Add documentation for default use cases

_automating-configurations/api/create-workflow.md

@@ -16,7 +16,7 @@ Creating a workflow adds the content of a workflow template to the flow framewor
 
 To obtain the validation template for workflow steps, call the [Get Workflow Steps API]({{site.url}}{{site.baseurl}}/automating-configurations/api/get-workflow-steps/).
 
-You can include placeholder expressions in the value of workflow step fields. For example, you can specify a credential field in a template as `openAI_key: '${{ openai_key }}'`. The expression will be substituted with the user-provided value during provisioning, using the format `${{ <value> }}`. You can pass the actual key as a parameter using the [Provision Workflow API]({{site.url}}{{site.baseurl}}/automating-configurations/api/provision-workflow/) or using this API with the `provision` parameter set to `true`.
+You can include placeholder expressions in the value of workflow step fields. For example, you can specify a credential field in a template as `openAI_key: '${{ openai_key }}'`. The expression will be substituted with the user-provided value during provisioning, using the format {% raw %}`${{ <value> }}`{% endraw %}. You can pass the actual key as a parameter by using the [Provision Workflow API]({{site.url}}{{site.baseurl}}/automating-configurations/api/provision-workflow/) or by using this API with the `provision` parameter set to `true`.
 
 Once a workflow is created, provide its `workflow_id` to other APIs.
 

