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‎docs/guide/async.asciidoc

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[[async]]
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== Using asyncio with Elasticsearch
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The `elasticsearch` package supports async/await with
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https://docs.python.org/3/library/asyncio.html[asyncio] and
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https://docs.aiohttp.org[aiohttp]. You can either install `aiohttp`
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directly or use the `[async]` extra:
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[source,bash]
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----
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$ python -m pip install elasticsearch aiohttp
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# - OR -
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$ python -m pip install elasticsearch[async]
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----
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=== Getting Started with Async
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After installation all async API endpoints are available via
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`~elasticsearch.AsyncElasticsearch` and are used in the same way as
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other APIs, just with an extra `await`:
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[source,python]
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----
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import asyncio
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from elasticsearch import AsyncElasticsearch
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client = AsyncElasticsearch()
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async def main():
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resp = await client.search(
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index="documents",
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body={"query": {"match_all": {}}},
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size=20,
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)
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print(resp)
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loop = asyncio.get_event_loop()
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loop.run_until_complete(main())
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----
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All APIs that are available under the sync client are also available
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under the async client.
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=== ASGI Applications and Elastic APM
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https://asgi.readthedocs.io[ASGI] (Asynchronous Server Gateway
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Interface) is a new way to serve Python web applications making use of
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async I/O to achieve better performance. Some examples of ASGI
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frameworks include FastAPI, Django 3.0+, and Starlette. If you're
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using one of these frameworks along with Elasticsearch then you should
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be using `~elasticsearch.AsyncElasticsearch` to avoid blocking the event
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loop with synchronous network calls for optimal performance.
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https://www.elastic.co/guide/en/apm/agent/python/current/index.html[Elastic
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APM] also supports tracing of async Elasticsearch queries just the same
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as synchronous queries. For an example on how to configure
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`AsyncElasticsearch` with a popular ASGI framework
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https://fastapi.tiangolo.com/[FastAPI] and APM tracing there is a
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https://github.com/elastic/elasticsearch-py/tree/master/examples/fastapi-apm[pre-built
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example] in the `examples/fastapi-apm` directory.
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=== Frequently Asked Questions
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==== ValueError when initializing `AsyncElasticsearch`?
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If when trying to use `AsyncElasticsearch` you receive
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`ValueError: You must have 'aiohttp' installed to use AiohttpHttpNode`
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you should ensure that you have `aiohttp` installed in your environment
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(check with `$ python -m pip freeze | grep aiohttp`). Otherwise,
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async support won't be available.
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==== What about the `elasticsearch-async` package?
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Previously asyncio was supported separately via the
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https://github.com/elastic/elasticsearch-py-async[elasticsearch-async]
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package. The `elasticsearch-async` package has been deprecated in favor
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of `AsyncElasticsearch` provided by the `elasticsearch` package in v7.8
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and onwards.
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==== Receiving 'Unclosed client session / connector' warning?
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This warning is created by `aiohttp` when an open HTTP connection is
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garbage collected. You'll typically run into this when closing your
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application. To resolve the issue ensure that
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`~elasticsearch.AsyncElasticsearch.close` is called before the
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`~elasticsearch.AsyncElasticsearch` instance is garbage collected.
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For example if using FastAPI that might look like this:
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[source,python]
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----
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import os
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from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from elasticsearch import AsyncElasticsearch
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ELASTICSEARCH_URL = os.environ["ELASTICSEARCH_URL"]
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client = None
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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global client
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client = AsyncElasticsearch(ELASTICSEARCH_URL)
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yield
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await client.close()
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app = FastAPI(lifespan=lifespan)
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@app.get("/")
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async def main():
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return await client.info()
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----
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You can run this example by saving it to `main.py` and executing
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`ELASTICSEARCH_URL=http://localhost:9200 uvicorn main:app`.
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=== Async Helpers
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Async variants of all helpers are available in `elasticsearch.helpers`
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and are all prefixed with `async_*`. You'll notice that these APIs
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are identical to the ones in the sync <<client-helpers>> documentation.
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All async helpers that accept an iterator or generator also accept async
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iterators and async generators.

