id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
47575929e1fa-7 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-8 | NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
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: ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
47575929e1fa-9 | alone. The default name for it is metadata.
Returns
List of documents
Return type
List[Document]
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using StarRocks¶
StarRocks | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.StarRocks.html |
fbdb098de978-0 | langchain.vectorstores.chroma.Chroma¶
class langchain.vectorstores.chroma.Chroma(collection_name: str = 'langchain', embedding_function: Optional[Embeddings] = None, persist_directory: Optional[str] = None, client_settings: Optional[chromadb.config.Settings] = None, collection_metadata: Optional[Dict] = None, client: O... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-1 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-2 | Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with Chroma.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_relevance_scores(...)
Return docs most similar to embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-3 | 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_images(uris: List[str], metadatas: Op... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-4 | Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-5 | lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-6 | Return docs most similar to embedding vector.
async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Docume... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-7 | Otherwise, the data will be ephemeral in-memory.
Parameters
collection_name (str) – Name of the collection to create.
persist_directory (Optional[str]) – Directory to persist the collection.
ids (Optional[List[str]]) – List of document IDs. Defaults to None.
documents (List[Document]) – List of documents to add to the ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-8 | ids (Optional[List[str]]) – List of document IDs. Defaults to None.
client_settings (Optional[chromadb.config.Settings]) – Chroma client settings
collection_metadata (Optional[Dict]) – Collection configurations.
Defaults to None.
Returns
Chroma vectorstore.
Return type
Chroma
get(ids: Optional[OneOrMany[ID]] = None, wh... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-9 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-10 | Persist the collection.
This can be used to explicitly persist the data to disk.
It will also be called automatically when the object is destroyed.
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 = ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-11 | Return docs most similar to embedding vector and similarity score.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – 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
t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
fbdb098de978-12 | Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
update_document(document_id: str, document: Document) → None[source]¶
Update a document in the collection.
Parameters
document_id (str) – ID of the document to update.
document (Document) – Document to update.
update_documents(ids: List[st... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.chroma.Chroma.html |
ca7b0729d867-0 | langchain.vectorstores.scann.dependable_scann_import¶
langchain.vectorstores.scann.dependable_scann_import() → Any[source]¶
Import scann if available, otherwise raise error. | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.dependable_scann_import.html |
a96a98c92f98-0 | langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch¶
class langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch(collection: Collection[CosmosDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[sou... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-1 | add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStor... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-2 | Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_index_name()
Returns the ind... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-3 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-4 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-5 | Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-6 | Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-7 | the search speed and latency will be slow.
After your initial setup, you should go ahead and tune
the numLists parameter using the above guidance.
Parameters
num_lists – This integer is the number of clusters that the
inverted file (IVF) index uses to group the vector data.
We recommend that numLists is set to document... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-8 | Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
Parameters
connection_string – The MongoDB vCore instance connection string
namespace – The namespace (database.collection)
embedding – The embedding utility
**kwargs – Dynamic keyword arguments
Returns
an instance of the vector store
classmethod... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-9 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
a96a98c92f98-10 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azure_cosmos_db.AzureCosmosDBVectorSearch.html |
0c52a28c8bf8-0 | langchain.vectorstores.clickhouse.Clickhouse¶
class langchain.vectorstores.clickhouse.Clickhouse(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any)[source]¶
ClickHouse VectorSearch vector store.
You need a clickhouse-connect python package, and a valid account
to connect to ClickHouse.
C... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-1 | amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-2 | similarity_search_with_relevance_scores(query)
Perform a similarity search with ClickHouse
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
__init__(embedding: Embeddings, config: Optional[ClickhouseSettings] = None, **kwargs: Any) → None[source]¶
ClickHouse Wrapper to LangChain
embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-3 | ids – Optional list of ids to associate with the texts.
batch_size – Batch size of insertion
metadata – Optional column data to be inserted
Returns
List of ids from adding the texts into the VectorStore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteri... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-4 | the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-5 | search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to q... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-6 | Return type
Optional[bool]
drop() → None[source]¶
Helper function: Drop data
escape_str(value: str) → str[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], emb... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-7 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-8 | where_str (Optional[str], optional) – where condition string.
