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### Summary Agentic search <img width="1149" height="1575" alt="image" src="https://github.com/user-attachments/assets/bce9a3e7-0517-4fb2-80a2-5d2a81a4da78" /> --------- Co-authored-by: Yingfeng Zhang <yingfeng.zhang@gmail.com>
78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
"""Route node — query classification (one-time, no KB dependency)."""
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import json
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import logging
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import re
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from rag.advanced_rag.harness.types import RouteDecision
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from rag.advanced_rag.harness.config import get_mode
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from rag.advanced_rag.harness.prompts.route_prompt import ROUTE_PROMPT
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_LOG = logging.getLogger(__name__)
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def _extract_json(text: str) -> dict:
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"""Extract JSON from LLM response, handling markdown fences and think tags."""
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text = re.sub(r"^.*</think>", "", text, flags=re.DOTALL).strip()
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text = re.sub(r"```(?:json)?\s*|\s*```", "", text).strip()
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try:
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import json_repair
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return json_repair.loads(text)
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except Exception:
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try:
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return json.loads(text)
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except Exception:
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_LOG.warning("route: failed to parse LLM output: %s", text[:200])
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return {}
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async def route_node(state: dict, tools) -> dict:
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"""Route node — analyze the question, produce RouteDecision."""
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question = state.get("question", "")
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if not question:
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return _fallback_route(question)
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mode_label = getattr(tools, "thinking_mode", "medium")
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mode = get_mode(mode_label)
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try:
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system = ROUTE_PROMPT.format(question=question)
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msg = await tools._fit_messages(system, question)
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ans = await tools.chat_mdl.async_chat(msg[0]["content"], msg[1:], {"temperature": 0.1})
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if isinstance(ans, tuple):
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ans = ans[0]
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result = _extract_json(ans)
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except Exception:
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_LOG.exception("route_node failed")
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result = {}
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question_type = result.get("question_type", "factual")
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requires_decomp = result.get("requires_decomposition", True)
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suggests_comp = result.get("suggests_compilation")
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route = RouteDecision(
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question=question,
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thinking_mode=mode_label,
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question_type=question_type,
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requires_decomposition=mode.requires_decomposition and requires_decomp,
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suggests_compilation=suggests_comp,
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execution_strategy=mode.execution_strategy,
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reasoning=result.get("reasoning", ""),
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)
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return {"route": route}
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def _fallback_route(question: str) -> dict:
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route = RouteDecision(
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question=question,
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thinking_mode="medium",
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question_type="factual",
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requires_decomposition=False,
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suggests_compilation=None,
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execution_strategy="direct_search",
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reasoning="fallback: empty question",
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)
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return {"route": route}
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