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https://github.com/lllin000/PaperForge.git
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2c21926444
- Add evidence_query class to build_query_plan() for intent='evidence' - Add ocr_evidence_available to enrich_query_plan_with_runtime() - Add OCR evidence enrichment in search.py and retrieve.py - Add routing contract tests (3 routing + 1 evidence hit) - All pre-existing tests pass
116 lines
4.3 KiB
Python
116 lines
4.3 KiB
Python
from __future__ import annotations
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import argparse
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import sys
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from paperforge import __version__ as PF_VERSION
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from paperforge.core.errors import ErrorCode
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from paperforge.core.result import PFError, PFResult
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from paperforge.memory.db import get_connection, get_memory_db_path
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from paperforge.memory.fts import search_papers
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from paperforge.query_planning import build_query_plan, enrich_query_plan_with_runtime
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def run(args: argparse.Namespace) -> int:
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vault = args.vault_path
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query = args.query
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db_path = get_memory_db_path(vault)
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if not db_path.exists():
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result = PFResult(
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ok=False,
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command="search",
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version=PF_VERSION,
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error=PFError(
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code=ErrorCode.PATH_NOT_FOUND,
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message="Memory database not found. Run paperforge memory build.",
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),
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)
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if args.json:
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print(result.to_json())
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else:
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print(f"Error: {result.error.message}", file=sys.stderr)
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return 1
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conn = get_connection(db_path, read_only=True)
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try:
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results = search_papers(
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conn,
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query,
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limit=args.limit,
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domain=args.domain or "",
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year_from=args.year_from or 0,
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year_to=args.year_to or 0,
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ocr_status=args.ocr or "",
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deep_status=args.deep or "",
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lifecycle=args.lifecycle or "",
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next_step=args.next_step or "",
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)
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data = {
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"query": query,
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"matches": results,
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"count": len(results),
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"filters_applied": {
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"domain": args.domain,
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"year_from": args.year_from,
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"year_to": args.year_to,
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"ocr": args.ocr,
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"deep": args.deep,
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"lifecycle": args.lifecycle,
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"next_step": args.next_step,
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},
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}
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warnings: list[str] = []
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next_actions: list[dict] = []
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if len(results) == 0:
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plan = enrich_query_plan_with_runtime(build_query_plan(query, "discover"), vault)
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data["query_diagnostic"] = {
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"query_class": plan["query_class"],
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"recommended_primary": plan["recommended_primary"],
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"query_writing_rules": plan["query_writing_rules"],
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"scope_assessment": plan.get("scope_assessment"),
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}
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if plan["query_class"] in {"mixed_query", "author_year"}:
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warnings.append("Zero results may reflect a noncanonical metadata query rather than library absence.")
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evidence_plan = enrich_query_plan_with_runtime(build_query_plan(query, "evidence"), vault)
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data["ocr_evidence_supported"] = evidence_plan.get("runtime", {}).get("ocr_evidence_available", False)
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next_actions.append(
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{
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"command": "paperforge query-plan",
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"reason": "Normalize the query and choose the correct first retrieval command.",
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}
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)
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if plan["recommended_primary"]["command"] != "search":
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next_actions.append(
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{
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"command": f"paperforge {plan['recommended_primary']['command']}",
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"reason": "The planning layer recommends a different first command for this query.",
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}
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)
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result = PFResult(
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ok=True, command="search", version=PF_VERSION, data=data, warnings=warnings, next_actions=next_actions
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)
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except Exception as exc:
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result = PFResult(
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ok=False,
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command="search",
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version=PF_VERSION,
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error=PFError(code=ErrorCode.INTERNAL_ERROR, message=str(exc)),
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)
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finally:
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conn.close()
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if args.json:
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print(result.to_json())
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else:
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if result.ok:
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matches = result.data["matches"]
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print(f"Found {len(matches)} results for: {query}")
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for m in matches:
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rank_val = m.get("rank", "")
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print(
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f" [{m['lifecycle']:16}] {m['zotero_key']} | {m['year']} | {m['first_author']} | {m['title'][:60]}"
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)
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else:
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print(f"Error: {result.error.message}", file=sys.stderr)
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return 0 if result.ok else 1
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