langchain_community.vectorstores.couchbase.CouchbaseVectorStore

class langchain_community.vectorstores.couchbase.CouchbaseVectorStore(cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, embedding: Embeddings, index_name: str, *, text_key: Optional[str] = 'text', embedding_key: Optional[str] = 'embedding', scoped_index: bool = True)

Couchbase Vector Store vector store.

To use it, you need - a recent installation of the couchbase library - a Couchbase database with a pre-defined Search index with support for

vector fields

Example

from langchain_community.vectorstores import CouchbaseVectorStore
from langchain_openai import OpenAIEmbeddings

from couchbase.cluster import Cluster
from couchbase.auth import PasswordAuthenticator
from couchbase.options import ClusterOptions
from datetime import timedelta

auth = PasswordAuthenticator(username, password)
options = ClusterOptions(auth)
connect_string = "couchbases://localhost"
cluster = Cluster(connect_string, options)

# Wait until the cluster is ready for use.
cluster.wait_until_ready(timedelta(seconds=5))

embeddings = OpenAIEmbeddings()

vectorstore = CouchbaseVectorStore(
    cluster=cluster,
    bucket_name="",
    scope_name="",
    collection_name="",
    embedding=embeddings,
    index_name="vector-index",
)

vectorstore.add_texts(["hello", "world"])
results = vectorstore.similarity_search("ola", k=1)

Initialize the Couchbase Vector Store.

Parameters

• cluster (Cluster) – couchbase cluster object with active connection.

• bucket_name (str) – name of bucket to store documents in.

• scope_name (str) – name of scope in the bucket to store documents in.

• collection_name (str) – name of collection in the scope to store documents in

• embedding (Embeddings) – embedding function to use.

• index_name (str) – name of the Search index to use.

• text_key (optional[str]) – key in document to use as text. Set to text by default.

• embedding_key (optional[str]) – key in document to use for the embeddings. Set to embedding by default.

• scoped_index (optional[bool]) – specify whether the index is a scoped index. Set to True by default.

Attributes

DEFAULT_BATCH_SIZE
embeddings	Return the query embedding object.

Methods

__init__(cluster, bucket_name, scope_name, ...)	Initialize the Couchbase Vector Store.
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, batch_size])	Run texts through the embeddings and persist in 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.
delete([ids])	Delete documents from the vector store by ids.
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])	Construct a Couchbase vector store from a list of texts.
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 specified search type.
similarity_search(query[, k, search_options])	Return documents most similar to embedding vector with their scores.
similarity_search_by_vector(embedding[, k, ...])	Return documents that are most similar to the vector embedding.
similarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])	Return documents that are most similar to the query with their scores.
similarity_search_with_score_by_vector(embedding)	Return docs most similar to embedding vector with their scores.

__init__(cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, embedding: Embeddings, index_name: str, *, text_key: Optional[str] = 'text', embedding_key: Optional[str] = 'embedding', scoped_index: bool = True) → None

Initialize the Couchbase Vector Store.

Parameters

• cluster (Cluster) – couchbase cluster object with active connection.

• bucket_name (str) – name of bucket to store documents in.

• scope_name (str) – name of scope in the bucket to store documents in.

• collection_name (str) – name of collection in the scope to store documents in

• embedding (Embeddings) – embedding function to use.

• index_name (str) – name of the Search index to use.

• text_key (optional[str]) – key in document to use as text. Set to text by default.

• embedding_key (optional[str]) – key in document to use for the embeddings. Set to embedding by default.

• scoped_index (optional[bool]) – specify whether the index is a scoped index. Set to True by default.

Return type

None

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.

• documents (List[Document]) –

• kwargs (Any) –

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.

Parameters

• texts (Iterable[str]) –

• metadatas (Optional[List[dict]]) –

• kwargs (Any) –

Return type

List[str]

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.

• documents (List[Document]) –

• kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, ids: Optional[List[str]] = None, batch_size: Optional[int] = None, **kwargs: Any) → List[str]

Run texts through the embeddings and persist in vectorstore.

If the document IDs are passed, the existing documents (if any) will be overwritten with the new ones.

Parameters

• texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

• metadatas (Optional[List[Dict]]) – Optional list of metadatas associated with the texts.

• ids (Optional[List[str]]) – Optional list of ids associated with the texts. IDs have to be unique strings across the collection. If it is not specified uuids are generated and used as ids.

• batch_size (Optional[int]) – Optional batch size for bulk insertions. Default is 100.

• kwargs (Any) –

Returns

List of ids from adding the texts into the vectorstore.

Return type

List[str]

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

Delete by vector ID or other criteria.

Parameters

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

• **kwargs (Any) – 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.

