Tony20100/langchain_full huggingface dataset:
langchain_community.vectorstores.redis.filters.RedisFilterExpression¶
class langchain_community.vectorstores.redis.filters.RedisFilterExpression(_filter: Optional[str] = None, operator: Optional[RedisFilterOperator] = None, left: Optional[RedisFilterExpression] = None, right: Optional[RedisFilterExpression] = None)[source]¶
Logical expression of RedisFilterFields.
RedisFilterExpressions can be combined using the & and | operators to create
complex logical expressions that evaluate to the Redis Query language.
This presents an interface by which users can create complex queries
without having to know the Redis Query language.
Filter expressions are not initialized directly. Instead they are built
by combining RedisFilterFields using the & and | operators.
Examples
>>> from langchain_community.vectorstores.redis import RedisTag, RedisNum
>>> brand_is_nike = RedisTag("brand") == "nike"
>>> price_is_under_100 = RedisNum("price") < 100
>>> filter = brand_is_nike & price_is_under_100
>>> print(str(filter))
(@brand:{nike} @price:[-inf (100)])
Methods
__init__([_filter, operator, left, right])
format_expression(left, right, operator_str)
Parameters
_filter (Optional[str]) – 
operator (Optional[RedisFilterOperator]) – 
left (Optional[RedisFilterExpression]) – 
right (Optional[RedisFilterExpression]) – 
__init__(_filter: Optional[str] = None, operator: Optional[RedisFilterOperator] = None, left: Optional[RedisFilterExpression] = None, right: Optional[RedisFilterExpression] = None)[source]¶
Parameters
_filter (Optional[str]) – 
operator (Optional[RedisFilterOperator]) – 
left (Optional[RedisFilterExpression]) –
langchain_community.vectorstores.vald.Vald¶
class langchain_community.vectorstores.vald.Vald(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[source]¶
Vald vector database.
To use, you should have the vald-client-python python package installed.
Example
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
texts = ['foo', 'bar', 'baz']
vald = Vald.from_texts(
    texts=texts,
    embedding=HuggingFaceEmbeddings(),
    host="localhost",
    port=8080,
    skip_strict_exist_check=False,
 )
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding[, host, port, ...])
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.
add_texts(texts[, metadatas, grpc_metadata, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
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])
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.
delete([ids, skip_strict_exist_check, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
param skip_strict_exist_check
Deprecated. This is not used basically.
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, radius, ...])
Return docs most similar to query.
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, ...])
Run similarity search with distance.
similarity_search_with_score_by_vector(embedding)
Parameters
embedding (Embeddings) – 
host (str) – 
port (int) – 
grpc_options (Tuple) – 
grpc_use_secure (bool) – 
grpc_credentials (Optional[Any]) – 
__init__(embedding: Embeddings, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None)[source]¶
Parameters
embedding (Embeddings) – 
host (str) – 
port (int) – 
grpc_options (Tuple) – 
grpc_use_secure (bool) – 
grpc_credentials (Optional[Any]) – 
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
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_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) → List[str][source]¶
Parameters
skip_strict_exist_check (bool) – Deprecated. This is not used basically.
texts (Iterable[str]) – 
metadatas (Optional[List[dict]]) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
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¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(ids: Optional[List[str]] = None, skip_strict_exist_check: bool = False, grpc_metadata: Optional[Any] = None, **kwargs: Any) → Optional[bool][source]¶
Parameters
skip_strict_exist_check (bool) – Deprecated. This is not used basically.
ids (Optional[List[str]]) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
Return type
Optional[bool]
classmethod from_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
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, host: str = 'localhost', port: int = 8080, grpc_options: Tuple = (('grpc.keepalive_time_ms', 10000), ('grpc.keepalive_timeout_ms', 10000)), grpc_use_secure: bool = False, grpc_credentials: Optional[Any] = None, grpc_metadata: Optional[Any] = None, skip_strict_exist_check: bool = False, **kwargs: Any) → Vald[source]¶
Parameters
skip_strict_exist_check (bool) – Deprecated. This is not used basically.
