verbose live UI + tool-level SSE events + Groq default + regression tests
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141
api/analyses/compare_periods.py
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141
api/analyses/compare_periods.py
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"""compare_periods Analysis — CoT with two queries.
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Both SQL queries are generated in one LLM call (paired prompt), then both
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executed, then a single interpretation pass diffs them. Single round-trip
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per LLM step → cheap and bounded latency.
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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from typing import Any
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from api import langfuse_client as lf
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from api.analyses.base import Analysis
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from api.analyses.types import Finding
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from api.llm import chat
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from api.prompts import load, render
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from api.runtime import events
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from api.tools.execute_sql import execute_sql
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from api.tools.schema import load_schema
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logger = logging.getLogger("nvi.analyses.compare_periods")
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class ComparePeriods(Analysis):
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name = "compare_periods"
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description = (
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"Compare a metric across two time windows (e.g. Q2 vs Q3, 1995 vs 1996). "
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"Generates two parallel SQL queries and diffs the results."
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)
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args_schema = {
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"question": {"type": "string", "description": "The metric / scope to compare."},
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"period_a": {"type": "string", "description": "First period label, e.g. '1995' or 'Q2 1996'."},
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"period_b": {"type": "string", "description": "Second period label, e.g. '1996' or 'Q3 1996'."},
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}
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async def run(self, args: dict[str, Any], question: str) -> Finding:
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sub_q = args.get("question") or question
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period_a = args.get("period_a", "")
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period_b = args.get("period_b", "")
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with lf.span("analysis.compare_periods", input=args) as span:
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try:
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await events.publish_current(events.tool_call_start(
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"generate_pair", input={"question": sub_q, "period_a": period_a, "period_b": period_b},
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))
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pair = _generate_pair(sub_q, period_a, period_b)
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await events.publish_current(events.tool_call_end(
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"generate_pair",
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output={"a": {"label": pair["a"]["label"], "sql": pair["a"]["sql"]},
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"b": {"label": pair["b"]["label"], "sql": pair["b"]["sql"]}},
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))
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await events.publish_current(events.tool_call_start("execute_sql", input={"label": pair["a"]["label"], "sql": pair["a"]["sql"]}))
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res_a = execute_sql(pair["a"]["sql"])
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await events.publish_current(events.tool_call_end(
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"execute_sql",
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output={"label": pair["a"]["label"], "row_count": res_a.row_count,
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"preview": res_a.as_dicts()[:5]},
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))
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await events.publish_current(events.tool_call_start("execute_sql", input={"label": pair["b"]["label"], "sql": pair["b"]["sql"]}))
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res_b = execute_sql(pair["b"]["sql"])
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await events.publish_current(events.tool_call_end(
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"execute_sql",
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output={"label": pair["b"]["label"], "row_count": res_b.row_count,
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"preview": res_b.as_dicts()[:5]},
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))
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interpret_system = load("compare_periods.interpret")
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interpret_user = render(
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"compare_periods.interpret.user",
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question=sub_q,
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label_a=pair["a"]["label"],
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label_b=pair["b"]["label"],
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rows_a=repr(res_a.as_dicts()[:20]),
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rows_b=repr(res_b.as_dicts()[:20]),
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)
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await events.publish_current(events.llm_call(
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"compare_periods.interpret",
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system_len=len(interpret_system),
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user_len=len(interpret_user),
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))
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summary = chat(system=interpret_system, user=interpret_user, max_tokens=512)
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except Exception as e:
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logger.exception("compare_periods failed")
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await events.publish_current(events.tool_call_end("compare_periods", error=str(e)))
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span.update(output={"error": str(e)})
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return Finding(analysis=self.name, summary="", error=str(e))
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span.update(output={"summary": summary})
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return Finding(
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analysis=self.name,
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summary=summary,
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rows=[
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{"period": pair["a"]["label"], **r} for r in res_a.as_dicts()[:20]
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] + [
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{"period": pair["b"]["label"], **r} for r in res_b.as_dicts()[:20]
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],
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sql=[pair["a"]["sql"], pair["b"]["sql"]],
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metadata={
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"label_a": pair["a"]["label"],
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"label_b": pair["b"]["label"],
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"row_count_a": res_a.row_count,
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"row_count_b": res_b.row_count,
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},
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)
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def _generate_pair(question: str, period_a: str, period_b: str) -> dict[str, dict[str, str]]:
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schema = load_schema()
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text = chat(
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system=load("compare_periods.pair"),
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user=render(
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"compare_periods.pair.user",
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question=question,
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period_a=period_a or "(infer)",
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period_b=period_b or "(infer)",
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schema_block=schema.render_tables(),
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metrics_block=schema.render_metrics(),
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),
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max_tokens=1024,
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)
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obj = json.loads(_extract_json(text))
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for k in ("a", "b"):
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if k not in obj or "sql" not in obj[k] or "label" not in obj[k]:
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raise ValueError(f"pair generator returned malformed payload: missing {k}")
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obj[k]["sql"] = obj[k]["sql"].strip().rstrip(";").strip()
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return obj
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def _extract_json(text: str) -> str:
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m = re.search(r"```(?:json)?\s*(\{.*\})\s*```", text, re.DOTALL)
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if m:
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return m.group(1)
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start, end = text.find("{"), text.rfind("}")
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if start >= 0 and end > start:
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return text[start:end + 1]
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return text
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