[Backport 2.x] Add sample templates (#510)

Add sample templates (#498) * Add sample templates * Change Query Assist template to use OpenAI * Add README to the folder * Address PR review comments on templates * Add chat agent template --------- (cherry picked from commit 13b1f93) Signed-off-by: Daniel Widdis <[email protected]> Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
opensearch-project · Feb 9, 2024 · ddeeee0 · ddeeee0
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diff --git a/sample-templates/README.md b/sample-templates/README.md
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+## Flow Framework Sample Templates
+
+This folder contains sample workflow templates that can be used with Flow Framework.
+
+Each template is provided in both YAML and JSON format with identical functionality.
+The YAML templates include comments which give more insight into the template's usage.
+Use the corresponding `Content-Type` (`application/yaml` or `application/json`) when providing them as the body of a REST request.
+
+You will need to update the `credentials` field with appropriate API keys.
+
+To create a workflow and provision the resources:
+
+```
+POST /_plugins/_flow_framework/workflow?provision=true
+{ template as body }
+```
+
+This will return a `workflow_id`. To get the IDs of created resources, call the workflow status API.
+
+```
+GET /_plugins/_flow_framework/workflow/<workflow_id>/_status
+```
+
+For the Query Assist Agent API, the `agent_id` of the `root_agent` can be used to query it.
+
+```
+POST /_plugins/_ml/agents/_<agent_id>/_execute
+{
+  "parameters": {
+    "question": "How many 5xx logs do I have?"
+  }
+}
+```
diff --git a/sample-templates/deploy-bedrock-claude-model.json b/sample-templates/deploy-bedrock-claude-model.json
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+{
+  "name": "Deploy Claude Model",
+  "description": "Deploy a model using a connector to Claude",
+  "use_case": "PROVISION",
+  "version": {
+    "template": "1.0.0",
+    "compatibility": [
+      "2.12.0",
+      "3.0.0"
+    ]
+  },
+  "workflows": {
+    "provision": {
+      "nodes": [
+        {
+          "id": "create_claude_connector",
+          "type": "create_connector",
+          "user_inputs": {
+            "name": "Claude Instant Runtime Connector",
+            "version": "1",
+            "protocol": "aws_sigv4",
+            "description": "The connector to BedRock service for Claude model",
+            "actions": [
+              {
+                "headers": {
+                  "x-amz-content-sha256": "required",
+                  "content-type": "application/json"
+                },
+                "method": "POST",
+                "request_body": "{ \"prompt\":\"${parameters.prompt}\", \"max_tokens_to_sample\":${parameters.max_tokens_to_sample}, \"temperature\":${parameters.temperature},  \"anthropic_version\":\"${parameters.anthropic_version}\" }",
+                "action_type": "predict",
+                "url": "https://bedrock-runtime.us-west-2.amazonaws.com/model/anthropic.claude-instant-v1/invoke"
+              }
+            ],
+            "credential": {
+              "access_key": "PUT_YOUR_ACCESS_KEY_HERE",
+              "secret_key": "PUT_YOUR_SECRET_KEY_HERE"
+            },
+            "parameters": {
+              "endpoint": "bedrock-runtime.us-west-2.amazonaws.com",
+              "content_type": "application/json",
+              "auth": "Sig_V4",
+              "max_tokens_to_sample": "8000",
+              "service_name": "bedrock",
+              "temperature": "0.0001",
+              "response_filter": "$.completion",
+              "region": "us-west-2",
+              "anthropic_version": "bedrock-2023-05-31"
+            }
+          }
+        },
+        {
+          "id": "register_claude_model",
+          "type": "register_remote_model",
+          "previous_node_inputs": {
+            "create_claude_connector": "connector_id"
+          },
+          "user_inputs": {
+            "name": "claude-instant"
+          }
+        },
+        {
+          "id": "deploy_claude_model",
+          "type": "deploy_model",
+          "previous_node_inputs": {
+            "register_claude_model": "model_id"
+          }
+        }
+      ]
+    }
+  }
+}
diff --git a/sample-templates/deploy-bedrock-claude-model.yml b/sample-templates/deploy-bedrock-claude-model.yml
@@ -0,0 +1,76 @@
+# This template creates a connector to the BedRock service for Claude model
+# It then registers a model using that connector and deploys it.
