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Chroma is a retrieval index, not the source of truth. This store owns exact
document structure, page/box provenance, relations, and the low-memory FTS5
lexical index used by hybrid retrieval.
"""
from __future__ import annotations
import json
import os
import sqlite3
from contextlib import contextmanager
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator, Sequence
from app.rag.models import (
AcademicDocument,
BoundingBox,
EvidenceRelation,
EvidenceUnit,
normalize_evidence_text,
)
EVIDENCE_SCHEMA_VERSION = 1
DEFAULT_EVIDENCE_ROOT = Path(os.path.expanduser("~/.studybuddy/evidence"))
@dataclass(frozen=True)
class LexicalCandidate:
evidence_id: str
rank: float
score: float
class EvidenceStore:
def __init__(self, root: Path | str | None = None) -> None:
configured = Path(root) if root is not None else DEFAULT_EVIDENCE_ROOT
self.path = configured if configured.suffix in {".db", ".sqlite", ".sqlite3"} else configured / "evidence.sqlite3"
self.path.parent.mkdir(parents=True, exist_ok=True)
self._initialize()
@contextmanager
def _connect(self) -> Iterator[sqlite3.Connection]:
conn = sqlite3.connect(self.path, timeout=30.0)
try:
conn.row_factory = sqlite3.Row
conn.execute("PRAGMA foreign_keys = ON")
conn.execute("PRAGMA journal_mode = WAL")
conn.execute("PRAGMA synchronous = NORMAL")
yield conn
finally:
conn.close()
def _initialize(self) -> None:
with self._connect() as conn:
conn.executescript(
"""
CREATE TABLE IF NOT EXISTS evidence_meta (
key TEXT PRIMARY KEY,
value TEXT NOT NULL
);
CREATE TABLE IF NOT EXISTS documents (
project_id TEXT NOT NULL,
document_id TEXT NOT NULL,
filename TEXT NOT NULL,
title TEXT NOT NULL,
authors_json TEXT NOT NULL,
abstract TEXT NOT NULL,
page_count INTEGER NOT NULL,
parse_quality TEXT NOT NULL,
quality_flags_json TEXT NOT NULL,
created_at TEXT NOT NULL,
PRIMARY KEY (project_id, document_id)
);
CREATE TABLE IF NOT EXISTS evidence_units (
evidence_id TEXT PRIMARY KEY,
project_id TEXT NOT NULL,
document_id TEXT NOT NULL,
element_type TEXT NOT NULL,
page_start INTEGER NOT NULL,
page_end INTEGER NOT NULL,
parent_id TEXT,
section_path_json TEXT NOT NULL,
ordinal INTEGER NOT NULL,
bbox_json TEXT,
raw_text TEXT NOT NULL,
retrieval_text TEXT NOT NULL,
caption TEXT NOT NULL,
table_markdown TEXT NOT NULL,
visual_description TEXT NOT NULL,
quality_flags_json TEXT NOT NULL,
annotation_ids_json TEXT NOT NULL,
source_kind TEXT NOT NULL,
metadata_json TEXT NOT NULL,
FOREIGN KEY (project_id, document_id)
REFERENCES documents(project_id, document_id)
ON DELETE CASCADE
);
CREATE INDEX IF NOT EXISTS idx_evidence_project_document
ON evidence_units(project_id, document_id);
CREATE INDEX IF NOT EXISTS idx_evidence_page
ON evidence_units(project_id, document_id, page_start, page_end);
CREATE INDEX IF NOT EXISTS idx_evidence_type
ON evidence_units(project_id, element_type);
CREATE TABLE IF NOT EXISTS evidence_relations (
source_evidence_id TEXT NOT NULL,
target_evidence_id TEXT NOT NULL,
relation_type TEXT NOT NULL,
confidence REAL NOT NULL,
derivation TEXT NOT NULL,
PRIMARY KEY (source_evidence_id, target_evidence_id, relation_type),
FOREIGN KEY (source_evidence_id) REFERENCES evidence_units(evidence_id) ON DELETE CASCADE,
FOREIGN KEY (target_evidence_id) REFERENCES evidence_units(evidence_id) ON DELETE CASCADE
);
CREATE VIRTUAL TABLE IF NOT EXISTS evidence_fts USING fts5(
evidence_id UNINDEXED,
project_id UNINDEXED,
document_id UNINDEXED,
content,
tokenize='unicode61 remove_diacritics 2'
);
"""
)
current = conn.execute("SELECT value FROM evidence_meta WHERE key = 'schema_version'").fetchone()
if current is None:
conn.execute(
"INSERT INTO evidence_meta(key, value) VALUES ('schema_version', ?)",
(str(EVIDENCE_SCHEMA_VERSION),),
)
elif int(current["value"]) != EVIDENCE_SCHEMA_VERSION:
raise RuntimeError(
f"unsupported evidence schema {current['value']}; clean rebuild required for {EVIDENCE_SCHEMA_VERSION}"
)
def replace_document(
self,
document: AcademicDocument,
units: Sequence[EvidenceUnit],
relations: Sequence[EvidenceRelation],
) -> None:
