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elasticmachine authored Mar 5, 2024
2 parents d331598 + 7c6120b commit bd8f11b
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2 changes: 1 addition & 1 deletion build-tools-internal/version.properties
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@@ -1,5 +1,5 @@
elasticsearch = 8.14.0
lucene = 9.9.2
lucene = 9.10.0

bundled_jdk_vendor = openjdk
bundled_jdk = 21.0.2+13@f2283984656d49d69e91c558476027ac
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Expand Up @@ -433,7 +433,7 @@ private void commonNodeConfig() {
if (node.getTestDistribution().equals(TestDistribution.INTEG_TEST)) {
node.defaultConfig.put("xpack.security.enabled", "false");
} else {
if (node.getVersion().onOrAfter("7.16.0")) {
if (hasDeprecationIndexing(node)) {
node.defaultConfig.put("cluster.deprecation_indexing.enabled", "false");
}
}
Expand Down Expand Up @@ -474,13 +474,17 @@ public void nextNodeToNextVersion() {
commonNodeConfig();
nodeIndex += 1;
if (node.getTestDistribution().equals(TestDistribution.DEFAULT)) {
if (node.getVersion().onOrAfter("7.16.0")) {
if (hasDeprecationIndexing(node)) {
node.setting("cluster.deprecation_indexing.enabled", "false");
}
}
node.start();
}

private static boolean hasDeprecationIndexing(ElasticsearchNode node) {
return node.getVersion().onOrAfter("7.16.0") && node.getSettingKeys().contains("stateless.enabled") == false;
}

@Override
public void extraConfigFile(String destination, File from) {
nodes.all(node -> node.extraConfigFile(destination, from));
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4 changes: 2 additions & 2 deletions docs/Versions.asciidoc
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@@ -1,8 +1,8 @@

include::{docs-root}/shared/versions/stack/{source_branch}.asciidoc[]

:lucene_version: 9.9.2
:lucene_version_path: 9_9_2
:lucene_version: 9.10.0
:lucene_version_path: 9_10_0
:jdk: 11.0.2
:jdk_major: 11
:build_type: tar
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13 changes: 13 additions & 0 deletions docs/changelog/105578.yaml
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@@ -0,0 +1,13 @@
pr: 105578
summary: Upgrade to Lucene 9.10.0
area: Search
type: enhancement
issues: []
highlight:
title: New Lucene 9.10 release
body: |-
- https://github.com/apache/lucene/pull/13090: Prevent humongous allocations in ScalarQuantizer when building quantiles.
- https://github.com/apache/lucene/pull/12962: Speedup concurrent multi-segment HNSW graph search
- https://github.com/apache/lucene/pull/13033: Range queries on numeric/date/ip fields now exit earlier on segments whose values don't intersect with the query range. This should especially help when there are other required clauses in the `bool` query and when the range filter is narrow, e.g. filtering on the last 5 minutes.
- https://github.com/apache/lucene/pull/13026: `bool` queries that mix `filter` and `should` clauses will now propagate minimum competitive scores through the `should` clauses. This should yield speedups when sorting by descending score.
notable: true
2 changes: 2 additions & 0 deletions docs/reference/ingest/processors/geoip.asciidoc
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Expand Up @@ -435,6 +435,8 @@ each node's <<es-tmpdir,temporary directory>> at `$ES_TMPDIR/geoip-databases/<no
Note that {es} will make a GET request to `${ingest.geoip.downloader.endpoint}?elastic_geoip_service_tos=agree`,
expecting the list of metadata about databases typically found in `overview.json`.

The GeoIP downloader uses the JDK's builtin cacerts. If you're using a custom endpoint, add the custom https endpoint cacert(s) to the JDK's truststore.

[[ingest-geoip-downloader-poll-interval]]
`ingest.geoip.downloader.poll.interval`::
(<<dynamic-cluster-setting,Dynamic>>, <<time-units,time value>>)
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5 changes: 5 additions & 0 deletions docs/reference/mapping/params/format.asciidoc
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Expand Up @@ -70,6 +70,11 @@ The following tables lists all the defaults ISO formats supported:
(separated by `T`), is optional.
Examples: `yyyy-MM-dd'T'HH:mm:ss.SSSZ` or `yyyy-MM-dd`.

NOTE: When using `date_optional_time`, the parsing is lenient and will attempt to parse
numbers as a year (e.g. `292278994` will be parsed as a year). This can lead to unexpected results
when paired with a numeric focused format like `epoch_second` and `epoch_millis`.
It is recommended you use `strict_date_optional_time` when pairing with a numeric focused format.