_automating-configurations/workflow-templates.md

@@ -0,0 +1,147 @@
+---
+layout: default
+title: Workflow templates
+nav_order: 25
+---
+
+# Workflow templates
+
+OpenSearch provides several workflow templates for some common machine learning (ML) use cases, such as semantic or conversational search. 
+
+You can specify a workflow template when you call the [Create Workflow API]({{site.url}}{{site.baseurl}}/automating-configurations/api/create-workflow/). To provision the workflow, specify `provision=true` as a query parameter. For example, you can configure [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/) by providing the `local_neural_sparse_search_bi_encoder` query parameter as `use_case`, as shown in the following request:
+
+```json
+POST /_plugins/_flow_framework/workflow?use_case=local_neural_sparse_search_bi_encoder
+```
+{% include copy-curl.html %}
+
+The workflow created using this template performs the following configuration steps:
+
+- Deploys the default pretrained sparse encoding model (`amazon/neural-sparse/opensearch-neural-sparse-encoding-v1`).
+- Creates an ingest pipeline that contains a `sparse_encoding` processor, which converts the text in a document field to vector embeddings using the deployed model.
+- Creates a sample index for sparse search, specifying the default pipeline as the newly created ingest pipeline.
+
+## 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](#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:
+
+```json
+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"
+}
+```
+{% include copy-curl.html %}
+
+## 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:
+
+```json
+POST /_plugins/_flow_framework/workflow?use_case=semantic_search_with_cohere_embedding_query_enricher&provision=true
+{
+    "create_connector.credential.key" : "<YOUR API KEY>"
+}
+```
+{% include copy-curl.html %}
+
+OpenSearch responds with a workflow ID for the created workflow:
+
+```json
+{
+  "workflow_id" : "8xL8bowB8y25Tqfenm50"
+}
+```
+
+The workflow in the previous step creates a default k-NN index. The default index name is `my-nlp-index`:
+
+```json
+{
+  "create_index.name": "my-nlp-index"
+}
+```
+
+For all default parameter values for this workflow template, see [Cohere Embed semantic search defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/cohere-embedding-semantic-search-defaults.json).
+
+### Step 2: Ingest documents into the index 
+
+To ingest documents into the index created in the previous step, send the following request:
+
+```json
+PUT /my-nlp-index/_doc/1
+{
+  "passage_text": "Hello world",
+  "id": "s1"
+}
+```
+{% include copy-curl.html %}
+
+### Step 3: Perform vector search
+
+To perform a vector search on your index, use a [`neural` query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/) clause:
+
+```json
+GET /my-nlp-index/_search
+{
+  "_source": {
+    "excludes": [
+      "passage_embedding"
+    ]
+  },
+  "query": {
+    "neural": {
+      "passage_embedding": {
+        "query_text": "Hi world",
+        "k": 100
+      }
+    }
+  }
+}
+```
+{% include copy-curl.html %}
+
+## 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]({{site.url}}{{site.baseurl}}/automating-configurations/api/get-workflow-status/) and provide the `workflowID` for your workflow:
+
+```json
+GET /_plugins/_flow_framework/workflow/8xL8bowB8y25Tqfenm50/_status
+```
+{% include copy-curl.html %}
+
+## 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](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/bedrock-titan-embedding-defaults.json)|
+| `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](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/bedrock-titan-multimodal-defaults.json). |
+| `cohere_embedding_model_deploy`| Creates and deploys a Cohere embedding model (by default, `embed-english-v3.0`).   | `create_connector.credential.key`         |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/cohere-embedding-defaults.json)                  |
+| `cohere_chat_model_deploy` | Creates and deploys a Cohere chat model (by default, Cohere Command).     | `create_connector.credential.key`         |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/cohere-chat-defaults.json)                  |
+| `open_ai_embedding_model_deploy` | Creates and deploys an OpenAI embedding model (by default, `text-embedding-ada-002`).  | `create_connector.credential.key`         |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/openai-embedding-defaults.json)                  |
+| `openai_chat_model_deploy`  | Creates and deploys an OpenAI chat model (by default, `gpt-3.5-turbo`).   | `create_connector.credential.key`         |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/openai-chat-defaults.json)                  |
+| `local_neural_sparse_search_bi_encoder`  | Configures [neural sparse search]({{site.url}}{{site.baseurl}}/search-plugins/neural-sparse-search/): <br> - Deploys a pretrained sparse encoding model.<br> - Creates an ingest pipeline with a sparse encoding processor. <br> - Creates a sample index to use for sparse search, specifying the newly created pipeline as the default pipeline.  | None            |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/local-sparse-search-biencoder-defaults.json)                  |
+| `semantic_search`                                      | Configures [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/): <br> - Creates an ingest pipeline with a `text_embedding` processor and a k-NN index <br> You must provide the model ID of the text embedding model to be used. | `create_ingest_pipeline.model_id`        |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/semantic-search-defaults.json)                  |
+| `semantic_search_with_query_enricher` | Configures [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/) similarly to the `semantic_search` template. Adds a [`query_enricher`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/neural-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](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/semantic-search-query-enricher-defaults.json)                  |
+| `semantic_search_with_cohere_embedding` | Configures [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/) and deploys a Cohere embedding model. You must provide the API key for the Cohere model.  | `create_connector.credential.key`         |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/cohere-embedding-semantic-search-defaults.json)                  |
+| `semantic_search_with_cohere_embedding_query_enricher` | Configures [semantic search]({{site.url}}{{site.baseurl}}/search-plugins/semantic-search/) and deploys a Cohere embedding model. Adds a [`query_enricher`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/neural-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](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/cohere-embedding-semantic-search-with-query-enricher-defaults.json)                  |
+| `multimodal_search`  | Configures an ingest pipeline with a `text_image_embedding` processor and a k-NN index for [multimodal search]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/). You must provide the model ID of the multimodal embedding model to be used. | `create_ingest_pipeline.model_id`       |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/multi-modal-search-defaults.json)                  |
+| `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]({{site.url}}{{site.baseurl}}/search-plugins/multimodal-search/). You must provide your AWS credentials. | `create_connector.credential.access_key`, `create_connector.credential.secret_key`, `create_connector.credential.session_token` |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/multimodal-search-bedrock-titan-defaults.json)                  |
+| `hybrid_search`  | Configures [hybrid search]({{site.url}}{{site.baseurl}}/search-plugins/hybrid-search/): <br> - 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](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/hybrid-search-defaults.json)                  |
+| `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]({{site.url}}{{site.baseurl}}/search-plugins/conversational-search/).  | `create_connector.credential.key`  |[Defaults](https://github.com/opensearch-project/flow-framework/blob/2.13/src/main/resources/defaults/conversational-search-defaults.json) |
+
+