Defaults to None.
NOTE – Please do not let end-user to fill this and always be aware
of SQL injection. When dealing with metadatas, remember to
use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
Returns
List... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
0c52a28c8bf8-9 | use {self.metadata_column}.attribute instead of attribute
alone. The default name for it is metadata.
Returns
List of (Document, similarity)
Return type
List[Document]
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using Clickhouse¶
... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.clickhouse.Clickhouse.html |
a81728c93802-0 | langchain.vectorstores.deeplake.DeepLake¶
class langchain.vectorstores.deeplake.DeepLake(dataset_path: str = './deeplake/', token: Optional[str] = None, embedding: Optional[Embeddings] = None, embedding_function: Optional[Embeddings] = None, read_only: bool = False, ingestion_batch_size: int = 1000, num_workers: int = ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-1 | ... )
>>>
>>> # Create a vector store in the Deep Lake Managed Tensor Database
>>> data = DeepLake(
... path = "hub://org_id/dataset_name",
... runtime = {"tensor_db": True},
... )
Parameters
dataset_path (str) – Path to existing dataset or where to create
a new one. Defaults to _LANGCHAIN_DEFAULT_DEEPLAK... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-2 | or connected to Deep Lake. Not for in-memory or local datasets.
tensor_db - Hosted Managed Tensor Database that isresponsible for storage and query execution. Only for data stored in
the Deep Lake Managed Database. Use runtime = {“db_engine”: True}
during dataset creation.
runtime (Dict, optional) – Parameters for crea... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-3 | Methods
__init__([dataset_path, token, embedding, ...])
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 the embeddings and add to the vectorstore... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-4 | delete([ids])
Delete the entities in the dataset.
delete_dataset()
Delete the collection.
ds()
force_delete_by_path(path)
Force delete dataset by path.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts[, embedding, metadatas, ...])
Create a Dee... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-5 | The DeepLakeVectorStore is located at the specified path.
Examples
>>> # Create a vector store with default tensors
>>> deeplake_vectorstore = DeepLake(
... path = <path_for_storing_Data>,
... )
>>>
>>> # Create a vector store in the Deep Lake Managed Tensor Database
>>> data = DeepLake(
... path = "hub:/... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-6 | location of the Vector Store. It is the default option.
python - Pure-python implementation that runs on the client.WARNING: using this with big datasets can lead to memory
issues. Data can be stored anywhere.
compute_engine - C++ implementation of the Deep Lake ComputeEngine that runs on the client. Can be used for an... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-7 | - “COS” corresponds to DistanceType.COSINE_SIMILARITY.
additional_params: Additional parameters for fine-tuning the index.
**kwargs – Other optional keyword arguments.
Raises
ValueError – If some condition is not met.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-8 | metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
ids (Optional[List[str]], optional) – Optional list of IDs.
embedding_function (Optional[Embeddings], optional) – Embedding function
to use to convert the text into embeddings.
**kwargs (Any) – Any additional keyword arguments passed is not suppor... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-9 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-10 | )
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, **kwa... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-11 | Delete the entities in the dataset.
Parameters
ids (Optional[List[str]], optional) – The document_ids to delete.
Defaults to None.
**kwargs – Other keyword arguments that subclasses might use.
- filter (Optional[Dict[str, str]], optional): The filter to delete by.
- delete_all (Optional[bool], optional): Whether to dro... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-12 | dataset_path (str) –
The full path to the dataset. Can be:
Deep Lake cloud path of the form hub://username/dataset_name.To write to Deep Lake cloud datasets,
ensure that you are logged in to Deep Lake
(use ‘activeloop login’ from command line)
AWS S3 path of the form s3://bucketname/path/to/dataset.Credentials are req... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-13 | >>> data = vector_store.max_marginal_relevance_search(
… query = <query_to_search>,
… embedding_function = <embedding_function_for_query>,
… k = <number_of_items_to_return>,
… exec_option = <preferred_exec_option>,
… )
Parameters
query – Text to look up documents similar to.
k – Number of Do... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-14 | whatever distance metric user specifies.
**kwargs – Additional keyword arguments
Returns
List of Documents selected by maximal marginal relevance.
