Tony20100/langchain_full huggingface dataset:
text_property_name (str) – 
metadata_property_name (str) – 
Return type
None
async amdelete(keys: Sequence[K]) → None¶
Delete the given keys and their associated values.
Parameters
keys (Sequence[K]) – A sequence of keys to delete.
Return type
None
async amget(keys: Sequence[K]) → List[Optional[V]]¶
Get the values associated with the given keys.
Parameters
keys (Sequence[K]) – A sequence of keys.
Returns
A sequence of optional values associated with the keys.
If a key is not found, the corresponding value will be None.
Return type
List[Optional[V]]
async amset(key_value_pairs: Sequence[Tuple[K, V]]) → None¶
Set the values for the given keys.
Parameters
key_value_pairs (Sequence[Tuple[K, V]]) – A sequence of key-value pairs.
Return type
None
async ayield_keys(*, prefix: Optional[str] = None) → Union[AsyncIterator[K], AsyncIterator[str]]¶
Get an iterator over keys that match the given prefix.
Parameters
prefix (str) – The prefix to match.
Returns
An iterator over keys that match the given prefix.
This method is allowed to return an iterator over either K or str
depending on what makes more sense for the given store.
Return type
Iterator[K | str]
mdelete(keys: Sequence[str]) → None[source]¶
Deletes a sequence of documents by key.
Parameters
keys (Sequence[str]) – A sequence of keys to delete.
Return type
None
mget(keys: Sequence[str]) → List[Optional[Document]][source]¶
Gets a batch of documents by id.
:param ids: List of ids for the text.
Returns
:param ids: List of ids for the text.
Returns
List of texts. If the key id is not found for any id record returns a Noneinstead.
Parameters
keys (Sequence[str]) – 
Return type
List[Optional[Document]]
mset(key_value_pairs: Sequence[Tuple[str, Document]]) → None[source]¶
Stores a series of documents using each keys
Parameters
key_value_pairs (Sequence[Tuple[K, V]]) – A sequence of key-value pairs.
Return type
None
yield_keys(*, prefix: Optional[str] = None) → Iterator[str][source]¶
Yields the keys of all documents in the storage.
Parameters
prefix (Optional[str]) – Ignored
Return type
Iterator[str]
langchain_community.vectorstores.baiduvectordb.ConnectionParams¶
class langchain_community.vectorstores.baiduvectordb.ConnectionParams(endpoint: str, api_key: str, account: str = 'root', connection_timeout_in_mills: int = 50000)[source]¶
Baidu VectorDB Connection params.
See the following documentation for details:
https://cloud.baidu.com/doc/VDB/s/6lrsob0wy
Attribute:
endpoint (str)The access address of the vector database serverthat the client needs to connect to.
api_key (str): API key for client to access the vector database server,which is used for authentication.
account (str) : Account for client to access the vector database server.
connection_timeout_in_mills (int) : Request Timeout.
Methods
__init__(endpoint, api_key[, account, ...])
Parameters
endpoint (str) – 
api_key (str) – 
account (str) – 
connection_timeout_in_mills (int) – 
__init__(endpoint: str, api_key: str, account: str = 'root', connection_timeout_in_mills: int = 50000)[source]¶
Parameters
endpoint (str) – 
api_key (str) – 
account (str) – 
connection_timeout_in_mills (int) – 
Examples using ConnectionParams¶
Baidu VectorDB
Tencent Cloud VectorDB
langchain_community.vectorstores.elasticsearch.ApproxRetrievalStrategy¶
class langchain_community.vectorstores.elasticsearch.ApproxRetrievalStrategy(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True)[source]¶
[Deprecated] Approximate retrieval strategy using the HNSW algorithm.
Notes
Deprecated since version 0.0.27: Use Use class in langchain-elasticsearch package instead.
Methods
__init__([query_model_id, hybrid, rrf])
before_index_setup(client, text_field, ...)
Executes before the index is created.
index(dims_length, vector_query_field, ...)
Create the mapping for the Elasticsearch index.
query(query_vector, query, k, fetch_k, ...)
Executes when a search is performed on the store.
require_inference()
Returns whether or not the strategy requires inference to be performed on the text before it is added to the index.
Parameters
query_model_id (Optional[str]) – 
hybrid (Optional[bool]) – 
rrf (Optional[Union[dict, bool]]) – 
__init__(query_model_id: Optional[str] = None, hybrid: Optional[bool] = False, rrf: Optional[Union[dict, bool]] = True)[source]¶
Parameters
query_model_id (Optional[str]) – 
hybrid (Optional[bool]) – 
rrf (Optional[Union[dict, bool]]) – 
before_index_setup(client: Elasticsearch, text_field: str, vector_query_field: str) → None¶
Executes before the index is created. Used for setting up
any required Elasticsearch resources like a pipeline.
Parameters
client (Elasticsearch) – The Elasticsearch client.
Parameters
client (Elasticsearch) – The Elasticsearch client.
text_field (str) – The field containing the text data in the index.
vector_query_field (str) – The field containing the vector
representations in the index.
Return type
None
index(dims_length: Optional[int], vector_query_field: str, similarity: Optional[DistanceStrategy]) → Dict[source]¶
Create the mapping for the Elasticsearch index.
Parameters
dims_length (Optional[int]) – 
vector_query_field (str) – 
similarity (Optional[DistanceStrategy]) – 
Return type
Dict
query(query_vector: Optional[List[float]], query: Optional[str], k: int, fetch_k: int, vector_query_field: str, text_field: str, filter: List[dict], similarity: Optional[DistanceStrategy]) → Dict[source]¶
Executes when a search is performed on the store.
