Tony20100/langchain_full huggingface dataset:
Parameters
texts (Iterable[str]) – Iterable of strings/text to add to the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadatas.
Defaults to None.
embeddings (Optional[List[List[float]]], optional) – Optional pre-generated
embeddings. Defaults to None.
keys (List[str]) or ids (List[str]) – Identifiers of entries.
Defaults to None.
batch_size (int, optional) – Batch size to use for writes. Defaults to 1000.
clean_metadata (bool) – 
kwargs (Any) – 
Returns
List of ids added to 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
texts (List[str]) – Texts to add to the vectorstore.
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]
as_retriever(**kwargs: Any) → RedisVectorStoreRetriever[source]¶
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. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
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
kwargs (Any) – 
Returns
Retriever class for VectorStore.
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
)
# 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_threshold",
    search_kwargs={'score_threshold': 0.8}
)
# 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, **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]
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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]]
static delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool[source]¶
Delete a Redis entry.
Parameters
ids (Optional[List[str]]) – List of ids (keys in redis) to delete.
redis_url – Redis connection url. This should be passed in the kwargs
or set as an environment variable: REDIS_URL.
kwargs (Any) – 
Returns
kwargs (Any) – 
Returns
Whether or not the deletions were successful.
Return type
bool
Raises
ValueError – If the redis python package is not installed.
ValueError – If the ids (keys in redis) are not provided
static drop_index(index_name: str, delete_documents: bool, **kwargs: Any) → bool[source]¶
Drop a Redis search index.
Parameters
index_name (str) – Name of the index to drop.
delete_documents (bool) – Whether to drop the associated documents.
kwargs (Any) – 
Returns
Whether or not the drop was successful.
Return type
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_existing_index(embedding: Embeddings, index_name: str, schema: Union[Dict[str, List[Dict[str, str]]], str, PathLike], key_prefix: Optional[str] = None, **kwargs: Any) → Redis[source]¶
Connect to an existing Redis index.
Example
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
# must pass in schema and key_prefix from another index
existing_rds = Redis.from_existing_index(
    embeddings,
    index_name="my-index",
    schema=rds.schema, # schema dumped from another index
    key_prefix=rds.key_prefix, # key prefix from another index
    redis_url="redis://username:password@localhost:6379",
)
Parameters
redis_url="redis://username:password@localhost:6379",
)
Parameters
embedding (Embeddings) – Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
index_name (str) – Name of the index to connect to.
schema (Union[Dict[str, str], str, os.PathLike, Dict[str, ListOfDict]]) – Schema of the index and the vector schema. Can be a dict, or path to
yaml file.
key_prefix (Optional[str]) – Prefix to use for all keys in Redis associated
with this index.
**kwargs (Any) – Additional keyword arguments to pass to the Redis client.
Returns
Redis VectorStore instance.
Return type
Redis
Raises
ValueError – If the index does not exist.
ImportError – If the redis python package is not installed.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, index_schema: Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, **kwargs: Any) → Redis[source]¶
Create a Redis vectorstore from a list of texts.
This is a user-friendly interface that:
Embeds documents.
Creates a new Redis index if it doesn’t already exist
Adds the documents to the newly created Redis index.
This method will generate schema based on the metadata passed in
if the index_schema is not defined. If the index_schema is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class
To examine the schema options, initialize an instance of this class
and print out the schema using the Redis.schema` property. This
will include the content and content_vector classes which are
always present in the langchain schema.
Example
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
    texts,
    embeddings,
    redis_url="redis://username:password@localhost:6379"
)
Parameters
texts (List[str]) – List of texts to add to the vectorstore.
embedding (Embeddings) – Embedding model class (i.e. OpenAIEmbeddings)
for embedding queries.
metadatas (Optional[List[dict]], optional) – Optional list of metadata dicts
to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional) – Optional name of the index to create
or add to. Defaults to None.
(Optional[Union[Dict[str (index_schema) – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
ListOfDict] – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
str – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
os.PathLike]] – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
index_schema (Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]]) – 
vector_schema (Optional[Dict[str, Union[str, int]]]) – 
kwargs (Any) – 
Return type
Redis
:paramoptional):Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
Parameters
schema. Defaults to None.
Parameters
vector_schema (Optional[Dict[str, Union[str, int]]], optional) – Optional
vector schema to use. Defaults to None.
**kwargs (Any) – Additional keyword arguments to pass to the Redis client.
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
index_name (Optional[str]) – 
index_schema (Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]]) – 
Returns
Redis VectorStore instance.
Return type
Redis
Raises
ValueError – If the number of metadatas does not match the number of texts.
ImportError – If the redis python package is not installed.
classmethod from_texts_return_keys(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, index_schema: Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, **kwargs: Any) → Tuple[Redis, List[str]][source]¶
Create a Redis vectorstore from raw documents.
This is a user-friendly interface that:
Embeds documents.
Creates a new Redis index if it doesn’t already exist
Adds the documents to the newly created Redis index.
Returns the keys of the newly created documents once stored.
This method will generate schema based on the metadata passed in
if the index_schema is not defined. If the index_schema is defined,
it will compare against the generated schema and warn if there are
differences. If you are purposefully defining the schema for the
metadata, then you can ignore that warning.
