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564b5ea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | """Episodic SQLite storage."""
import json
import sqlite3
import time
import logging
from typing import Dict, Any, List
from api.deps import load_config, get_logger
logger = get_logger("kapo.memory.episodic")
class EpisodicDB:
def __init__(self):
cfg = load_config()
self.db_path = cfg.get("DB_PATH") or "./episodic.db"
self._init_db()
def _init_db(self):
conn = sqlite3.connect(self.db_path)
cur = conn.cursor()
cur.execute(
"""
CREATE TABLE IF NOT EXISTS experiences (
id INTEGER PRIMARY KEY AUTOINCREMENT,
task TEXT,
plan TEXT,
tools_used TEXT,
result TEXT,
success INTEGER,
timestamp TEXT
)
"""
)
conn.commit()
conn.close()
def insert_experience(self, task: str, plan: Dict[str, Any], tools_used: Dict[str, Any], result: Dict[str, Any], success: int):
conn = sqlite3.connect(self.db_path)
cur = conn.cursor()
cur.execute(
"""INSERT INTO experiences(task, plan, tools_used, result, success, timestamp)
VALUES(?,?,?,?,?,?)""",
(task, json.dumps(plan), json.dumps(tools_used), json.dumps(result), success, time.strftime("%Y-%m-%dT%H:%M:%S")),
)
conn.commit()
conn.close()
def list_recent(self, limit: int = 20) -> List[Dict[str, Any]]:
conn = sqlite3.connect(self.db_path)
cur = conn.cursor()
cur.execute("SELECT task, plan, tools_used, result, success, timestamp FROM experiences ORDER BY id DESC LIMIT ?", (limit,))
rows = cur.fetchall()
conn.close()
out = []
for r in rows:
out.append({
"task": r[0],
"plan": json.loads(r[1]),
"tools_used": json.loads(r[2]),
"result": json.loads(r[3]),
"success": r[4],
"timestamp": r[5],
})
return out
def search_similar(self, text: str, top_k: int = 3):
"""??? ?????? ??? embeddings ?????? (?????? ?????)."""
try:
from sentence_transformers import SentenceTransformer
cfg = load_config()
model = cfg.get("EMBED_MODEL") or "sentence-transformers/all-MiniLM-L6-v2"
embedder = SentenceTransformer(model)
records = self.list_recent(limit=200)
if not records:
return []
texts = [r.get("task", "") for r in records]
vectors = embedder.encode(texts, show_progress_bar=False)
qv = embedder.encode([text])[0]
# cosine similarity
def cos(a, b):
import math
dot = sum(x*y for x, y in zip(a, b))
na = math.sqrt(sum(x*x for x in a))
nb = math.sqrt(sum(x*x for x in b))
return dot / (na*nb + 1e-9)
scored = [(cos(qv, v), r) for v, r in zip(vectors, records)]
scored.sort(key=lambda x: x[0], reverse=True)
return [r for _, r in scored[:top_k]]
except Exception:
logger.exception("Similarity search failed")
return []
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