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#!/usr/bin/env python3
from __future__ import annotations
import json
import os
from pathlib import Path
import sqlite3
import sys
from typing import Any
LOCAL_CACHE_DIR = Path("models/hf")
os.environ.setdefault("HF_HOME", str(LOCAL_CACHE_DIR.resolve()))
os.environ.setdefault("SENTENCE_TRANSFORMERS_HOME", str(LOCAL_CACHE_DIR.resolve()))
import numpy as np
from sentence_transformers import SentenceTransformer
from cheat_runtime import DEFAULT_SOCKET_PATH, send_server_request
DB_PATH = Path("cheat.db")
CARDS_PATH = Path("./cards.jsonl")
MODEL_NAME = "sentence-transformers/all-MiniLM-L6-v2"
REQUIRED_FIELDS = {
"id",
"intent",
"command",
"alternatives",
"explanation",
"requires",
"packages",
"tags",
"platform",
"shell",
"safety",
}
def load_cards(path: Path) -> list[dict[str, Any]]:
cards: list[dict[str, Any]] = []
with path.open("r", encoding="utf-8") as f:
for line_no, line in enumerate(f, start=1):
line = line.strip()
if not line:
continue
try:
card = json.loads(line)
except json.JSONDecodeError as e:
raise ValueError(f"Invalid JSON on line {line_no}: {e}") from e
missing = REQUIRED_FIELDS - set(card.keys())
if missing:
raise ValueError(f"Missing required fields on line {line_no}: {sorted(missing)}")
cards.append(card)
return cards
def build_search_text(card: dict[str, Any]) -> str:
"""
Build a compact semantic representation for embedding.
This is what the retriever will search over.
"""
parts: list[str] = []
intents = card.get("intent", [])
tags = card.get("tags", [])
command = card.get("command", "")
explanation = card.get("explanation", "")
alternatives = card.get("alternatives", [])
requires = card.get("requires", [])
platform = card.get("platform", [])
if intents:
parts.append("Intents: " + " | ".join(intents))
if tags:
parts.append("Tags: " + ", ".join(tags))
if command:
parts.append("Command: " + command)
if alternatives:
parts.append("Alternatives: " + " | ".join(alternatives))
if explanation:
parts.append("Explanation: " + explanation)
if requires:
parts.append("Requires: " + ", ".join(requires))
if platform:
parts.append("Platform: " + ", ".join(platform))
return "\n".join(parts)
def serialize_embedding(vec: np.ndarray) -> bytes:
return vec.astype(np.float32).tobytes()
def upsert_card(conn: sqlite3.Connection, card: dict[str, Any], search_text: str) -> None:
conn.execute("""
INSERT INTO cards (
id, command, explanation, intent_json, alternatives_json, requires_json,
packages_json, tags_json, platform_json, shell_json, safety, search_text, updated_at
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)
ON CONFLICT(id) DO UPDATE SET
command=excluded.command,
explanation=excluded.explanation,
intent_json=excluded.intent_json,
alternatives_json=excluded.alternatives_json,
requires_json=excluded.requires_json,
packages_json=excluded.packages_json,
tags_json=excluded.tags_json,
platform_json=excluded.platform_json,
shell_json=excluded.shell_json,
safety=excluded.safety,
search_text=excluded.search_text,
updated_at=CURRENT_TIMESTAMP
""", (
card["id"],
card["command"],
card["explanation"],
json.dumps(card["intent"], ensure_ascii=False),
json.dumps(card["alternatives"], ensure_ascii=False),
json.dumps(card["requires"], ensure_ascii=False),
json.dumps(card["packages"], ensure_ascii=False),
json.dumps(card["tags"], ensure_ascii=False),
json.dumps(card["platform"], ensure_ascii=False),
json.dumps(card["shell"], ensure_ascii=False),
card["safety"],
search_text,
))
def upsert_embedding(
conn: sqlite3.Connection,
card_id: str,
model_name: str,
vec: np.ndarray,
) -> None:
conn.execute("""
INSERT INTO card_embeddings (
card_id, model_name, embedding_blob, embedding_dim
)
VALUES (?, ?, ?, ?)
ON CONFLICT(card_id) DO UPDATE SET
model_name=excluded.model_name,
embedding_blob=excluded.embedding_blob,
embedding_dim=excluded.embedding_dim
""", (
card_id,
model_name,
serialize_embedding(vec),
int(vec.shape[0]),
))
def main() -> None:
if not DB_PATH.exists():
raise FileNotFoundError(f"Database not found at {DB_PATH}. Run init_db.py first.")
if not CARDS_PATH.exists():
raise FileNotFoundError(f"Cards file not found at {CARDS_PATH}")
cards = load_cards(CARDS_PATH)
model = SentenceTransformer(
MODEL_NAME,
cache_folder=str(LOCAL_CACHE_DIR.resolve()),
local_files_only=True,
)
search_texts = [build_search_text(card) for card in cards]
embeddings = model.encode(
search_texts,
normalize_embeddings=True,
convert_to_numpy=True,
show_progress_bar=True,
)
conn = sqlite3.connect(DB_PATH)
try:
conn.execute("PRAGMA foreign_keys=ON;")
for card, vec, search_text in zip(cards, embeddings, search_texts):
upsert_card(conn, card, search_text)
upsert_embedding(conn, card["id"], MODEL_NAME, vec)
conn.commit()
print(f"Indexed {len(cards)} cards into {DB_PATH}")
finally:
conn.close()
try:
response = send_server_request(
{"action": "reload"},
socket_path=DEFAULT_SOCKET_PATH,
timeout=1.0,
)
except OSError:
return
if response.get("ok"):
print(f"Reloaded query server at {DEFAULT_SOCKET_PATH}")
else:
print(
f"Warning: query server reload failed: {response.get('error', 'unknown error')}",
file=sys.stderr,
)
if __name__ == "__main__":
main()
|
