id stringlengths 14 15 | text stringlengths 49 2.47k | source stringlengths 61 166 |
|---|---|---|
21f14b288daa-1 | 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(...)
Retu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-2 | search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter, fetch_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(que... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-3 | (List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-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.scann.ScaNN.html |
21f14b288daa-5 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-6 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(text_embeddings: List[... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-7 | scann = ScaNN.from_texts(texts, embeddings)
classmethod load_local(folder_path: str, embedding: Embeddings, index_name: str = 'index', **kwargs: Any) → ScaNN[source]¶
Load ScaNN index, docstore, and index_to_docstore_id from disk.
Parameters
folder_path – folder path to load index, docstore,
and index_to_docstore_id fr... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-8 | 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 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-9 | Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
fetch_k – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Returns
List of Documents most similar to... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
21f14b288daa-10 | L2 distance in float. Lower score represents more similarity.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
embedding – Embedding ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.ScaNN.html |
7d4af12ffb99-0 | 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 embedding... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-1 | Return docs most similar to query.
create_results(json_result)
create_results_with_score(json_result)
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ids, ...])
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, config])
Retu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-2 | 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.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-3 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-4 | )
# 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-5 | False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, ids: Optional[List[str]] = None, config: Optional[AlibabaCloudOpenSearchSettings] = None, **kwargs: Any) → AlibabaCloudOpenSearch[source]¶
Return VectorStore initialized from... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-6 | 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.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
7d4af12ffb99-7 | Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, search_filter: Optional[dict] = None, **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
qu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch.html |
bda6925ed94f-0 | langchain.vectorstores.base.VectorStoreRetriever¶
class langchain.vectorstores.base.VectorStoreRetriever[source]¶
Bases: BaseRetriever
Retriever class for VectorStore.
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 vali... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
bda6925ed94f-1 | Add documents to vectorstore.
async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Document]¶
Asynchronously get documents relevant to a query.
:param query: string to find relevant documents for
:par... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
bda6925ed94f-2 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
bda6925ed94f-3 | These tags will be associated with each call to this retriever,
and passed as arguments to the handlers defined in callbacks.
Parameters
metadata – Optional metadata associated with the retriever. Defaults to None
This metadata will be associated with each call to this retriever,
and passed as arguments to the handlers... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
bda6925ed94f-4 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedN... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
03a4b7c7dd7e-0 | langchain.vectorstores.docarray.base.DocArrayIndex¶
class langchain.vectorstores.docarray.base.DocArrayIndex(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
Initialize a vector store from DocArray’s DocIndex.
Attributes
doc_cls
embeddings
Access the query embedding object if available.
Methods
__init__(doc_ind... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-1 | Return docs most similar to query.
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_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-2 | 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 texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-3 | as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-4 | 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, **kwargs: Any) → List[Document]¶
Return docs most similar to query u... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-5 | Return VectorStore initialized from texts and embeddings.
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 AN... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-6 | 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 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 s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
03a4b7c7dd7e-7 | List of documents most similar to the query text and
cosine distance in float for each.
Lower score represents more similarity. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.base.DocArrayIndex.html |
ec100a5eca0c-0 | langchain.vectorstores.hologres.Hologres¶
class langchain.vectorstores.hologres.Hologres(connection_string: str, embedding_function: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, logger: Optional[Logger] = None)[source]¶
VectorStore implementation using Holog... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-1 | 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_retr... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-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 Hologres with distance.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_sco... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-3 | Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
embeddings – List o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-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.hologres.Hologres.html |
ec100a5eca0c-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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-7 | faiss = Hologres.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_existing_index(embedding: Embeddings, ndims: int = 1536, table_name: str = 'langchain_pg_embedding', pre_delete_table: bool = False, **kwargs: Any) → Hologres[source]¶
Get intsance of an existing Hologres store.This method will
return t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
ec100a5eca0c-8 | 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.hologres.Hologres.html |
ec100a5eca0c-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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query vector.
similarity_search_with_r... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.hologres.Hologres.html |
e51583e5bef9-0 | langchain.vectorstores.sklearn.JsonSerializer¶
class langchain.vectorstores.sklearn.JsonSerializer(persist_path: str)[source]¶
Serializes data in json using the json package from python standard library.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.JsonSerializer.html |
e52bec73e359-0 | langchain.vectorstores.qdrant.sync_call_fallback¶
langchain.vectorstores.qdrant.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. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.qdrant.sync_call_fallback.html |
00d9d75e4521-0 | langchain.vectorstores.xata.XataVectorStore¶
class langchain.vectorstores.xata.XataVectorStore(api_key: str, db_url: str, embedding: Embeddings, table_name: str)[source]¶
VectorStore for a Xata database. Assumes you have a Xata database
created with the right schema. See the guide at:
https://integrations.langchain.com... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
00d9d75e4521-1 | 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 most similar to query.
delete([ids, dele... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
00d9d75e4521-2 | 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.xata.XataVectorStore.html |
00d9d75e4521-3 | 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.xata.XataVectorStore.html |
00d9d75e4521-4 | )
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
00d9d75e4521-5 | ids – List of ids to delete.
delete_all – Delete all records in the table.
