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Feature/structured query tool integration (#493)
* Agent integration to structured query * Update tests
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5 changed files with 553 additions and 2 deletions
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@ -12,11 +12,11 @@ import logging
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logger = logging.getLogger(__name__)
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from ... base import AgentService, TextCompletionClientSpec, PromptClientSpec
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from ... base import GraphRagClientSpec, ToolClientSpec
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from ... base import GraphRagClientSpec, ToolClientSpec, StructuredQueryClientSpec
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from ... schema import AgentRequest, AgentResponse, AgentStep, Error
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from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl, PromptImpl
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from . tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl, PromptImpl, StructuredQueryImpl
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from . agent_manager import AgentManager
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from ..tool_filter import validate_tool_config, filter_tools_by_group_and_state, get_next_state
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@ -80,6 +80,13 @@ class Processor(AgentService):
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)
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)
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self.register_specification(
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StructuredQueryClientSpec(
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request_name = "structured-query-request",
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response_name = "structured-query-response",
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)
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)
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async def on_tools_config(self, config, version):
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logger.info(f"Loading configuration version {version}")
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@ -138,6 +145,12 @@ class Processor(AgentService):
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template_id=data.get("template"),
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arguments=arguments
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)
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elif impl_id == "structured-query":
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impl = functools.partial(
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StructuredQueryImpl,
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collection=data.get("collection")
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)
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arguments = StructuredQueryImpl.get_arguments()
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else:
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raise RuntimeError(
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f"Tool type {impl_id} not known"
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@ -85,6 +85,43 @@ class McpToolImpl:
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return json.dumps(output)
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# This tool implementation knows how to query structured data using natural language
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class StructuredQueryImpl:
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def __init__(self, context, collection=None):
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self.context = context
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self.collection = collection # For multi-tenant scenarios
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@staticmethod
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def get_arguments():
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return [
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Argument(
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name="question",
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type="string",
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description="Natural language question about structured data (tables, databases, etc.)"
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)
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]
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async def invoke(self, **arguments):
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client = self.context("structured-query-request")
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logger.debug("Structured query question...")
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result = await client.structured_query(
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arguments.get("question")
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)
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# Format the result for the agent
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if isinstance(result, dict):
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if result.get("error"):
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return f"Error: {result['error']['message']}"
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elif result.get("data"):
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# Pretty format JSON data for agent consumption
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return json.dumps(result["data"], indent=2)
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else:
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return "No data returned"
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else:
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return str(result)
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# This tool implementation knows how to execute prompt templates
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class PromptImpl:
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def __init__(self, context, template_id, arguments=None):
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