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toor
2026-09-27 23:06:24 +08:00
parent 2d4f9230b1
commit 22450e5bb3
40 changed files with 3040 additions and 697 deletions
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backend/app/services/narrator.py
+10 -10
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@@ -306,24 +306,24 @@ class Narrator:
try:
forecast_analysis = forecast.analyze(metrics_history)
except Exception as e:
logger.debug("Forecast analysis failed: %s", e)
logger.debug("预测分析失败：%s", e)
try:
causal_links = forecast.discover_causality(simulation_id)
causal_map = ReportAnalyzer.format_causal_links(causal_links)
except Exception as e:
logger.debug("Causal discovery failed: %s", e)
logger.debug("因果关系分析失败：%s", e)
try:
counterfactual_results = forecast.compute_counterfactuals(simulation_id)
counterfactual_data = ReportAnalyzer.format_counterfactuals(counterfactual_results)
except Exception as e:
logger.debug("Counterfactual computation failed: %s", e)
logger.debug("反事实计算失败：%s", e)
try:
from app.services.forecast import _SimState
sim_state = forecast._sims.get(simulation_id)
if sim_state and sim_state.coherence_scores:
coherence_data = ReportAnalyzer.format_coherence_scores(sim_state.coherence_scores)
except Exception as e:
logger.debug("Coherence formatting failed: %s", e)
logger.debug("一致性结果格式化失败：%s", e)
# --- Pass 2: Metric interpretation + insights ---
metrics_summary = "\n".join(
@@ -363,8 +363,8 @@ class Narrator:
+ (f"\n\nCausal analysis:\n{causal_summary}" if causal_summary else "")
)
response_p2 = await self.llm.generate(system=system_p2, user=user_p2, json_mode=True, max_tokens=2000)
pass2 = parse_json(response_p2)
response_p2 = await self.llm.generate(system=system_p2, user=user_p2, json_mode=True, max_tokens=2000, purpose="报告指标分析")
pass2 = parse_json(response_p2, purpose="报告指标分析")
explanations = pass2.get("metric_explanations", {})
scorecard = []
@@ -397,8 +397,8 @@ class Narrator:
) + f"\n\n{tail}"
user_p3 = f"Segment data:\n{segment_input}"
response_p3 = await self.llm.generate(system=system_p3, user=user_p3, json_mode=True, max_tokens=1500)
pass3 = parse_json(response_p3)
response_p3 = await self.llm.generate(system=system_p3, user=user_p3, json_mode=True, max_tokens=1500, purpose="报告分群分析")
pass3 = parse_json(response_p3, purpose="报告分群分析")
llm_segments = {s["name"]: s for s in pass3.get("segments", [])}
segments_final = []
@@ -437,8 +437,8 @@ class Narrator:
rules="; ".join(blueprint.rules),
) + f"\n\n{tail}"
response_p4 = await self.llm.generate(system=system_p4, user=synthesis_context, json_mode=True, max_tokens=1500)
pass4 = parse_json(response_p4)
response_p4 = await self.llm.generate(system=system_p4, user=synthesis_context, json_mode=True, max_tokens=1500, purpose="报告行动建议")
pass4 = parse_json(response_p4, purpose="报告行动建议")
# --- Assemble final report ---
key_moments = []