Parameters
query_vector (Optional[List[float]]) – The query vector,
or None if not using vector-based query.
query (Optional[str]) – The text query, or None if not using text-based query.
k (int) – The total number of results to retrieve.
fetch_k (int) – The number of results to fetch initially.
vector_query_field (str) – The field containing the vector
representations in the index.
text_field (str) – The field containing the text data in the index.
filter (List[dict]) – List of filter clauses to apply to the query.
similarity (Optional[DistanceStrategy]) – The similarity strategy to use, or None if not using one.
Returns
The Elasticsearch query body.
Return type
Dict
require_inference() → bool¶
Returns whether or not the strategy requires inference
to be performed on the text before it is added to the index.
Returns
Whether or not the strategy requires inference
add_texts(texts[, metadatas, ids])
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 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(...)
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.
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].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
connect()
create_collection()
create_hnsw_extension()
create_hnsw_index([max_elements, dims, m, ...])
create_tables_if_not_exists()
delete([ids])
Delete by vector ID or other criteria.
delete_collection()
drop_tables()
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
from_existing_index(embedding[, ...])
from_texts(texts, embedding[, metadatas, ...])
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_collection(session)
get_connection_string(kwargs)
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
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, filter])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
collection_name (str) – 
collection_metadata (Optional[dict]) – 
pre_delete_collection (bool) – 
logger (Optional[logging.Logger]) – 
__init__(connection_string: str, embedding_function: Embeddings, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, pre_delete_collection: bool = False, logger: Optional[Logger] = None) → None[source]¶
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
collection_name (str) – 
collection_metadata (Optional[dict]) – 
pre_delete_collection (bool) – 
logger (Optional[Logger]) – 
Return type
None
logger (Optional[Logger]) – 
Return type
None
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
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.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
add_embeddings(texts: List[str], embeddings: List[List[float]], metadatas: List[dict], ids: List[str], **kwargs: Any) → None[source]¶
Parameters
texts (List[str]) – 
embeddings (List[List[float]]) – 
metadatas (List[dict]) – 
ids (List[str]) – 
kwargs (Any) – 
Return type
None
ids (List[str]) – 
kwargs (Any) – 
Return type
None
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
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
ids (Optional[List[str]]) – 
**kwargs – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – 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 documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
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].
0 is dissimilar, 1 is most similar.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
connect() → Connection[source]¶
Return type
Connection
create_collection() → None[source]¶
Return type
None
create_hnsw_extension() → None[source]¶
Return type
None
None
create_hnsw_extension() → None[source]¶
Return type
None
create_hnsw_index(max_elements: int = 10000, dims: int = 1536, m: int = 8, ef_construction: int = 16, ef_search: int = 16) → None[source]¶
Parameters
max_elements (int) – 
dims (int) – 
m (int) – 
ef_construction (int) – 
ef_search (int) – 
Return type
None
create_tables_if_not_exists() → None[source]¶
Return type
None
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
delete_collection() → None[source]¶
Return type
None
drop_tables() → None[source]¶
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGEmbedding
kwargs (Any) – 
Return type
PGEmbedding
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Parameters
text_embeddings (List[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGEmbedding
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Parameters
embedding (Embeddings) – 
collection_name (str) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGEmbedding
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
collection_name (str) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) –
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGEmbedding
get_collection(session: Session) → Optional[CollectionStore][source]¶
Parameters
session (Session) – 
Return type
Optional[CollectionStore]
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
Parameters
kwargs (Dict[str, Any]) – 
Return type
str
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query (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.
lambda_mult (float) – 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.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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 relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
Parameters
embedding (List[float]) – Embedding 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.
lambda_mult (float) – 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.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
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 (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
filter (Optional[dict]) – 
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) –
langchain_community.vectorstores.redis.filters.RedisText¶
class langchain_community.vectorstores.redis.filters.RedisText(field: str)[source]¶
RedisFilterField representing a text field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
SUPPORTED_VAL_TYPES
escaper
Methods
__init__(field)
equals(other)
Parameters
field (str) – 
__init__(field: str)¶
Parameters
field (str) – 
equals(other: RedisFilterField) → bool¶
Parameters
other (RedisFilterField) – 
Return type
bool
Examples using RedisText¶
Redis
The connection args used for this class comes in the form of a dict,
here are a few of the options:
host (str): The host of Hippo instance. Default at “localhost”.
port (str/int): The port of Hippo instance. Default at 7788.
user (str): Use which user to connect to Hippo instance. If user and
password are provided, we will add related header in every RPC call.
password (str): Required when user is provided. The passwordcorresponding to the user.
Example
from langchain_community.vectorstores import Hippo
from langchain_community.embeddings import OpenAIEmbeddings
embedding = OpenAIEmbeddings()
# Connect to a hippo instance on localhost
vector_store = Hippo.from_documents(
docs,
embedding=embeddings,
table_name=”langchain_test”,
connection_args=HIPPO_CONNECTION
)
Raises
ValueError – If the hippo-api python package is not installed.
Parameters
embedding_function (Embeddings) – 
table_name (str) – 
database_name (str) – 
number_of_shards (int) – 
number_of_replicas (int) – 
connection_args (Optional[Dict[str, Any]]) – 
index_params (Optional[dict]) – 
drop_old (Optional[bool]) – 
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function[, table_name, ...])
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.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.