82 lines
3.4 KiB
Python
82 lines
3.4 KiB
Python
"""direct_answer Analysis — CoT, one-shot.
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Pattern: text-to-SQL → execute → ask the LLM to interpret the rows. No loop.
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For questions that resolve to a single SQL query and a short interpretation.
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"""
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from __future__ import annotations
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import logging
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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.text_to_sql import text_to_sql
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logger = logging.getLogger("nvi.analyses.direct_answer")
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class DirectAnswer(Analysis):
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name = "direct_answer"
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description = (
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"Answer a question that resolves to a single SQL query. Best for "
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"lookup / aggregation questions with a single, well-defined metric."
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)
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args_schema = {
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"question": {"type": "string", "description": "Refined question this Analysis should answer."},
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"hint_tables": {"type": "array", "items": "string", "description": "Tables the planner thinks are relevant (optional)."},
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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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hint_tables = args.get("hint_tables")
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with lf.span("analysis.direct_answer", input={"question": sub_q}) as span:
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try:
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await events.publish_current(events.tool_call_start("text_to_sql", input={"question": sub_q}))
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t2s = text_to_sql(sub_q, hint_tables=hint_tables)
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await events.publish_current(events.tool_call_end(
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"text_to_sql", output={"sql": t2s.sql, "tables": t2s.used_tables},
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))
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await events.publish_current(events.tool_call_start("execute_sql", input={"sql": t2s.sql}))
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result = execute_sql(t2s.sql)
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await events.publish_current(events.tool_call_end(
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"execute_sql",
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output={"row_count": result.row_count, "truncated": result.truncated,
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"preview": result.as_dicts()[:5]},
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))
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interpret_system = load("direct_answer.interpret")
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interpret_user = render(
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"direct_answer.interpret.user",
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question=sub_q,
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sql=t2s.sql,
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rows=repr(result.as_dicts()[:20]),
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)
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await events.publish_current(events.llm_call(
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"direct_answer.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("direct_answer failed")
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await events.publish_current(events.tool_call_end("direct_answer", 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, "rows": result.row_count})
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return Finding(
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analysis=self.name,
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summary=summary,
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rows=result.as_dicts()[:20],
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sql=[t2s.sql],
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metadata={"row_count": result.row_count, "truncated": result.truncated},
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)
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