+#
+# To use:
+#  - update the "credential" fields under the create_claude_connector node.
+#  - if needed, update region
+#
+# After provisioning:
+#  - returns a workflow ID
+#  - use the status API to get the deployed model ID
+---
+name: Deploy Claude Model
+description: Deploy a model using a connector to Claude
+use_case: PROVISION
+version:
+  template: 1.0.0
+  compatibility:
+  - 2.12.0
+  - 3.0.0
+# This section defines the provision workflow. Nodes are connected in a graph.
+# Either previous_node_inputs or explicit edges can be used to enforce ordering.
+workflows:
+  provision:
+    # Each id field in a workflow must be unique.
+    nodes:
+    - id: create_claude_connector
+      type: create_connector
+      user_inputs:
+        name: Claude Instant Runtime Connector
+        version: '1'
+        protocol: aws_sigv4
+        description: The connector to BedRock service for Claude model
+        actions:
+        - headers:
+            x-amz-content-sha256: required
+            content-type: application/json
+          method: POST
+          request_body: '{
+              "prompt":"${parameters.prompt}",
+              "max_tokens_to_sample":${parameters.max_tokens_to_sample},
+              "temperature":${parameters.temperature},  "anthropic_version":"${parameters.anthropic_version}"
+            }'
+          action_type: predict
+          url: https://bedrock-runtime.us-west-2.amazonaws.com/model/anthropic.claude-instant-v1/invoke
+        credential:
+          access_key: 'PUT_YOUR_ACCESS_KEY_HERE'
+          secret_key: 'PUT_YOUR_SECRET_KEY_HERE'
+        parameters:
+          endpoint: bedrock-runtime.us-west-2.amazonaws.com
+          content_type: application/json
+          auth: Sig_V4
+          max_tokens_to_sample: '8000'
+          service_name: bedrock
+          temperature: '0.0001'
+          response_filter: "$.completion"
+          region: us-west-2
+          anthropic_version: bedrock-2023-05-31
+    - id: register_claude_model
+      type: register_remote_model
+      previous_node_inputs:
+        create_claude_connector: connector_id
+      user_inputs:
+        name: claude-instant
+        # Using deploy: true here would both registers and deploy the model
+        # and the deploy model step below could be deleted
+        # deploy: true
+    - id: deploy_claude_model
+      type: deploy_model
+      previous_node_inputs:
+        register_claude_model: model_id
+    # Because the above nodes use previous_node_inputs, these edges are automatically generated.
+    # edges:
+    # - source: create_claude_connector
+    #   dest: register_claude_model
+    # - source: register_claude_model
+    #   dest: deploy_claude_model
diff --git a/sample-templates/deploy-openai-model.json b/sample-templates/deploy-openai-model.json
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+{
+  "name": "Deploy OpenAI Model",
+  "description": "Deploy a model using a connector to OpenAI",
+  "use_case": "PROVISION",
+  "version": {
+    "template": "1.0.0",
+    "compatibility": [
+      "2.12.0",
+      "3.0.0"
+    ]
+  },
+  "workflows": {
+    "provision": {
+      "nodes": [
+        {
+          "id": "create_openai_connector",
+          "type": "create_connector",
+          "user_inputs": {
+            "name": "OpenAI Chat Connector",
+            "description": "The connector to public OpenAI model service for GPT 3.5",
+            "version": "1",
+            "protocol": "http",
+            "parameters": {
+              "endpoint": "api.openai.com",
+              "model": "gpt-3.5-turbo",
+              "response_filter": "$.choices[0].message.content"
+            },
+            "credential": {
+              "openAI_key": "PUT_YOUR_API_KEY_HERE"
+            },
+            "actions": [
+              {
+                "action_type": "predict",
+                "method": "POST",
+                "url": "https://${parameters.endpoint}/v1/chat/completions"
+              }
+            ]
+          }
+        },
+        {
+          "id": "register_openai_model",
+          "type": "register_remote_model",
+          "previous_node_inputs": {
+            "create_openai_connector": "connector_id"
+          },
+          "user_inputs": {
+            "name": "openAI-gpt-3.5-turbo"
+          }
+        },
+        {
+          "id": "deploy_openai_model",
+          "type": "deploy_model",
+          "previous_node_inputs": {
+            "register_openai_model": "model_id"
+          }
+        }
+      ]
+    }
+  }
+}
diff --git a/sample-templates/deploy-openai-model.yml b/sample-templates/deploy-openai-model.yml
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+# This template creates a connector to the public OpenAI model service for GPT 3.5
+# It then registers a model using that connector and deploys it.
