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PPL tool
Introduced 2.13
The PPLTool
translates natural language into a PPL query. The tool provides an execute
flag to specify whether to run the query. If you set the flag to true
, the PPLTool
runs the query and returns the query and the results.
Prerequisite
To create a PPL tool, you need a fine-tuned model that translates natural language into PPL queries. Alternatively, you can use large language models for prompt-based translation. The PPL tool supports the Anthropic Claude and OpenAI models.
Step 1: Create a connector for a model
The following example request creates a connector for a model hosted on Amazon SageMaker:
POST /_plugins/_ml/connectors/_create
{
"name": "sagemaker: t2ppl",
"description": "Test connector for Sagemaker t2ppl model",
"version": 1,
"protocol": "aws_sigv4",
"credential": {
"access_key": "<YOUR ACCESS KEY>",
"secret_key": "<YOUR SECRET KEY>"
},
"parameters": {
"region": "us-east-1",
"service_name": "sagemaker"
},
"actions": [
{
"action_type": "predict",
"method": "POST",
"headers": {
"content-type": "application/json"
},
"url": "<YOUR SAGEMAKER ENDPOINT>",
"request_body": """{"prompt":"${parameters.prompt}"}"""
}
]
}
OpenSearch responds with a connector ID:
{
"connector_id": "eJATWo0BkIylWTeYToTn"
}
For information about connecting to an Anthropic Claude model or OpenAI models, see Connectors.
Step 2: Register and deploy the model
To register and deploy the model to OpenSearch, send the following request, providing the connector ID from the previous step:
POST /_plugins/_ml/models/_register?deploy=true
{
"name": "remote-inference",
"function_name": "remote",
"description": "test model",
"connector_id": "eJATWo0BkIylWTeYToTn"
}
OpenSearch responds with a model ID:
{
"task_id": "7X7pWI0Bpc3sThaJ4I8R",
"status": "CREATED",
"model_id": "h5AUWo0BkIylWTeYT4SU"
}
Step 3: Register a flow agent that will run the PPLTool
A flow agent runs a sequence of tools in order and returns the last tool’s output. To create a flow agent, send the following register agent request, providing the model ID in the model_id
parameter. To run the generated query, set execute
to true
:
POST /_plugins/_ml/agents/_register
{
"name": "Test_Agent_For_PPL",
"type": "flow",
"description": "this is a test agent",
"memory": {
"type": "demo"
},
"tools": [
{
"type": "PPLTool",
"name": "TransferQuestionToPPLAndExecuteTool",
"description": "Use this tool to transfer natural language to generate PPL and execute PPL to query inside. Use this tool after you know the index name, otherwise, call IndexRoutingTool first. The input parameters are: {index:IndexName, question:UserQuestion}",
"parameters": {
"model_id": "h5AUWo0BkIylWTeYT4SU",
"model_type": "FINETUNE",
"execute": true
}
}
]
}
For parameter descriptions, see Register parameters.
OpenSearch responds with an agent ID:
{
"agent_id": "9X7xWI0Bpc3sThaJdY9i"
}
Step 4: Run the agent
Before you run the agent, make sure that you add the sample OpenSearch Dashboards Sample web logs
dataset. To learn more, see Adding sample data.
Then, run the agent by sending the following request:
POST /_plugins/_ml/agents/9X7xWI0Bpc3sThaJdY9i/_execute
{
"parameters": {
"verbose": true,
"question": "what is the error rate yesterday",
"index": "opensearch_dashboards_sample_data_logs"
}
}
OpenSearch returns the PPL query and the query results:
{
"inference_results": [
{
"output": [
{
"name": "response",
"result":"{\"ppl\":\"source\=opensearch_dashboards_sample_data_logs| where timestamp \> DATE_SUB(NOW(), INTERVAL 1 DAY) AND timestamp \< NOW() | eval is_error\=IF(response\=\'200\', 0, 1.0) | stats AVG(is_error) as error_rate\",\"executionResult\":\"{\\n \\\"schema\\\": [\\n {\\n \\\"name\\\": \\\"error_rate\\\",\\n \\\"type\\\": \\\"double\\\"\\n }\\n ],\\n \\\"datarows\\\": [\\n [\\n null\\n ]\\n ],\\n \\\"total\\\": 1,\\n \\\"size\\\": 1\\n}\"}"
}
]
}
]
}
If you set execute
to false
, OpenSearch only returns the query but does not run it:
{
"inference_results": [
{
"output": [
{
"name": "response",
"result": "source=opensearch_dashboards_sample_data_logs| where timestamp > DATE_SUB(NOW(), INTERVAL 1 DAY) AND timestamp < NOW() | eval is_error=IF(response='200', 0, 1.0) | stats AVG(is_error) as error_rate"
}
]
}
]
}
Register parameters
The following table lists all tool parameters that are available when registering an agent.
Parameter | Type | Required/Optional | Description |
---|---|---|---|
model_id | String | Required | The model ID of the large language model (LLM) to use for translating text into a PPL query. |
model_type | String | Optional | The model type. Valid values are CLAUDE (Anthropic Claude model), OPENAI (OpenAI models), and FINETUNE (custom fine-tuned model). |
prompt | String | Optional | The prompt to provide to the LLM. |
execute | Boolean | Optional | Specifies whether to run the PPL query. Default is true . |
input | Object | Optional | Contains two parameters that specify the index to search and the question for the LLM. For example, "input": "{\"index\": \"${parameters.index}\", \"question\": ${parameters.question} }" . |
head | Integer | Optional | Limits the number of returned execution results if execute is set to true . Default is -1 (no limit). |
Execute parameters
The following table lists all tool parameters that are available when running the agent.
Parameter | Type | Required/Optional | Description |
---|---|---|---|
index | String | Required | The index on which to run the PPL query. |
question | String | Required | The natural language question to send to the LLM. |
verbose | Boolean | Optional | Whether to provide verbose output. Default is false . |