id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
3e67be62930b-7 | filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score) | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.matching_engine.MatchingEngine.html |
0cde69f1f1ac-0 | langchain.vectorstores.elastic_vector_search.ElasticVectorSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticVectorSearch(elasticsearch_url: str, index_name: str, embedding: Embeddings, *, ssl_verify: Optional[Dict[str, Any]] = None)[source]¶
Bases: VectorStore, ABC
Wrapper around Elasticsearch as a vect... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-1 | Example
from langchain import ElasticVectorSearch
from langchain.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
elastic_host = "cluster_id.region_id.gcp.cloud.es.io"
elasticsearch_url = f"https://username:password@{elastic_host}:9243"
elastic_vector_search = ElasticVectorSearch(
elasticsearch_url... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-2 | 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)
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-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]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-4 | Return VectorStore initialized from texts and embeddings.
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.
async amax_marginal_relevance_search_by_vector(embedding: List[f... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-5 | Delete by vector IDs.
Parameters
ids – List of ids to delete.
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] =... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-6 | to maximum diversity and 1 to minimum diversity.
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]¶
Return docs selected using the max... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
0cde69f1f1ac-7 | 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[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameter... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
63ae3bb8772c-0 | langchain.vectorstores.docarray.base.DocArrayIndex¶
class langchain.vectorstores.docarray.base.DocArrayIndex(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
Bases: VectorStore, ABC
Initialize a vector store from DocArray’s DocIndex.
Methods
__init__(doc_index, embedding)
Initialize a vector store from DocArray... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
63ae3bb8772c-1 | Delete by vector ID.
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 marg... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
63ae3bb8772c-2 | 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]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Option... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
63ae3bb8772c-3 | 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 query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embeddin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
63ae3bb8772c-4 | fetch_k – Number of Documents to fetch to pass to MMR algorithm.
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.
max_mar... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
63ae3bb8772c-5 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
3be938d05c07-0 | langchain.vectorstores.tigris.Tigris¶
class langchain.vectorstores.tigris.Tigris(client: TigrisClient, embeddings: Embeddings, index_name: str)[source]¶
Bases: VectorStore
Initialize Tigris vector store
Methods
__init__(client, embeddings, index_name)
Initialize Tigris vector store
aadd_documents(documents, **kwargs)
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
3be938d05c07-1 | Delete by vector ID.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
3be938d05c07-2 | (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]¶
Run more texts through the embeddings and add to t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
3be938d05c07-3 | as_retriever(**kwargs: Any) → VectorStoreRetriever¶
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 query.
async asimi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
3be938d05c07-4 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
3be938d05c07-5 | 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 ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tigris.Tigris.html |
f99039699e47-0 | langchain.vectorstores.typesense.Typesense¶
class langchain.vectorstores.typesense.Typesense(typesense_client: Client, embedding: Embeddings, *, typesense_collection_name: Optional[str] = None, text_key: str = 'text')[source]¶
Bases: VectorStore
Wrapper around Typesense vector search.
To use, you should have the typese... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-1 | add_texts(texts[, metadatas, ids])
Run more texts through the embedding and add to the vectorstore.
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_marg... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-2 | Return typesense documents 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, filter])
Return typesense documents mos... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-3 | Returns
List of ids from adding the texts into the vectorstore.
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: Option... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-4 | Return docs most similar to query.
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_client_params(embedding: Embeddings, *, host: str = 'loca... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-5 | Construct Typesense wrapper from raw text.
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 selec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-6 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 10, filter: Optional[str] = '', **kwargs: Any) → List[Document][source]¶
Return typesense documents most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Def... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
f99039699e47-7 | Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.typesense.Typesense.html |
b2af55023b25-0 | langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch¶
class langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any)[source]¶
Bases: VectorStore
Alibaba Cloud OpenSearch Vector Store
Methods
__init__(embedding, c... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
b2af55023b25-1 | create_results_with_score(json_result)
delete(ids)
Delete by vector ID.
from_documents(documents, embedding[, ids, ...])
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, config])
Return VectorStore initialized from texts and embeddings.
inner_embedding_query(embeddi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
b2af55023b25-2 | (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]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
b2af55023b25-3 | 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 query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embeddin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
b2af55023b25-4 | 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 – Text to l... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
b2af55023b25-5 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, search_filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, search_filter: Optional[dic... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
a181d7159d27-0 | langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever¶
class langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever(*, vectorstore: AzureSearch, search_type: str = 'hybrid', k: int = 4)[source]¶
Bases: BaseRetriever, BaseModel
Create a new model by parsing and validating input data from keyword... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
0f88982f0c78-0 | langchain.vectorstores.singlestoredb.SingleStoreDBRetriever¶
class langchain.vectorstores.singlestoredb.SingleStoreDBRetriever(*, vectorstore: SingleStoreDB, search_type: str = 'similarity', search_kwargs: dict = None, k: int = 4)[source]¶
Bases: VectorStoreRetriever
Retriever for SingleStoreDB vector stores.
