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560d4048d2
* Add Amazon Bedrock Inference API to docs * fix example errors * update semantic search tutorial; add changelog * fix typo * fix error; accept suggestions
192 lines
5 KiB
Text
192 lines
5 KiB
Text
////
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[source,console]
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----
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DELETE _ingest/pipeline/*_embeddings
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----
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// TEST
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// TEARDOWN
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////
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// tag::cohere[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/cohere_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "cohere_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::cohere[]
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// tag::hugging-face[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/hugging_face_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "hugging_face_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::hugging-face[]
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// tag::openai[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/openai_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "openai_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::openai[]
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// tag::azure-openai[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/azure_openai_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "azure_openai_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::azure-openai[]
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// tag::azure-ai-studio[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/azure_ai_studio_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "azure_ai_studio_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::azure-ai-studio[]
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// tag::mistral[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/mistral_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "mistral_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::mistral[]
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// tag::amazon-bedrock[]
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[source,console]
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--------------------------------------------------
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PUT _ingest/pipeline/amazon_bedrock_embeddings
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{
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"processors": [
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{
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"inference": {
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"model_id": "amazon_bedrock_embeddings", <1>
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"input_output": { <2>
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"input_field": "content",
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"output_field": "content_embedding"
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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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<1> The name of the inference endpoint you created by using the
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<<put-inference-api>>, it's referred to as `inference_id` in that step.
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<2> Configuration object that defines the `input_field` for the {infer} process
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and the `output_field` that will contain the {infer} results.
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// end::amazon-bedrock[]
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