33488fd634
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
19 lines
641 B
Python
19 lines
641 B
Python
"""Fallback agent tools -- handles unmatched intents."""
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from __future__ import annotations
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from langchain_core.tools import tool
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@tool
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def fallback_respond(query: str) -> str:
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"""Provide a helpful response when the user's intent doesn't match a specific agent."""
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return (
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"I'm here to help with order inquiries and actions. "
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"Here's what I can do:\n"
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"- Check order status (e.g., 'What is the status of order 1042?')\n"
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"- Get tracking information (e.g., 'Track order 1042')\n"
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"- Cancel an order (e.g., 'Cancel order 1042')\n\n"
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"Could you please rephrase your request?"
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)
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