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Auto-generated API code
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‎docs/doc_examples/120fcf9f55128d6a81d5e87a9c235bbd.asciidoc‎

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[source, js]
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----
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const response = await client.inference.streamInference({
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task_type: "chat_completion",
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const response = await client.inference.chatCompletionUnified({
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inference_id: "openai-completion",
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model: "gpt-4o",
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messages: [
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{
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role: "user",
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content: "What is Elastic?",
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},
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],
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chat_completion_request: {
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model: "gpt-4o",
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messages: [
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{
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role: "user",
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content: "What is Elastic?",
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},
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],
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},
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});
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console.log(response);
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----

‎docs/doc_examples/13ecdf99114098c76b050397d9c3d4e6.asciidoc‎

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[source, js]
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----
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const response = await client.inference.inference({
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task_type: "sparse_embedding",
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const response = await client.inference.sparseEmbedding({
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inference_id: "my-elser-model",
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input:
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"The sky above the port was the color of television tuned to a dead channel.",

‎docs/doc_examples/141ef0ebaa3b0772892b79b9bb85efb0.asciidoc‎

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[source, js]
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----
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const response = await client.inference.put({
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task_type: "my-inference-endpoint",
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inference_id: "_update",
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const response = await client.inference.update({
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inference_id: "my-inference-endpoint",
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inference_config: {
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service_settings: {
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api_key: "<API_KEY>",

‎docs/doc_examples/45954b8aaedfed57012be8b6538b0a24.asciidoc‎

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[source, js]
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----
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const response = await client.inference.streamInference({
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task_type: "chat_completion",
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const response = await client.inference.chatCompletionUnified({
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inference_id: "openai-completion",
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messages: [
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{
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role: "user",
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content: [
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{
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type: "text",
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text: "What's the price of a scarf?",
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chat_completion_request: {
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messages: [
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{
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role: "user",
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content: [
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{
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type: "text",
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text: "What's the price of a scarf?",
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},
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],
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},
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],
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tools: [
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{
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type: "function",
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function: {
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name: "get_current_price",
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description: "Get the current price of a item",
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parameters: {
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type: "object",
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properties: {
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item: {
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id: "123",
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},
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},
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},
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},
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],
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},
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],
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tools: [
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{
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},
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],
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tool_choice: {
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type: "function",
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function: {
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name: "get_current_price",
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description: "Get the current price of a item",
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parameters: {
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type: "object",
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properties: {
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item: {
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id: "123",
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},
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},
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},
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},
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},
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],
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tool_choice: {
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type: "function",
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function: {
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name: "get_current_price",
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},
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},
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});
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console.log(response);

‎docs/doc_examples/4b91ad7c9b44e07db4a4e81390f19ad3.asciidoc‎

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[source, js]
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----
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const response = await client.inference.streamInference({
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task_type: "completion",
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const response = await client.inference.streamCompletion({
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inference_id: "openai-completion",
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input: "What is Elastic?",
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});

‎docs/doc_examples/7429b16221fe741fd31b0584786dd0b0.asciidoc‎

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[source, js]
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----
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const response = await client.inference.inference({
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task_type: "text_embedding",
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const response = await client.inference.textEmbedding({
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inference_id: "my-cohere-endpoint",
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input:
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"The sky above the port was the color of television tuned to a dead channel.",

‎docs/doc_examples/82bb6c61dab959f4446dc5ecab7ecbdf.asciidoc‎

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[source, js]
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----
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const response = await client.inference.streamInference({
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task_type: "chat_completion",
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const response = await client.inference.chatCompletionUnified({
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inference_id: "openai-completion",
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messages: [
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{
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role: "assistant",
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content: "Let's find out what the weather is",
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tool_calls: [
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{
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id: "call_KcAjWtAww20AihPHphUh46Gd",
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type: "function",
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function: {
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name: "get_current_weather",
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arguments: '{"location":"Boston, MA"}',
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chat_completion_request: {
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messages: [
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{
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role: "assistant",
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content: "Let's find out what the weather is",
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tool_calls: [
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{
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id: "call_KcAjWtAww20AihPHphUh46Gd",
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type: "function",
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function: {
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name: "get_current_weather",
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arguments: '{"location":"Boston, MA"}',
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},
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},
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},
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],
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},
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{
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role: "tool",
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content: "The weather is cold",
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tool_call_id: "call_KcAjWtAww20AihPHphUh46Gd",
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},
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],
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],
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},
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{
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role: "tool",
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content: "The weather is cold",
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tool_call_id: "call_KcAjWtAww20AihPHphUh46Gd",
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},
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],
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},
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});
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console.log(response);
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----

‎docs/doc_examples/b45a8c6fc746e9c90fd181e69a605fad.asciidoc‎

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[source, js]
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----
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const response = await client.inference.inference({
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task_type: "completion",
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const response = await client.inference.completion({
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inference_id: "openai_chat_completions",
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input: "What is Elastic?",
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});

‎docs/doc_examples/f1b24217b1d9ba6ea5e4fa6e6f412022.asciidoc‎

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[source, js]
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const response = await client.inference.inference({
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task_type: "rerank",
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const response = await client.inference.rerank({
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inference_id: "cohere_rerank",
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input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
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query: "star wars main character",

‎docs/reference/api-reference.md‎

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- **`task_type` (Optional, Enum("sparse_embedding" | "text_embedding" | "rerank" | "completion" | "chat_completion"))**: The task type
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- **`inference_id` (Optional, string)**: The inference Id
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## client.inference.postEisChatCompletion [_inference.post_eis_chat_completion]
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Perform a chat completion task through the Elastic Inference Service (EIS).
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Perform a chat completion inference task with the `elastic` service.
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[Endpoint documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-post-eis-chat-completion)
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```ts
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client.inference.postEisChatCompletion({ eis_inference_id })
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```
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### Arguments [_arguments_inference.post_eis_chat_completion]
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#### Request (object) [_request_inference.post_eis_chat_completion]
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- **`eis_inference_id` (string)**: The unique identifier of the inference endpoint.
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- **`chat_completion_request` (Optional, { messages, model, max_completion_tokens, stop, temperature, tool_choice, tools, top_p })**
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## client.inference.put [_inference.put]
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Create an inference endpoint.
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When you create an inference endpoint, the associated machine learning model is automatically deployed if it is not already running.
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- **`task_settings` (Optional, { input_type, return_documents, top_n, truncate })**: Settings to configure the inference task.
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These settings are specific to the task type you specified.
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## client.inference.putEis [_inference.put_eis]
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Create an Elastic Inference Service (EIS) inference endpoint.
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Create an inference endpoint to perform an inference task through the Elastic Inference Service (EIS).
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[Endpoint documentation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-inference-put-eis)
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```ts
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client.inference.putEis({ task_type, eis_inference_id, service, service_settings })
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```
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### Arguments [_arguments_inference.put_eis]
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#### Request (object) [_request_inference.put_eis]
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- **`task_type` (Enum("chat_completion"))**: The type of the inference task that the model will perform.
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NOTE: The `chat_completion` task type only supports streaming and only through the _stream API.
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- **`eis_inference_id` (string)**: The unique identifier of the inference endpoint.
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- **`service` (Enum("elastic"))**: The type of service supported for the specified task type. In this case, `elastic`.
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- **`service_settings` ({ model_id, rate_limit })**: Settings used to install the inference model. These settings are specific to the `elastic` service.
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## client.inference.putElasticsearch [_inference.put_elasticsearch]
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Create an Elasticsearch inference endpoint.
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