Create a ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
0f88982f0c78-1 | model Config¶
Bases: object
Configuration for this pydantic object.
arbitrary_types_allowed = True¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDBRetriever.html |
cd3116ded434-0 | 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)[source]¶
Bases: Vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-1 | 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)
asearch(query, search_type,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-2 | Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-3 | 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_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-4 | Return connection string from database parameters.
create_collection() → None[source]¶
create_table_if_not_exists() → None[source]¶
delete(ids: List[str]) → Optional[bool]¶
Delete by vector ID.
Parameters
ids – List of ids to delete.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Retu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-5 | 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 algorithm.
lambda_mult – Number between 0 and 1 that deter... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-6 | Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] =... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
cd3116ded434-7 | filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.analyticdb.AnalyticDB.html |
2c6a0ff0f93b-0 | langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch¶
class langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
Bases: DocArrayIndex
Wrapper around HnswLib storage.
To use it, you should have the docarray package with version >=0.32.0 installed.
You can in... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-1 | 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.
delete(ids)
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-2 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-3 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
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) → Lis... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-4 | Initialize DocArrayHnswSearch store.
Parameters
embedding (Embeddings) – Embedding function.
work_dir (str) – path to the location where all the data will be stored.
n_dim (int) – dimension of an embedding.
dist_metric (str) – Distance metric for DocArrayHnswSearch can be one of:
“cosine”, “ip”, and “l2”. Defaults to “... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-5 | Defaults to None.
work_dir (str) – path to the location where all the data will be stored.
n_dim (int) – dimension of an embedding.
**kwargs – Other keyword arguments to be passed to the __init__ method.
Returns
DocArrayHnswSearch Vector Store
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lam... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-6 | 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, **kwargs: Any... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
2c6a0ff0f93b-7 | Return docs most similar to query.
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 text and
cosine distance in float for each.
Lower score represents more similarity.
property doc_cls: Type[BaseDoc]¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
ec00cc0f3028-0 | langchain.vectorstores.cassandra.Cassandra¶
class langchain.vectorstores.cassandra.Cassandra(embedding: Embeddings, session: Session, keyspace: str, table_name: str, ttl_seconds: int | None = None)[source]¶
Bases: VectorStore
Wrapper around Cassandra embeddings platform.
There is no notion of a default table name, sinc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-1 | 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 mo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-2 | 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. :param fetch_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-4 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-5 | Create a Cassandra vectorstore from a document list.
No support for specifying text IDs
Returns
a Cassandra vectorstore.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → CVST[source]¶
Create a Cassandra vectorstore from raw texts.
No support for sp... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-6 | :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.
Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs m... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ec00cc0f3028-7 | similarity_search_with_score_by_vector(embedding: List[float], k: int = 4) → List[Tuple[Document, float]][source]¶
Return docs most similar to embedding vector.
No support for filter query (on metadata) along with vector search.
Parameters
embedding (str) – Embedding to look up documents similar to.
k (int) – Number of... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.cassandra.Cassandra.html |
ecea860391e1-0 | langchain.vectorstores.starrocks.debug_output¶
langchain.vectorstores.starrocks.debug_output(s: Any) → None[source]¶
Print a debug message if DEBUG is True.
:param s: The message to print | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.debug_output.html |
143b60873b11-0 | langchain.vectorstores.starrocks.StarRocksSettings¶
class langchain.vectorstores.starrocks.StarRocksSettings(_env_file: Optional[Union[str, PathLike, List[Union[str, PathLike]], Tuple[Union[str, PathLike], ...]]] = '<object object>', _env_file_encoding: Optional[str] = None, _env_nested_delimiter: Optional[str] = None,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocksSettings.html |
143b60873b11-1 | ‘metadata’: ‘metadata_dictionary_in_json’,
}
Defaults to identity map.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param column_map: Dict[str, str] = {'document': 'document', 'embedding': 'embedding', 'i... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocksSettings.html |
a1433904eeac-0 | langchain.vectorstores.clickhouse.Clickhouse¶
class langchain.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any)[source]¶
Bases: VectorStore
Wrapper around ClickHouse vector database
You need a clickhouse-connect python package, and a valid account
to c... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**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 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-4 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[ClickhouseSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → Clickhouse[source]... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-5 | 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]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevan... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a1433904eeac-6 | Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with ClickHouse by vectors
Parameters
query (str) – query string
k (int, optional) – Top K neighbors ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
fe71c72a6a85-0 | langchain.vectorstores.weaviate.Weaviate¶
class langchain.vectorstores.weaviate.Weaviate(client: ~typing.Any, index_name: str, text_key: str, embedding: ~typing.Optional[~langchain.embeddings.base.Embeddings] = None, attributes: ~typing.Optional[~typing.List[str]] = None, relevance_score_fn: ~typing.Optional[~typing.Ca... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-1 | 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)
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-2 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-4 | Embeds documents.
