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#!/usr/bin/env python3
from __future__ import annotations
import json
import os
from pathlib import Path
import socket
import sqlite3
from typing import Any
import numpy as np
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()))
from sentence_transformers import SentenceTransformer
DB_PATH = Path("cheat.db")
DEFAULT_SOCKET_PATH = Path(os.environ.get("CHEAT_SOCKET_PATH", "/tmp/cheat.sock"))
MAX_MESSAGE_BYTES = 1024 * 1024
def deserialize_embedding(blob: bytes, dim: int) -> np.ndarray:
vec = np.frombuffer(blob, dtype=np.float32)
if vec.shape[0] != dim:
raise ValueError(f"Embedding length mismatch: expected {dim}, got {vec.shape[0]}")
return vec
def load_index(conn: sqlite3.Connection) -> tuple[list[dict[str, Any]], np.ndarray, str]:
rows = conn.execute("""
SELECT
c.id,
c.command,
c.explanation,
c.intent_json,
c.alternatives_json,
c.requires_json,
c.packages_json,
c.tags_json,
c.platform_json,
c.shell_json,
c.safety,
e.model_name,
e.embedding_blob,
e.embedding_dim
FROM cards c
JOIN card_embeddings e ON c.id = e.card_id
ORDER BY c.id
""").fetchall()
if not rows:
raise RuntimeError("No indexed cards found. Run build_index.py first.")
cards: list[dict[str, Any]] = []
vectors: list[np.ndarray] = []
model_name: str | None = None
for row in rows:
(
card_id,
command,
explanation,
intent_json,
alternatives_json,
requires_json,
packages_json,
tags_json,
platform_json,
shell_json,
safety,
row_model_name,
embedding_blob,
embedding_dim,
) = row
if model_name is None:
model_name = row_model_name
elif model_name != row_model_name:
raise RuntimeError("Mixed embedding models found in the index.")
cards.append({
"id": card_id,
"command": command,
"explanation": explanation,
"intent": json.loads(intent_json),
"alternatives": json.loads(alternatives_json),
"requires": json.loads(requires_json),
"packages": json.loads(packages_json),
"tags": json.loads(tags_json),
"platform": json.loads(platform_json),
"shell": json.loads(shell_json),
"safety": safety,
})
vectors.append(deserialize_embedding(embedding_blob, embedding_dim))
matrix = np.vstack(vectors)
return cards, matrix, model_name
class SearchEngine:
def __init__(self, db_path: Path = DB_PATH) -> None:
self.db_path = db_path
self.cards: list[dict[str, Any]] = []
self.matrix = np.empty((0, 0), dtype=np.float32)
self.model_name = ""
self._model: SentenceTransformer | None = None
self.reload()
def reload(self) -> None:
conn = sqlite3.connect(self.db_path)
try:
cards, matrix, model_name = load_index(conn)
finally:
conn.close()
model = self._model
if model is None or model_name != self.model_name:
model = SentenceTransformer(
model_name,
cache_folder=str(LOCAL_CACHE_DIR.resolve()),
local_files_only=True,
)
self.cards = cards
self.matrix = matrix
self.model_name = model_name
self._model = model
def search(self, query: str, top_k: int = 5) -> list[dict[str, Any]]:
if top_k < 1:
raise ValueError("top_k must be at least 1")
if self._model is None:
raise RuntimeError("Search engine is not initialized.")
qvec = self._model.encode(
[query],
normalize_embeddings=True,
convert_to_numpy=True,
)[0]
scores = self.matrix @ qvec
top_indices = np.argsort(scores)[::-1][:top_k]
results: list[dict[str, Any]] = []
for idx in top_indices:
card = dict(self.cards[idx])
card["score"] = float(scores[idx])
results.append(card)
return results
def send_server_request(
payload: dict[str, Any],
socket_path: Path = DEFAULT_SOCKET_PATH,
timeout: float = 5.0,
) -> dict[str, Any]:
encoded = (json.dumps(payload) + "\n").encode("utf-8")
with socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) as client:
client.settimeout(timeout)
client.connect(str(socket_path))
client.sendall(encoded)
client.shutdown(socket.SHUT_WR)
chunks: list[bytes] = []
total_bytes = 0
while True:
chunk = client.recv(65536)
if not chunk:
break
chunks.append(chunk)
total_bytes += len(chunk)
if total_bytes > MAX_MESSAGE_BYTES:
raise RuntimeError("Server response exceeded the maximum allowed size.")
if b"\n" in chunk:
break
message = b"".join(chunks).decode("utf-8").strip()
if not message:
raise RuntimeError("Server closed the connection without responding.")
return json.loads(message)
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