self._validate_document_batch(document, units, relations)
with self._connect() as conn:
conn.execute("BEGIN IMMEDIATE")
self._delete_document_rows(conn, document.project_id, document.document_id)
conn.execute(
"""
INSERT INTO documents(
project_id, document_id, filename, title, authors_json, abstract,
page_count, parse_quality, quality_flags_json, created_at
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
document.project_id,
document.document_id,
document.filename,
document.title,
_json(document.authors),
document.abstract,
document.page_count,
document.parse_quality,
_json(document.quality_flags),
document.created_at.isoformat(),
),
)
conn.executemany(
"""
INSERT INTO evidence_units(
evidence_id, project_id, document_id, element_type, page_start,
page_end, parent_id, section_path_json, ordinal, bbox_json,
raw_text, retrieval_text, caption, table_markdown,
visual_description, quality_flags_json, annotation_ids_json,
source_kind, metadata_json
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
[self._unit_row(unit) for unit in units],
)
fts_rows = [
(unit.evidence_id, unit.project_id, unit.document_id, _fts_content(document, unit))
for unit in units
if unit.index_text.strip()
]
if fts_rows:
conn.executemany(
"INSERT INTO evidence_fts(evidence_id, project_id, document_id, content) VALUES (?, ?, ?, ?)",
fts_rows,
)
if relations:
conn.executemany(
"""
INSERT INTO evidence_relations(
source_evidence_id, target_evidence_id, relation_type, confidence, derivation
) VALUES (?, ?, ?, ?, ?)
""",
[
(
relation.source_evidence_id,
relation.target_evidence_id,
str(relation.relation_type),
relation.confidence,
relation.derivation,
)
for relation in relations
],
)
conn.commit()
def get_document(self, project_id: str, document_id: str) -> AcademicDocument | None:
with self._connect() as conn:
row = conn.execute(
"SELECT * FROM documents WHERE project_id = ? AND document_id = ?",
(project_id, document_id),
).fetchone()
return _row_to_document(row) if row else None
def list_documents(self, project_id: str) -> list[AcademicDocument]:
with self._connect() as conn:
rows = conn.execute(
"SELECT * FROM documents WHERE project_id = ? ORDER BY filename COLLATE NOCASE",
(project_id,),
).fetchall()
return [_row_to_document(row) for row in rows]
def get_units(
self,
project_id: str,
*,
evidence_ids: Sequence[str] | None = None,
document_ids: Sequence[str] | None = None,
) -> list[EvidenceUnit]:
clauses = ["project_id = ?"]
params: list[object] = [project_id]
if evidence_ids:
clauses.append(f"evidence_id IN ({','.join('?' for _ in evidence_ids)})")
params.extend(evidence_ids)
if document_ids:
clauses.append(f"document_id IN ({','.join('?' for _ in document_ids)})")
params.extend(document_ids)
sql = f"SELECT * FROM evidence_units WHERE {' AND '.join(clauses)} ORDER BY document_id, ordinal"
with self._connect() as conn:
rows = conn.execute(sql, params).fetchall()
units = [_row_to_unit(row) for row in rows]
if evidence_ids:
order = {evidence_id: index for index, evidence_id in enumerate(evidence_ids)}
units.sort(key=lambda unit: order.get(unit.evidence_id, len(order)))
return units
def get_relations(
self,
evidence_ids: Sequence[str],
*,
outgoing: bool = True,
incoming: bool = True,
) -> list[EvidenceRelation]:
if not evidence_ids or (not outgoing and not incoming):
return []
placeholders = ",".join("?" for _ in evidence_ids)
clauses: list[str] = []
params: list[object] = []
if outgoing:
clauses.append(f"source_evidence_id IN ({placeholders})")
params.extend(evidence_ids)
if incoming:
clauses.append(f"target_evidence_id IN ({placeholders})")
params.extend(evidence_ids)
with self._connect() as conn:
rows = conn.execute(
f"SELECT * FROM evidence_relations WHERE {' OR '.join(clauses)}",
params,
).fetchall()
return [EvidenceRelation(**dict(row)) for row in rows]
def lexical_search(
self,
project_id: str,
query: str,
limit: int = 30,
document_ids: Sequence[str] | None = None,
) -> list[LexicalCandidate]:
terms = _fts_query(query)
if not project_id or not terms:
return []
clauses = ["project_id = ?", "evidence_fts MATCH ?"]
