jpmschweitzerandClaude Opus 4.5
a077121b39
Changes preprocessing to use get_delegation_tools() instead of get_scoped_tools(). Expert agents now get delegation wrappers (delegate_to_librarian) while core tools are returned directly. This reduces Tatlock's cognitive load from 16+ tools to ~3-5. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
"""
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Request preprocessing pipeline.
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Analyzes requests via the Steward and creates scoped toolsets for Tatlock.
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"""
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Any, Optional
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from src.agents.steward import analyze_request, format_steward_note
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from src.agents.steward.schemas import StewardRecommendation
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from src.core.household_registry import get_household_registry
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from src.core.logging_config import get_logger
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logger = get_logger(__name__)
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def _inject_temporal_context(request: str) -> str:
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"""
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Append current time context to user request.
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Provides Tatlock with temporal awareness for time-sensitive queries.
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Args:
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request: Original user request
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Returns:
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Request with appended time context
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"""
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now = datetime.now()
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time_str = now.strftime("%Y-%m-%d %H:%M")
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return f"{request}\n\n[Current time: {time_str}]"
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@dataclass
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class EnrichedRequest:
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"""
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Request enriched with Steward's analysis.
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Attributes:
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original_request: The user's original message
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steward_note: Formatted note for Tatlock (includes context analysis)
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scoped_tools: List of tools from recommended capabilities
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recommendation: Full Steward recommendation
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steward_reasoning: Plain text reasoning for streaming to user
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"""
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original_request: str
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steward_note: str
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scoped_tools: list[Any] # PydanticAI tool definitions
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recommendation: StewardRecommendation
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steward_reasoning: str
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async def preprocess_request(
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user_request: str,
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conversation_history: list[dict],
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conversation_id: Optional[str] = None,
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) -> EnrichedRequest:
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"""
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Analyze request via Steward and prepare scoped context for Tatlock.
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This is the main preprocessing pipeline that:
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1. Calls Steward with full conversation history
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2. Gets capability recommendations
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3. Creates scoped toolset from recommended capabilities
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4. Formats a note for Tatlock with context analysis
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Args:
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user_request: Current user message to analyze
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conversation_history: Full conversation history (all previous turns)
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conversation_id: Optional conversation ID for tracking
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Returns:
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EnrichedRequest with scoped tools and Steward analysis
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Example:
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>>> enriched = await preprocess_request(
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... "What's sqrt(144)?",
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... conversation_history=[],
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... )
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>>> print(enriched.recommendation.recommended_capabilities)
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['tatlock_core']
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>>> print(len(enriched.scoped_tools))
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5 # All tatlock_core tools
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"""
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# Inject temporal context for time-aware processing
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enriched_request = _inject_temporal_context(user_request)
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logger.info(
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"preprocessing_request",
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request_preview=user_request[:100],
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history_length=len(conversation_history),
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conversation_id=conversation_id,
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)
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# Call Steward with full conversation history
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recommendation = await analyze_request(
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enriched_request,
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conversation_history=conversation_history,
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conversation_id=conversation_id,
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)
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# Format note for Tatlock (includes conversation context)
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steward_note = await format_steward_note(recommendation)
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# Get delegation tools from household registry
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# Uses agent-as-tool pattern: expert agents get delegation wrappers,
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# core tools are returned directly
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registry = get_household_registry()
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scoped_tools = registry.get_delegation_tools(
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recommendation.recommended_capabilities
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)
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logger.info(
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"preprocessing_complete",
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recommended_capabilities=recommendation.recommended_capabilities,
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tool_count=len(scoped_tools),
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complexity=recommendation.estimated_complexity,
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has_context=recommendation.conversation_context.has_previous_context,
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)
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return EnrichedRequest(
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original_request=enriched_request,
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steward_note=steward_note,
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scoped_tools=scoped_tools,
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recommendation=recommendation,
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steward_reasoning=recommendation.reasoning,
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)
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