langchain.vectorstores.redis.Redis

class langchain.vectorstores.redis.Redis(redis_url: str, index_name: str, embedding_function: Callable, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', relevance_score_fn: Optional[Callable[[float], float]] = None, distance_metric: Literal['COSINE', 'IP', 'L2'] = 'COSINE', **kwargs: Any)

Bases: VectorStore

Wrapper around Redis vector database.

To use, you should have the redis python package installed.

Example

from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Redis(
    redis_url="redis://username:password@localhost:6379"
    index_name="my-index",
    embedding_function=embeddings.embed_query,
)

To use a redis replication setup with multiple redis server and redis sentinels set “redis_url” to “redis+sentinel://” scheme. With this url format a path is needed holding the name of the redis service within the sentinels to get the correct redis server connection. The default service name is “mymaster”.

An optional username or password is used for booth connections to the rediserver and the sentinel, different passwords for server and sentinel are not supported. And as another constraint only one sentinel instance can be given:

Example

vectorstore = Redis(
    redis_url="redis+sentinel://username:password@sentinelhost:26379/mymaster/0"
    index_name="my-index",
    embedding_function=embeddings.embed_query,
)

Initialize with necessary components.

Methods

__init__(redis_url, index_name, ...[, ...])	Initialize with necessary components.
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, embeddings, ...])	Add more texts to the vectorstore.
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)
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 most similar to query.
delete([ids])	Delete a Redis entry.
drop_index(index_name, delete_documents, ...)	Drop a Redis search index.
from_documents(documents, embedding, **kwargs)	Return VectorStore initialized from documents and embeddings.
from_existing_index(embedding, index_name[, ...])	Connect to an existing Redis index.
from_texts(texts, embedding[, metadatas, ...])	Create a Redis vectorstore from raw documents.
from_texts_return_keys(texts, embedding[, ...])	Create a Redis vectorstore from raw documents.
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])	Returns the most similar indexed documents to the query text.
similarity_search_by_vector(embedding[, k])	Return docs most similar to embedding vector.
similarity_search_limit_score(query[, k, ...])	Returns the most similar indexed documents to the query text within the score_threshold range.
similarity_search_with_relevance_scores(query)	Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])	Return docs most similar to query.

Attributes

embeddings

Access the query embedding object if available.

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.

Returns

List of IDs of the added texts.

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, batch_size: int = 1000, **kwargs: Any) → List[str]

Add more texts to the vectorstore.

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.

Returns

List of ids added to the vectorstore

Return type

List[str]

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.

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) → RedisVectorStoreRetriever

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

static delete(ids: Optional[List[str]] = None, **kwargs: Any) → bool

Delete a Redis entry.

Parameters

ids – List of ids (keys) to delete.

Returns

Whether or not the deletions were successful.

Return type

bool

static drop_index(index_name: str, delete_documents: bool, **kwargs: Any) → bool

Drop a Redis search index.

Parameters

• index_name (str) – Name of the index to drop.

• delete_documents (bool) – Whether to drop the associated documents.

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.

classmethod from_existing_index(embedding: Embeddings, index_name: str, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', **kwargs: Any) → Redis

Connect to an existing Redis index.

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', **kwargs: Any) → Redis

Create a Redis vectorstore from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in Redis. 3. Adds the documents to the newly created Redis index.

This is intended to be a quick way to get started.

Example

from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch = RediSearch.from_texts(
    texts,
    embeddings,
    redis_url="redis://username:password@localhost:6379"
)

classmethod from_texts_return_keys(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, index_name: Optional[str] = None, content_key: str = 'content', metadata_key: str = 'metadata', vector_key: str = 'content_vector', distance_metric: Literal['COSINE', 'IP', 'L2'] = 'COSINE', **kwargs: Any) → Tuple[Redis, List[str]]

Create a Redis vectorstore from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Creates a new index for the embeddings in Redis. 3. Adds the documents to the newly created Redis index. 4. Returns the keys of the newly created documents.

This is intended to be a quick way to get started.

Example

from langchain.vectorstores import Redis
from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redisearch, keys = RediSearch.from_texts_return_keys(
    texts,
    embeddings,
    redis_url="redis://username:password@localhost:6379"
)

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 – 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

List of Documents selected by maximal marginal relevance.

max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]

Return docs selected using the maximal marginal relevance.

Maximal marginal 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]

Returns the most similar indexed documents to the query text.

Parameters

• query (str) – The query text for which to find similar documents.

• k (int) – The number of documents to return. Default is 4.

Returns

A list of documents that are most similar to the query text.

Return type

List[Document]

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

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_limit_score(query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any) → List[Document]

Returns the most similar indexed documents to the query text within the score_threshold range.

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 score required for a document to be considered a match. Defaults to 0.2. Because the similarity calculation algorithm is based on cosine similarity, the smaller the angle, the higher the similarity.

Returns

A list of documents that are most similar to the query text,

including 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.

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) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query and score for each

property embeddings: Optional[langchain.embeddings.base.Embeddings]

Access the query embedding object if available.

Examples using Redis

• Redis