Raises
ValueError – when MRR search is on but embedding function is
not specified.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: i... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-15 | any data stored in or connected to Deep Lake. It cannot be 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 runt... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-16 | query (str) – Text to look up similar documents.
**kwargs – Additional keyword arguments include:
embedding (Callable): Embedding function to use. Defaults to None.
distance_metric (str): ‘L2’ for Euclidean, ‘L1’ for Nuclear, ‘max’
for L-infinity, ‘cos’ for cosine, ‘dot’ for dot product.
Defaults to ‘L2’.
filter (Union... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-17 | 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.
Examples
>>> # Search using an embedding
>>> data = vector_store.similarit... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-18 | for data stored in the Deep Lake Managed Database.
To store datasets in this database, specify
runtime = {“db_engine”: True} during dataset creation.
distance_metric (str): L2 for Euclidean, L1 for Nuclear,max for L-infinity distance, cos for cosine similarity,
‘dot’ for dot product. Defaults to L2.
deep_memory (bool):... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-19 | >>> data = vector_store.similarity_search_with_score(
… query=<your_query>,
… embedding=<your_embedding_function>
… k=<number_of_items_to_return>,
… exec_option=<preferred_exec_option>,
… )
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
**kwargs ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
a81728c93802-20 | in the Vector Store initialization. If True, the distance metric
is set to “deepmemory_distance”, which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to “COS” or
whatever distance metric user specifies.
Returns
List of d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.deeplake.DeepLake.html |
11c02fe49fb4-0 | langchain.vectorstores.redis.filters.RedisFilterExpression¶
class langchain.vectorstores.redis.filters.RedisFilterExpression(_filter: Optional[str] = None, operator: Optional[RedisFilterOperator] = None, left: Optional[RedisFilterExpression] = None, right: Optional[RedisFilterExpression] = None)[source]¶
A logical expr... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.filters.RedisFilterExpression.html |
8d37689a5587-0 | langchain.vectorstores.elasticsearch.ElasticsearchStore¶
class langchain.vectorstores.elasticsearch.ElasticsearchStore(index_name: str, *, embedding: ~typing.Optional[~langchain.schema.embeddings.Embeddings] = None, es_connection: ~typing.Optional[Elasticsearch] = None, es_url: ~typing.Optional[str] = None, es_cloud_id... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-1 | es_connection – Optional pre-existing Elasticsearch connection.
vector_query_field – Optional. Name of the field to store
the embedding vectors in.
query_field – Optional. Name of the field to store the texts in.
strategy – Optional. Retrieval strategy to use when searching the index.
Defaults to ApproxRetrievalStrateg... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-2 | If you want to use the Brute force / Exact strategy for searching vectors, you
can pass in the ExactRetrievalStrategy to the ElasticsearchStore constructor.
Example
from langchain.vectorstores import ElasticsearchStore
from langchain.embeddings.openai import OpenAIEmbeddings
vectorstore = ElasticsearchStore(
embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-3 | add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_embeddings(text_embeddings[, metadatas, ...])
Add the given texts and embeddings to the vectorstore.
add_texts(texts[, metadatas, ids, ...])
Run more texts through the embeddings and add to the vectorstore.
adel... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-4 | from_texts(texts[, embedding, metadatas, ...])
Construct ElasticsearchStore wrapper from raw documents.
get_user_agent()
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 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-5 | embed the query text within the stack. Requires
embedding model to be deployed to Elasticsearch.
hybrid – Optional. If True, will perform a hybrid search
using both the knn query and a text query.
Defaults to False.
rrf – Optional. rrf is Reciprocal Rank Fusion.
When hybrid is True,
and rrf is True, then rrf: {}.
and r... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-6 | within the stack. Requires embedding model to be
deployed to Elasticsearch.
__init__(index_name: str, *, embedding: ~typing.Optional[~langchain.schema.embeddings.Embeddings] = None, es_connection: ~typing.Optional[Elasticsearch] = None, es_url: ~typing.Optional[str] = None, es_cloud_id: ~typing.Optional[str] = None, es... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-7 | (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, refresh_indices: bool = True, create_index_if_not_e... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-8 | refresh_indices – Whether to refresh the Elasticsearch indices
after adding the texts.
create_index_if_not_exists – Whether to create the Elasticsearch
index if it doesn’t already exist.
