diff --git a/docs/reference/search/search-your-data/semantic-search-inference.asciidoc b/docs/reference/search/search-your-data/semantic-search-inference.asciidoc index f74bc65e31bf0..1b5f072a19ce6 100644 --- a/docs/reference/search/search-your-data/semantic-search-inference.asciidoc +++ b/docs/reference/search/search-your-data/semantic-search-inference.asciidoc @@ -9,15 +9,19 @@ The instructions in this tutorial shows you how to use the {infer} API workflow IMPORTANT: For the easiest way to perform semantic search in the {stack}, refer to the <> end-to-end tutorial. -The following examples use Cohere's `embed-english-v3.0` model, the `all-mpnet-base-v2` model from HuggingFace, and OpenAI's `text-embedding-ada-002` second generation embedding model. +The following examples use the: + +* `embed-english-v3.0` model for https://docs.cohere.com/docs/cohere-embed[Cohere] +* `all-mpnet-base-v2` model from https://huggingface.co/sentence-transformers/all-mpnet-base-v2[HuggingFace] +* `text-embedding-ada-002` second generation embedding model for OpenAI +* models available through https://ai.azure.com/explore/models?selectedTask=embeddings[Azure AI Studio] or https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models[Azure OpenAI] +* `text-embedding-004` model for https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api[Google Vertex AI] +* `mistral-embed` model for https://docs.mistral.ai/getting-started/models/[Mistral] +* `amazon.titan-embed-text-v1` model for https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html[Amazon Bedrock] + You can use any Cohere and OpenAI models, they are all supported by the {infer} API. For a list of recommended models available on HuggingFace, refer to <>. -Azure based examples use models available through https://ai.azure.com/explore/models?selectedTask=embeddings[Azure AI Studio] -or https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models[Azure OpenAI]. -Mistral examples use the `mistral-embed` model from https://docs.mistral.ai/getting-started/models/[the Mistral API]. -Amazon Bedrock examples use the `amazon.titan-embed-text-v1` model from https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html[the Amazon Bedrock base models]. - Click the name of the service you want to use on any of the widgets below to review the corresponding instructions. [discrete] @@ -73,8 +77,8 @@ Once the upload is complete, you can see an index named `test-data` with 182469 [[reindexing-data-infer]] ==== Ingest the data through the {infer} ingest pipeline -Create the embeddings from the text by reindexing the data through the {infer} -pipeline that uses the chosen model as the inference model. +Create embeddings from the text by reindexing the data through the {infer} pipeline that uses your chosen model. +This step uses the {ref}/docs-reindex.html[reindex API] to simulate data ingestion through a pipeline. include::{es-ref-dir}/tab-widgets/inference-api/infer-api-reindex-widget.asciidoc[] @@ -113,5 +117,6 @@ include::{es-ref-dir}/tab-widgets/inference-api/infer-api-search-widget.asciidoc You can also find tutorials in an interactive Colab notebook format using the {es} Python client: + * https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/integrations/cohere/inference-cohere.ipynb[Cohere {infer} tutorial notebook] * https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/search/07-inference.ipynb[OpenAI {infer} tutorial notebook] diff --git a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc b/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc index 997dbbe8a20e6..00adc08b77dfc 100644 --- a/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc +++ b/docs/reference/tab-widgets/inference-api/infer-api-ingest-pipeline-widget.asciidoc @@ -37,6 +37,12 @@ id="infer-api-ingest-azure-ai-studio"> Azure AI Studio + + + + + +