Jeff Vestal 0b542a9706 Add ElasticsearchEmbeddings class for generating embeddings using Elasticsearch models (#3401) ... This PR introduces a new module, `elasticsearch_embeddings.py`, which provides a wrapper around Elasticsearch embedding models. The new ElasticsearchEmbeddings class allows users to generate embeddings for documents and query texts using a [model deployed in an Elasticsearch cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding). ### Main features: 1. The ElasticsearchEmbeddings class initializes with an Elasticsearch connection object and a model_id, providing an interface to interact with the Elasticsearch ML client through [infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model) . 2. The `embed_documents()` method generates embeddings for a list of documents, and the `embed_query()` method generates an embedding for a single query text. 3. The class supports custom input text field names in case the deployed model expects a different field name than the default `text_field`. 4. The implementation is compatible with any model deployed in Elasticsearch that generates embeddings as output. ### Benefits: 1. Simplifies the process of generating embeddings using Elasticsearch models. 2. Provides a clean and intuitive interface to interact with the Elasticsearch ML client. 3. Allows users to easily integrate Elasticsearch-generated embeddings. Related issue https://github.com/hwchase17/langchain/issues/3400 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>		2023-05-23 14:50:33 -07:00
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examples	Add ElasticsearchEmbeddings class for generating embeddings using Elasticsearch models (#3401)	2023-05-23 14:50:33 -07:00