59 lines
2.2 KiB
Python
59 lines
2.2 KiB
Python
from langgraph.graph import END, START, StateGraph
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from app.modules.agent.engine.graphs.progress import emit_progress_sync
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from app.modules.agent.llm import AgentLlmService
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from app.modules.agent.engine.graphs.state import AgentGraphState
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class BaseGraphFactory:
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def __init__(self, llm: AgentLlmService) -> None:
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self._llm = llm
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def build(self, checkpointer=None):
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graph = StateGraph(AgentGraphState)
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graph.add_node("context", self._context_node)
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graph.add_node("answer", self._answer_node)
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graph.add_edge(START, "context")
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graph.add_edge("context", "answer")
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graph.add_edge("answer", END)
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return graph.compile(checkpointer=checkpointer)
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def _context_node(self, state: AgentGraphState) -> dict:
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emit_progress_sync(
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state,
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stage="graph.default.context",
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message="Готовлю контекст ответа по данным запроса.",
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)
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rag = state.get("rag_context", "")
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conf = state.get("confluence_context", "")
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emit_progress_sync(
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state,
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stage="graph.default.context.done",
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message="Контекст собран, перехожу к формированию ответа.",
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)
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return {"rag_context": rag, "confluence_context": conf}
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def _answer_node(self, state: AgentGraphState) -> dict:
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emit_progress_sync(
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state,
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stage="graph.default.answer",
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message="Формирую текст ответа для пользователя.",
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)
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msg = state.get("message", "")
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rag = state.get("rag_context", "")
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conf = state.get("confluence_context", "")
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user_input = "\n\n".join(
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[
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f"User request:\n{msg}",
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f"RAG context:\n{rag}",
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f"Confluence context:\n{conf}",
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]
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)
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answer = self._llm.generate("general_answer", user_input)
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emit_progress_sync(
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state,
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stage="graph.default.answer.done",
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message="Черновик ответа подготовлен.",
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)
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return {"answer": answer}
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