id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
3c70074abbe6-4 | 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, refresh_indices: bool = True, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – It... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-5 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-6 | search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-7 | Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
client_search(client: Any, index_name: str, script_query: Dict, size: int) → Any[source]¶
create_index(client: Any, index_n... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-8 | )
static get_user_agent() → str[source]¶
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selecte... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-9 | Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][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.
R... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
3c70074abbe6-10 | :param k: Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query.
Examples using ElasticVectorSearch¶
Elasticsearch
Memory in the Multi-Input Chain | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticVectorSearch.html |
fd5318317f8c-0 | langchain.vectorstores.tiledb.TileDB¶
class langchain.vectorstores.tiledb.TileDB(embedding: Embeddings, index_uri: str, metric: str, *, vector_index_uri: str = '', docs_array_uri: str = '', config: Optional[Mapping[str, Any]] = None, timestamp: Any = None, **kwargs: Any)[source]¶
Wrapper around TileDB vector database.
... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-1 | Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilar... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-3 | Returns
List of IDs of the added texts.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, timestamp: int = 0, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts – Iterable of ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-4 | Return docs selected using the maximal marginal relevance.
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs: Any) → VectorStore... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-5 | search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-6 | Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
consolidate_updates(**kwargs: Any) → None[source]¶
classmethod create(index_uri: str, index_type: str, dimensions: int, vec... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-7 | metadatas – List of metadata dictionaries to associate with documents.
metric – Optional, Metric to use for indexing. Defaults to “euclidean”.
index_type – Optional, Vector index type (“FLAT”, IVF_FLAT”)
config – Optional, TileDB config
index_timestamp – Optional, timestamp to write new texts with.
Example
from langcha... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-8 | index = TileDB.from_texts(texts, embeddings)
classmethod load(index_uri: str, embedding: Embeddings, *, metric: str = 'euclidean', config: Optional[Mapping[str, Any]] = None, timestamp: Any = None, **kwargs: Any) → TileDB[source]¶
Load a TileDB index from a URI.
Parameters
index_uri – The URI of the TileDB vector index... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-9 | Return docs selected using the maximal marginal relevance.
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 befor... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-10 | Returns
List of Documents and similarity scores selected by maximal marginalrelevance and score for each.
process_index_results(ids: List[int], scores: List[float], *, k: int = 4, filter: Optional[Dict[str, Any]] = None, score_threshold: float = 1.7976931348623157e+308) → List[Tuple[Document, float]][source]¶
Turns Til... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-11 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
fd5318317f8c-12 | 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
Distance as float. Lower score represents mor... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.TileDB.html |
2246864f3a53-0 | langchain.vectorstores.tencentvectordb.IndexParams¶
class langchain.vectorstores.tencentvectordb.IndexParams(dimension: int, shard: int = 1, replicas: int = 2, index_type: str = 'HNSW', metric_type: str = 'L2', params: Optional[Dict] = None)[source]¶
Tencent vector DB Index params.
See the following documentation for d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tencentvectordb.IndexParams.html |
c122f513a8cc-0 | langchain.vectorstores.pgvector.PGVector¶
class langchain.vectorstores.pgvector.PGVector(connection_string: str, embedding_function: Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = Fa... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-1 | collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
)
Attributes
distance_strategy
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectors... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-2 | Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
connect()
connection_string_from_db_params(driver, ...)
Return connection string from database parameters.
create_collection()
create_tables_if_not_exi... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-3 | Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(connection_string: str, embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-4 | Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str][source]¶
Add embeddings to the vectorstore.
Parameters
texts – Iterable of strings to add ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-5 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-6 | 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 top 5
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-7 | 0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples o... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-8 | “Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-9 | Postgres connection string is required
“Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
get_collection(session: Session) → Optional['CollectionStore'][source]¶
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
max_marginal_relevance_search(query: str, k: ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-10 | Maximal marginal relevance optimizes for similarity to query AND diversityamong selected documents.
Parameters
embedding (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mul... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-11 | Returns
List of Documents selected by maximal marginalrelevance to the query and score for each.
Return type
List[Tuple[Document, float]]
max_marginal_relevance_search_with_score_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs:... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-12 | Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] =... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
c122f513a8cc-13 | filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query and score for each.
