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# Backport This will backport the following commits from `main` to `8.14`: - [[DOCS] Add gpt-4o model support to Playground (#183372)](https://github.com/elastic/kibana/pull/183372) <!--- Backport version: 9.4.3 --> ### Questions ? Please refer to the [Backport tool documentation](https://github.com/sqren/backport) <!--BACKPORT [{"author":{"name":"Liam Thompson","email":"32779855+leemthompo@users.noreply.github.com"},"sourceCommit":{"committedDate":"2024-05-14T20:08:13Z","message":"[DOCS] Add gpt-4o model support to Playground (#183372)","sha":"b245f0dab8db740be9484bc0b8b5ddfc97166e4c","branchLabelMapping":{"^v8.15.0$":"main","^v(\\d+).(\\d+).\\d+$":"$1.$2"}},"sourcePullRequest":{"labels":["release_note:skip","Team:Search","docs","v8.14.0","v8.15.0"],"title":"[DOCS] Add gpt-4o model support to Playground","number":183372,"url":"https://github.com/elastic/kibana/pull/183372","mergeCommit":{"message":"[DOCS] Add gpt-4o model support to Playground (#183372)","sha":"b245f0dab8db740be9484bc0b8b5ddfc97166e4c"}},"sourceBranch":"main","suggestedTargetBranches":["8.14"],"targetPullRequestStates":[{"branch":"8.14","label":"v8.14.0","branchLabelMappingKey":"^v(\\d+).(\\d+).\\d+$","isSourceBranch":false,"state":"NOT_CREATED"},{"branch":"main","label":"v8.15.0","branchLabelMappingKey":"^v8.15.0$","isSourceBranch":true,"state":"MERGED","url":"https://github.com/elastic/kibana/pull/183372","number":183372,"mergeCommit":{"message":"[DOCS] Add gpt-4o model support to Playground (#183372)","sha":"b245f0dab8db740be9484bc0b8b5ddfc97166e4c"}}]}] BACKPORT--> Co-authored-by: Liam Thompson <32779855+leemthompo@users.noreply.github.com>
204 lines
7.3 KiB
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204 lines
7.3 KiB
Text
[role="xpack"]
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[[playground]]
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= Playground
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preview::[]
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// Variable (attribute) definition
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:x: Playground
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Use {x} to combine your Elasticsearch data with the power of large language models (LLMs) for retrieval augmented generation (RAG).
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The chat interface translates your natural language questions into {es} queries, retrieves the most relevant results from your {es} documents, and passes those documents to the LLM to generate tailored responses.
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Once you start chatting, use the UI to view and modify the Elasticsearch queries that search your data.
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You can also view the underlying Python code that powers the chat interface, and download this code to integrate into your own application.
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Learn how to get started on this page.
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Refer to the following for more advanced topics:
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* <<playground-context>>
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* <<playground-query>>
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* <<playground-troubleshooting>>
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[float]
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[[playground-how-it-works]]
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== How {x} works
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Here's a simpified overview of how {x} works:
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* User *creates a connection* to LLM provider
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* User *selects a model* to use for generating responses
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* User *define the model's behavior and tone* with initial instructions
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** *Example*: "_You are a friendly assistant for question-answering tasks. Keep responses as clear and concise as possible._"
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* User *selects {es} indices* to search
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* User *enters a question* in the chat interface
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* {x} *autogenerates an {es} query* to retrieve relevant documents
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** User can *view and modify underlying {es} query* in the UI
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* {x} *auto-selects relevant fields* from retrieved documents to pass to the LLM
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** User can *edit fields targeted*
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* {x} passes *filtered documents* to the LLM
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** The LLM generates a response based on the original query, initial instructions, chat history, and {es} context
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* User can *view the Python code* that powers the chat interface
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** User can also *Download the code* to integrate into application
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[float]
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[[playground-availability-prerequisites]]
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== Availability and prerequisites
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For Elastic Cloud and self-managed deployments {x} is available in the *Search* space in {kib}, under *Content* > *{x}*.
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For Elastic Serverless, {x} is available in your {es} project UI.
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// TODO: Confirm URL path for Serverless
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To use {x}, you'll need the following:
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1. An Elastic *v8.14.0+* deployment or {es} *Serverless* project. (Start a https://cloud.elastic.co/registration[free trial]).
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2. At least one *{es} index* with documents to search.
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** See <<playground-getting-started-ingest, ingest data>> if you'd like to ingest sample data.
