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70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
from langgraph.graph import StateGraph
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from .state import State
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from .nodes import reformulate_user_query, write_answer_outline, process_sections, handle_qna_workflow
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from .configuration import Configuration, ResearchMode
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from typing import TypedDict, List, Dict, Any, Optional
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# Define what keys are in our state dict
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class GraphState(TypedDict):
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# Intermediate data produced during workflow
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answer_outline: Optional[Any]
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# Final output
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final_written_report: Optional[str]
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def build_graph():
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"""
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Build and return the LangGraph workflow.
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This function constructs the researcher agent graph with conditional routing
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based on research_mode - QNA mode uses a direct Q&A workflow while other modes
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use the full report generation pipeline.
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Returns:
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A compiled LangGraph workflow
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"""
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# Define a new graph with state class
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workflow = StateGraph(State, config_schema=Configuration)
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# Add nodes to the graph
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workflow.add_node("reformulate_user_query", reformulate_user_query)
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workflow.add_node("handle_qna_workflow", handle_qna_workflow)
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workflow.add_node("write_answer_outline", write_answer_outline)
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workflow.add_node("process_sections", process_sections)
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# Define the edges
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workflow.add_edge("__start__", "reformulate_user_query")
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# Add conditional edges from reformulate_user_query based on research mode
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def route_after_reformulate(state: State, config) -> str:
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"""Route based on research_mode after reformulating the query."""
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configuration = Configuration.from_runnable_config(config)
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if configuration.research_mode == ResearchMode.QNA.value:
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return "handle_qna_workflow"
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else:
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return "write_answer_outline"
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workflow.add_conditional_edges(
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"reformulate_user_query",
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route_after_reformulate,
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{
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"handle_qna_workflow": "handle_qna_workflow",
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"write_answer_outline": "write_answer_outline"
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}
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)
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# QNA workflow path
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workflow.add_edge("handle_qna_workflow", "__end__")
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# Report generation workflow path
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workflow.add_edge("write_answer_outline", "process_sections")
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workflow.add_edge("process_sections", "__end__")
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# Compile the workflow into an executable graph
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graph = workflow.compile()
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graph.name = "Surfsense Researcher" # This defines the custom name in LangSmith
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return graph
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# Compile the graph once when the module is loaded
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graph = build_graph()
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