langchain_community.vectorstores.astradb.AstraDB

class langchain_community.vectorstores.astradb.AstraDB(*, embedding: Embeddings, collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[Any] = None, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, pre_delete_collection: bool = False)

Wrapper around DataStax Astra DB for vector-store workloads.

To use it, you need a recent installation of the astrapy library and an Astra DB cloud database.

For quickstart and details, visit:

docs.datastax.com/en/astra/home/astra.html

Example

      from langchain_community.vectorstores import AstraDB
      from langchain_community.embeddings.openai import OpenAIEmbeddings

      embeddings = OpenAIEmbeddings()
      vectorstore = AstraDB(
        embedding=embeddings,
        collection_name="my_store",
        token="AstraCS:...",
        api_endpoint="https://<DB-ID>-us-east1.apps.astra.datastax.com"
      )

      vectorstore.add_texts(["Giraffes", "All good here"])
      results = vectorstore.similarity_search("Everything's ok", k=1)

Constructor Args (only keyword-arguments accepted):
    embedding (Embeddings): embedding function to use.
    collection_name (str): name of the Astra DB collection to create/use.
    token (Optional[str]): API token for Astra DB usage.
    api_endpoint (Optional[str]): full URL to the API endpoint,
        such as "https://<DB-ID>-us-east1.apps.astra.datastax.com".
    astra_db_client (Optional[Any]): *alternative to token+api_endpoint*,
        you can pass an already-created 'astrapy.db.AstraDB' instance.
    namespace (Optional[str]): namespace (aka keyspace) where the
        collection is created. 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).

Advanced arguments (coming with sensible defaults):
    batch_size (Optional[int]): Size of batches for bulk insertions.
    bulk_insert_batch_concurrency (Optional[int]): Number of threads
        to insert batches concurrently.
    bulk_insert_overwrite_concurrency (Optional[int]): Number of
        threads in a batch to insert pre-existing entries.
    bulk_delete_concurrency (Optional[int]): Number of threads
        (for deleting multiple rows concurrently).
    pre_delete_collection (Optional[bool]): whether to delete the collection
        before creating it. If False and the collection already exists,
        the collection will be used as is.

A note on concurrency: 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.

Create an AstraDB vector store object. See class docstring for help.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(*, embedding, collection_name[, ...])	Create an AstraDB vector store object.
aadd_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])	Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)	Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids, ...])	Run texts through the embeddings and add them to the vectorstore.
adelete([ids])	Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])	Return VectorStore initialized from texts and embeddings.
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)	Return docs most similar to query using specified search type.
asimilarity_search(query[, k])	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)	Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)	Run similarity search with distance asynchronously.
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 (str).
delete_collection()	Completely delete the collection from the database (as opposed to 'clear()', which empties it only).
from_documents(documents, embedding, **kwargs)	Create an Astra DB vectorstore from a document list.
from_texts(texts, embedding[, metadatas, ids])	Create an Astra DB vectorstore from raw texts.
max_marginal_relevance_search(query[, k, ...])	Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :type query: str :param k: Number of Documents to return. :type k: int = 4 :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :type fetch_k: int = 20 :param lambda_mult: 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. Optional. :type lambda_mult: float = 0.5.
max_marginal_relevance_search_by_vector(...)	Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param lambda_mult: 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.
search(query, search_type, **kwargs)	Return docs most similar to query using 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])	Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)	Return docs most similar to embedding vector.
similarity_search_with_score_id(query[, k, ...])
similarity_search_with_score_id_by_vector(...)	Return docs most similar to embedding vector.

__init__(*, embedding: Embeddings, collection_name: str, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[Any] = None, namespace: Optional[str] = None, metric: Optional[str] = None, batch_size: Optional[int] = None, bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, pre_delete_collection: bool = False) → None

Create an AstraDB vector store object. See class docstring for help.

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

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]

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

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

Run more documents through the embeddings and add to the vectorstore.

Parameters

(List[Document] (documents) – Documents to add to the vectorstore.

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, *, batch_size: Optional[int] = None, batch_concurrency: Optional[int] = None, overwrite_concurrency: Optional[int] = None, **kwargs: Any) → 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 (Optional[List[dict]], optional) – Optional list of metadatas.

• ids (Optional[List[str]], optional) – Optional list of ids.

• batch_size (Optional[int]) – Number of documents in each API call. Check the underlying Astra DB HTTP API specs for the max value (20 at the time of writing this). If not provided, defaults to the instance-level setting.

• batch_concurrency (Optional[int]) – number of threads to process insertion batches concurrently. Defaults to instance-level setting if not provided.

• overwrite_concurrency (Optional[int]) – number of threads to process pre-existing documents in each batch (which require individual API calls). Defaults to instance-level setting if not provided.

A note on metadata: there are constraints on the allowed field names in this dictionary, coming from the underlying Astra DB API. For instance, the $ (dollar sign) cannot be used in the dict keys. See this document for details:

docs.datastax.com/en/astra-serverless/docs/develop/dev-with-json.html

Returns

List of ids of the added texts.

Return type

List[str]

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – Other keyword arguments that subclasses might use.

Returns

True if deletion is successful, False otherwise, None if not implemented.

Return type

Optional[bool]

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

as_retriever(**kwargs: Any) → VectorStoreRetriever

Return VectorStoreRetriever initialized from this VectorStore.

Parameters

• 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]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_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], asynchronously.

0 is dissimilar, 1 is most similar.

Parameters

• query – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  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)

async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

Run similarity search with distance asynchronously.

clear() → None

Empty the collection of all its stored entries.

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

Delete by vector ids.

Parameters

• ids (Optional[List[str]]) – List of ids to delete.

• concurrency (Optional[int]) – max number of threads issuing single-doc delete requests. Defaults to instance-level setting.

Returns

True if deletion is successful,

False otherwise, None if not implemented.

Return type

Optional[bool]

delete_by_document_id(document_id: str) → bool

Remove a single document from the store, given its document_id (str). Return True if a document has indeed been deleted, False if ID not found.

delete_collection() → None

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.

classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → ADBVST

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 AstraDB vectorstore.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → ADBVST

Create an Astra DB vectorstore from raw texts.

Parameters

• texts (List[str]) – the texts to insert.

• embedding (Embeddings) – the embedding function to use in the store.

• metadatas (Optional[List[dict]]) – metadata dicts for the texts.

• ids (Optional[List[str]]) – ids to associate to the texts.

• arguments* (*Additional) – you can pass any argument that you would to ‘add_texts’ and/or to the ‘AstraDB’ class constructor (see these methods for details). These arguments will be routed to the respective methods as they are.

Returns

an AstraDb vectorstore.

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

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param query: Text to look up documents similar to. :type query: str :param k: Number of Documents to return. :type k: int = 4 :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :type fetch_k: int = 20 :param lambda_mult: 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. Optional.

Returns

List of Documents selected by maximal marginal relevance.

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

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. :param fetch_k: Number of Documents to fetch to pass to MMR algorithm. :param lambda_mult: 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.

Returns

List of Documents selected by maximal marginal relevance.

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

Return docs most similar to query using specified search type.

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

Return docs most similar to query.

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

Return docs most similar to embedding vector.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query vector.

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 – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  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)

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

Run similarity search with distance.

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

Return docs most similar to embedding vector.

Parameters

• embedding (str) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

Returns

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

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

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

Return docs most similar to embedding vector.

Parameters

• embedding (str) – Embedding to look up documents similar to.

• k (int) – Number of Documents to return. Defaults to 4.

Returns

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