+#
+# To use:
+#  - update the "credential" field under the create_openai_connector node.
+#
+# After provisioning:
+#  - returns a workflow ID
+#  - use the status API to get the deployed model ID
+---
+name: Deploy OpenAI Model
+description: Deploy a model using a connector to OpenAI
+use_case: PROVISION
+version:
+  template: 1.0.0
+  compatibility:
+  - 2.12.0
+  - 3.0.0
+# This section defines the provision workflow. Nodes are connected in a graph.
+# Either previous_node_inputs or explicit edges can be used to enforce ordering.
+workflows:
+  provision:
+    # Each id field in a workflow must be unique.
+    nodes:
+    - id: create_openai_connector
+      type: create_connector
+      user_inputs:
+        name: OpenAI Chat Connector
+        description: The connector to public OpenAI model service for GPT 3.5
+        version: '1'
+        protocol: http
+        parameters:
+          endpoint: api.openai.com
+          model: gpt-3.5-turbo
+          response_filter: '$.choices[0].message.content'
+        credential:
+          openAI_key: 'PUT_YOUR_API_KEY_HERE'
+        actions:
+        - action_type: predict
+          method: POST
+          url: https://${parameters.endpoint}/v1/chat/completions
+    - id: register_openai_model
+      type: register_remote_model
+      previous_node_inputs:
+        create_openai_connector: connector_id
+      user_inputs:
+        name: openAI-gpt-3.5-turbo
+        # Using deploy: true here would both registers and deploy the model
+        # and the deploy model step below could be deleted
+        # deploy: true
+    - id: deploy_openai_model
+      type: deploy_model
+      previous_node_inputs:
+        register_openai_model: model_id
+    # Because the above nodes use previous_node_inputs, these edges are automatically generated.
+    # edges:
+    # - source: create_openai_connector
+    #   dest: register_openai_model
+    # - source: register_openai_model
+    #   dest: deploy_openai_model
diff --git a/sample-templates/deploy-sagemaker-mistral-model.json b/sample-templates/deploy-sagemaker-mistral-model.json
@@ -0,0 +1,64 @@
+{
+  "name": "Deploy Mistral Model",
+  "description": "Deploy a model using a connector to SageMaker Mistral model",
+  "use_case": "PROVISION",
+  "version": {
+    "template": "1.0.0",
+    "compatibility": [
+      "2.12.0",
+      "3.0.0"
+    ]
+  },
+  "workflows": {
+    "provision": {
+      "nodes": [
+        {
+          "id": "create_mistral_connector",
+          "type": "create_connector",
+          "user_inputs": {
+            "name": "sagemaker: mistral",
+            "description": "Test connector for Sagemaker mistral model",
+            "version": "1",
+            "protocol": "aws_sigv4",
+            "credential": {
+              "access_key": "PUT_YOUR_ACCESS_KEY_HERE",
+              "secret_key": "PUT_YOUR_SECRET_KEY_HERE"
+            },
+            "parameters": {
+              "region": "us-east-1",
+              "service_name": "sagemaker"
+            },
+            "actions": [
+              {
+                "action_type": "predict",
+                "method": "POST",
+                "headers": {
+                  "content-type": "application/json"
+                },
+                "url": "https://PUT_YOUR_CUSTOM_SAGEMAKER_ENDPOINT_HERE",
+                "request_body": "{\"prompt\":\"${parameters.prompt}\"}"
+              }
+            ]
+          }
+        },
+        {
+          "id": "register_mistral_model",
+          "type": "register_remote_model",
+          "previous_node_inputs": {
+            "create_mistral_connector": "connector_id"
+          },
+          "user_inputs": {
+            "name": "mistral fine-tuned model"
+          }
+        },
+        {
+          "id": "deploy_mistral_model",
+          "type": "deploy_model",
+          "previous_node_inputs": {
+            "register_mistral_model": "model_id"
+          }
+        }
+      ]
+    }
+  }
+}