Parameters

• documents (List[Document]) –

• embedding (Embeddings) –

• kwargs (Any) –

Return type

VST

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

Return VectorStore initialized from texts and embeddings.

Parameters

• texts (List[str]) –

• embedding (Embeddings) –

• metadatas (Optional[List[dict]]) –

• kwargs (Any) –

Return type

VST

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.

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

Parameters

• query (str) – Text to look up documents similar to.

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

• 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. Defaults to 0.5.

• kwargs (Any) –

Returns

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, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Parameters

• embedding (List[float]) –

• k (int) –

• fetch_k (int) –

• lambda_mult (float) –

• kwargs (Any) –

Return type

List[Document]

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

• kwargs (Any) –

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.

Parameters

• query (str) –

• search_type (str) –

• kwargs (Any) –

Return type

List[Document]

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

Return docs most similar to query.

Parameters

• query (str) –

• k (int) –

• kwargs (Any) –

Return type

List[Document]

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

Return docs most similar to embedding vector.

Parameters

• embedding (List[float]) –

• k (int) –

• kwargs (Any) –

Return type

List[Document]

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

Run similarity search with distance asynchronously.

Parameters

• args (Any) –

• kwargs (Any) –

Return type

List[Tuple[Document, float]]

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

Delete documents from the vector store by ids.

Parameters

• ids (List[str]) – List of IDs of the documents to delete.

• batch_size (Optional[int]) – Optional batch size for bulk deletions.

• kwargs (Any) –

Returns

True if all the documents were deleted successfully, False otherwise.

Return type

bool

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

Return VectorStore initialized from documents and embeddings.

Parameters

• documents (List[Document]) –

• embedding (Embeddings) –

• kwargs (Any) –

Return type

VST

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

Construct a Couchbase vector store from a list of texts.

Example

from langchain_community.vectorstores import CouchbaseVectorStore from langchain_openai import OpenAIEmbeddings

from couchbase.cluster import Cluster from couchbase.auth import PasswordAuthenticator from couchbase.options import ClusterOptions from datetime import timedelta

auth = PasswordAuthenticator(username, password) options = ClusterOptions(auth) connect_string = “couchbases://localhost” cluster = Cluster(connect_string, options)

# Wait until the cluster is ready for use. cluster.wait_until_ready(timedelta(seconds=5))

embeddings = OpenAIEmbeddings()

texts = [“hello”, “world”]

vectorstore = CouchbaseVectorStore.from_texts(

texts, embedding=embeddings, cluster=cluster, bucket_name=””, scope_name=””, collection_name=””, index_name=”vector-index”,

)

Parameters

• texts (List[str]) – list of texts to add to the vector store.

• embedding (Embeddings) – embedding function to use.

• metadatas (optional[List[Dict]) – list of metadatas to add to documents.

• **kwargs – Keyword arguments used to initialize the vector store with and/or passed to add_texts method. Check the constructor and/or add_texts for the list of accepted arguments.

Returns

A Couchbase vector store.

Return type

CouchbaseVectorStore

max_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.

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

Parameters

• query (str) – Text to look up documents similar to.

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

• 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. Defaults to 0.5.

• kwargs (Any) –

Returns

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, **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.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

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

• 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. Defaults to 0.5.

• kwargs (Any) –

Returns

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 specified search type.

Parameters

• query (str) –

• search_type (str) –

• kwargs (Any) –

Return type

List[Document]

similarity_search(query: str, k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any) → List[Document]

Return documents most similar to embedding vector with their scores.

Parameters

• query (str) – Query to look up for similar documents

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

• search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary

• fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index.

• kwargs (Any) –

Returns

List of Documents most similar to the query.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any) → List[Document]

Return documents that are most similar to the vector embedding.

Parameters

• embedding (List[float]) – Embedding to look up documents similar to.

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

• search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.

• fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to document text and metadata fields.

• kwargs (Any) –

Returns

List of Documents most similar to the query.

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, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any) → List[Tuple[Document, float]]

Return documents that are most similar to the query with their scores.

Parameters

• query (str) – Query to look up for similar documents

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

• search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.

• fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to text and metadata fields.

• kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float]]

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to embedding vector with their scores.

Parameters

• embedding (List[float]) – Embedding vector to look up documents similar to.

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

• search_options (Optional[Dict[str, Any]]) – Optional search options that are passed to Couchbase search. Defaults to empty dictionary.

• fields (Optional[List[str]]) – Optional list of fields to include in the metadata of results. Note that these need to be stored in the index. If nothing is specified, defaults to all the fields stored in the index.

• kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float]]

Examples using CouchbaseVectorStore

• Wait until the cluster is ready for use.