id stringlengths 14 15 | text stringlengths 35 2.51k | source stringlengths 61 154 |
|---|---|---|
6d61979c76cd-2 | Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[sou... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
6d61979c76cd-3 | Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
on_tool_st... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html |
18f352ccaac5-0 | langchain.callbacks.whylabs_callback.import_langkit¶
langchain.callbacks.whylabs_callback.import_langkit(sentiment: bool = False, toxicity: bool = False, themes: bool = False) → Any[source]¶
Import the langkit python package and raise an error if it is not installed.
Parameters
sentiment – Whether to import the langkit... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.import_langkit.html |
a18e7b38ddc9-0 | langchain.callbacks.utils.load_json¶
langchain.callbacks.utils.load_json(json_path: Union[str, Path]) → str[source]¶
Load json file to a string.
Parameters
json_path (str) – The path to the json file.
Returns
The string representation of the json file.
Return type
(str) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.load_json.html |
7be9e841fc09-0 | langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler¶
class langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler(pl_id_callback: Optional[Callable[[...], Any]] = None, pl_tags: Optional[List[str]] = [])[source]¶
Bases: BaseCallbackHandler
Callback handler for promptlayer.
Initialize the Pr... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler.html |
7be9e841fc09-1 | Run when Retriever errors.
on_retriever_start(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler.html |
7be9e841fc09-2 | Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, ru... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler.html |
7be9e841fc09-3 | Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Ret... | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.promptlayer_callback.PromptLayerCallbackHandler.html |
7fa00c580b93-0 | langchain.docstore.arbitrary_fn.DocstoreFn¶
class langchain.docstore.arbitrary_fn.DocstoreFn(lookup_fn: Callable[[str], Union[Document, str]])[source]¶
Bases: Docstore
Langchain Docstore via arbitrary lookup function.
This is useful when:
it’s expensive to construct an InMemoryDocstore/dict
you retrieve documents from ... | https://api.python.langchain.com/en/latest/docstore/langchain.docstore.arbitrary_fn.DocstoreFn.html |
06c27a7688b0-0 | langchain.docstore.in_memory.InMemoryDocstore¶
class langchain.docstore.in_memory.InMemoryDocstore(_dict: Dict[str, Document])[source]¶
Bases: Docstore, AddableMixin
Simple in memory docstore in the form of a dict.
Initialize with dict.
Methods
__init__(_dict)
Initialize with dict.
add(texts)
Add texts to in memory dic... | https://api.python.langchain.com/en/latest/docstore/langchain.docstore.in_memory.InMemoryDocstore.html |
35410f99db1f-0 | langchain.docstore.base.Docstore¶
class langchain.docstore.base.Docstore[source]¶
Bases: ABC
Interface to access to place that stores documents.
Methods
__init__()
search(search)
Search for document.
abstract search(search: str) → Union[str, Document][source]¶
Search for document.
If page exists, return the page summar... | https://api.python.langchain.com/en/latest/docstore/langchain.docstore.base.Docstore.html |
b04789bfcb86-0 | langchain.docstore.wikipedia.Wikipedia¶
class langchain.docstore.wikipedia.Wikipedia[source]¶
Bases: Docstore
Wrapper around wikipedia API.
Check that wikipedia package is installed.
Methods
__init__()
Check that wikipedia package is installed.
search(search)
Try to search for wiki page.
search(search: str) → Union[str... | https://api.python.langchain.com/en/latest/docstore/langchain.docstore.wikipedia.Wikipedia.html |
b4c0912a9df7-0 | langchain.docstore.base.AddableMixin¶
class langchain.docstore.base.AddableMixin[source]¶
Bases: ABC
Mixin class that supports adding texts.
Methods
__init__()
add(texts)
Add more documents.
abstract add(texts: Dict[str, Document]) → None[source]¶
Add more documents. | https://api.python.langchain.com/en/latest/docstore/langchain.docstore.base.AddableMixin.html |
9b49231f99a2-0 | langchain.vectorstores.deeplake.DeepLake¶
class langchain.vectorstores.deeplake.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding_function: Optional[Embeddings] = None, read_only: bool = False, ingestion_batch_size: int = 1000, num_workers: int = 0, verbose: bool = True, exec_option: st... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-1 | ... path = "hub://org_id/dataset_name",
... exec_option = "tensor_db",
... )
Parameters
dataset_path (str) – Path to existing dataset or where to create
a new one. Defaults to _LANGCHAIN_DEFAULT_DEEPLAKE_PATH.
token (str, optional) – Activeloop token, for fetching credentials
to the dataset at path if it ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-2 | Raises
ValueError – If some condition is not met.