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Examples using PGVector¶
PGV... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.PGVector.html |
8dc6f73f3905-0 | langchain.vectorstores.redis.filters.RedisText¶
class langchain.vectorstores.redis.filters.RedisText(field: str)[source]¶
A RedisFilterField representing a text field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
escaper
Methods
__init__(field)
equals(other)
__init__(field: str)¶
equals(other: RedisFilterField) →... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.filters.RedisText.html |
dcad88efba58-0 | langchain.vectorstores.redis.filters.RedisNum¶
class langchain.vectorstores.redis.filters.RedisNum(field: str)[source]¶
A RedisFilterField representing a numeric field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
escaper
Methods
__init__(field)
equals(other)
__init__(field: str)¶
equals(other: RedisFilterField) ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.filters.RedisNum.html |
654dbce58785-0 | langchain.vectorstores.llm_rails.LLMRailsRetriever¶
class langchain.vectorstores.llm_rails.LLMRailsRetriever[source]¶
Bases: VectorStoreRetriever
Retriever for LLMRails.
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 va... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-1 | Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
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.
add_documents(documents: List[... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-2 | Subclasses should override this method if they can run asynchronously.
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
a... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-3 | Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
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.
bind(**kwargs: Any) → R... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-4 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
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 creat... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-5 | namespace is [“langchain”, “llms”, “openai”]
get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
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 tha... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-6 | purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]]... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-7 | 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, **kwargs: Optional[Any... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-8 | Returns
A new Runnable that will try the original runnable, and then each
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 Ru... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
654dbce58785-9 | The type of input this runnable accepts specified as a type annotation.
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
The type of output this runnable produces specified as a type annotation.
allowed_search_types: ClassVar[Collection[str]] = ('similarity', 'similarity_score_threshold', 'mmr')¶
prop... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.llm_rails.LLMRailsRetriever.html |
ff961d09f7f0-0 | langchain.vectorstores.tair.Tair¶
class langchain.vectorstores.tair.Tair(embedding_function: Embeddings, url: str, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', search_params: Optional[dict] = None, **kwargs: Any)[source]¶
Tair vector store.
Attributes
embeddings
Access the query embedd... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-1 | Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
create_index_if_not_exist(dim, ...)
delete([ids])
Delete ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-2 | async aadd_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]
async aadd_texts(texts: Iterable[s... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-3 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-4 | )
# 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-5 | **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)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, floa... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-6 | Connect to an existing Tair index.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: str = 'langchain', content_key: str = 'content', metadata_key: str = 'metadata', **kwargs: Any) → Tair[source]¶
Return VectorStore initialized from texts and embeddings.... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-7 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
ff961d09f7f0-8 | filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Examples using Tair¶
Tair | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tair.Tair.html |
e11dff38b67b-0 | langchain.vectorstores.redis.schema.read_schema¶
langchain.vectorstores.redis.schema.read_schema(index_schema: Optional[Union[Dict[str, List[Any]], str, PathLike]]) → Dict[str, Any][source]¶
Reads in the index schema from a dict or yaml file.
Check if it is a dict and return RedisModel otherwise, check if it’s a path a... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.redis.schema.read_schema.html |
dd0b6d2eaadb-0 | langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch¶
class langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
HnswLib storage using DocArray package.
To use it, you should have the docarray package with version >=0.32.0 installed.
You can install it with... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-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 an... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-2 | Initialize a vector store from DocArray’s DocIndex.
async aadd_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.
Retu... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-4 | Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-5 | Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-6 | Parameters
embedding (Embeddings) – Embedding function.
work_dir (str) – path to the location where all the data will be stored.
n_dim (int) – dimension of an embedding.
dist_metric (str) – Distance metric for DocArrayHnswSearch can be one of:
“cosine”, “ip”, and “l2”. Defaults to “cosine”.
max_elements (int) – Maximum... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-7 | work_dir (str) – path to the location where all the data will be stored.
n_dim (int) – dimension of an embedding.
**kwargs – Other keyword arguments to be passed to the __init__ method.
Returns
DocArrayHnswSearch Vector Store
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
dd0b6d2eaadb-9 | Return docs most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of documents most similar to the query text and
cosine distance in float for each.
Lower score represents more similarity.
Examples using DocArrayHnswSearch¶
DocArra... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch.html |
30110f6d6bf1-0 | langchain.vectorstores.scann.normalize¶
langchain.vectorstores.scann.normalize(x: ndarray) → ndarray[source]¶
Normalize vectors to unit length. | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.scann.normalize.html |
ffb1942e1148-0 | langchain.vectorstores.pgvector.DistanceStrategy¶
class langchain.vectorstores.pgvector.DistanceStrategy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies.
EUCLIDEAN = 'l2'¶
COSINE = 'cosine'¶
MAX_INNER_PRODUCT = 'inner'¶ | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.pgvector.DistanceStrategy.html |
316b771b376a-0 | langchain.vectorstores.elastic_vector_search.ElasticKnnSearch¶
class langchain.vectorstores.elastic_vector_search.ElasticKnnSearch(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None,... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-1 | that contains the original text data.
