id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
6eb4c8868dda-29 | Returns
List of Documents most similar to the query.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. D... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
6eb4c8868dda-30 | consistency – Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the maj... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
6eb4c8868dda-31 | queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
’majority’ - query all replicas, but return values present in themajority of replicas
’quorum’ - query the majority of replicas, return values present inall of them
’all’ - query all replicas,... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.Qdrant.html |
23930305d771-0 | langchain.vectorstores.sklearn.SKLearnVectorStoreException¶
class langchain.vectorstores.sklearn.SKLearnVectorStoreException[source]¶
Exception raised by SKLearnVectorStore. | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStoreException.html |
900f19c6bd56-0 | langchain.vectorstores.scann.ScaNN¶
class langchain.vectorstores.scann.ScaNN(embedding: Embeddings, index: Any, docstore: Docstore, index_to_docstore_id: Dict[int, str], relevance_score_fn: Optional[Callable[[float], float]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCL... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-1 | 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.
am... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-2 | Return docs selected using the maximal marginal relevance.
save_local(folder_path[, index_name])
Save ScaNN index, docstore, and index_to_docstore_id to disk.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter, fetch_k])
Return docs mo... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-3 | Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added text... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-4 | False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: O... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-5 | lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-6 | Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Docume... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-7 | Embeds documents.
Creates an in memory docstore
Initializes the ScaNN database
This is intended to be a quick way to get started.
Example
from langchain.vectorstores import ScaNN
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_em... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-8 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-9 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Document... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-10 | **kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Dict[str, Any]] = No... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
900f19c6bd56-11 | filter the resulting set of retrieved docs
Returns
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
Examples using ScaNN¶
ScaNN | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
75543a56dc0b-0 | langchain.vectorstores.singlestoredb.SingleStoreDB¶
class langchain.vectorstores.singlestoredb.SingleStoreDB(embedding: Embeddings, *, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = 'metadata', vector_field: str =... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-1 | Defaults to “vector”.
pool (Following arguments pertain to the connection) –
pool_size (int, optional) – Determines the number of active connections in
the pool. Defaults to 5.
max_overflow (int, optional) – Determines the maximum number of connections
allowed beyond the pool_size. Defaults to 10.
timeout (float, opti... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-2 | Automatically enabled if ssl_ca is specified.
ssl_verify_identity (bool, optional) – Verifies the server’s identity.
conv (dict[int, Callable], optional) – A dictionary of data conversion
functions.
credential_type (str, optional) – Specifies the type of authentication to
use: auth.PASSWORD, auth.JWT, or auth.BROWSER_S... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-3 | Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, *[, distance_strategy, ...])
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts thr... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-4 | 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, ...])
Create a SingleStoreDB vectorstore from raw documents. This is a user-friendly interface that: 1. Embeds do... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-5 | Initialize with necessary components.
Parameters
embedding (Embeddings) – A text embedding model.
distance_strategy (DistanceStrategy, optional) – Determines the strategy employed for calculating
the distance between vectors in the embedding space.
Defaults to DOT_PRODUCT.
Available options are:
- DOT_PRODUCT: Computes... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-6 | connections, 80 for HTTP connections, and 443 for HTTPS connections.
database (str, optional) – Database name.
the (Additional optional arguments provide further customization over) –
connection –
pure_python (bool, optional) – Toggles the connector mode. If True,
operates in pure Python mode.
local_infile (bool, opt... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-7 | vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
host="https://user:password@127.0.0.1:3306/database"
)
Advanced Usage:
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import SingleStoreDB
vectorstore = SingleStoreDB(
OpenAIEmbeddings(),
distance_strategy=DistanceStrategy.E... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-8 | (List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]¶
Add more texts to the vectorstore.
... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-9 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-10 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-11 | Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-12 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-13 | Returns the most similar indexed documents to the query text.
Uses cosine similarity.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filter (dict) – A dictionary of metadata fields and values to filter by.
Returns
A list of documen... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
75543a56dc0b-14 | k – Number of Documents to return. Defaults to 4.
filter – A dictionary of metadata fields and values to filter by.
Defaults to None.
Returns
List of Documents most similar to the query and score for each
Examples using SingleStoreDB¶
SingleStoreDB | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
1c32523e0f24-0 | langchain.vectorstores.analyticdb.AnalyticDB¶
class langchain.vectorstores.analyticdb.AnalyticDB(connection_string: str, embedding_function: Embeddings, embedding_dimension: int = 1536, collection_name: str = 'langchain_document', pre_delete_collection: bool = False, logger: Optional[Logger] = None, engine_args: Option... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-1 | 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.
am... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-2 | search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with AnalyticDB with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-3 | 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: int = 500, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-4 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-5 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-6 | Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-7 | Either pass it as a parameter
or set the PG_CONNECTION_STRING environment variable.
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
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 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-8 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document]... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
1c32523e0f24-9 | filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
0cd518e0780e-0 | langchain.vectorstores.usearch.USearch¶
class langchain.vectorstores.usearch.USearch(embedding: Embeddings, index: Any, docstore: Docstore, ids: List[str])[source]¶
USearch vector store.
To use, you should have the usearch python package installed.
Initialize with necessary components.
Attributes
embeddings
Access the ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-1 | 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]... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-2 | Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-4 | 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 t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-5 | Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-6 | from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
usearch = USearch.from_texts(texts, embeddings)
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.... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-7 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most simi... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
0cd518e0780e-8 | Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of documents most similar to the query with distance.