params: list[object] = [project_id, terms]
if document_ids:
clauses.append(f"document_id IN ({','.join('?' for _ in document_ids)})")
params.extend(document_ids)
params.append(max(1, int(limit)))
with self._connect() as conn:
rows = conn.execute(
f"""
SELECT evidence_id, bm25(evidence_fts) AS rank
FROM evidence_fts
WHERE {' AND '.join(clauses)}
ORDER BY rank
LIMIT ?
""",
params,
).fetchall()
return [
LexicalCandidate(
evidence_id=row["evidence_id"],
rank=float(row["rank"]),
score=1.0 / (1.0 + abs(float(row["rank"]))),
)
for row in rows
]
def resolve_selection(
self,
project_id: str,
document_id: str,
page_number: int,
boxes: Sequence[BoundingBox] = (),
text: str = "",
limit: int = 8,
) -> list[str]:
with self._connect() as conn:
rows = conn.execute(
"""
SELECT * FROM evidence_units
WHERE project_id = ? AND document_id = ?
AND page_start <= ? AND page_end >= ?
ORDER BY ordinal
""",
(project_id, document_id, page_number, page_number),
).fetchall()
normalized_text = normalize_evidence_text(text).casefold()
scored: list[tuple[float, int, str]] = []
for row in rows:
unit = _row_to_unit(row)
box_score = 0.0
if unit.bbox_norm and boxes:
box_score = max(unit.bbox_norm.intersection_ratio(box) for box in boxes)
unit_text = normalize_evidence_text(unit.raw_text or unit.caption or unit.table_markdown).casefold()
text_score = _text_overlap(normalized_text, unit_text)
score = box_score * 2.0 + text_score
if score > 0.0:
scored.append((score, -unit.ordinal, unit.evidence_id))
scored.sort(reverse=True)
return [evidence_id for _, _, evidence_id in scored[: max(1, limit)]]
def resolve_region(self, project_id: str, document_id: str, region_id: str) -> list[str]:
if not project_id or not document_id or not region_id:
return []
with self._connect() as conn:
rows = conn.execute(
"""
SELECT evidence_id FROM evidence_units
WHERE project_id = ? AND document_id = ?
AND json_extract(metadata_json, '$.region_id') = ?
