viruPVS/langchain-docs huggingface dataset:
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
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]) – 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, 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.
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
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.
index_schema (Optional[Union[Dict[str, str], str, os.PathLike]], optional) – Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
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.
Returns
Redis VectorStore instance.
Return type
Redis
Raises
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, 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
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.
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.
index_schema (Optional[Union[Dict[str, str], str, os.PathLike]], optional) – Optional fields to index within the metadata. Overrides generated
schema. Defaults to None.
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.
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
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.
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.
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 – 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 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]¶
Return docs most similar to query using specified search type.
Return docs most similar to query using specified search type.
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.
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.
Returns
A list of documents that are most similar to the querytext.
Return type
List[Document]
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.
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.[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.
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 0.0.272: 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 – 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 of (doc, similarity_score)
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.
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.
Examples using Redis¶
Redis
langchain_community.vectorstores.vespa.VespaStore¶
class langchain_community.vectorstores.vespa.VespaStore(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None)[source]¶
Vespa vector store.
To use, you should have the python client library pyvespa installed.
Example
from langchain_community.vectorstores import VespaStore
from langchain_community.embeddings.openai import OpenAIEmbeddings
from vespa.application import Vespa
# Create a vespa client dependent upon your application,
# e.g. either connecting to Vespa Cloud or a local deployment
# such as Docker. Please refer to the PyVespa documentation on
# how to initialize the client.
vespa_app = Vespa(url="...", port=..., application_package=...)
# You need to instruct LangChain on which fields to use for embeddings
vespa_config = dict(
    page_content_field="text",
    embedding_field="embedding",
    input_field="query_embedding",
    metadata_fields=["date", "rating", "author"]
)
embedding_function = OpenAIEmbeddings()
vectorstore = VespaStore(vespa_app, embedding_function, **vespa_config)
Initialize with a PyVespa client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(app[, embedding_function, ...])
Initialize with a PyVespa client.
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.
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, ids])
Add texts 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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ids])
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_by_vector_with_score(...)
Performs similarity search from a embeddings vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
Run similarity search with distance.
__init__(app: Any, embedding_function: Optional[Embeddings] = None, page_content_field: Optional[str] = None, embedding_field: Optional[str] = None, input_field: Optional[str] = None, metadata_fields: Optional[List[str]] = None) → None[source]¶
Initialize with a PyVespa client.
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[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
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, **kwargs: Any) → List[str][source]¶
Add texts to the vectorstore.
Parameters
texts – Iterable of strings to add to the vectorstore.
metadatas – Optional list of metadatas associated with the texts.
ids – Optional list of ids associated with the texts.
kwargs – vectorstore specific parameters
Returns
List of ids from adding the texts into the vectorstore.
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other 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]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
async classmethod afrom_texts(texts: List[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_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
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) → VectorStoreRetriever[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
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
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)
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.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
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: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → VespaStore[source]¶
Return VectorStore initialized from texts and embeddings.
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult – Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal 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 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]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
Returns
List of Documents most similar to the query vector.
similarity_search_by_vector_with_score(query_embedding: List[float], k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