gongzhiyongandClaude Sonnet 4.6
47921d1a45
## Fixes - [#3] Status events now add/update a status ActivityNode in timeline (upsertWsStatusNode) - [#4] Done callback adds done node; error callback adds error node + sets session.status="error" - [#5] handleRegenerate fully synced with workspace: status/tool/card/done/error all handled - [#1][#2] localStorage persistence: completed/error sessions auto-saved, lazy-loaded on demand - [#1] handleSelectConversation preloads workspace sessions from localStorage for history messages - Refactored completeWsSession to include done ActivityNode in timeline - Added errorWsSession, upsertWsStatusNode, saveWsToStorage, loadWsFromStorage helpers ## Known limitation - [#7] workspace_card merge:true not yet used by backend (all cards are append-only for now) - History workspace recovery depends on localStorage (browser-local, not cross-device) Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
"""Build and compile the LangGraph agent.
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Phase 1 used a simple single-node StateGraph.
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Phase 2 upgrades to create_react_agent (ReAct pattern) with dynamic tool binding.
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When no tools are requested, we fall back to a plain single-node graph so the
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agent does not produce unnecessary tool-call reasoning.
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"""
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from __future__ import annotations
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from langchain_openai import AzureChatOpenAI
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from langgraph.graph import StateGraph
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from langgraph.prebuilt import create_react_agent
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from app.config import settings
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from app.graph.nodes import call_model
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from app.graph.state import ChatState
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from app.store.memory import get_checkpointer
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# Model parameter presets
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MODEL_PARAMS: dict[str, dict] = {
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"flash": {"max_tokens": 500, "temperature": 0.2},
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"pro": {"max_tokens": 4096, "temperature": 0.3},
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}
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# System prompt that instructs the ReAct agent
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SYSTEM_PROMPT = (
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"You are SOC Assistant, an enterprise AI assistant. "
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"You help users with knowledge base queries, ticket management, "
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"and general questions. "
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"When the user has enabled specific tools, you may use them if relevant. "
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"If you decide not to use an available tool, briefly explain why. "
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"Always respond in the same language the user uses. "
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"Be concise, accurate, and helpful.\n\n"
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"When web search tools are available:\n"
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"- Use search_web first to find relevant pages\n"
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"- Use read_url to get full content from the most relevant URLs (1-3 max)\n"
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"- Use sort_by_relevance to rank results if you have many documents\n"
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)
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# Cache compiled graphs to avoid re-creation on every request.
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# Key: (model, frozenset(tool_names))
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_graph_cache: dict[tuple, object] = {}
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def _get_llm(model: str) -> AzureChatOpenAI:
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"""Create an AzureChatOpenAI instance with preset parameters."""
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params = MODEL_PARAMS.get(model, MODEL_PARAMS["flash"])
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return AzureChatOpenAI(
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azure_endpoint=settings.azure_openai_endpoint,
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api_key=settings.azure_openai_api_key,
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api_version=settings.azure_openai_api_version,
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azure_deployment=settings.azure_openai_deployment,
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max_tokens=params["max_tokens"],
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temperature=params["temperature"],
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streaming=True,
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)
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# MCP tool names that should not be cached (bound to per-request client)
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_MCP_TOOL_NAMES = {"search_web", "read_url", "sort_by_relevance"}
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async def get_chat_graph(model: str = "flash", tools: list | None = None):
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"""Get or create a compiled graph for the given model and tool set.
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When tools are provided, creates a ReAct agent that can call tools.
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When no tools, falls back to a simple single-node graph.
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MCP tools are bound to a per-request client session, so graphs
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containing them are never cached.
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"""
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tools = tools or []
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tool_names = frozenset(t.name for t in tools)
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has_mcp_tools = bool(tool_names & _MCP_TOOL_NAMES)
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cache_key = (model, tool_names)
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if not has_mcp_tools and cache_key in _graph_cache:
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return _graph_cache[cache_key]
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checkpointer = await get_checkpointer()
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llm = _get_llm(model)
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if tools:
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# ReAct agent with tool calling
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graph = create_react_agent(
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llm,
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tools=tools,
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checkpointer=checkpointer,
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prompt=SYSTEM_PROMPT,
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)
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else:
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# Simple graph without tools (Phase 1 style)
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builder = StateGraph(ChatState)
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builder.add_node("agent", call_model)
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builder.set_entry_point("agent")
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builder.set_finish_point("agent")
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graph = builder.compile(checkpointer=checkpointer)
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if not has_mcp_tools:
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_graph_cache[cache_key] = graph
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return graph
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