Search for an agent
Introduced 2.13
Use this command to search for agents you’ve already created. You can provide any OpenSearch search query in the request body.
Endpoints
GET /_plugins/_ml/agents/_search
POST /_plugins/_ml/agents/_search
Example request: Searching for all agents
POST /_plugins/_ml/agents/_search
{
"query": {
"match_all": {}
},
"size": 1000
}
Example request: Searching for agents of a certain type
POST /_plugins/_ml/agents/_search
{
"query": {
"term": {
"type": {
"value": "flow"
}
}
}
}
Example: Searching for an agent by description
GET _plugins/_ml/agents/_search
{
"query": {
"bool": {
"should": [
{
"match": {
"description": "test agent"
}
}
]
}
},
"size": 1000
}
Example response
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 6,
"relation": "eq"
},
"max_score": 0.15019803,
"hits": [
{
"_index": ".plugins-ml-agent",
"_id": "8HXlkI0BfUsSoeNTP_0P",
"_version": 1,
"_seq_no": 17,
"_primary_term": 2,
"_score": 0.13904166,
"_source": {
"created_time": 1707532959502,
"last_updated_time": 1707532959502,
"name": "Test_Agent_For_RagTool",
"description": "this is a test flow agent",
"type": "flow",
"tools": [
{
"description": "A description of the tool",
"include_output_in_agent_response": false,
"type": "RAGTool",
"parameters": {
"inference_model_id": "gnDIbI0BfUsSoeNT_jAw",
"embedding_model_id": "Yg7HZo0B9ggZeh2gYjtu_2",
"input": "${parameters.question}",
"source_field": """["text"]""",
"embedding_field": "embedding",
"index": "my_test_data",
"query_type": "neural",
"prompt": """
Human:You are a professional data analyst. You will always answer question based on the given context first. If the answer is not directly shown in the context, you will analyze the data and find the answer. If you don't know the answer, just say don't know.
Context:
${parameters.output_field}
Human:${parameters.question}
Assistant:"""
}
}
]
}
}
]
}
}
Response body fields
For response field descriptions, see Register Agent API request fields.