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[... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
00d9d75e4521-6 | Maximal marginal relevance optimizes for similarity to query AND diversity
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 t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
00d9d75e4521-7 | 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 of (doc, similarity_score)
similarity_search_wit... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.xata.XataVectorStore.html |
a1136f4dc4af-0 | langchain.vectorstores.faiss.dependable_faiss_import¶
langchain.vectorstores.faiss.dependable_faiss_import(no_avx2: Optional[bool] = None) → Any[source]¶
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Paramet... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.dependable_faiss_import.html |
123ee61fbb88-0 | langchain.vectorstores.pgembedding.QueryResult¶
class langchain.vectorstores.pgembedding.QueryResult[source]¶
Attributes
EmbeddingStore
distance
Methods
__init__()
__init__()¶ | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgembedding.QueryResult.html |
1225a4093ea8-0 | langchain.vectorstores.clarifai.Clarifai¶
class langchain.vectorstores.clarifai.Clarifai(user_id: Optional[str] = None, app_id: Optional[str] = None, pat: Optional[str] = None, number_of_docs: Optional[int] = None, api_base: Optional[str] = None)[source]¶
Wrapper around Clarifai AI platform’s vector store.
To use, you ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-1 | Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ids])
Add texts to the Clarifai vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore init... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-2 | similarity_search(query[, k])
Run similarity search using Clarifai.
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 ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-3 | 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.clarifai.Clarifai.html |
1225a4093ea8-4 | 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.clarifai.Clarifai.html |
1225a4093ea8-5 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-6 | False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Optional[Embeddings] = None, user_id: Optional[str] = None, app_id: Optional[str] = None, pat: Optional[str] = None, number_of_docs: Optional[int] = None, api_base: Optional[str] = None,... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-7 | None. (Defaults to) –
api_base (Optional[str]) – API base. Defaults to None.
metadatas (Optional[List[dict]]) – Optional list of metadatas.
None. –
Returns
Clarifai vectorstore.
Return type
Clarifai
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → Lis... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-8 | 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, **kwargs: Any... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
1225a4093ea8-9 | Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None, namespace: Optional[str] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with score using Clarifai.
Parameters
query (str) – Query text to search for... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
558607c39cd8-0 | langchain.vectorstores.redis.RedisVectorStoreRetriever¶
class langchain.vectorstores.redis.RedisVectorStoreRetriever[source]¶
Bases: VectorStoreRetriever
Retriever for Redis VectorStore.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be pa... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html |
558607c39cd8-1 | Add documents to vectorstore.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, max_concurrency: Optional[int] = None) → List[Output]¶
add_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Add documents to vectorstore.
async aget_relevant_d... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html |
558607c39cd8-2 | Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html |
558607c39cd8-3 | Retrieve documents relevant to a query.
:param query: string to find relevant documents for
:param callbacks: Callback manager or list of callbacks
:param tags: Optional list of tags associated with the retriever. Defaults to None
These tags will be associated with each call to this retriever,
and passed as arguments t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html |
558607c39cd8-4 | classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None) → Iterator[Output]¶
to... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.RedisVectorStoreRetriever.html |
bfd71cb1c28b-0 | langchain.vectorstores.rocksetdb.Rockset¶
class langchain.vectorstores.rocksetdb.Rockset(client: Any, embeddings: Embeddings, collection_name: str, text_key: str, embedding_key: str, workspace: str = 'commons')[source]¶
Wrapper arpund Rockset vector database.
To use, you should have the rockset python package installed... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-1 | Rockset ingest transformation.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(client, embeddings, ...[, workspace])
Initialize with Rockset client. :param client: Rockset client object :param collection: Rockset collection to insert docs / query :param embeddings: Langchain Embed... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-2 | 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 most similar to query.
delete([ids])
Delete by vector ID or other criteria.
delete_texts(ids)
Delete a ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-3 | Initialize with Rockset client.