‎docs/guide/examples.asciidoc

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----------------------------
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client.delete(index="test-index", id=1)
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----------------------------
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[discrete]
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[[ex-interactive]]
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=== Interactive examples
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The https://github.com/elastic/elasticsearch-labs[elasticsearch-labs]
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repo contains interactive and executable
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https://github.com/elastic/elasticsearch-labs/tree/main/notebooks[Python
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notebooks], sample apps, and resources for testing out Elasticsearch,
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using the Python client. These examples are mainly focused on vector
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search, hybrid search and generative AI use cases, but you'll also find
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examples of basic operations like creating index mappings and performing
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lexical search.
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[discrete]
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==== Search notebooks
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The
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https://github.com/elastic/elasticsearch-labs/tree/main/notebooks/search[Search]
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folder is a good place to start if you're new to Elasticsearch. This
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folder contains a number of notebooks that demonstrate the fundamentals
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of Elasticsearch, like indexing vectors, running lexical, semantic and
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_hybrid_ searches, and more.
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The following notebooks are available:
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[arabic, start=0]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/00-quick-start.ipynb[Quick
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start]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/01-keyword-querying-filtering.ipynb[Keyword&#44;
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querying&#44; filtering]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/02-hybrid-search.ipynb[Hybrid
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search]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/03-ELSER.ipynb[Semantic
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search with ELSER]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/04-multilingual.ipynb[Multilingual
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semantic search]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/05-query-rules.ipynb[Query
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rules]
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/06-synonyms-api.ipynb[Synonyms
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API quick start]
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Here's a brief overview of what you'll learn in each notebook.
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[discrete]
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===== Quick start
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/00-quick-start.ipynb[00-quick-start.ipynb]
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notebook you'll learn how to:
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* Use the Elasticsearch Python client for various operations.
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* Create and define an index for a sample dataset with
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`dense_vector` fields.
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* Transform book titles into embeddings using
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https://www.sbert.net[Sentence Transformers] and index them into
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Elasticsearch.
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* Perform k-nearest neighbors (knn) semantic searches.
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* Integrate traditional text-based search with semantic search, for a
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hybrid search system.
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* Use reciprocal rank fusion (RRF) to intelligently combine search
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results from different retrieval systems.
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[discrete]
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===== Keyword, querying, filtering
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/01-keyword-querying-filtering.ipynb[01-keyword-querying-filtering.ipynb]
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notebook, you'll learn how to:
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* Use
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https://www.elastic.co/guide/en/elasticsearch/reference/current/query-filter-context.html[query
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and filter contexts] to search and filter documents in Elasticsearch.
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* Execute full-text searches with `match` and `multi-match` queries.
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* Query and filter documents based on `text`, `number`, `date`, or
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`boolean` values.
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* Run multi-field searches using the `multi-match` query.
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* Prioritize specific fields in the `multi-match` query for tailored
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results.
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[discrete]
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===== Hybrid search
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/02-hybrid-search.ipynb[02-hybrid-search.ipynb]
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notebook, you'll learn how to:
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* Combine results of traditional text-based search with semantic search,
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for a hybrid search system.
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* Transform fields in the sample dataset into embeddings using the
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Sentence Transformer model and index them into Elasticsearch.
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* Use the
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https://www.elastic.co/guide/en/elasticsearch/reference/current/rrf.html#rrf-api[RRF
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API] to combine the results of a `match` query and a `kNN` semantic
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search.
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* Walk through a super simple toy example that demonstrates, step by
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step, how RRF ranking works.
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[discrete]
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===== Semantic search with ELSER
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/03-ELSER.ipynb[03-ELSER.ipynb]
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notebook, you'll learn how to:
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* Use the Elastic Learned Sparse Encoder (ELSER) for text
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expansion-powered semantic search, out of the box — without training,
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fine-tuning, or embeddings generation.
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* Download and deploy the ELSER model in your Elastic environment.
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* Create an Elasticsearch index named [.title-ref]#search-movies# with
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specific mappings and index a dataset of movie descriptions.
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* Create an ingest pipeline containing an inference processor for ELSER
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model execution.
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* Reindex the data from [.title-ref]#search-movies# into another index,
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[.title-ref]#elser-movies#, using the ELSER pipeline for text expansion.
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* Observe the results of running the documents through the model by
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inspecting the additional terms it adds to documents, which enhance
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searchability.
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* Perform simple keyword searches on the [.title-ref]#elser-movies#
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index to assess the impact of ELSER's text expansion.
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* Execute ELSER-powered semantic searches using the `text_expansion`
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query.
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[discrete]
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===== Multilingual semantic search
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/04-multilingual.ipynb[04-multilingual.ipynb]
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notebook, you'll learn how to:
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* Use a multilingual embedding model for semantic search across
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languages.
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* Transform fields in the sample dataset into embeddings using the
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Sentence Transformer model and index them into Elasticsearch.
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* Use filtering with a `kNN` semantic search.
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* Walk through a super simple toy example that demonstrates, step by
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step, how multilingual search works across languages, and within
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non-English languages.
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[discrete]
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===== Query rules
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/05-query-rules.ipynb[05-query-rules.ipynb]
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notebook, you'll learn how to:
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* Use the query rules management APIs to create and edit promotional
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rules based on contextual queries.
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* Apply these query rules by using the `rule_query` in Query DSL.
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[discrete]
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===== Synonyms API quick start
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In the
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https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/search/06-synonyms-api.ipynb[06-synonyms-api.ipynb]
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notebook, you'll learn how to:
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* Use the synonyms management API to create a synonyms set to enhance
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your search recall.
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* Configure an index to use search-time synonyms.
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* Update synonyms in real time.
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* Run queries that are enhanced by synonyms.
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[discrete]
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==== Other notebooks
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* https://github.com/elastic/elasticsearch-labs/tree/main/notebooks/generative-ai[Generative
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AI]. Notebooks that demonstrate various use cases for Elasticsearch as
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the retrieval engine and vector store for LLM-powered applications.
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* https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/integrations[Integrations].
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Notebooks that demonstrate how to integrate popular services and
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projects with Elasticsearch, including OpenAI, Hugging Face, and
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LlamaIndex
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* https://github.com/elastic/elasticsearch-labs/tree/main/notebooks/langchain[Langchain].
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Notebooks that demonstrate how to integrate Elastic with LangChain, a
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framework for developing applications powered by language models.