Methods
__init__([dataset_path, token, ...])
Creates an empty DeepLakeVectorStore or loads an existing one.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through th... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-3 | Return VectorStore initialized from documents and embeddings.
from_texts(texts[, embedding, metadatas, ...])
Create a Deep Lake dataset from a raw documents.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs sel... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-4 | Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Examples
>>> ids = deeplake_vectorstore.add_texts(
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-5 | 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.deeplake.DeepLake.html |
9b49231f99a2-6 | Return type
bool
delete_dataset() → None[source]¶
Delete the collection.
classmethod force_delete_by_path(path: str) → None[source]¶
Force delete dataset by path.
Parameters
path (str) – path of the dataset to delete.
Raises
ValueError – if deeplake is not installed.
classmethod from_documents(documents: List[Document]... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-7 | in either the environment
Local file system path of the form ./path/to/dataset or~/path/to/dataset or path/to/dataset.
In-memory path of the form mem://path/to/dataset which doesn’tsave the dataset, but keeps it in memory instead.
Should be used only for testing as it does not persist.
texts (List[Document]) – List of ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-8 | fetch_k – Number of Documents for MMR algorithm.
lambda_mult – Value between 0 and 1. 0 corresponds
to maximum diversity and 1 to minimum.
Defaults to 0.5.
exec_option (str) – Supports 3 ways to perform searching.
- “python” - Pure-python implementation running on the client.
Can be used for data stored anywhere. WARNI... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-9 | … k=<number_of_items_to_return>,
… exec_option=<preferred_exec_option>,
… )
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 for MMR algorithm.
lambda_mult – Number between 0 and 1 determining the deg... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-10 | Return docs most similar to query.
Examples
>>> # Search using an embedding
>>> data = vector_store.similarity_search(
... query=<your_query>,
... k=<num_items>,
... exec_option=<preferred_exec_option>,
... )
>>> # Run tql search:
>>> data = vector_store.tql_search(
... tql_query="SELECT * WHERE id == <... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-11 | Use runtime = {“db_engine”: True} during dataset creation.
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_by_vector(embedding: Union[List[float], ndarray], k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Example... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-12 | used with in-memory or local datasets.
”tensor_db” - Performant, fully-hosted Managed Tensor Database.Responsible for storage and query execution. Only available
for data stored in the Deep Lake Managed Database.
To store datasets in this database, specify
runtime = {“db_engine”: True} during dataset creation.
distance... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
9b49231f99a2-13 | k (int) – Number of results to return. Defaults to 4.
**kwargs – Additional keyword arguments. Some of these arguments are:
distance_metric: L2 for Euclidean, L1 for Nuclear, max L-infinity
distance, cos for cosine similarity, ‘dot’ for dot product.
Defaults to L2.
filter (Optional[Dict[str, str]]): Filter by metadata.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
59475d609aa0-0 | langchain.vectorstores.annoy.dependable_annoy_import¶
langchain.vectorstores.annoy.dependable_annoy_import() → Any[source]¶
Import annoy if available, otherwise raise error. | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.annoy.dependable_annoy_import.html |
478cc75ed024-0 | langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch¶
class langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding')[source]¶
Bases: VectorStore
Wra... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-1 | 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-2 | Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query, *[, k, ...])
Return MongoDB documents most similar to query, along with scores.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Para... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-3 | 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.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-4 | 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_connection_string(connection_string: str, namespace: str, embedding: Embeddings, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶
classmethod from_docum... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-5 | 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.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-6 | Parameters
query – Text to look up documents similar to.
k – Optional Number of Documents to return. Defaults to 4.
pre_filter – Optional Dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline – Optional Pipeline of MongoDB aggregation stages
following the knnBeta search.
Returns
List of Docume... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
478cc75ed024-7 | This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
early access users. It is not recommended for production deployments as we
may introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch.html |
13b7f5434f76-0 | langchain.vectorstores.alibabacloud_opensearch.create_metadata¶
langchain.vectorstores.alibabacloud_opensearch.create_metadata(fields: Dict[str, Any]) → Dict[str, Any][source]¶
Create metadata from fields.
Parameters
fields – The fields of the document. The fields must be a dict.
