Tony20100/langchain_full huggingface dataset:
kwargs (Any) – 
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
The most k similar documents and scores of the specified query.
:param embeddings: embedding vector of the query.
:param k: The k most similar documents to the text query.
:param min_score: the score of similar documents to the text query
Returns
The k most similar documents to the specified text query.
0 is dissimilar, 1 is the most similar.
Parameters
embedding (List[float]) – 
k (int) – 
kwargs (Any) – 
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
The most k similar documents and scores of the specified query.
:param embeddings: embedding vector of the query.
:param k: The k most similar documents to the text query.
:param min_score: the score of similar documents to the text query
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[, metadata, k])
Return docs most similar to query using specified search type.
asimilarity_search(query[, k, metadata])
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 most similar to query.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by Zep vector UUIDs.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts[, embedding, metadatas, ...])
Class method that returns a ZepVectorStore instance initialized from 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[, metadata, k])
Return docs most similar to query using specified search type.
similarity_search(query[, k, metadata])
Return docs most similar to query.
similarity_search(query[, k, metadata])
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, ...])
Run similarity search with distance.
__init__(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None) → None[source]¶
Parameters
collection_name (str) – 
api_url (str) – 
api_key (Optional[str]) – 
config (Optional[CollectionConfig]) – 
embedding (Optional[Embeddings]) – 
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[str, Any]]] = None, document_ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – 
metadatas (Optional[List[Dict[str, Any]]]) – 
document_ids (Optional[List[str]]) – 
kwargs (Any) – 
Return type
List[str]
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
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[str, Any]]] = None, document_ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
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[str, Any]]]) – Optional list of metadatas associated with the texts.
document_ids (Optional[List[str]]) – Optional list of document ids associated with the texts.
kwargs (Any) – vectorstore specific parameters
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]) – List of Documents to add to the vectorstore.
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
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Parameters
query (str) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) –
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
metadata (Optional[Dict[str, Any]]) – 
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(
# 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, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – 
search_type (str) – 
metadata (Optional[Dict[str, Any]]) – 
k (int) – 
kwargs (Any) – 
Return type
List[Document]
async asimilarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) –
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Document]
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query (str) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
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) → None[source]¶
Delete by Zep vector UUIDs.
Parameters
ids (Optional[List[str]]) – The UUIDs of the vectors to delete.
kwargs (Any) – 
Raises
ValueError – If no UUIDs are provided.
Return type
None
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
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = None, collection_name: str = '', api_url: str = '', api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, **kwargs: Any) → ZepVectorStore[source]¶
Class method that returns a ZepVectorStore instance initialized from texts.
If the collection does not exist, it will be created.
Parameters
texts (List[str]) – The list of texts to add to the vectorstore.
embedding (Optional[Embeddings]) – Optional embedding function to use to
embed the texts.
metadatas (Optional[List[Dict[str, Any]]]) – Optional list of metadata
associated with the texts.
collection_name (str) – The name of the collection in the Zep store.
api_url (str) – The URL of the Zep API.
api_key (Optional[str]) – The API key for the Zep API.
config (Optional[CollectionConfig]) – The configuration for the collection.
**kwargs – Additional parameters specific to the vectorstore.
Returns
An instance of ZepVectorStore.
Return type
ZepVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, metadata: Optional[Dict[str, Any]] = 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) – Text to look up documents similar to.
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.
Zep determines this automatically and this parameter is
ignored.
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.
metadata (Optional[Dict[str, Any]]) – Optional, metadata to filter the resulting set of retrieved docs
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, metadata: Optional[Dict[str, Any]] = 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. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
Zep determines this automatically and this parameter is
ignored.
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.
metadata (Optional[Dict[str, Any]]) – Optional, metadata to filter the resulting set of retrieved docs
kwargs (Any) – 
Returns
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – 
search_type (str) – 
metadata (Optional[Dict[str, Any]]) – 
k (int) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search(query: str, k: int = 4, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, metadata: Optional[Dict[str, Any]] = 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.
metadata (Optional[Dict[str, Any]]) – Optional, metadata filter
kwargs (Any) – 
Returns
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Parameters
query (str) – 
k (int) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using ZepVectorStore¶
Zep
langchain_community.vectorstores.alibabacloud_opensearch.create_metadata¶
langchain_community.vectorstores.alibabacloud_opensearch.create_metadata(fields: Dict[str, Any]) → Dict[str, Any][source]¶
Create metadata from fields.
Parameters
fields (Dict[str, Any]) – The fields of the document. The fields must be a dict.
Returns
The metadata of the document. The metadata must be a dict.
Return type
metadata
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)[source]¶
[Deprecated] 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)
Notes
Deprecated since version 0.2.4.
Initialize the Couchbase Vector Store.
Parameters
cluster (Cluster) – couchbase cluster object with active connection.
bucket_name (str) – name of bucket to store documents in.
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(...)
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].
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.
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[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]¶