f0699436c5
- API versioning: all REST endpoints prefixed with /api/v1/ - Structured logging: replaced stdlib logging with structlog (console/JSON modes) - Alembic migrations: versioned DB schema with initial migration - Error standardization: global exception handlers for consistent envelope format - Interrupt cleanup: asyncio background task for expired interrupt removal - Integration tests: +30 tests (analytics, replay, openapi, error, session APIs) - Frontend tests: +57 tests (all components, pages, useWebSocket hook) - Backend: 557 tests, 89.75% coverage | Frontend: 80 tests, 16 test files
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
"""LangGraph Supervisor construction -- connects registry, agents, LLM, and persistence."""
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from langchain.agents import create_agent
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from langgraph_supervisor import create_supervisor
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from app.agents import get_tools_by_names
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from app.graph_context import GraphContext
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if TYPE_CHECKING:
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from langchain_core.language_models import BaseChatModel
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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from app.intent import IntentClassifier
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from app.registry import AgentRegistry
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import structlog
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logger = structlog.get_logger()
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SUPERVISOR_PROMPT = (
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"You are a customer support supervisor. "
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"Route customer requests to the appropriate agent based on their description.\n\n"
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"Available agents and their roles:\n"
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"{agent_descriptions}\n\n"
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"Routing rules:\n"
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"- For order status and tracking queries, use the order_lookup agent.\n"
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"- For order modifications like cancellations, use the order_actions agent.\n"
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"- For discounts, promotions, or coupon codes, use the discount agent.\n"
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"- For anything else or when uncertain, use the fallback agent.\n"
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"- If the user's request involves multiple actions, execute them in order.\n"
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"- If a previous intent classification is provided, follow it.\n"
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)
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def _format_agent_descriptions(registry: AgentRegistry) -> str:
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"""Build agent description text for the supervisor prompt."""
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lines = []
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for agent in registry.list_agents():
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lines.append(f"- {agent.name}: {agent.description}")
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return "\n".join(lines)
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def build_agent_nodes(
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registry: AgentRegistry,
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llm: BaseChatModel,
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) -> list:
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"""Create LangGraph react agent nodes from registry configurations."""
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agent_nodes = []
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for agent_config in registry.list_agents():
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tools = get_tools_by_names(agent_config.tools)
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system_prompt = (
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f"You are the {agent_config.name} agent. "
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f"Personality: {agent_config.personality.tone}. "
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f"{agent_config.description} "
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f"Permission level: {agent_config.permission}."
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)
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agent_node = create_agent(
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model=llm,
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tools=tools,
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name=agent_config.name,
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system_prompt=system_prompt,
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)
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agent_nodes.append(agent_node)
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return agent_nodes
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def build_graph(
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registry: AgentRegistry,
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llm: BaseChatModel,
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checkpointer: AsyncPostgresSaver,
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intent_classifier: IntentClassifier | None = None,
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) -> GraphContext:
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"""Build and compile the LangGraph supervisor graph.
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Returns a GraphContext that bundles the compiled graph with its
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associated registry and intent classifier.
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"""
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agent_nodes = build_agent_nodes(registry, llm)
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agent_descriptions = _format_agent_descriptions(registry)
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prompt = SUPERVISOR_PROMPT.format(agent_descriptions=agent_descriptions)
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workflow = create_supervisor(
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agents=agent_nodes,
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model=llm,
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prompt=prompt,
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output_mode="full_history",
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)
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compiled = workflow.compile(checkpointer=checkpointer)
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return GraphContext(
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graph=compiled,
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registry=registry,
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intent_classifier=intent_classifier,
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)
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