*bulk_kwargs – Additional arguments to pass to Elasticsearch bulk.
- chunk_size: Optional. Number of texts to add to the
index at a t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-9 | 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]) ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-10 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-11 | Run similarity search with distance asynchronously.
static connect_to_elasticsearch(*, es_url: Optional[str] = None, cloud_id: Optional[str] = None, api_key: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None) → Elasticsearch[source]¶
delete(ids: Optional[List[str]] = None, refresh_ind... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-12 | es_connection – Optional pre-existing Elasticsearch connection.
vector_query_field – Optional. Name of the field
to store the embedding vectors in.
query_field – Optional. Name of the field to store the texts in.
bulk_kwargs – Optional. Additional arguments to pass to
Elasticsearch bulk.
classmethod from_texts(texts: L... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-13 | strategy to use. Defaults to “COSINE”.
can be one of “COSINE”,
“EUCLIDEAN_DISTANCE”, “DOT_PRODUCT”.
bulk_kwargs – Optional. Additional arguments to pass to
Elasticsearch bulk.
static get_user_agent() → str[source]¶
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, fields... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-14 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
8d37689a5587-15 | Return Elasticsearch documents most similar to query, along with scores.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
filter – Array of Elasticsearch filter clauses to apply to the query.
Returns
List of Documents most similar to the embedding and s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elasticsearch.ElasticsearchStore.html |
1fc5e8e9327a-0 | langchain.vectorstores.starrocks.has_mul_sub_str¶
langchain.vectorstores.starrocks.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string has multiple substrings.
:param s: The string to check
:param *args: The substrings to check for in the string
Returns
True if all substrings are present in the string... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.starrocks.has_mul_sub_str.html |
5996468194e5-0 | langchain.vectorstores.sklearn.ParquetSerializer¶
class langchain.vectorstores.sklearn.ParquetSerializer(persist_path: str)[source]¶
Serializes data in Apache Parquet format using the pyarrow package.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.sklearn.ParquetSerializer.html |
b27397df202a-0 | langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever¶
class langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever[source]¶
Bases: BaseRetriever
Retriever that uses Azure Cognitive Search.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-1 | Subclasses should override this method if they can batch more efficiently;
e.g., if the underlying runnable uses an API which supports a batch mode.
async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-2 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-3 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic m... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-4 | exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, i... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-5 | Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific co... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-6 | Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclu... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-7 | classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-8 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
b27397df202a-9 | The type of output this runnable produces specified as a type annotation.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this runnable.
property input_schema: Type[pydantic.main.BaseModel]¶
The type of input this runnable accepts specified as a pydantic ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.azuresearch.AzureSearchVectorStoreRetriever.html |
cd32967808bb-0 | langchain.vectorstores.zep.ZepVectorStore¶
class langchain.vectorstores.zep.ZepVectorStore(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None)[source]¶
Zep vector store.
It provides methods for adding texts or documents... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-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 distance.
__init__(collection_name: str, api_url: s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-3 | Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
document_ids – Optional list of document ids associated with the texts.
kwargs – vectorstore specific parameters
Returns
L... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-4 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-5 | )
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(query: str, search_type: str, metad... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-6 | Raises
ValueError – If no UUIDs are provided.
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: Optional[Embeddings] = None, metadatas: Optional[List[dict]] ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-7 | 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.
Zep determines this automatically and this para... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-8 | Returns
List of Documents selected by maximal marginal relevance.
search(query: str, search_type: str, metadata: Optional[Dict[str, Any]] = None, k: int = 3, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, metadata: Option... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
cd32967808bb-9 | Run similarity search with distance.
Examples using ZepVectorStore¶
Zep | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.zep.ZepVectorStore.html |
2f690b24f704-0 | langchain.vectorstores.redis.schema.HNSWVectorField¶
class langchain.vectorstores.redis.schema.HNSWVectorField[source]¶
Bases: RedisVectorField
Schema for HNSW vector fields in Redis.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parse... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.HNSWVectorField.html |
2f690b24f704-1 | exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, i... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.HNSWVectorField.html |
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