[[strict-date-time-nanos]]`strict_date_optional_time_nanos`::

A generic ISO datetime parser, where the date must include the year at a minimum, and the time
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Expand Up @@ -4,18 +4,21 @@
<titleabbrev>Semantic search with the {infer} API</titleabbrev>
++++

The instructions in this tutorial shows you how to use the {infer} API with the
Open AI service to perform semantic search on your data. The following example
uses OpenAI's `text-embedding-ada-002` second generation embedding model. You
can use any OpenAI models, they are all supported by the {infer} API.
The instructions in this tutorial shows you how to use the {infer} API with
various services to perform semantic search on your data. The following examples
use Cohere's `embed-english-light-v3.0` model and OpenAI's
`text-embedding-ada-002` second generation embedding model. You can use any
Cohere and OpenAI models, they are all supported by the {infer} API.

Click the name of the service you want to use on any of the widgets below to
review the corresponding instructions.


[discrete]
[[infer-openai-requirements]]
[[infer-service-requirements]]
==== Requirements

An https://openai.com/[OpenAI account] is required to use the {infer} API with
the OpenAI service.
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-requirements-widget.asciidoc[]


[discrete]
Expand All @@ -24,113 +27,30 @@ the OpenAI service.

Create the {infer} task by using the <<put-inference-api>>:

[source,console]
------------------------------------------------------------
PUT _inference/text_embedding/openai_embeddings <1>
{
"service": "openai",
"service_settings": {
"api_key": "<api_key>" <2>
},
"task_settings": {
"model": "text-embedding-ada-002" <3>
}
}
------------------------------------------------------------
// TEST[skip:TBD]
<1> The task type is `text_embedding` in the path.
<2> The API key of your OpenAI account. You can find your OpenAI API keys in
your OpenAI account under the
https://platform.openai.com/api-keys[API keys section]. You need to provide
your API key only once. The <<get-inference-api>> does not return your API
key.
<3> The name of the embedding model to use. You can find the list of OpenAI
embedding models
https://platform.openai.com/docs/guides/embeddings/embedding-models[here].
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-task-widget.asciidoc[]


[discrete]
[[infer-openai-mappings]]
[[infer-service-mappings]]
==== Create the index mapping

The mapping of the destination index - the index that contains the embeddings
that the model will create based on your input text - must be created. The
destination index must have a field with the <<dense-vector, `dense_vector`>>
field type to index the output of the OpenAI model.
field type to index the output of the used model.

[source,console]
--------------------------------------------------
PUT openai-embeddings
{
"mappings": {
"properties": {
"content_embedding": { <1>
"type": "dense_vector", <2>
"dims": 1536, <3>
"element_type": "float",
"similarity": "dot_product" <4>
},
"content": { <5>
"type": "text" <6>
}
}
}
}
--------------------------------------------------
<1> The name of the field to contain the generated tokens. It must be refrenced
in the {infer} pipeline configuration in the next step.
<2> The field to contain the tokens is a `dense_vector` field.
<3> The output dimensions of the model. Find this value in the
https://platform.openai.com/docs/guides/embeddings/embedding-models[OpenAI documentation]
of the model you use.
<4> The faster` dot_product` function can be used to calculate similarity
because OpenAI embeddings are normalised to unit length. You can check the
https://platform.openai.com/docs/guides/embeddings/which-distance-function-should-i-use[OpenAI docs]
about which similarity function to use.
<5> The name of the field from which to create the sparse vector representation.
In this example, the name of the field is `content`. It must be referenced in
the {infer} pipeline configuration in the next step.
<6> The field type which is text in this example.
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-mapping-widget.asciidoc[]


[discrete]
[[infer-openai-inference-ingest-pipeline]]
[[infer-service-inference-ingest-pipeline]]
==== Create an ingest pipeline with an inference processor

Create an <<ingest,ingest pipeline>> with an
<<inference-processor,{infer} processor>> and use the OpenAI model you created
above to infer against the data that is being ingested in the
pipeline.
<<inference-processor,{infer} processor>> and use the model you created above to
infer against the data that is being ingested in the pipeline.

[source,console]
--------------------------------------------------
PUT _ingest/pipeline/openai_embeddings
{
"processors": [
{
"inference": {
"model_id": "openai_embeddings", <1>
"input_output": { <2>
"input_field": "content",
"output_field": "content_embedding"
}
}
}
]
}
--------------------------------------------------
<1> The name of the inference model you created by using the
<<put-inference-api>>.
<2> Configuration object that defines the `input_field` for the {infer} process
and the `output_field` that will contain the {infer} results.

////
[source,console]
----
DELETE _ingest/pipeline/openai_embeddings
----
// TEST[continued]
////
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc[]


[discrete]
Expand All @@ -157,32 +77,10 @@ you can see an index named `test-data` with 182469 documents.
[[reindexing-data-infer]]
==== Ingest the data through the {infer} ingest pipeline

Create the embeddings from the text by reindexing the data throught the {infer}
pipeline that uses the OpenAI model as the inference model.
Create the embeddings from the text by reindexing the data through the {infer}
pipeline that uses the chosen model as the inference model.