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3. An account with a *supported LLM provider*. {x} supports the following:
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+
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[cols="2a,2a,1a", options="header"]
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|===
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| Provider | Models | Notes
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| *Amazon Bedrock*
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a|
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* Anthropic: Claude 3 Sonnet
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* Anthropic: Claude 3 Haiku
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a|
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Does not currently support streaming.
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| *OpenAI*
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a|
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* GPT-3 turbo
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* GPT-4 turbo
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* GPT-4 omni
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a|
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| *Azure OpenAI*
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a|
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* GPT-3 turbo
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* GPT-4 turbo
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a|
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|===
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[float]
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[[playground-getting-started]]
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== Getting started
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[float]
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[[playground-getting-started-connect]]
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=== Connect to LLM provider
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To get started with {x}, you need to create a <<action-types,connector>> for your LLM provider.
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Follow these steps on the {x} landing page:
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. Under *Connect to LLM*, click *Create connector*.
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. Select your *LLM provider*.
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. *Name* your connector.
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. Select a *URL endpoint* (or use the default).
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. Enter *access credentials* for your LLM provider.
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[TIP]
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====
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If you need to update a connector, or add a new one, click the wrench button (🔧) under *Model settings*.
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====
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[float]
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[[playground-getting-started-ingest]]
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=== Ingest data (optional)
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_You can skip this step if you already have data in one or more {es} indices._
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There are many options for ingesting data into {es}, including:
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* The {enterprise-search-ref}/crawler.html[Elastic crawler] for web content (*NOTE*: Not yet available in _Serverless_)
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* {enterprise-search-ref}/connectors.html[Elastic connectors] for data synced from third-party sources
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* The {es} {ref}/docs-bulk.html[Bulk API] for JSON documents
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+
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.*Expand* for example
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[%collapsible]
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==============
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To add a few documents to an index called `books` run the following in Dev Tools Console:
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[source,console]
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----
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POST /_bulk
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{ "index" : { "_index" : "books" } }
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{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}
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{ "index" : { "_index" : "books" } }
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{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
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{ "index" : { "_index" : "books" } }
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{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
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{ "index" : { "_index" : "books" } }
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{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
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{ "index" : { "_index" : "books" } }
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{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
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{ "index" : { "_index" : "books" } }
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{"name": "The Handmaids Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}
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----
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==============
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We've also provided some Jupyter notebooks to easily ingest sample data into {es}.
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Find these in the https://github.com/elastic/elasticsearch-labs/blob/main/notebooks/ingestion-and-chunking[elasticsearch-labs] repository.
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These notebooks use the official {es} Python client.
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// TODO: [The above link will be broken until https://github.com/elastic/elasticsearch-labs/pull/232 is merged]
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[float]
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[[playground-getting-started-index]]
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=== Select {es} indices
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Once you've connected to your LLM provider, it's time to choose the data you want to search.
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Follow the steps under *Select indices*:
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. Select one or more {es} indices under *Add index*.
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. Click *Start* to launch the chat interface.
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+
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[.screenshot]
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image::select-indices.png[width=400]
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Learn more about the underlying {es} queries used to search your data in <<playground-query>>.
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[float]
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[[playground-getting-started-setup-chat]]
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=== Set up the chat interface
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You can start chatting with your data immediately, but you might want to tweak some defaults first.
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[.screenshot]
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image::chat-interface.png[]
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You can adjust the following under *Model settings*:
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* *Model*. The model used for generating responses.
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* *Instructions*. Also known as the _system prompt_, these initial instructions and guidelines define the behavior of the model throughout the conversation. Be *clear and specific* for best results.
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* *Include citations*. A toggle to include citations from the relevant {es} documents in responses.
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{x} also uses another LLM under the hood, to encode all previous questions and responses, and make them available to the main model.
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This ensures the model has "conversational memory".
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Under *Indices*, you can edit which {es} indices will be searched.
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This will affect the underlying {es} query.
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[TIP]
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====
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Click *✨ Regenerate* to resend the last query to the model for a fresh response.
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Click *⟳ Clear chat* to clear chat history and start a new conversation.
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====
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[float]
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[[playground-next-steps]]
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=== Next steps
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Once you've got {x} up and running, and you've tested out the chat interface, you might want to explore some more advanced topics:
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* <<playground-context>>
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* <<playground-query>>
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* <<playground-troubleshooting>>
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include::playground-context.asciidoc[]
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include::playground-query.asciidoc[]
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include::playground-troubleshooting.asciidoc[]
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