AsyncElasticsearchEmbeddingsCache

AsyncElasticsearchEmbeddingsCache(
  self,
  index_name: str,
  *,
  store_input: bool = True,
  metadata

Bases

ByteStore

Constructors

Attributes

Methods

View source on GitHub

Parameters

Name	Type	Description
`index_name`*	`str`	The name of the index or alias to use for the cache. If it doesn't exist, an index is created according to the default mapping.
`store_input`	`bool`	Default:`True` Whether to store the input text in the cache. Default True.
`metadata`	`dict`	Default:`None` Additional metadata to store in the cache for filtering. Must be JSON serializable.
`namespace`	`str`	Default:`None`
`maximum_duplicates_allowed`	`int`	Default:`1`
`client`	`AsyncElasticsearch`	Default:`None`
`es_url`	`str`	Default:`None`
`es_cloud_id`	`str`	Default:`None`
`es_user`	`str`	Default:`None`
`es_api_key`	`str`	Default:`None`
`es_password`	`str`	Default:`None`

constructor

__init__

Name	Type
index_name	str
store_input	bool
metadata	Optional[Dict[str, Any]]
namespace	Optional[str]
maximum_duplicates_allowed	int
client	Optional[AsyncElasticsearch]
es_url	Optional[str]
es_cloud_id	Optional[str]
es_user	Optional[str]
es_api_key	Optional[str]
es_password	Optional[str]

Setup:

Install langchain_elasticsearch and start Elasticsearch locally using the start-local script.

pip install -qU langchain_elasticsearch
curl -fsSL https://elastic.co/start-local | sh

This will create an elastic-start-local folder. To start Elasticsearch and Kibana:

cd elastic-start-local
./start.sh

Elasticsearch will be available at http://localhost:9200. The password for the elastic user and API key are stored in the .env file in the elastic-start-local folder.

Key init args:

index_name: str The name of the index or alias to use for the cache. store_input: bool Whether to store the input text in the cache. Default True. metadata: Optional[Dict[str, Any]] Additional metadata to store in the cache for filtering. namespace: Optional[str] A namespace to organize the cache. maximum_duplicates_allowed: int Maximum duplicate keys permitted when using aliases. Default 1.

Key init args — client params: client: Optional[AsyncElasticsearch or Elasticsearch] Pre-existing Elasticsearch connection. Either provide this OR credentials. es_url: Optional[str] URL of the Elasticsearch instance to connect to. es_cloud_id: Optional[str] Cloud ID of the Elasticsearch instance to connect to. es_user: Optional[str] Username to use when connecting to Elasticsearch. es_api_key: Optional[str] API key to use when connecting to Elasticsearch. es_password: Optional[str] Password to use when connecting to Elasticsearch.

Instantiate:

from langchain_elasticsearch import ElasticsearchEmbeddingsCache

cache = ElasticsearchEmbeddingsCache(
    index_name="embeddings-cache",
    es_url="http://localhost:9200"
)

Instantiate with API key:

from langchain_elasticsearch import ElasticsearchEmbeddingsCache

cache = ElasticsearchEmbeddingsCache(
    index_name="embeddings-cache",
    es_url="http://localhost:9200",
    es_api_key="your-api-key"
)

Instantiate from cloud:

from langchain_elasticsearch import ElasticsearchEmbeddingsCache

cache = ElasticsearchEmbeddingsCache(
    index_name="embeddings-cache",
    es_cloud_id="<cloud_id>",
    es_api_key="your-api-key"
)

Instantiate from existing connection:

from langchain_elasticsearch import ElasticsearchEmbeddingsCache
from elasticsearch import Elasticsearch

client = Elasticsearch("http://localhost:9200")
cache = ElasticsearchEmbeddingsCache(
    index_name="embeddings-cache",
    client=client
)

Use with CacheBackedEmbeddings:

from langchain.embeddings import CacheBackedEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_elasticsearch import ElasticsearchEmbeddingsCache

underlying_embeddings = OpenAIEmbeddings()
cache = ElasticsearchEmbeddingsCache(
    index_name="embeddings-cache",
    es_url="http://localhost:9200"
)
cached_embeddings = CacheBackedEmbeddings.from_bytes_store(
    underlying_embeddings,
    cache,
    namespace=underlying_embeddings.model
)

For synchronous applications, use the ElasticsearchEmbeddingsCache class. For asynchronous applications, use the AsyncElasticsearchEmbeddingsCache class.