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| --- | ||
| layout: default | ||
| title: Workflow templates | ||
| nav_order: 25 | ||
| --- | ||
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| # Workflow templates | ||
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| OpenSearch provides several workflow templates for some common machine learning (ML) use cases, such as semantic or conversational search. | ||
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| 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 using the `local_neural_sparse_search_bi_encoder` workflow template, as shown in the following request: | ||
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| ```json | ||
| POST /_plugins/_flow_framework/workflow?use_case=local_neural_sparse_search_bi_encoder | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| The workflow created using this template performs the following configuration steps: | ||
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| - 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 | ||
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| ## Parameters | ||
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| Each workflow template has a defined schema and a set of APIs with predefined defaults for each step. For more information about template parameter default values, see [Supported workflow templates](#supported-workflow-templates). | ||
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| ### Overwriting default values | ||
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| To overwrite the 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: | ||
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| ```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 %} | ||
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| ## Example | ||
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| In this example, you'll configure the `semantic_search_with_cohere_embedding` workflow template. The workflow created using this template performs the following configuration steps: | ||
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| - Deploys a Cohere externally hosted 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 | ||
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| ### Step 1: Create and provision the workflow | ||
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| Send the following request to create and provision a workflow using the `semantic_search_with_cohere_embedding` workflow template. The only required request body field for this template is the API key for the Cohere Embed model: | ||
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| ```json | ||
| POST /_plugins/_flow_framework/workflow?use_case=semantic_search_with_cohere_embedding&provision=true | ||
| { | ||
| "create_connector.credential.key" : "<YOUR API KEY>" | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| OpenSearch responds with a workflow ID for the created workflow: | ||
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| ```json | ||
| { | ||
| "workflow_id" : "8xL8bowB8y25Tqfenm50" | ||
| } | ||
| ``` | ||
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| The workflow in the previous step creates a default k-NN index. The default index name is `my-nlp-index`: | ||
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| ```json | ||
| { | ||
| "create_index.name": "my-nlp-index" | ||
| } | ||
| ``` | ||
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| 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). | ||
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| ### Step 2: Ingest documents into the index | ||
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| To ingest documents into the index created in the previous step, send the following request: | ||
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| ```json | ||
| PUT /my-nlp-index/_doc/1 | ||
| { | ||
| "passage_text": "Hello world", | ||
| "id": "s1" | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ### Step 3: Perform vector search | ||
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| To perform a vector search on your index, use a [`neural` query]({{site.url}}{{site.baseurl}}/query-dsl/specialized/neural/) clause: | ||
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| ```json | ||
| GET /my-nlp-index/_search | ||
| { | ||
| "_source": { | ||
| "excludes": [ | ||
| "passage_embedding" | ||
| ] | ||
| }, | ||
| "query": { | ||
| "neural": { | ||
| "passage_embedding": { | ||
| "query_text": "Hi world", | ||
| "k": 100 | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Viewing workflow resources | ||
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| 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: | ||
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| ```json | ||
| GET /_plugins/_flow_framework/workflow/8xL8bowB8y25Tqfenm50/_status | ||
| ``` | ||
| {% include copy-curl.html %} | ||
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| ## Supported workflow templates | ||
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| | Template name | 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 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 a model ID of the text embedding model to use. | `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 is defaulted for neural queries. You must provide a model ID of the text embedding model to use. | `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. Adds a [`query_enricher`]({{site.url}}{{site.baseurl}}/search-plugins/search-pipelines/neural-query-enricher/) search processor that sets a default model ID is defaulted 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-defaults.json) | | ||
| | `multi_modal_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 a model ID of the multimodal embedding model to use. | `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) | | ||
| | `multi_modal_search_with_bedrock_titan_multi_modal` | 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 a model ID of the text embedding model to use. | `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 an LLM model (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) | | ||
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