Overhaul docs retrieval and web search quality

Replace the abandoned llms-txt-mcp/Chroma docs backend with an in-repo
MCP service: SQLite WAL + FTS5 + sentence-transformer embeddings,
transactional source replacement, persisted state across restarts,
singleflight refresh with conditional requests, hybrid lexical/semantic
ranking with exact-duplicate collapse, source/host filters, and
explicit-by-default content retrieval. Add docs_rebuild and a
docs-rebuild CLI command.

Add deterministic llms-full.txt snapshot generation for machine-local
menus with hash-validated provenance manifests; lifecycle commands
promote a local menu to its snapshot only when the manifest validates.
Switch public source profiles to content-bearing llms-full.txt feeds.

Improve web search: bounded provider fallback with per-attempt
diagnostics and cancellation, an optional Brave Search API provider,
strict SearXNG engine selection, capped link/media extraction, and a
real engine=browser renderer that routes every request through the
existing SSRF vetting while blocking WebSockets, non-GET traffic, and
private destinations.

Extend release checks with offline unit suites and isolated candidate
container tests for both images.
This commit is contained in:
2026-07-25 08:49:26 -07:00
parent 29bcb123fa
commit 51dceee224
60 changed files with 3207 additions and 107 deletions

View File

@@ -0,0 +1,282 @@
from __future__ import annotations
import re
import sqlite3
import threading
from pathlib import Path
import numpy as np
from .models import PreparedDocument, SourceState, SourceUpdate, StoredDocument
class IndexStore:
def __init__(self, path: str | Path):
self.path = Path(path)
self.path.parent.mkdir(parents=True, exist_ok=True)
self.connection = sqlite3.connect(self.path, check_same_thread=False, isolation_level=None)
self.connection.row_factory = sqlite3.Row
self._lock = threading.RLock()
self._initialize()
def _initialize(self) -> None:
with self.connection:
self.connection.execute("PRAGMA journal_mode=WAL")
self.connection.execute("PRAGMA foreign_keys=ON")
self.connection.execute("PRAGMA busy_timeout=5000")
self.connection.executescript(
"""
CREATE TABLE IF NOT EXISTS sources (
configured_source TEXT PRIMARY KEY,
resolved_source TEXT,
active INTEGER NOT NULL DEFAULT 1,
etag TEXT,
last_modified TEXT,
body_hash TEXT,
raw_body TEXT,
parser_fingerprint TEXT,
embedding_fingerprint TEXT,
checked_at REAL,
indexed_at REAL,
stale_at REAL,
last_error TEXT
);
CREATE TABLE IF NOT EXISTS documents (
id TEXT PRIMARY KEY,
configured_source TEXT NOT NULL REFERENCES sources(configured_source) ON DELETE CASCADE,
resolved_source TEXT NOT NULL,
source_host TEXT NOT NULL,
canonical_url TEXT NOT NULL,
canonical_host TEXT NOT NULL,
title TEXT NOT NULL,
description TEXT NOT NULL,
heading_path TEXT NOT NULL,
content TEXT NOT NULL,
content_hash TEXT NOT NULL,
embedding BLOB NOT NULL,
embedding_dim INTEGER NOT NULL
);
CREATE INDEX IF NOT EXISTS documents_source ON documents(configured_source);
CREATE INDEX IF NOT EXISTS documents_hash ON documents(content_hash);
CREATE INDEX IF NOT EXISTS documents_hosts ON documents(source_host, canonical_host);
CREATE VIRTUAL TABLE IF NOT EXISTS documents_fts USING fts5(
doc_id UNINDEXED, title, description, heading_path, content, canonical_url,
tokenize='unicode61 remove_diacritics 2 tokenchars ''_-'''
);
"""
)
def close(self) -> None:
self.connection.close()
def configure_sources(self, sources: list[str]) -> None:
with self._lock, self.connection:
self.connection.execute("UPDATE sources SET active = 0")
self.connection.executemany(
"INSERT INTO sources(configured_source, active) VALUES(?, 1) "
"ON CONFLICT(configured_source) DO UPDATE SET active = 1",
[(source,) for source in dict.fromkeys(sources)],
)
def replace_source(self, update: SourceUpdate) -> None:
with self._lock:
self.connection.execute("BEGIN IMMEDIATE")
try:
self._replace_source(update)
except Exception:
self.connection.rollback()
raise
else:
self.connection.commit()
def _replace_source(self, update: SourceUpdate) -> None:
self.connection.execute(
"""INSERT INTO sources(
configured_source, resolved_source, active, etag, last_modified,
body_hash, raw_body, parser_fingerprint, embedding_fingerprint,
checked_at, indexed_at, stale_at, last_error
) VALUES(?, ?, 1, ?, ?, ?, ?, ?, ?, ?, ?, ?, NULL)
ON CONFLICT(configured_source) DO UPDATE SET
resolved_source=excluded.resolved_source, etag=excluded.etag,
last_modified=excluded.last_modified, body_hash=excluded.body_hash,
raw_body=excluded.raw_body, parser_fingerprint=excluded.parser_fingerprint,
embedding_fingerprint=excluded.embedding_fingerprint,
checked_at=excluded.checked_at, indexed_at=excluded.indexed_at,
stale_at=excluded.stale_at, last_error=NULL""",
(
update.configured_source,
update.resolved_source,
update.etag,
update.last_modified,
update.body_hash,
update.raw_body,
update.parser_fingerprint,
update.embedding_fingerprint,
update.checked_at,
update.indexed_at,
update.stale_at,
),
)
old_ids = [row[0] for row in self.connection.execute("SELECT id FROM documents WHERE configured_source=?", (update.configured_source,))]
if old_ids:
