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aa4269d 025a350 aa4269d 025a350 aa4269d 025a350 aa4269d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """LangGraph workflows.
Standard mode — router-driven Q&A:
route -> retrieve -> (qa | analyst | verify) -> confidence
Advanced mode — full verification pipeline:
retrieve -> analyst -> verify -> risk -> report
Both graphs share one state schema; nodes read/write only the keys they own.
"""
from __future__ import annotations
import json
import time
from typing import TypedDict
from langgraph.graph import END, StateGraph
from src.agents import analyst_agent, qa_agent, router, verifier_agent
from src.analysis import confidence as confidence_mod
from src.analysis import risk as risk_mod
from src.reporting import report as report_mod
from src.retrieval.hybrid import HybridRetriever, RetrievedChunk
class AnalysisState(TypedDict, total=False):
# inputs
question: str
history: str
doc_ids: list[str]
# working state
route: str
retrieved: list[RetrievedChunk]
tool_trace: list[dict]
findings: list[dict] # evidence-matrix rows from the verifier
verification_text: str
risk: risk_mod.RiskAssessment
ratio_results: list[dict]
# outputs
answer: str
confidence: confidence_mod.ConfidenceReport
report: str
timings: dict[str, float]
def _timed(state: AnalysisState, key: str, started: float) -> dict:
timings = dict(state.get("timings") or {})
timings[key] = round(time.perf_counter() - started, 2)
return timings
def _balanced_retrieve(retriever: HybridRetriever, question: str,
doc_ids: list[str] | None,
per_doc: int = 5) -> list[RetrievedChunk]:
"""Retrieve top chunks from EVERY document separately, then merge.
Plain top-k retrieval can return chunks from a single document, which
leaves the verifier with nothing to cross-check. Balanced retrieval
guarantees each uploaded document contributes evidence.
"""
ids = doc_ids or retriever.store.doc_ids()
merged: dict[str, RetrievedChunk] = {}
for doc_id in ids:
for r in retriever.search(question, k=per_doc, doc_ids=[doc_id]):
merged.setdefault(r.chunk.chunk_id, r)
return sorted(merged.values(), key=lambda r: r.score, reverse=True)
def build_standard_graph(retriever: HybridRetriever):
def route_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
decided = router.route(state["question"])
return {"route": decided, "timings": _timed(state, "route", t0)}
def retrieve_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
# verification/comparison questions need evidence from every document
if state.get("route") in ("verification", "comparison"):
retrieved = _balanced_retrieve(retriever, state["question"],
state.get("doc_ids") or None, per_doc=4)
else:
retrieved = retriever.search(state["question"], k=6,
doc_ids=state.get("doc_ids") or None)
return {"retrieved": retrieved, "timings": _timed(state, "retrieve", t0)}
def qa_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
answer = qa_agent.answer(state["question"], state["retrieved"],
state.get("history", ""))
return {"answer": answer, "timings": _timed(state, "qa_agent", t0)}
def analyst_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
answer, trace = analyst_agent.answer(state["question"], state["retrieved"],
state.get("history", ""))
return {"answer": answer, "tool_trace": trace,
"timings": _timed(state, "analyst_agent", t0)}
def verify_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
findings = verifier_agent.verify(state["question"], state["retrieved"])
answer = verifier_agent.narrative(findings)
return {"answer": answer, "findings": findings,
"timings": _timed(state, "verifier_agent", t0)}
def confidence_node(state: AnalysisState) -> dict:
report = confidence_mod.score(state.get("answer", ""),
state.get("retrieved", []),
state.get("findings"))
return {"confidence": report}
def pick_agent(state: AnalysisState) -> str:
return {"factual": "qa", "analysis": "analyst",
"comparison": "analyst", "verification": "verify"}[state["route"]]
g = StateGraph(AnalysisState)
g.add_node("route", route_node)
g.add_node("retrieve", retrieve_node)
g.add_node("qa", qa_node)
g.add_node("analyst", analyst_node)
g.add_node("verify", verify_node)
g.add_node("confidence", confidence_node)
g.set_entry_point("route")
g.add_edge("route", "retrieve")
g.add_conditional_edges("retrieve", pick_agent,
{"qa": "qa", "analyst": "analyst", "verify": "verify"})
g.add_edge("qa", "confidence")
g.add_edge("analyst", "confidence")
g.add_edge("verify", "confidence")
g.add_edge("confidence", END)
return g.compile()
def build_advanced_graph(retriever: HybridRetriever):
def retrieve_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
retrieved = retriever.search(state["question"], k=14,
doc_ids=state.get("doc_ids") or None)
return {"retrieved": retrieved, "timings": _timed(state, "retrieve", t0)}
def analyst_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
answer, trace = analyst_agent.answer(state["question"], state["retrieved"])
ratio_results = [
json.loads(t["result"])
for t in trace if t["tool"] == "calculate_ratio" and t["result"].startswith("{")
]
return {"answer": answer, "tool_trace": trace, "ratio_results": ratio_results,
"timings": _timed(state, "analyst_agent", t0)}
def verify_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
# re-retrieve balanced across documents so every doc is represented
evidence = _balanced_retrieve(retriever, state["question"],
state.get("doc_ids") or None, per_doc=5)
findings = verifier_agent.verify(state["question"], evidence)
text = verifier_agent.narrative(findings)
return {"findings": findings, "verification_text": text,
"timings": _timed(state, "verifier_agent", t0)}
def risk_node(state: AnalysisState) -> dict:
assessment = risk_mod.assess(state.get("findings", []),
state.get("ratio_results", []))
return {"risk": assessment}
def report_node(state: AnalysisState) -> dict:
t0 = time.perf_counter()
text = report_mod.generate(
question=state["question"],
analysis_text=state.get("answer", ""),
verification_text=state.get("verification_text", ""),
findings=state.get("findings", []),
risk=state["risk"],
ratio_results=state.get("ratio_results", []),
doc_ids=state.get("doc_ids", []),
)
conf = confidence_mod.score(state.get("answer", ""),
state.get("retrieved", []),
state.get("findings"))
return {"report": text, "confidence": conf,
"timings": _timed(state, "report", t0)}
g = StateGraph(AnalysisState)
g.add_node("retrieve", retrieve_node)
g.add_node("analyst", analyst_node)
g.add_node("verify", verify_node)
g.add_node("risk", risk_node)
g.add_node("report", report_node)
g.set_entry_point("retrieve")
g.add_edge("retrieve", "analyst")
g.add_edge("analyst", "verify")
g.add_edge("verify", "risk")
g.add_edge("risk", "report")
g.add_edge("report", END)
return g.compile()
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