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Workflow templates
OpenSearch provides several workflow templates for some common machine learning (ML) use cases. Using a template simplifies complex setups and provides many default values for use cases like semantic or conversational search.
You can specify a workflow template when you call the Create Workflow API:
-
To use an OpenSearch-provided workflow template, specify the template use case as the
use_case
query parameter (see the Example). For a list of OpenSearch-provided templates, see Supported workflow templates. -
To use a custom workflow template, provide the complete template in the request body. For an example of a custom template, see an example JSON template or an example YAML template.
To provision the workflow, specify provision=true
as a query parameter.
Example
In this example, you’ll configure the semantic_search_with_cohere_embedding_query_enricher
workflow template. The workflow created using this template performs the following configuration steps:
- Deploys an externally hosted Cohere model
- Creates an ingest pipeline using the model
- Creates a sample k-NN index and configures a search pipeline to define the default model ID for that index
Step 1: Create and provision the workflow
Send the following request to create and provision a workflow using the semantic_search_with_cohere_embedding_query_enricher
workflow template. The only required request body field for this template is the API key for the Cohere Embed model:
POST /_plugins/_flow_framework/workflow?use_case=semantic_search_with_cohere_embedding_query_enricher&provision=true
{
"create_connector.credential.key" : "<YOUR API KEY>"
}
OpenSearch responds with a workflow ID for the created workflow:
{
"workflow_id" : "8xL8bowB8y25Tqfenm50"
}
The workflow in the previous step creates a default k-NN index. The default index name is my-nlp-index
:
{
"create_index.name": "my-nlp-index"
}
For all default parameter values for this workflow template, see Cohere Embed semantic search defaults.
Step 2: Ingest documents into the index
To ingest documents into the index created in the previous step, send the following request:
PUT /my-nlp-index/_doc/1
{
"passage_text": "Hello world",
"id": "s1"
}
Step 3: Perform vector search
To perform a vector search on your index, use a neural
query clause:
GET /my-nlp-index/_search
{
"_source": {
"excludes": [
"passage_embedding"
]
},
"query": {
"neural": {
"passage_embedding": {
"query_text": "Hi world",
"k": 100
}
}
}
}
Parameters
Each workflow template has a defined schema and a set of APIs with predefined default values for each step. For more information about template parameter defaults, see Supported workflow templates.
Overriding default values
To override a template’s default values, provide the new values in the request body when sending a create workflow request. For example, the following request changes the Cohere model, the name of the text_embedding
processor output field, and the name of the sparse index of the semantic_search_with_cohere_embedding
template:
POST /_plugins/_flow_framework/workflow?use_case=semantic_search_with_cohere_embedding
{
"create_connector.model" : "embed-multilingual-v3.0",
"text_embedding.field_map.output": "book_embedding",
"create_index.name": "sparse-book-index"
}
Viewing workflow resources
The workflow you created provisioned all the necessary resources for semantic search. To view the provisioned resources, call the Get Workflow Status API and provide the workflowID
for your workflow:
GET /_plugins/_flow_framework/workflow/8xL8bowB8y25Tqfenm50/_status
Supported workflow templates
The following table lists the supported workflow templates. To use a workflow template, specify it in the use_case
query parameter when creating a workflow.
Template use case | Description | Required parameters | Defaults |
bedrock_titan_embedding_model_deploy | Creates and deploys an Amazon Bedrock embedding model (by default, titan-embed-text-v1 ). | create_connector.credential.access_key , create_connector.credential.secret_key , create_connector.credential.session_token | Defaults |
bedrock_titan_multimodal_model_deploy | Creates and deploys an Amazon Bedrock multimodal embedding model (by default, titan-embed-image-v1 ). | create_connector.credential.access_key , create_connector.credential.secret_key , create_connector.credential.session_token | Defaults. |
cohere_embedding_model_deploy | Creates and deploys a Cohere embedding model (by default, embed-english-v3.0 ). | create_connector.credential.key | Defaults |
cohere_chat_model_deploy | Creates and deploys a Cohere chat model (by default, Cohere Command). | create_connector.credential.key | Defaults |
open_ai_embedding_model_deploy | Creates and deploys an OpenAI embedding model (by default, text-embedding-ada-002 ). | create_connector.credential.key | Defaults |
openai_chat_model_deploy | Creates and deploys an OpenAI chat model (by default, gpt-3.5-turbo ). | create_connector.credential.key | Defaults |
local_neural_sparse_search_bi_encoder | Configures neural sparse search: - Deploys a pretrained sparse encoding model. - Creates an ingest pipeline with a sparse encoding processor. - Creates a sample index to use for sparse search, specifying the newly created pipeline as the default pipeline. | None | Defaults |
semantic_search | Configures semantic search: - Creates an ingest pipeline with a text_embedding processor and a k-NN index You must provide the model ID of the text embedding model to be used. | create_ingest_pipeline.model_id | Defaults |
semantic_search_with_query_enricher | Configures semantic search similarly to the semantic_search template. Adds a query_enricher search processor that sets a default model ID for neural queries. You must provide the model ID of the text embedding model to be used. | create_ingest_pipeline.model_id | Defaults |
semantic_search_with_cohere_embedding | Configures semantic search and deploys a Cohere embedding model. You must provide the API key for the Cohere model. | create_connector.credential.key | Defaults |
semantic_search_with_cohere_embedding_query_enricher | Configures semantic search and deploys a Cohere embedding model. Adds a query_enricher search processor that sets a default model ID for neural queries. You must provide the API key for the Cohere model. | create_connector.credential.key | Defaults |
multimodal_search | Configures an ingest pipeline with a text_image_embedding processor and a k-NN index for multimodal search. You must provide the model ID of the multimodal embedding model to be used. | create_ingest_pipeline.model_id | Defaults |
multimodal_search_with_bedrock_titan | Deploys an Amazon Bedrock multimodal model and configures an ingest pipeline with a text_image_embedding processor and a k-NN index for multimodal search. You must provide your AWS credentials. | create_connector.credential.access_key , create_connector.credential.secret_key , create_connector.credential.session_token | Defaults |
hybrid_search | Configures hybrid search: - Creates an ingest pipeline, a k-NN index, and a search pipeline with a normalization_processor . You must provide the model ID of the text embedding model to be used. | create_ingest_pipeline.model_id | Defaults |
conversational_search_with_llm_deploy | Deploys a large language model (LLM) (by default, Cohere Chat) and configures a search pipeline with a retrieval_augmented_generation processor for conversational search. | create_connector.credential.key | Defaults |