Returns
The metadata of the document. T... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.alibabacloud_opensearch.create_metadata.html |
f3ecbcdac98a-0 | langchain.vectorstores.awadb.AwaDB¶
class langchain.vectorstores.awadb.AwaDB(table_name: str = 'langchain_awadb', embedding: Optional[Embeddings] = None, log_and_data_dir: Optional[str] = None, client: Optional[awadb.Client] = None)[source]¶
Bases: VectorStore
Interface implemented by AwaDB vector stores.
Initialize wi... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f3ecbcdac98a-1 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs most similar to query.
create_table(table_name, **kwargs)
Create a new table.
delete(ids)
Delete by vector ID.
from_documents(documents[, embedding, ...])
Create an AwaDB vectorstore from a list of documents.
from_... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f3ecbcdac98a-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.awadb.AwaDB.html |
f3ecbcdac98a-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.awadb.AwaDB.html |
f3ecbcdac98a-4 | Create an AwaDB vectorstore from a list of documents.
If a log_and_data_dir specified, the table will be persisted there.
Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
embedding (Optional[Embeddings]) – Embedding function. Defaults to None.
table_name (str) – Name of the table to ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f3ecbcdac98a-5 | load_local(table_name: str, **kwargs: Any) → bool[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 maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diver... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
f3ecbcdac98a-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.
similarity_search_by_vector(embedding: Optional[List[float]] = None, k: int = 4, scores: Optional[list] = None, **kwargs: Any) → List[Docum... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.awadb.AwaDB.html |
cd73d016d4d2-0 | langchain.vectorstores.sklearn.BaseSerializer¶
class langchain.vectorstores.sklearn.BaseSerializer(persist_path: str)[source]¶
Bases: ABC
Abstract base class for saving and loading data.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.BaseSerializer.html |
0204bd178800-0 | langchain.vectorstores.faiss.FAISS¶
class langchain.vectorstores.faiss.FAISS(embedding_function: ~typing.Callable, index: ~typing.Any, docstore: ~langchain.docstore.base.Docstore, index_to_docstore_id: ~typing.Dict[int, str], relevance_score_fn: ~typing.Optional[~typing.Callable[[float], float]] = <function _default_re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-1 | 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.faiss.FAISS.html |
0204bd178800-2 | Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Return docs most similar to query.
similari... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-3 | ids – Optional list of unique IDs.
Returns
List of ids from adding the texts into the vectorstore.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
tex... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-4 | 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.faiss.FAISS.html |
0204bd178800-5 | faiss = FAISS.from_embeddings(text_embedding_pairs, embeddings)
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → FAISS[source]¶
Construct FAISS wrapper from raw documents.
This is a user friendly interface that:
Emb... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-6 | 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_marginal_relevance_search_by_vector(embedding... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-7 | k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch before filtering 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.
Return... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-8 | Defaults to 20.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
0204bd178800-9 | 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 the query text with
L2 distance in float. Lower score represents ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.faiss.FAISS.html |
5d650ce4fd1f-0 | langchain.vectorstores.base.VectorStoreRetriever¶
class langchain.vectorstores.base.VectorStoreRetriever(*, vectorstore: VectorStore, search_type: str = 'similarity', search_kwargs: dict = None)[source]¶
Bases: BaseRetriever, BaseModel
Create a new model by parsing and validating input data from keyword arguments.
Rais... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.base.VectorStoreRetriever.html |
d3493c26021d-0 | langchain.vectorstores.vectara.Vectara¶
class langchain.vectorstores.vectara.Vectara(vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None)[source]¶
Bases: VectorStore
Implementation of Vector Store using Vectara (https://vectara.com).
.. rubric:: Exam... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
d3493c26021d-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)
Delet... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
d3493c26021d-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.vectara.Vectara.html |
d3493c26021d-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) → VectaraRetr... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
d3493c26021d-4 | Construct Vectara wrapper from raw documents.
This is intended to be a quick way to get started.