Type
str, optional
Usage:>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_cont... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-2 | Type
str, optional
Usage:>>> from embeddings import Embeddings
>>> embedding = Embeddings.load('glove')
>>> es_search = ElasticKnnSearch('my_index', embedding)
>>> es_search.add_texts(['Hello world!', 'Another text'])
>>> results = es_search.knn_search('Hello')
[(Document(page_content='Hello world!', metadata={}), 0.9)... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-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 and relevance scores in the range [0, 1]... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-4 | similarity_search_with_score(query[, k])
Pass through to knn_search including score
__init__(index_name: str, embedding: Embeddings, es_connection: Optional['Elasticsearch'] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_password: Optional[str] = None, vector_query_field: Optional[str] = '... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-5 | to associate with the texts.
model_id (str, optional) – The ID of the model to use for transforming the
texts into vectors.
refresh_indices (bool, optional) – Whether to refresh the Elasticsearch
indices after adding the texts.
**kwargs – Arbitrary keyword arguments.
Returns
A list of IDs for the added texts.
async ade... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-6 | 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]) – Keyword arguments to pass to the
search function. ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-7 | 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 using specified search type.
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
R... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-8 | Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embed... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-9 | Perform a hybrid k-NN and text search on the Elasticsearch index.
Parameters
query (str, optional) – The query text to search for.
k (int, optional) – The number of nearest neighbors to return.
query_vector (List[float], optional) – The query vector to search for.
model_id (str, optional) – The ID of the model to use f... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-10 | query_vector (List[float], optional) – The query vector to search for.
model_id (str, optional) – The ID of the model to use for transforming the
query text into a vector.
size (int, optional) – The number of search results to return.
source (bool, optional) – Whether to return the source of the search results.
fields ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-11 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
316b771b376a-12 | filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
similarity_search_with_score(query: str, k: int = 10, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Pass through to knn_search including score | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.elastic_vector_search.ElasticKnnSearch.html |
ff97be09cfdb-0 | langchain.vectorstores.neo4j_vector.SearchType¶
class langchain.vectorstores.neo4j_vector.SearchType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies.
VECTOR = 'vector'¶
HYBRID = 'hybrid'¶ | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.neo4j_vector.SearchType.html |
add3c1fbb7b3-0 | langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch¶
class langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
In-memory DocArray storage for exact search.
To use it, you should have the docarray package with version >=0.32.0 installed.
... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-1 | 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 and relevance scores in the range [0, 1]... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-2 | Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-3 | Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_marginal_relevance_search(query: str, k: int = 4, fetch_... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-4 | Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
search_type="mmr",
search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the t... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-5 | Return docs and relevance scores in the range [0, 1], asynchronously.
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 ... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-6 | **kwargs – Other keyword arguments to be passed to the get_doc_cls method.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, **kwargs: Any) → DocArrayInMemorySearch[source]¶
Create an DocArrayInMemorySearch store and insert data.
Parameters
texts (List[str... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-7 | Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevan... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
add3c1fbb7b3-8 | Returns
List of Documents most similar to the query vector.
similarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]¶
Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to re... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.docarray.in_memory.DocArrayInMemorySearch.html |
b1b9609ea896-0 | langchain.vectorstores.tiledb.get_documents_array_uri¶
langchain.vectorstores.tiledb.get_documents_array_uri(uri: str) → str[source]¶ | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.tiledb.get_documents_array_uri.html |
888d179489b8-0 | langchain.vectorstores.dashvector.DashVector¶
class langchain.vectorstores.dashvector.DashVector(collection: Any, embedding: Embeddings, text_field: str)[source]¶
DashVector vector store.
To use, you should have the dashvector python package installed.
Example
from langchain.vectorstores import dashvector
from langchai... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-1 | 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_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-2 | Initialize with DashVector collection.
async aadd_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[... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-3 | **kwargs – Other keyword arguments that subclasses might use.
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 documen... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-4 | 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... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-5 | 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 and r... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
888d179489b8-6 | Return DashVector VectorStore initialized from texts and embeddings.
This is the quick way to get started with dashvector vector store.
Example
from langchain.vectorstores import DashVector
from langchain.embeddings import OpenAIEmbeddings
import dashvector
embeddings = OpenAIEmbeddings()
dashvector = DashVector.from_d... | lang/api.python.langchain.com/en/latest/vectorstores/langchain.vectorstores.dashvector.DashVector.html |
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