Elasticsearch open inference API adds support for OpenAI chat completions

PUT _inference/completion/openai_chat_completions
{
    "service": "openai",
    "service_settings": {
        "api_key": <api-key>,
        "model_id": "gpt-3.5-turbo"
    }
}

POST _inference/completion/openai_chat_completions
{
    "input": "What is Elastic?"
}

{
    "completion": [
        {
            "result": "Elastic is a software company that provides a range of products and solutions for search, logging, security, and analytics. Its flagship product, Elasticsearch, is a distributed, RESTful search and analytics engine that is used for full-text search, structured search, and analytics. Elastic also offers other products such as Logstash for log collection and parsing, Kibana for data visualization and dashboarding, and Beats for lightweight data shippers. These products can be combined to create powerful data analysis and monitoring solutions for organizations of all sizes."
        }
    ]
}

POST _bulk
{ "index" : { "_index" : "docs" } }
{"content": "You know, for search (and analysis) Elasticsearch is the distributed search and analytics engine at the heart of the Elastic Stack. Logstash and Beats facilitate collecting, aggregating, and enriching your data and storing it in Elasticsearch. Kibana enables you to interactively explore, visualize, and share insights into your data and manage and monitor the stack. Elasticsearch is where the indexing, search, and analysis magic happens. Elasticsearch provides near real-time search and analytics for all types of data. Whether you have structured or unstructured text, numerical data, or geospatial data, Elasticsearch can efficiently store and index it in a way that supports fast searches. You can go far beyond simple data retrieval and aggregate information to discover trends and patterns in your data. And as your data and query volume grows, the distributed nature of Elasticsearch enables your deployment to grow seamlessly right along with it. While not every problem is a search problem, Elasticsearch offers speed and flexibility to handle data in a wide variety of use cases: Add a search box to an app or website Store and analyze logs, metrics, and security event data Use machine learning to automatically model the behavior of your data in real time Use Elasticsearch as a vector database to create, store, and search vector embeddings Automate business workflows using Elasticsearch as a storage engine Manage, integrate, and analyze spatial information using Elasticsearch as a geographic information system (GIS) Store and process genetic data using Elasticsearch as a bioinformatics research tool We’re continually amazed by the novel ways people use search. But whether your use case is similar to one of these, or you’re using Elasticsearch to tackle a new problem, the way you work with your data, documents, and indices in Elasticsearch is the same."}

PUT _ingest/pipeline/summarization_pipeline
{
    "processors": [
        {
            "script": {
                "source": "ctx.prompt = 'Please summarize the following text: ' + ctx.content"
            }
        },
        {
            "inference": {
                "model_id": "openai_chat_completions",
                "input_output": {
                    "input_field": "prompt",
                    "output_field": "summary"
                }
            }
        },
        {
            "remove": {
                "field": "prompt"
            }
        }
  ]
}

POST _reindex
{
    "source": {
        "index": "docs",
        "size": 50
    },
    "dest": {
        "index": "docs_summaries",
        "pipeline": "summarization_pipeline"
    }
}

POST docs_summaries/_search
{
    "query": {
        "match_all": { }
    }
}