Tony20100/langchain_full huggingface dataset:
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[str, Any]]] = 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[Any, Any]]] = None, **kwargs: Any) → CouchbaseVectorStore[source]¶
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.
langchain_community.vectorstores.neo4j_vector.sort_by_index_name¶
langchain_community.vectorstores.neo4j_vector.sort_by_index_name(lst: List[Dict[str, Any]], index_name: str) → List[Dict[str, Any]][source]¶
Sort first element to match the index_name if exists
Parameters
lst (List[Dict[str, Any]]) – 
index_name (str) – 
Return type
List[Dict[str, Any]]
langchain_qdrant.vectorstores.sync_call_fallback¶
langchain_qdrant.vectorstores.sync_call_fallback(method: Callable) → Callable[source]¶
Decorator to call the synchronous method of the class if the async method is not
implemented. This decorator might be only used for the methods that are defined
as async in the class.
Parameters
method (Callable) – 
Return type
Callable
langchain_community.vectorstores.sklearn.SKLearnVectorStore¶
class langchain_community.vectorstores.sklearn.SKLearnVectorStore(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any)[source]¶
Simple in-memory vector store based on the scikit-learn library
NearestNeighbors.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, *[, persist_path, ...])
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 VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
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 by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
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. :param k: Number of Documents to return. Defaults to 4. :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. Defaults to 0.5.
max_marginal_relevance_search_by_vector(...)
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. Defaults to 4. :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. Defaults to 0.5.
persist()
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
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.
Parameters
embedding (Embeddings) – 
persist_path (Optional[str]) – 
serializer (Literal['json', 'bson', 'parquet']) – 
metric (str) – 
kwargs (Any) – 
__init__(embedding: Embeddings, *, persist_path: Optional[str] = None, serializer: Literal['json', 'bson', 'parquet'] = 'json', metric: str = 'cosine', **kwargs: Any) → None[source]¶
Parameters
embedding (Embeddings) – 
persist_path (Optional[str]) – 
serializer (Literal['json', 'bson', 'parquet']) – 
metric (str) – 
kwargs (Any) – 
Return type
None
metric (str) – 
kwargs (Any) – 
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
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, **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]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
ids (Optional[List[str]]) – 
**kwargs – 
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.
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, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
kwargs (Any) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, persist_path: Optional[str] = None, **kwargs: Any) → SKLearnVectorStore[source]¶
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.
ids (Optional[List[str]]) – 
persist_path (Optional[str]) – 
kwargs (Any) – 
Return type
SKLearnVectorStore
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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.
:param query: Text to look up documents similar to.
:param k: Number of Documents to return. Defaults to 4.
: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.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
query (str) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
kwargs (Any) – 
Return type
List[Document]
kwargs (Any) – 
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][source]¶
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. Defaults to 4.
: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.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
kwargs (Any) – 
Return type
List[Document]
persist() → None[source]¶
Return type
None
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using 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.
Return type
List[Document]
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
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, **kwargs: Any) → List[Document]¶
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.
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, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) –
add_texts(texts[, metadatas])
Upload texts with metadata (properties) to Marqo.
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
bulk_similarity_search(queries[, k])
Search the marqo index for the most similar documents in bulk with multiple queries.
bulk_similarity_search_with_score(queries[, k])
Return documents from Marqo that are similar to the query as well as their scores using a batch of queries.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents[, embedding])
Return VectorStore initialized from documents.
from_texts(texts[, embedding, metadatas, ...])
Return Marqo initialized from texts.
get_indexes()
Return Marqo initialized from texts.
get_indexes()
Helper to see your available indexes in marqo, useful if the from_texts method was used without an index name specified
get_number_of_documents()
Helper to see the number of documents in the index
marqo_bulk_similarity_search(queries[, k])
Return documents from Marqo using a bulk search, exposes Marqo's output directly
marqo_similarity_search(query[, k])
Return documents from Marqo exposing Marqo's output directly
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 the marqo index for the most similar documents.
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])
Return documents from Marqo that are similar to the query as well as their scores.
Parameters
client (marqo.Client) – 
index_name (str) – 
add_documents_settings (Optional[Dict[str, Any]]) – 
searchable_attributes (Optional[List[str]]) – 
page_content_builder (Optional[Callable[[Dict[str, Any]], str]]) –
page_content_builder (Optional[Callable[[Dict[str, Any]], str]]) – 
__init__(client: marqo.Client, index_name: str, add_documents_settings: Optional[Dict[str, Any]] = None, searchable_attributes: Optional[List[str]] = None, page_content_builder: Optional[Callable[[Dict[str, Any]], str]] = None)[source]¶
Initialize with Marqo client.
Parameters
client (marqo.Client) – 
index_name (str) – 
add_documents_settings (Optional[Dict[str, Any]]) – 
searchable_attributes (Optional[List[str]]) – 
page_content_builder (Optional[Callable[[Dict[str, Any]], str]]) – 
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.
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
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, **kwargs: Any) → List[str][source]¶
Upload texts with metadata (properties) to Marqo.
You can either have marqo generate ids for each document or you can provide
your own by including a “_id” field in the metadata objects.
Parameters
texts (Iterable[str]) – am iterator of texts - assumed to preserve an
metadatas. (order that matches the) – 
metadatas (Optional[List[dict]], optional) – a list of metadatas.
kwargs (Any) – 
Raises
ValueError – if metadatas is provided and the number of metadatas differs
from the number of texts. – 
Returns
The list of ids that were added.
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.