Backend: - FastAPI WebSocket /ws endpoint with streaming via LangGraph astream - LangGraph Supervisor connecting 3 mock agents (order_lookup, order_actions, fallback) - YAML Agent Registry with Pydantic validation and immutable configs - PostgresSaver checkpoint persistence via langgraph-checkpoint-postgres - Session TTL with 30-min sliding window and interrupt extension - LLM provider abstraction (Anthropic/OpenAI/Google) - Token usage + cost tracking callback handler - Input validation: message size cap, thread_id format, content length - Security: no hardcoded defaults, startup API key validation, no input reflection Frontend: - React 19 + TypeScript + Vite chat UI - WebSocket hook with reconnect + exponential backoff - Streaming token display with agent attribution - Interrupt approval/reject UI for write operations - Collapsible tool call viewer Testing: - 87 unit tests, 87% coverage (exceeds 80% requirement) - Ruff lint + format clean Infrastructure: - Docker Compose (PostgreSQL 16 + backend) - pyproject.toml with full dependency management
31 lines
1007 B
Python
31 lines
1007 B
Python
"""Agent tools registry -- maps tool name strings to actual tool functions."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from langchain_core.tools import BaseTool
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from app.agents.fallback import fallback_respond
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from app.agents.order_actions import cancel_order
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from app.agents.order_lookup import get_order_status, get_tracking_info
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_TOOL_MAP: dict[str, BaseTool] = {
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"get_order_status": get_order_status,
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"get_tracking_info": get_tracking_info,
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"cancel_order": cancel_order,
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"fallback_respond": fallback_respond,
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}
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def get_tools_by_names(tool_names: list[str]) -> list[BaseTool]:
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"""Resolve tool name strings from YAML config to actual tool objects."""
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tools = []
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for name in tool_names:
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if name not in _TOOL_MAP:
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available = ", ".join(sorted(_TOOL_MAP.keys()))
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raise ValueError(f"Unknown tool '{name}'. Available tools: {available}")
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tools.append(_TOOL_MAP[name])
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return tools
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