To examine the schema options, initialize an instance of this class
To examine the schema options, initialize an instance of this class
and print out the schema using the Redis.schema` property. This
will include the content and content_vector classes which are
always present in the langchain schema.
Example
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redis, keys = Redis.from_texts_return_keys(
    texts,
    embeddings,
    redis_url="redis://localhost:6379"
)
Parameters
texts (List[str]) – List of texts to add to the vectorstore.
embedding (Embeddings) – Embeddings to use for the vectorstore.
metadatas (Optional[List[dict]], optional) – Optional list of metadata
dicts to add to the vectorstore. Defaults to None.
index_name (Optional[str], optional) – Optional name of the index to
create or add to. Defaults to None.
(Optional[Union[Dict[str (index_schema) – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
ListOfDict] – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
str – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
os.PathLike]] – optional):
Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
index_schema (Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]]) – 
vector_schema (Optional[Dict[str, Union[str, int]]]) – 
kwargs (Any) – 
Return type
Tuple[Redis, List[str]]
:paramoptional):Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
Parameters
schema. Defaults to None.
Parameters
vector_schema (Optional[Dict[str, Union[str, int]]], optional) – Optional
vector schema to use. Defaults to None.
**kwargs (Any) – Additional keyword arguments to pass to the Redis client.
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
index_name (Optional[str]) – 
index_schema (Optional[Union[Dict[str, List[Dict[str, str]]], str, PathLike]]) – 
Returns
Tuple of the Redis instance and the keys ofthe newly created documents.
Return type
Tuple[Redis, List[str]]
Raises
ValueError – If the number of metadatas does not match the number of texts.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[RedisFilterExpression] = None, return_metadata: bool = True, distance_threshold: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversityamong 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.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
return_metadata (bool, optional) – Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional) – Maximum vector distance
between selected documents and the query vector. Defaults to None.
kwargs (Any) – 
Returns
A 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
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]
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[RedisFilterExpression] = None, return_metadata: bool = True, distance_threshold: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
return_metadata (bool, optional) – Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional) – Maximum vector distance
between selected documents and the query vector. Defaults to None.
kwargs (Any) – 
Returns
A list of documents that are most similar to the querytext.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[RedisFilterExpression] = None, return_metadata: bool = True, distance_threshold: Optional[float] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search between a query vector and the indexed vectors.
Parameters
embedding (List[float]) – The query vector for which to find similar
documents.
k (int) – The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
return_metadata (bool, optional) – Whether to return metadata.
Defaults to True.
distance_threshold (Optional[float], optional) – Maximum vector distance
between selected documents and the query vector. Defaults to None.
kwargs (Any) – 
Returns
A list of documents that are most similar to the querytext.
Return type
Returns
A list of documents that are most similar to the querytext.
Return type
List[Document]
similarity_search_limit_score(query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any) → List[Document][source]¶
[Deprecated] Returns the most similar indexed documents to the query text within the
score_threshold range.
Deprecated: Use similarity_search with distance_threshold instead.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
score_threshold (float) – The minimum matching distance required
for a document to be considered a match. Defaults to 0.2.
kwargs (Any) – 
Returns
A list of documents that are most similar to the query textincluding the match score for each document.
Return type
List[Document]
Note
If there are no documents that satisfy the score_threshold value,
an empty list is returned.
Notes
Deprecated since version langchain-community==0.0.1: Use similarity_search(distance_threshold=0.1) instead.
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
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[RedisFilterExpression] = None, return_metadata: bool = True, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with vector distance.
The “scores” returned from this function are the raw vector
distances from the query vector. For similarity scores, use
similarity_search_with_relevance_scores.
Parameters
query (str) – The query text for which to find similar documents.
k (int) – The number of documents to return. Default is 4.
filter (RedisFilterExpression, optional) – Optional metadata filter.
Defaults to None.
return_metadata (bool, optional) – Whether to return metadata.
Defaults to True.
kwargs (Any) – 
Returns
A list of documents that aremost similar to the query with the distance for each document.
Return type
List[Tuple[Document, float]]
write_schema(path: Union[str, PathLike]) → None[source]¶
Write the schema to a yaml file.
Parameters
path (Union[str, PathLike]) – 
Return type
None
Examples using Redis¶
Redis
langchain_community.vectorstores.apache_doris.ApacheDoris¶
class langchain_community.vectorstores.apache_doris.ApacheDoris(embedding: Embeddings, *, config: Optional[ApacheDorisSettings] = None, **kwargs: Any)[source]¶
Apache Doris vector store.
You need a pymysql python package, and a valid account
to connect to Apache Doris.
For more information, please visit[Apache Doris official site](https://doris.apache.org/)
[Apache Doris github](https://github.com/apache/doris)
Constructor for Apache Doris.
Parameters
embedding (Embeddings) – Text embedding model.
config (ApacheDorisSettings) – Apache Doris client configuration information.
kwargs (Any) – 
Attributes
embeddings
Access the query embedding object if available.
metadata_column
Methods
__init__(embedding, *[, config])
Constructor for Apache Doris.
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, batch_size, ids])
Insert 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.