.. rubric:: Example
from langchain import Vectara
vectara = Vectara.from_texts(
texts,
vectara_customer_id=customer_id,
vectara_corpus_id=corpus_id,
vectara_api_key=api_key,
)
max_marginal_relevance_search(q... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
d3493c26021d-5 | 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.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
d3493c26021d-6 | 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. 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 re... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.vectara.Vectara.html |
01d42f6ac0d3-0 | langchain.vectorstores.starrocks.StarRocks¶
class langchain.vectorstores.starrocks.StarRocks(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any)[source]¶
Bases: VectorStore
Wrapper around StarRocks vector database
You need a pymysql python package, and a valid account
to connect to StarRoc... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
01d42f6ac0d3-1 | 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.starrocks.StarRocks.html |
01d42f6ac0d3-2 | (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.starrocks.StarRocks.html |
01d42f6ac0d3-3 | 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.starrocks.StarRocks.html |
01d42f6ac0d3-4 | 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], embedding: Embeddings, metadatas: Optional[List[Dict[... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
01d42f6ac0d3-5 | 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_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
01d42f6ac0d3-6 | use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
c8f9596f9b7b-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]¶
Bases: VectorStore
Wrapper around Clarifai AI platform’s vector ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
c8f9596f9b7b-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 |
c8f9596f9b7b-2 | similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with score using Clarifai.
async aadd_documents(documents: Li... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
c8f9596f9b7b-3 | Parameters
texts (Iterable[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]], optional) – Optional list of IDs.
Returns
List of IDs of the added texts.
Return type
List[str]
async classmethod afrom_documents(documents: List[Documen... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
c8f9596f9b7b-4 | 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.clarifai.Clarifai.html |
c8f9596f9b7b-5 | Returns
Clarifai vectorstore.
Return type
Clarifai
classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict]] = 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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
c8f9596f9b7b-6 | 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_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
c8f9596f9b7b-7 | 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[Document, float]]¶
Return docs and relevance scores ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clarifai.Clarifai.html |
568505c854ba-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 =... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-1 | vector_field (str, optional) – Specifies the field to store the vector.
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 connec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-2 | ssl_disabled (bool, optional) – Disables SSL usage.
ssl_verify_cert (bool, optional) – Verifies the server’s certificate.
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.... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-3 | from langchain.vectorstores import SingleStoreDB
os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db'
vectorstore = SingleStoreDB(OpenAIEmbeddings())
Methods
__init__(embedding, *[, distance_strategy, ...])
Initialize with necessary components.
aadd_documents(documents, **kwargs)
Run more documents through... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-4 | 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 documents. 2. Creates a new table for the embeddings in SingleStoreDB. 3. Adds the documents to the ne... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-5 | 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.singlestoredb.SingleStoreDB.html |
568505c854ba-6 | 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) → SingleStore... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-7 | Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, distance_strategy: DistanceStrategy = DistanceStrategy.DOT_PRODUCT, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = ... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
568505c854ba-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.singlestoredb.SingleStoreDB.html |
568505c854ba-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... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.singlestoredb.SingleStoreDB.html |
e3cc07d99ef0-0 | langchain.vectorstores.pinecone.Pinecone¶
class langchain.vectorstores.pinecone.Pinecone(index: Any, embedding_function: Callable, text_key: str, namespace: Optional[str] = None)[source]¶
Bases: VectorStore
Wrapper around Pinecone vector database.
To use, you should have the pinecone-client python package installed.
Ex... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
e3cc07d99ef0-1 | 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])
Return docs most similar to query.
asimilarity_search_by_vec... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
e3cc07d99ef0-2 | 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.pinecone.Pinecone.html |
e3cc07d99ef0-3 | 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.pinecone.Pinecone.html |
e3cc07d99ef0-4 | Return VectorStore initialized from documents and embeddings.
classmethod from_existing_index(index_name: str, embedding: Embeddings, text_key: str = 'text', namespace: Optional[str] = None) → Pinecone[source]¶
Load pinecone vectorstore from index name.
classmethod from_texts(texts: List[str], embedding: Embeddings, me... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
e3cc07d99ef0-5 | 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.pinecone.Pinecone.html |
e3cc07d99ef0-6 | Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Dictionary of argument(s) to filter on metadata
namespace – Namespace to search in. Default will search in ‘’ namespace.
Returns
List of Documents most similar to the query and score for each
similarity_s... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
e3cc07d99ef0-7 | filter – Dictionary of argument(s) to filter on metadata
namespace – Namespace to search in. Default will search in ‘’ namespace.
Returns
List of Documents most similar to the query and score for each | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pinecone.Pinecone.html |
7fd9d4a411bb-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]¶
Bases: VectorStore
Wrapper around OpenSearch as a vector database.
Example
fr... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
7fd9d4a411bb-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 most similar to query.
delete(ids)
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
... | https://api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.opensearch_vector_search.OpenSearchVectorSearch.html |
7fd9d4a411bb-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.opensearch_vector_search.OpenSearchVectorSearch.html |
7fd9d4a411bb-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.opensearch_vector_search.OpenSearchVectorSearch.html |
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