:param client: Rockset client object
:param collection: Rockset collection to insert docs / query
:param embeddings: Langchain Embeddings object to use to generate
embedding for given text.
Parameters
text_key – column in Rockset collection to use to store the text
embedding_key – column... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-4 | ids: Optional list of ids to associate with the texts.
batch_size: Send documents in batches to rockset.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-5 | fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-6 | 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 embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs most ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-7 | 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.rocksetdb.Rockset.html |
bfd71cb1c28b-8 | Same as similarity_search_with_relevance_scores but
doesn’t return the scores.
similarity_search_by_vector(embedding: List[float], k: int = 4, distance_func: DistanceFunction = DistanceFunction.COSINE_SIM, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Accepts a query_embedding (vector), and ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
bfd71cb1c28b-9 | List of documents with their relevance score
Return type
List[Tuple[Document, float]]
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using Rockset¶
Rockset | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.rocksetdb.Rockset.html |
d148dd57db4b-0 | langchain.vectorstores.zilliz.Zilliz¶
class langchain.vectorstores.zilliz.Zilliz(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-1 | uri (str): The uri of Zilliz instance. Example uri:“https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com”,
host (str): The host of Zilliz instance. Default at “localhost”,PyMilvus will fill in the default host if only port is provided.
port (str/int): The port of Zilliz instance. Default at 19530, PyMilvuswill fill... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-2 | embedding_function = embedding,
collection_name = “LangChainCollection”,
connection_args = {
“uri”: “https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com”,
“user”: “temp”,
“password”: “temp”,
“token”: “temp”, # API key as replacements for user and password
“secure”: True
}
drop_old: True,
)
Raises
ValueError – If t... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-3 | 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 most similar to query.
delete([ids])
Del... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-4 | Perform a search on a query string and return results with score.
__init__(embedding_function: Embeddings, collection_name: str = 'LangChainCollection', connection_args: Optional[dict[str, Any]] = None, consistency_level: str = 'Session', index_params: Optional[dict] = None, search_params: Optional[dict] = None, drop_o... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-5 | embedding and the columns are decided by the first metadata dict.
Metada keys will need to be present for all inserted values. At
the moment there is no None equivalent in Milvus.
Parameters
texts (Iterable[str]) – The texts to embed, it is assumed
that they all fit in memory.
metadatas (Optional[List[dict]]) – Metadat... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-6 | as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-7 | 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, **kwargs: Any) → List[Document]¶
Return docs most similar to query u... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-8 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'LangChainCollection', connection_args: dict[str, Any] = {}, consistency_level: str = 'Session', index_params: Optional[dict] = Non... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-9 | Returns
Zilliz Vector Store
Return type
Zilliz
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document]¶
Perform a search and return results that are reorder... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-10 | Parameters
embedding (str) – The embedding vector being searched.
k (int, optional) – How many results to give. Defaults to 4.
fetch_k (int, optional) – Total results to select k from.
Defaults to 20.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-11 | Returns
Document results for search.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, param: Optional[dict] = None, expr: Optional[str] = None, timeout: Optional[int] = None, **kwargs: Any) → List[Document]¶
Perform a similarity search against the query string.
Parameters
embed... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-12 | Perform a search on a query string and return results with score.
For more information about the search parameters, take a look at the pymilvus
documentation found here:
https://milvus.io/api-reference/pymilvus/v2.2.6/Collection/search().md
Parameters
query (str) – The text being searched.
k (int, optional) – The amoun... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
d148dd57db4b-13 | Defaults to None.
kwargs – Collection.search() keyword arguments.
Returns
Result doc and score.
Return type
List[Tuple[Document, float]] | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zilliz.Zilliz.html |
68b354b073e2-0 | langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch¶
class langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch(opensearch_url: str, index_name: str, embedding_function: Embeddings, **kwargs: Any)[source]¶
Wrapper around OpenSearch as a vector database.
Example
from langchain import... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-1 | 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.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-2 | (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 more texts through the embeddings and add to the vectorstore.
add_documents(docu... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-3 | 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.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-4 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-5 | Return docs most similar to query.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
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 im... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-6 | ef_search: Size of the dynamic list used during k-NN searches. Higher values
lead to more accurate but slower searches; default: 512
ef_construction: Size of the dynamic list used during k-NN graph creation.
Higher values lead to more accurate graph but slower indexing speed;
default: 512
m: Number of bidirectional lin... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
68b354b073e2-7 | 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.opensearch_vector_search.OpenSearchVectorSearch.html |
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