Add explain analysis workflow and UI
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280
backend/services/research_db.py
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280
backend/services/research_db.py
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# -*- coding: utf-8 -*-
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"""Query-oriented storage for explain/research data."""
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from __future__ import annotations
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import json
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import sqlite3
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, Iterable
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from backend.data.schema import CompanyNews
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SCHEMA = """
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CREATE TABLE IF NOT EXISTS news_items (
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id TEXT PRIMARY KEY,
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ticker TEXT NOT NULL,
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published_at TEXT,
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trade_date TEXT,
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source TEXT,
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title TEXT NOT NULL,
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summary TEXT,
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url TEXT,
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related TEXT,
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category TEXT,
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raw_json TEXT NOT NULL,
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ingest_run_date TEXT,
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created_at TEXT NOT NULL
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);
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CREATE INDEX IF NOT EXISTS idx_news_items_ticker_date
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ON news_items (ticker, trade_date DESC, published_at DESC);
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"""
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def _json_dumps(value: Any) -> str:
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return json.dumps(value, ensure_ascii=False, sort_keys=True, default=str)
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def _resolve_news_id(ticker: str, item: CompanyNews, fallback_index: int) -> str:
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base = item.url or item.title or f"{ticker}-{fallback_index}"
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return f"{ticker}:{base}"
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def _resolve_trade_date(date_value: str | None) -> str | None:
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if not date_value:
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return None
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normalized = str(date_value).strip()
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if not normalized:
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return None
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if "T" in normalized:
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return normalized.split("T", 1)[0]
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if " " in normalized:
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return normalized.split(" ", 1)[0]
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return normalized[:10]
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class ResearchDb:
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"""Small SQLite helper for explain-oriented news storage."""
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def __init__(self, db_path: Path):
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self.db_path = Path(db_path)
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self.db_path.parent.mkdir(parents=True, exist_ok=True)
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self._init_db()
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def _connect(self) -> sqlite3.Connection:
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conn = sqlite3.connect(self.db_path)
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conn.row_factory = sqlite3.Row
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conn.execute("PRAGMA journal_mode=WAL")
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conn.execute("PRAGMA foreign_keys=ON")
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return conn
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def _init_db(self):
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with self._connect() as conn:
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conn.executescript(SCHEMA)
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def upsert_news_items(
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self,
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*,
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ticker: str,
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items: Iterable[CompanyNews],
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ingest_run_date: str | None = None,
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) -> list[dict[str, Any]]:
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"""Persist provider news and return normalized rows."""
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normalized_rows: list[dict[str, Any]] = []
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timestamp = datetime.utcnow().isoformat(timespec="seconds")
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symbol = str(ticker or "").strip().upper()
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if not symbol:
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return normalized_rows
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with self._connect() as conn:
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for index, item in enumerate(items):
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news_id = _resolve_news_id(symbol, item, index)
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trade_date = _resolve_trade_date(item.date)
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payload = item.model_dump()
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row = {
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"id": news_id,
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"ticker": symbol,
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"published_at": item.date,
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"trade_date": trade_date,
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"source": item.source,
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"title": item.title,
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"summary": item.summary,
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"url": item.url,
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"related": item.related,
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"category": item.category,
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"raw_json": _json_dumps(payload),
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"ingest_run_date": ingest_run_date,
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"created_at": timestamp,
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}
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conn.execute(
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"""
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INSERT INTO news_items
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(id, ticker, published_at, trade_date, source, title, summary, url,
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related, category, raw_json, ingest_run_date, created_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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ON CONFLICT(id) DO UPDATE SET
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ticker = excluded.ticker,
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published_at = excluded.published_at,
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trade_date = excluded.trade_date,
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source = excluded.source,
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title = excluded.title,
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summary = excluded.summary,
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url = excluded.url,
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related = excluded.related,
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category = excluded.category,
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raw_json = excluded.raw_json,
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ingest_run_date = excluded.ingest_run_date
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""",
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(
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row["id"],
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row["ticker"],
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row["published_at"],
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row["trade_date"],
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row["source"],
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row["title"],
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row["summary"],
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row["url"],
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row["related"],
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row["category"],
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row["raw_json"],
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row["ingest_run_date"],
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row["created_at"],
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),
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)
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normalized_rows.append(row)
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return normalized_rows
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def get_news_items(
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self,
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*,
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ticker: str,
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start_date: str | None = None,
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end_date: str | None = None,
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limit: int = 20,
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) -> list[dict[str, Any]]:
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"""Return normalized news rows for explain UI."""