Creates a new index for the embeddings in the Weaviate instance.
Adds the documents to the newly created Weaviate index.
This is intended to be a quick way to get started.
Example
from langchain.vectorstores.weaviate import Weaviate
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAI... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-5 | among selected documents.
Parameters
embedding – Embedding 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 algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 correspondi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
fe71c72a6a85-6 | 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 to
filter the resulting set of retrieved docs
Returns
List of Tuples o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.weaviate.Weaviate.html |
2d923f592c7d-0 | langchain.vectorstores.sklearn.BsonSerializer¶
class langchain.vectorstores.sklearn.BsonSerializer(persist_path: str)[source]¶
Bases: BaseSerializer
Serializes data in binary json using the bson python package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
loa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BsonSerializer.html |
74f9b4aca3cc-0 | langchain.vectorstores.clickhouse.has_mul_sub_str¶
langchain.vectorstores.clickhouse.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string contains multiple substrings.
:param s: string to check.
:param *args: substrings to check.
Returns
True if all substrings are in the string, False otherwise. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.has_mul_sub_str.html |
3cb51f09fb96-0 | langchain.vectorstores.myscale.MyScale¶
class langchain.vectorstores.myscale.MyScale(embedding: Embeddings, config: Optional[MyScaleSettings] = None, **kwargs: Any)[source]¶
Bases: VectorStore
Wrapper around MyScale vector database
You need a clickhouse-connect python package, and a valid account
to connect to MyScale.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**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 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-2 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-4 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[MyScaleSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → MyScale[source]¶
Crea... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-5 | 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]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevan... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3cb51f09fb96-6 | Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with MyScale by vectors
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.MyScale.html |
3da3b15250bc-0 | langchain.vectorstores.sklearn.ParquetSerializer¶
class langchain.vectorstores.sklearn.ParquetSerializer(persist_path: str)[source]¶
Bases: BaseSerializer
Serializes data in Apache Parquet format using the pyarrow package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (witho... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.ParquetSerializer.html |
7f1d1814a4df-0 | langchain.vectorstores.sklearn.SKLearnVectorStoreException¶
class langchain.vectorstores.sklearn.SKLearnVectorStoreException[source]¶
Bases: RuntimeError
Exception raised by SKLearnVectorStore.
add_note()¶
Exception.add_note(note) –
add a note to the exception
with_traceback()¶
Exception.with_traceback(tb) –
set self._... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.SKLearnVectorStoreException.html |
dd9af551ffd5-0 | langchain.vectorstores.myscale.has_mul_sub_str¶
langchain.vectorstores.myscale.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string contains multiple substrings.
:param s: string to check.
:param *args: substrings to check.
Returns
True if all substrings are in the string, False otherwise. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.myscale.has_mul_sub_str.html |
7d170faf2760-0 | langchain.vectorstores.base.VectorStore¶
class langchain.vectorstores.base.VectorStore[source]¶
Bases: ABC
Interface for vector stores.
Methods
__init__()
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the e... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
7d170faf2760-1 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
7d170faf2760-2 | Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
7d170faf2760-3 | Return docs most similar to query.
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
R... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
7d170faf2760-4 | to maximum diversity and 1 to minimum diversity.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
7d170faf2760-5 | Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStore.html |
76cf626360dc-0 | langchain.vectorstores.lancedb.LanceDB¶
class langchain.vectorstores.lancedb.LanceDB(connection: Any, embedding: Embeddings, vector_key: Optional[str] = 'vector', id_key: Optional[str] = 'id', text_key: Optional[str] = 'text')[source]¶
Bases: VectorStore
Wrapper around LanceDB vector database.
To use, you should have l... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
76cf626360dc-1 | 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 mo... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
76cf626360dc-2 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.lancedb.LanceDB.html |
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