self.connection.executemany("DELETE FROM documents_fts WHERE doc_id=?", [(identifier,) for identifier in old_ids])
self.connection.execute("DELETE FROM documents WHERE configured_source=?", (update.configured_source,))
for document in update.documents:
vector = np.asarray(document.embedding, dtype=np.float32)
self.connection.execute(
"""INSERT INTO documents VALUES(
?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?
)""",
(
document.id,
document.configured_source,
document.resolved_source,
document.source_host,
document.canonical_url,
document.canonical_host,
document.title,
document.description,
document.heading_path,
document.content,
document.content_hash,
vector.tobytes(),
vector.size,
),
)
self.connection.execute(
"INSERT INTO documents_fts VALUES(?, ?, ?, ?, ?, ?)",
(
document.id,
document.title,
document.description,
document.heading_path,
document.content,
document.canonical_url,
),
)
def mark_checked(self, source: str, checked_at: float, stale_at: float, error: str | None = None) -> None:
with self._lock, self.connection:
self.connection.execute(
"UPDATE sources SET checked_at=?, stale_at=?, last_error=? WHERE configured_source=?",
(checked_at, stale_at, error, source),
)
def list_sources(self, include_inactive: bool = False) -> list[SourceState]:
condition = "" if include_inactive else "WHERE s.active=1"
rows = self.connection.execute(
f"""SELECT s.*, COUNT(d.id) AS doc_count FROM sources s
LEFT JOIN documents d ON d.configured_source=s.configured_source
{condition} GROUP BY s.configured_source ORDER BY s.configured_source"""
).fetchall()
return [self._source(row) for row in rows]
def get_source(self, source: str) -> SourceState | None:
row = self.connection.execute(
"""SELECT s.*, COUNT(d.id) AS doc_count FROM sources s
LEFT JOIN documents d ON d.configured_source=s.configured_source
WHERE s.configured_source=? GROUP BY s.configured_source""",
(source,),
).fetchone()
return self._source(row) if row else None
def get_document(
self,
identifier: str,
sources: list[str] | None = None,
hosts: list[str] | None = None,
) -> StoredDocument | None:
where, parameters = self._filters(sources, hosts, alias="d")
row = self.connection.execute(
f"SELECT d.* FROM documents d JOIN sources s ON s.configured_source=d.configured_source "
f"WHERE s.active=1 AND d.id=? {where}",
[identifier, *parameters],
).fetchone()
return self._document(row) if row else None
def lexical_search(
self,
query: str,
limit: int,
sources: list[str] | None = None,
hosts: list[str] | None = None,
) -> list[StoredDocument]:
terms = re.findall(r"[\w.-]+", query, flags=re.UNICODE)
if not terms:
return []
expression = " AND ".join(f'"{term.replace(chr(34), chr(34) * 2)}"' for term in terms)
where, parameters = self._filters(sources, hosts, alias="d")
rows = self.connection.execute(
f"""SELECT d.* FROM documents_fts f
JOIN documents d ON d.id=f.doc_id
JOIN sources s ON s.configured_source=d.configured_source
WHERE documents_fts MATCH ? AND s.active=1 {where}
ORDER BY bm25(documents_fts, 0, 8, 3, 5, 1, 2) LIMIT ?""",
[expression, *parameters, limit],
).fetchall()
return [self._document(row) for row in rows]
def semantic_candidates(
self,
sources: list[str] | None = None,
hosts: list[str] | None = None,
) -> list[StoredDocument]:
where, parameters = self._filters(sources, hosts, alias="d")
rows = self.connection.execute(
f"SELECT d.* FROM documents d JOIN sources s ON s.configured_source=d.configured_source "
f"WHERE s.active=1 {where}",
parameters,
).fetchall()
return [self._document(row) for row in rows]
@staticmethod
def _filters(sources: list[str] | None, hosts: list[str] | None, alias: str) -> tuple[str, list[str]]:
clauses: list[str] = []
parameters: list[str] = []
if sources:
clauses.append(f"{alias}.configured_source IN ({','.join('?' for _ in sources)})")
parameters.extend(sources)
if hosts:
clauses.append(
f"({alias}.source_host IN ({','.join('?' for _ in hosts)}) OR "
f"{alias}.canonical_host IN ({','.join('?' for _ in hosts)}))"
)
parameters.extend(hosts)
parameters.extend(hosts)
return (" AND " + " AND ".join(clauses) if clauses else "", parameters)
@staticmethod
def _source(row: sqlite3.Row) -> SourceState:
return SourceState(
configured_source=row["configured_source"],
resolved_source=row["resolved_source"],
active=bool(row["active"]),
etag=row["etag"],
last_modified=row["last_modified"],
body_hash=row["body_hash"],
raw_body=row["raw_body"],
parser_fingerprint=row["parser_fingerprint"],
embedding_fingerprint=row["embedding_fingerprint"],
checked_at=row["checked_at"],
indexed_at=row["indexed_at"],
stale_at=row["stale_at"],
last_error=row["last_error"],
doc_count=row["doc_count"],
)
@staticmethod
def _document(row: sqlite3.Row) -> StoredDocument:
return StoredDocument(
id=row["id"],
configured_source=row["configured_source"],
resolved_source=row["resolved_source"],
source_host=row["source_host"],
canonical_url=row["canonical_url"],
canonical_host=row["canonical_host"],
title=row["title"],
description=row["description"],
heading_path=row["heading_path"],
content=row["content"],
content_hash=row["content_hash"],
embedding=np.frombuffer(row["embedding"], dtype=np.float32, count=row["embedding_dim"]).copy(),
)