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
host (str) –
metadatas (Optional[List[dict]]) – 
host (str) – 
port (int) – 
grpc_options (Tuple) – 
grpc_use_secure (bool) – 
grpc_credentials (Optional[Any]) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
Return type
Vald
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
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.
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
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, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
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.
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.
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
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]
Return type
List[Document]
similarity_search(query: str, k: int = 4, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = 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.
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
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, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
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.
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
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
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, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, radius: float = -1.0, epsilon: float = 0.01, timeout: int = 3000000000, grpc_metadata: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) – 
radius (float) – 
epsilon (float) – 
timeout (int) – 
grpc_metadata (Optional[Any]) – 
kwargs (Any) – 
Return type
langchain_community.vectorstores.starrocks.has_mul_sub_str¶
langchain_community.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, False otherwise
Return type
bool
Parameters
s (str) – 
args (Any) –
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.
delete([ids])
Delete by vector ID or other criteria.
drop()
Helper function: Drop data
escape_str(value)
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Create StarRocks wrapper with existing texts
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, where_str])
Perform a similarity search with StarRocks
similarity_search_by_vector(embedding[, k, ...])
Perform a similarity search with StarRocks by vectors
similarity_search_with_relevance_scores(query)
similarity_search_with_relevance_scores(query)
Perform a similarity search with StarRocks
similarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
Parameters
embedding (Embeddings) – 
config (Optional[StarRocksSettings]) – 
kwargs (Any) – 
__init__(embedding: Embeddings, config: Optional[StarRocksSettings] = None, **kwargs: Any) → None[source]¶
StarRocks Wrapper to LangChain
embedding_function (Embeddings):
config (StarRocksSettings): Configuration to StarRocks Client
Parameters
embedding (Embeddings) – 
config (Optional[StarRocksSettings]) – 
kwargs (Any) – 
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
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_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, batch_size: int = 32, ids: Optional[Iterable[str]] = None, **kwargs: Any) → List[str][source]¶
Insert more texts through the embeddings and add to the VectorStore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the VectorStore.
ids (Optional[Iterable[str]]) – Optional list of ids to associate with the texts.
batch_size (int) – Batch size of insertion
metadata – Optional column data to be inserted
metadatas (Optional[List[dict]]) – 
kwargs (Any) – 
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
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]
drop() → None[source]¶
Helper function: Drop data
Return type
None
escape_str(value: str) → str[source]¶
Parameters
value (str) – 
Return type
str
classmethod from_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
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict[Any, Any]]] = None, config: Optional[StarRocksSettings] = None, text_ids: Optional[Iterable[str]] = None, batch_size: int = 32, **kwargs: Any) → StarRocks[source]¶
Create StarRocks wrapper with existing texts
Parameters
embedding_function (Embeddings) – Function to extract text embedding
texts (Iterable[str]) – List or tuple of strings to be added
config (StarRocksSettings, Optional) – StarRocks configuration
text_ids (Optional[Iterable], optional) – IDs for the texts.
Defaults to None.
batch_size (int, optional) – Batchsize when transmitting data to StarRocks.
Defaults to 32.
metadata (List[dict], optional) – metadata to texts. Defaults to None.
embedding (Embeddings) –
embedding (Embeddings) – 
metadatas (Optional[List[Dict[Any, Any]]]) – 
kwargs (Any) – 
Returns
StarRocks Index
Return type
StarRocks
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.
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
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, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with StarRocks
Parameters
query (str) – query string
k (int, optional) – Top K neighbors to retrieve. Defaults to 4.
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.
kwargs (Any) – 
Returns
List of Documents
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, where_str: Optional[str] = None, **kwargs: Any) → List[Document][source]¶
Perform a similarity search with StarRocks by vectors
Parameters