Tony20100/langchain_full huggingface dataset:
langchain_couchbase.vectorstores.CouchbaseVectorStore¶
class langchain_couchbase.vectorstores.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)[source]¶
Couchbase vector store.
To use it, you need
a Couchbase database with a pre-defined Search index with support forvector fields
Example
from langchain_couchbase 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.
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)
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
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.
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[source]¶
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
documents (List[Document]) – Documents to add to the vectorstore.
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
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
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
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
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, **kwargs: Any) → List[str][source]¶
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]
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.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → CouchbaseVectorStore[source]¶
Construct a Couchbase vector store from a list of texts.
Example
from langchain_couchbase 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.
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.
Return type
List[Document]
similarity_search(query: str, k: int = 4, search_options: Optional[Dict[str, Any]] = {}, **kwargs: Any) → List[Document][source]¶
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][source]¶
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.
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]][source]¶
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
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]][source]¶
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¶
Couchbase
langchain_community.vectorstores.pinecone.Pinecone¶
class langchain_community.vectorstores.pinecone.Pinecone(index: Any, embedding: Union[Embeddings, Callable], text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
[Deprecated] Pinecone vector store.
To use, you should have the pinecone-client python package installed.
This version of Pinecone is deprecated. Please use langchain_pinecone.Pinecone
instead.
Notes
Deprecated since version 0.0.18.
Initialize with Pinecone client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(index, embedding, text_key[, ...])
Initialize with Pinecone client.
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 more texts through the embeddings and add 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 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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids, delete_all, namespace, filter])
Delete by vector IDs or filter.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_existing_index(index_name, embedding[, ...])
Load pinecone vectorstore from index name.
from_texts(texts, embedding[, metadatas, ...])
DEPRECATED: use langchain_pinecone.PineconeVectorStore.from_texts instead: Construct Pinecone wrapper from raw documents.
get_pinecone_index(index_name[, pool_threads])
Return a Pinecone Index instance.
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, filter, namespace])
Return pinecone documents most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(...)
Return pinecone documents most similar to embedding, along with scores.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return pinecone documents most similar to query, along with scores.
Parameters
index (Any) – 
embedding (Union[Embeddings, Callable]) – 
text_key (str) – 
namespace (Optional[str]) – 
distance_strategy (Optional[DistanceStrategy]) – 
__init__(index: Any, embedding: Union[Embeddings, Callable], text_key: str, namespace: Optional[str] = None, distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE)[source]¶
Initialize with Pinecone client.
Parameters
index (Any) – 
embedding (Union[Embeddings, Callable]) – 
text_key (str) – 
namespace (Optional[str]) – 
distance_strategy (Optional[DistanceStrategy]) – 
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
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]) – Iterable of strings to add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
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
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
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, namespace: Optional[str] = None, batch_size: int = 32, embedding_chunk_size: int = 1000, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Upsert optimization is done by chunking the embeddings and upserting them.
This is done to avoid memory issues and optimize using HTTP based embeddings.
For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index,
embedding_chunk_size>1000 and batch_size~64 for best performance.
:param texts: Iterable of strings to add to the vectorstore.
:param metadatas: Optional list of metadatas associated with the texts.
:param ids: Optional list of ids to associate with the texts.
:param namespace: Optional pinecone namespace to add the texts to.
:param batch_size: Batch size to use when adding the texts to the vectorstore.
:param embedding_chunk_size: Chunk size to use when embedding the texts.
Returns
:param embedding_chunk_size: Chunk size to use when embedding the texts.
Returns
List of ids from adding the texts into the vectorstore.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
ids (Optional[List[str]]) – 
namespace (Optional[str]) – 
batch_size (int) – 
embedding_chunk_size (int) – 
kwargs (Any) – 
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]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
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]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
Returns
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]]¶
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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, delete_all: Optional[bool] = None, namespace: Optional[str] = None, filter: Optional[dict] = None, **kwargs: Any) → None[source]¶
Delete by vector IDs or filter.
:param ids: List of ids to delete.
:param filter: Dictionary of conditions to filter vectors to delete.
Parameters
ids (Optional[List[str]]) – 
delete_all (Optional[bool]) – 
namespace (Optional[str]) – 
filter (Optional[dict]) – 
kwargs (Any) – 
Return type
None