‎docs/guide/getting-started.asciidoc

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client.indices.create(index="my_index")
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----
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Optionally, you can first define the expected types of your features with a
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custom mapping.
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[source,py]
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----
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mappings = {
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"properties": {
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"foo": {"type": "text"},
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"bar": {
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"type": "text",
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"fields": {
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"keyword": {
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"type": "keyword",
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"ignore_above": 256,
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}
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},
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},
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}
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}
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client.indices.create(index="my_index", mappings=mappings)
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----
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[discrete]
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==== Indexing documents
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This is a simple way of indexing a document:
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This indexes a document with the index API:
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[source,py]
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)
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----
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You can also index multiple documents at once with the bulk helper function:
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[source,py]
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----
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from elasticsearch import helpers
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def generate_docs():
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for i in range(10):
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yield {
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"_index": "my_index",
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"foo": f"foo {i}",
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"bar": "bar",
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}
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helpers.bulk(client, generate_docs())
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----
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These helpers are the recommended way to perform bulk ingestion. While it is
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also possible to perform bulk ingestion using `client.bulk` directly, the
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helpers handle retries, ingesting chunk by chunk and more. See the
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<<client-helpers>> page for more details.
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[discrete]
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==== Getting documents
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[source,py]
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----
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client.update(index="my_index", id="my_document_id", doc={
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"foo": "bar",
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"new_field": "new value",
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})
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client.update(
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index="my_index",
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id="my_document_id",
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doc={
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"foo": "bar",
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"new_field": "new value",
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}
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)
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----
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