to change.

Add or update documents in the vectorstore.

The upsert functionality should utilize the ID field of the Document object if it is provided. If the ID is not provided, the upsert method is free to generate an ID for the document.

Parameters:

items (Sequence[Document]) – Sequence of Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

A response object that contains the list of IDs that were successfully added or updated in the vectorstore and the list of IDs that failed to be added or updated.

Return type:

UpsertResponse

Added in version 0.2.11.