langchain-elasticsearch 0.1.2

An integration package connecting Elasticsearch and LangChain

langchain-elasticsearch

This package contains the LangChain integration with Elasticsearch.

Installation

pip install -U langchain-elasticsearch

Elasticsearch setup

Elastic Cloud

You need a running Elasticsearch deployment. The easiest way to start one is through Elastic Cloud. You can sign up for a free trial.

1. Create a deployment
2. Get your Cloud ID:
   1. In the Elastic Cloud console, click "Manage" next to your deployment
   2. Copy the Cloud ID and paste it into the es_cloud_id parameter below
3. Create an API key:
   1. In the Elastic Cloud console, click "Open" next to your deployment
   2. In the left-hand side menu, go to "Stack Management", then to "API Keys"
   3. Click "Create API key"
   4. Enter a name for the API key and click "Create"
   5. Copy the API key and paste it into the es_api_key parameter below

Elastic Cloud

Alternatively, you can run Elasticsearch via Docker as described in the docs.

Usage

ElasticsearchStore

The ElasticsearchStore class exposes Elasticsearch as a vector store.

from langchain_elasticsearch import ElasticsearchStore

embeddings = ... # use a LangChain Embeddings class or ElasticsearchEmbeddings

vectorstore = ElasticsearchStore(
    es_cloud_id="your-cloud-id",
    es_api_key="your-api-key",
    index_name="your-index-name",
    embeddings=embeddings,
)

ElasticsearchRetriever

The ElasticsearchRetriever class can be user to implement more complex queries. This can be useful for power users and necessary if data was ingested outside of LangChain (for example using a web crawler).

def fuzzy_query(search_query: str) -> Dict:
    return {
        "query": {
            "match": {
                text_field: {
                    "query": search_query,
                    "fuzziness": "AUTO",
                }
            },
        },
    }


fuzzy_retriever = ElasticsearchRetriever.from_es_params(
    es_cloud_id="your-cloud-id",
    es_api_key="your-api-key",
    index_name="your-index-name",
    body_func=fuzzy_query,
    content_field=text_field,
)

fuzzy_retriever.get_relevant_documents("fooo")

ElasticsearchEmbeddings

The ElasticsearchEmbeddings class provides an interface to generate embeddings using a model deployed in an Elasticsearch cluster.

from langchain_elasticsearch import ElasticsearchEmbeddings

embeddings = ElasticsearchEmbeddings.from_credentials(
    model_id="your-model-id",
    input_field="your-input-field",
    es_cloud_id="your-cloud-id",
    es_api_key="your-api-key",
)

ElasticsearchChatMessageHistory

The ElasticsearchChatMessageHistory class stores chat histories in Elasticsearch.

from langchain_elasticsearch import ElasticsearchChatMessageHistory

chat_history = ElasticsearchChatMessageHistory(
    index="your-index-name",
    session_id="your-session-id",
    es_cloud_id="your-cloud-id",
    es_api_key="your-api-key",
)