langchain_community.vectorstores.redis.base.Redis¶

class langchain_community.vectorstores.redis.base.Redis(redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, str], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, key_prefix: Optional[str] = None, **kwargs: Any)[source]¶

Redis vector database.

To use, you should have the redis python package installed and have a running Redis Enterprise or Redis-Stack server

For production use cases, it is recommended to use Redis Enterprise as the scaling, performance, stability and availability is much better than Redis-Stack.

For testing and prototyping, however, this is not required. Redis-Stack is available as a docker container the full vector search API available.

# to run redis stack in docker locally
docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

Once running, you can connect to the redis server with the following url schemas: - redis://<host>:<port> # simple connection - redis://<username>:<password>@<host>:<port> # connection with authentication - rediss://<host>:<port> # connection with SSL - rediss://<username>:<password>@<host>:<port> # connection with SSL and auth

Examples:

The following examples show various ways to use the Redis VectorStore with LangChain.

For all the following examples assume we have the following imports:

from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings
Initialize, create index, and load Documents
from langchain_community.vectorstores import Redis
from langchain_community.embeddings import OpenAIEmbeddings

rds = Redis.from_documents(
    documents, # a list of Document objects from loaders or created
    embeddings, # an Embeddings object
    redis_url="redis://localhost:6379",
)
Initialize, create index, and load Documents with metadata
rds = Redis.from_texts(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    redis_url="redis://localhost:6379",
)

Initialize, create index, and load Documents with metadata and return keys

rds, keys = Redis.from_texts_return_keys(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    redis_url="redis://localhost:6379",
)

For use cases where the index needs to stay alive, you can initialize with an index name such that it’s easier to reference later

rds = Redis.from_texts(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    index_name="my-index",
    redis_url="redis://localhost:6379",
)

Initialize and connect to an existing index (from above)

# must pass in schema and key_prefix from another index
existing_rds = Redis.from_existing_index(
    embeddings, # an Embeddings object
    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://localhost:6379",
)

Advanced examples:

Custom vector schema can be supplied to change the way that Redis creates the underlying vector schema. This is useful for production use cases where you want to optimize the vector schema for your use case. ex. using HNSW instead of FLAT (knn) which is the default

vector_schema = {
    "algorithm": "HNSW"
}

rds = Redis.from_texts(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    vector_schema=vector_schema,
    redis_url="redis://localhost:6379",
)

Custom index schema can be supplied to change the way that the metadata is indexed. This is useful for you would like to use the hybrid querying (filtering) capability of Redis.

By default, this implementation will automatically generate the index schema according to the following rules:

  • All strings are indexed as text fields

  • All numbers are indexed as numeric fields

  • All lists of strings are indexed as tag fields (joined by

    langchain_community.vectorstores.redis.constants.REDIS_TAG_SEPARATOR)

  • All None values are not indexed but still stored in Redis these are

    not retrievable through the interface here, but the raw Redis client can be used to retrieve them.

  • All other types are not indexed

To override these rules, you can pass in a custom index schema like the following

tag:
    - name: credit_score
text:
    - name: user
    - name: job

Typically, the credit_score field would be a text field since it’s a string, however, we can override this behavior by specifying the field type as shown with the yaml config (can also be a dictionary) above and the code below.

rds = Redis.from_texts(
    texts, # a list of strings
    metadata, # a list of metadata dicts
    embeddings, # an Embeddings object
    index_schema="path/to/index_schema.yaml", # can also be a dictionary
    redis_url="redis://localhost:6379",
)

When connecting to an existing index where a custom schema has been applied, it’s important to pass in the same schema to the from_existing_index method. Otherwise, the schema for newly added samples will be incorrect and metadata will not be returned.

Initialize Redis vector store with necessary components.

Attributes

DEFAULT_VECTOR_SCHEMA

embeddings

Access the query embedding object if available.

schema

Return the schema of the index.

Methods

__init__(redis_url, index_name, embedding[, ...])

Initialize Redis vector store 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.

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 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 a list of texts.

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, filter, ...])

Run similarity search

similarity_search_by_vector(embedding[, k, ...])

Run similarity search between a query vector and the indexed vectors.

similarity_search_limit_score(query[, k, ...])

[Deprecated] 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, ...])

Run similarity search with vector distance.

write_schema(path)

Write the schema to a yaml file.
Parameters

  • redis_url (str) –

  • index_name (str) –

  • embedding (Embeddings) –

  • index_schema (Optional[Union[Dict[str, str], str, os.PathLike]]) –

  • vector_schema (Optional[Dict[str, Union[str, int]]]) –

  • relevance_score_fn (Optional[Callable[[float], float]]) –

  • key_prefix (Optional[str]) –

  • kwargs (Any) –

__init__(redis_url: str, index_name: str, embedding: Embeddings, index_schema: Optional[Union[Dict[str, str], str, PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, key_prefix: Optional[str] = None, **kwargs: Any)[source]¶

Initialize Redis vector store with necessary components.

Parameters

  • redis_url (str) –

  • index_name (str) –

  • embedding (Embeddings) –

  • index_schema (Optional[Union[Dict[str, str], str, PathLike]]) –

  • vector_schema (Optional[Dict[str, Union[str, int]]]) –

  • relevance_score_fn (Optional[Callable[[float], float]]) –

  • key_prefix (Optional[str]) –

  • kwargs (Any) –

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.

  • documents (List[Document]) –

  • 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]) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

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

  • (List[Document] (documents) – Documents to add to the vectorstore.

  • documents (List[Document]) –

  • kwargs (Any) –

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, clean_metadata: bool = True, **kwargs: Any) → List[str][source]¶

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.

  • 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]) –

  • embedding (Embeddings) –

  • 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]) –

  • embedding (Embeddings) –

  • metadatas (Optional[List[dict]]) –

  • kwargs (Any) –

Return type

VST

Return docs selected using the maximal marginal relevance.

Parameters

  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

Return type

List[Document]

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.

Parameters

  • embedding (List[float]) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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
# 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) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

Return docs most similar to query.

Parameters

  • query (str) –

  • k (int) –

  • kwargs (Any) –

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]) –

  • k (int) –

  • kwargs (Any) –

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], asynchronously.

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 asynchronously.

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

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]) –

  • embedding (Embeddings) –

  • kwargs (Any) –

Return type

VST

classmethod from_existing_index(embedding: Embeddings, index_name: str, schema: Union[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

  • 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:

  1. Embeds documents.

  2. Creates a new Redis index if it doesn’t already exist

  3. 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

  • 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:

  1. Embeds documents.

  2. Creates a new Redis index if it doesn’t already exist

  3. Adds the documents to the newly created Redis index.

  4. 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.

  • 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 of

the newly created documents.

Return type

Tuple[Redis, List[str]]

Raises

ValueError – If the number of metadatas does not match the number of texts.

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.

  • 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) –

  • search_type (str) –

  • kwargs (Any) –

Return type

List[Document]

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 query

text.

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 query

text.

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 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.

Notes

Deprecated since version 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

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 are

most 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¶