ORDER BY ordinal
""",
(project_id, document_id, region_id),
).fetchall()
return [str(row["evidence_id"]) for row in rows]
def delete_document(self, project_id: str, document_id: str) -> None:
with self._connect() as conn:
conn.execute("BEGIN IMMEDIATE")
self._delete_document_rows(conn, project_id, document_id)
conn.commit()
def clear_derived_data(self, project_id: str | None = None) -> None:
with self._connect() as conn:
conn.execute("BEGIN IMMEDIATE")
if project_id:
document_ids = [
row["document_id"]
for row in conn.execute(
"SELECT document_id FROM documents WHERE project_id = ?", (project_id,)
).fetchall()
]
for document_id in document_ids:
self._delete_document_rows(conn, project_id, document_id)
else:
conn.execute("DELETE FROM evidence_fts")
conn.execute("DELETE FROM documents")
conn.commit()
def counts(self, project_id: str) -> dict[str, int]:
with self._connect() as conn:
documents = conn.execute(
"SELECT COUNT(*) AS n FROM documents WHERE project_id = ?", (project_id,)
).fetchone()["n"]
units = conn.execute(
"SELECT COUNT(*) AS n FROM evidence_units WHERE project_id = ?", (project_id,)
).fetchone()["n"]
fts = conn.execute(
"SELECT COUNT(*) AS n FROM evidence_fts WHERE project_id = ?", (project_id,)
).fetchone()["n"]
return {"documents": int(documents), "evidence_units": int(units), "fts_rows": int(fts)}
@staticmethod
def _validate_document_batch(
document: AcademicDocument,
units: Sequence[EvidenceUnit],
relations: Sequence[EvidenceRelation],
) -> None:
ids: set[str] = set()
for unit in units:
if unit.project_id != document.project_id or unit.document_id != document.document_id:
raise ValueError("evidence unit does not belong to document")
if unit.evidence_id in ids:
raise ValueError(f"duplicate evidence id: {unit.evidence_id}")
ids.add(unit.evidence_id)
for relation in relations:
if relation.source_evidence_id not in ids or relation.target_evidence_id not in ids:
raise ValueError("relation endpoint is not part of the document batch")
@staticmethod
def _unit_row(unit: EvidenceUnit) -> tuple[object, ...]:
return (
unit.evidence_id,
unit.project_id,
unit.document_id,
str(unit.element_type),
unit.page_start,
unit.page_end,
unit.parent_id,
_json(unit.section_path),
unit.ordinal,
_json(unit.bbox_norm.rounded()) if unit.bbox_norm else None,
unit.raw_text,
unit.retrieval_text,
unit.caption,
unit.table_markdown,
unit.visual_description,
_json(unit.quality_flags),
_json(unit.annotation_ids),
str(unit.source_kind),
_json(unit.metadata),
)
@staticmethod
def _delete_document_rows(conn: sqlite3.Connection, project_id: str, document_id: str) -> None:
conn.execute(
"DELETE FROM evidence_fts WHERE project_id = ? AND document_id = ?",
(project_id, document_id),
)
conn.execute(
"DELETE FROM documents WHERE project_id = ? AND document_id = ?",
(project_id, document_id),
)
def _json(value: object) -> str:
return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
def _loads(value: str | None, fallback: object) -> object:
return json.loads(value) if value else fallback
def _row_to_document(row: sqlite3.Row) -> AcademicDocument:
return AcademicDocument(
project_id=row["project_id"],
document_id=row["document_id"],
filename=row["filename"],
title=row["title"],
authors=_loads(row["authors_json"], []),
abstract=row["abstract"],
page_count=row["page_count"],
parse_quality=row["parse_quality"],
quality_flags=_loads(row["quality_flags_json"], []),
created_at=row["created_at"],
)
def _row_to_unit(row: sqlite3.Row) -> EvidenceUnit:
bbox_payload = _loads(row["bbox_json"], None)
return EvidenceUnit(
evidence_id=row["evidence_id"],
project_id=row["project_id"],
document_id=row["document_id"],
element_type=row["element_type"],
page_start=row["page_start"],
page_end=row["page_end"],
parent_id=row["parent_id"],
section_path=_loads(row["section_path_json"], []),
ordinal=row["ordinal"],
bbox_norm=BoundingBox(**bbox_payload) if bbox_payload else None,
raw_text=row["raw_text"],
retrieval_text=row["retrieval_text"],
caption=row["caption"],
table_markdown=row["table_markdown"],
visual_description=row["visual_description"],
quality_flags=_loads(row["quality_flags_json"], []),
annotation_ids=_loads(row["annotation_ids_json"], []),
source_kind=row["source_kind"],
metadata=_loads(row["metadata_json"], {}),
)
def _fts_content(document: AcademicDocument, unit: EvidenceUnit) -> str:
return "\n".join(
part
for part in (
document.title,
" > ".join(unit.section_path),
unit.raw_text,
unit.caption,
unit.table_markdown,
unit.visual_description,
)
if part.strip()
)
def _fts_query(query: str) -> str:
tokens = [token for token in normalize_evidence_text(query).split(" ") if len(token) > 1]
escaped = [f'"{token.replace(chr(34), chr(34) * 2)}"' for token in tokens[:32]]
return " OR ".join(escaped)
def _text_overlap(needle: str, haystack: str) -> float:
if not needle or not haystack:
return 0.0
if needle in haystack:
return 1.0
needle_terms = set(needle.split())
if not needle_terms:
return 0.0
return len(needle_terms.intersection(haystack.split())) / len(needle_terms)
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