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symbol = str(ticker or "").strip().upper()
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if not symbol:
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return []
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sql = """
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SELECT id, ticker, published_at, trade_date, source, title, summary,
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url, related, category
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FROM news_items
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WHERE ticker = ?
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"""
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params: list[Any] = [symbol]
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if start_date:
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sql += " AND COALESCE(trade_date, substr(published_at, 1, 10)) >= ?"
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params.append(start_date)
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if end_date:
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sql += " AND COALESCE(trade_date, substr(published_at, 1, 10)) <= ?"
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params.append(end_date)
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sql += " ORDER BY COALESCE(published_at, trade_date) DESC LIMIT ?"
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params.append(max(1, int(limit)))
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with self._connect() as conn:
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rows = conn.execute(sql, params).fetchall()
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return [
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{
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"id": row["id"],
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"ticker": row["ticker"],
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"date": row["published_at"] or row["trade_date"],
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"trade_date": row["trade_date"],
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"source": row["source"],
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"title": row["title"],
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"summary": row["summary"],
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"url": row["url"],
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"related": row["related"],
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"category": row["category"],
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}
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for row in rows
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]
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def get_news_timeline(
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self,
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*,
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ticker: str,
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start_date: str | None = None,
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end_date: str | None = None,
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) -> list[dict[str, Any]]:
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"""Aggregate news counts per trade date for chart markers."""
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symbol = str(ticker or "").strip().upper()
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if not symbol:
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return []
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sql = """
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SELECT COALESCE(trade_date, substr(published_at, 1, 10)) AS date,
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COUNT(*) AS count,
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COUNT(DISTINCT source) AS source_count,
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MAX(title) AS top_title
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FROM news_items
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WHERE ticker = ?
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"""
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params: list[Any] = [symbol]
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if start_date:
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sql += " AND COALESCE(trade_date, substr(published_at, 1, 10)) >= ?"
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params.append(start_date)
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if end_date:
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sql += " AND COALESCE(trade_date, substr(published_at, 1, 10)) <= ?"
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params.append(end_date)
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sql += """
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GROUP BY COALESCE(trade_date, substr(published_at, 1, 10))
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ORDER BY date ASC
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"""
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with self._connect() as conn:
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rows = conn.execute(sql, params).fetchall()
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return [
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{
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"date": row["date"],
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"count": int(row["count"] or 0),
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"source_count": int(row["source_count"] or 0),
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"top_title": row["top_title"] or "",
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}
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for row in rows
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if row["date"]
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]
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def get_news_by_ids(
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self,
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*,
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ticker: str,
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article_ids: Iterable[str],
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) -> list[dict[str, Any]]:
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"""Return selected persisted news items."""
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symbol = str(ticker or "").strip().upper()
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ids = [str(article_id).strip() for article_id in article_ids if str(article_id).strip()]
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if not symbol or not ids:
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return []
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placeholders = ",".join("?" for _ in ids)
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sql = f"""
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SELECT id, ticker, published_at, trade_date, source, title, summary,
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url, related, category
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FROM news_items
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WHERE ticker = ? AND id IN ({placeholders})
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ORDER BY COALESCE(published_at, trade_date) DESC
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"""
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with self._connect() as conn:
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rows = conn.execute(sql, [symbol, *ids]).fetchall()
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return [
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{
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"id": row["id"],
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"ticker": row["ticker"],
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"date": row["published_at"] or row["trade_date"],
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"trade_date": row["trade_date"],
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"source": row["source"],
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"title": row["title"],
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"summary": row["summary"],
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"url": row["url"],
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"related": row["related"],
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"category": row["category"],
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}
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for row in rows
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]
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