[source,console]
----
POST _reindex?wait_for_completion=false
{
"source": {
"index": "test-data",
"size": 50 <1>
},
"dest": {
"index": "openai-embeddings",
"pipeline": "openai_embeddings"
}
}
----
// TEST[skip:TBD]
<1> The default batch size for reindexing is 1000. Reducing `size` to a smaller
number makes the update of the reindexing process quicker which enables you to
follow the progress closely and detect errors early.

NOTE: The
https://platform.openai.com/account/limits[rate limit of your OpenAI account]
may affect the throughput of the reindexing process. If this happens, change
`size` to `3` or a similar value in magnitude.
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc[]

The call returns a task ID to monitor the progress:

Expand Down Expand Up @@ -214,63 +112,4 @@ provide the query text and the model you have used to create the embeddings.
NOTE: If you cancelled the reindexing process, you run the query only a part of
the data which affects the quality of your results.

[source,console]
--------------------------------------------------
GET openai-embeddings/_search
{
"knn": {
"field": "content_embedding",
"query_vector_builder": {
"text_embedding": {
"model_id": "openai_embeddings",
"model_text": "Calculate fuel cost"
}
},
"k": 10,
"num_candidates": 100
},
"_source": [
"id",
"content"
]
}
--------------------------------------------------
// TEST[skip:TBD]

As a result, you receive the top 10 documents that are closest in meaning to the
query from the `openai-embeddings` index sorted by their proximity to the query:

[source,consol-result]
--------------------------------------------------
"hits": [
{
"_index": "openai-embeddings",
"_id": "DDd5OowBHxQKHyc3TDSC",
"_score": 0.83704096,
"_source": {
"id": 862114,
"body": "How to calculate fuel cost for a road trip. By Tara Baukus Mello • Bankrate.com. Dear Driving for Dollars, My family is considering taking a long road trip to finish off the end of the summer, but I'm a little worried about gas prices and our overall fuel cost.It doesn't seem easy to calculate since we'll be traveling through many states and we are considering several routes.y family is considering taking a long road trip to finish off the end of the summer, but I'm a little worried about gas prices and our overall fuel cost. It doesn't seem easy to calculate since we'll be traveling through many states and we are considering several routes."
}
},
{
"_index": "openai-embeddings",
"_id": "ajd5OowBHxQKHyc3TDSC",
"_score": 0.8345704,
"_source": {
"id": 820622,
"body": "Home Heating Calculator. Typically, approximately 50% of the energy consumed in a home annually is for space heating. When deciding on a heating system, many factors will come into play: cost of fuel, installation cost, convenience and life style are all important.This calculator can help you estimate the cost of fuel for different heating appliances.hen deciding on a heating system, many factors will come into play: cost of fuel, installation cost, convenience and life style are all important. This calculator can help you estimate the cost of fuel for different heating appliances."
}
},
{
"_index": "openai-embeddings",
"_id": "Djd5OowBHxQKHyc3TDSC",
"_score": 0.8327426,
"_source": {
"id": 8202683,
"body": "Fuel is another important cost. This cost will depend on your boat, how far you travel, and how fast you travel. A 33-foot sailboat traveling at 7 knots should be able to travel 300 miles on 50 gallons of diesel fuel.If you are paying $4 per gallon, the trip would cost you $200.Most boats have much larger gas tanks than cars.uel is another important cost. This cost will depend on your boat, how far you travel, and how fast you travel. A 33-foot sailboat traveling at 7 knots should be able to travel 300 miles on 50 gallons of diesel fuel."
}
},
(...)
]
--------------------------------------------------
// NOTCONSOLE
include::{es-repo-dir}/tab-widgets/inference-api/infer-api-search-widget.asciidoc[]
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++++
<div class="tabs" data-tab-group="model">
<div role="tablist" aria-label="model">
<button role="tab"
aria-selected="true"
aria-controls="infer-api-ingest-cohere-tab"
id="infer-api-ingest-cohere">
Cohere
</button>
<button role="tab"
aria-selected="false"
aria-controls="infer-api-ingest-openai-tab"
id="infer-api-ingest-openai">
OpenAI
</button>
</div>
<div tabindex="0"
role="tabpanel"
id="infer-api-ingest-cohere-tab"
aria-labelledby="infer-api-ingest-cohere">
++++

include::infer-api-ingest-pipeline.asciidoc[tag=cohere]

++++
</div>
<div tabindex="0"
role="tabpanel"
id="infer-api-ingest-openai-tab"
aria-labelledby="infer-api-ingest-openai"
hidden="">
++++

include::infer-api-ingest-pipeline.asciidoc[tag=openai]

++++
</div>
</div>
++++
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