Examples using USearch¶
USearch | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.usearch.USearch.html |
63d2bf29694c-0 | langchain.vectorstores.redis.filters.RedisFilterField¶
class langchain.vectorstores.redis.filters.RedisFilterField(field: str)[source]¶
Base class for RedisFilterFields.
Attributes
OPERATORS
escaper
Methods
__init__(field)
equals(other)
__init__(field: str)[source]¶
equals(other: RedisFilterField) → bool[source]¶ | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.filters.RedisFilterField.html |
5f6a93946439-0 | langchain.vectorstores.pgembedding.EmbeddingStore¶
class langchain.vectorstores.pgembedding.EmbeddingStore(**kwargs)[source]¶
Embedding store.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attr... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.EmbeddingStore.html |
2df56939d06c-0 | langchain.vectorstores.utils.filter_complex_metadata¶
langchain.vectorstores.utils.filter_complex_metadata(documents: ~typing.List[~langchain.schema.document.Document], *, allowed_types: ~typing.Tuple[~typing.Type, ...] = (<class 'str'>, <class 'bool'>, <class 'int'>, <class 'float'>)) → List[Document][source]¶
Filter ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.utils.filter_complex_metadata.html |
996b17452510-0 | langchain.vectorstores.starrocks.get_named_result¶
langchain.vectorstores.starrocks.get_named_result(connection: Any, query: str) → List[dict[str, Any]][source]¶
Get a named result from a query.
:param connection: The connection to the database
:param query: The query to execute
Returns
The result of the query
Return t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.get_named_result.html |
527d16b8947d-0 | langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch¶
class langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any)[source]¶
Alibaba Cloud OpenSearch vector store.
Attributes
embeddings
Access the query embeddin... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-1 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-2 | similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any) → None[source]¶
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the v... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-3 | True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], emb... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-4 | lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-5 | Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Docume... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-6 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
delete_documents_with_document_id(id_list: List[str]) → bool[source]¶
Delete documents based on their IDs.
Parameters
id_list – List of document IDs.
Returns
Whether the deletion was successful or not.
delete_do... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-7 | Returns
Alibaba cloud opensearch vector store instance.
Return type
AlibabaCloudOpenSearch
inner_embedding_query(embedding: List[float], search_filter: Optional[Dict[str, Any]] = None, k: int = 4) → Dict[str, Any][source]¶
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
527d16b8947d-8 | to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, search_filter... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
027fea99a41d-0 | langchain.vectorstores.vespa.VespaStore¶
class langchain.vectorstores.vespa.VespaStore(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None)[source]¶
Vesp... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-1 | 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])
Add texts to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, emb... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-2 | 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])
Return docs most similar to... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-3 | Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add texts to the vectorstore.
Parameter... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-4 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-5 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-6 | Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID or other criteria.
Par... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-7 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
027fea99a41d-8 | Performs similarity search from a embeddings vector.
Parameters
query_embedding – Embeddings vector to search for.
k – Number of results to return.
custom_query – Use this custom query instead default query (kwargs)
kwargs – other vector store specific parameters
Returns
List of ids from adding the texts into the vecto... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vespa.VespaStore.html |
1ffba0461693-0 | langchain.vectorstores.azuresearch.AzureSearch¶
class langchain.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', f... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-1 | 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.... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-2 | 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(*args, **kwargs)
Run similarity sea... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-3 | Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Ad... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-4 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-5 | )
# 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, **kwa... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-6 | Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embed... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-7 | Returns
List of Documents most similar to the query and score for each
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 que... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-8 | Return docs most similar to query using specified search type.
semantic_hybrid_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Returns the most similar indexed documents to the query text.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of docume... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-9 | Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Docume... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
1ffba0461693-10 | k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query and score for each
Examples using AzureSearch¶
Azure Cognitive Search | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearch.html |
f4dbeb7f9213-0 | langchain.vectorstores.sklearn.BaseSerializer¶
class langchain.vectorstores.sklearn.BaseSerializer(persist_path: str)[source]¶
Base class for serializing data.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BaseSerializer.html |
7fc2f1ba1f5c-0 | langchain.vectorstores.pgvector.BaseModel¶
class langchain.vectorstores.pgvector.BaseModel(**kwargs: Any)[source]¶
Base model for the SQL stores.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
a... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.BaseModel.html |
12da4fb431b7-0 | langchain.vectorstores.tigris.Tigris¶
class langchain.vectorstores.tigris.Tigris(client: TigrisClient, embeddings: Embeddings, index_name: str)[source]¶
Tigris vector store.
Initialize Tigris vector store.
Attributes
embeddings
Access the query embedding object if available.
search_index
Methods
__init__(client, embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-1 | 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 by vector ID or other criteria.
from... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-2 | Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documen... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-4 | 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 t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-5 | Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-6 | Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
12da4fb431b7-7 | Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
47575929e1fa-0 | langchain.vectorstores.starrocks.StarRocks¶
class langchain.vectorstores.starrocks.StarRocks(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any)[source]¶
StarRocks vector store.
You need a pymysql python package, and a valid account
to connect to StarRocks.
Right now StarRocks has only imp... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-1 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-2 | similarity_search_with_relevance_scores(query)
Perform a similarity search with StarRocks
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any) → None[source]¶
StarRocks Wrapper to LangChain
embedding... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-3 | batch_size – Batch size of insertion
metadata – Optional column data to be inserted
Returns
List of ids from adding the texts into the VectorStore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Ot... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-4 | 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 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-5 | search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to q... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-6 | Return type
Optional[bool]
drop() → None[source]¶
Helper function: Drop data
escape_str(value: str) → str[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], emb... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
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