mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
synced 2026-04-18 01:53:47 +08:00
fix: long-term memory bug
This commit is contained in:
saboteur7
@@ -28,7 +28,7 @@ class MemoryConfig:
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# Search config
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max_results: int = 10
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min_score: float = 0.3
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min_score: float = 0.1
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# Hybrid search weights
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vector_weight: float = 0.7
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@@ -213,7 +213,6 @@ class MemoryManager:
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memory_chunks.append(MemoryChunk(
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id=chunk_id,
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agent_id="default",
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user_id=user_id,
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scope=scope,
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source=source,
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@@ -330,7 +329,6 @@ class MemoryManager:
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memory_chunks.append(MemoryChunk(
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id=chunk_id,
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agent_id="default",
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user_id=user_id,
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scope=scope,
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source=source,
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@@ -428,7 +426,7 @@ class MemoryManager:
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return success
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def build_memory_guidance(self, lang: str = "en", include_context: bool = True) -> str:
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def build_memory_guidance(self, lang: str = "zh", include_context: bool = True) -> str:
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"""
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Build natural memory guidance for agent system prompt
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@@ -450,7 +448,7 @@ class MemoryManager:
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if lang == "zh":
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guidance = f"""## 记忆召回
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回答关于过去工作、决策、日期、人物、偏好或待办事项的问题前：先用 memory_search 搜索 MEMORY.md + memory/*.md；然后用 memory_get 只读取需要的行。如果搜索后仍不确定，说明你已检查过。
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下方"背景知识"包含你的核心长期记忆，可直接使用。如果背景知识中没有相关信息，再用 memory_search 搜索历史记录（memory/*.md 日期文件）。
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## 记忆存储
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当用户分享持久偏好、决策或重要事实时（无论是否明确要求"记住"），主动存储：
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@@ -465,7 +463,7 @@ class MemoryManager:
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- 自然使用记忆，就像你本来就知道这些信息"""
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else:
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guidance = f"""## Memory Recall
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Before answering anything about prior work, decisions, dates, people, preferences, or todos: run memory_search on MEMORY.md + memory/*.md; then use memory_get to pull only the needed lines. If low confidence after search, say you checked.
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"Background Knowledge" below contains your core long-term memories - use them directly. If information is not in Background Knowledge, use memory_search to search, then use memory_get to read files (path format: memory/MEMORY.md, memory/2026-01-30.md).
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## Memory Storage
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When user shares durable preferences, decisions, or important facts (whether or not they explicitly say "remember"), proactively store:
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@@ -50,11 +50,45 @@ class MemoryStorage:
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def _init_db(self):
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"""Initialize database with schema"""
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self.conn = sqlite3.connect(str(self.db_path))
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self.conn.row_factory = sqlite3.Row
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# Enable JSON support
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self.conn.execute("PRAGMA journal_mode=WAL")
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try:
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self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
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self.conn.row_factory = sqlite3.Row
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# Check database integrity
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try:
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result = self.conn.execute("PRAGMA integrity_check").fetchone()
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if result[0] != 'ok':
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print(f"⚠️ Database integrity check failed: {result[0]}")
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print(f" Recreating database...")
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self.conn.close()
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self.conn = None
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# Remove corrupted database
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self.db_path.unlink(missing_ok=True)
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# Remove WAL files
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Path(str(self.db_path) + '-wal').unlink(missing_ok=True)
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Path(str(self.db_path) + '-shm').unlink(missing_ok=True)
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# Reconnect to create new database
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self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
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self.conn.row_factory = sqlite3.Row
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except sqlite3.DatabaseError:
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# Database is corrupted, recreate it
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print(f"⚠️ Database is corrupted, recreating...")
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if self.conn:
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self.conn.close()
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self.conn = None
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self.db_path.unlink(missing_ok=True)
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Path(str(self.db_path) + '-wal').unlink(missing_ok=True)
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Path(str(self.db_path) + '-shm').unlink(missing_ok=True)
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self.conn = sqlite3.connect(str(self.db_path), check_same_thread=False)
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self.conn.row_factory = sqlite3.Row
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# Enable WAL mode for better concurrency
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self.conn.execute("PRAGMA journal_mode=WAL")
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# Set busy timeout to avoid "database is locked" errors
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self.conn.execute("PRAGMA busy_timeout=5000")
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except Exception as e:
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print(f"⚠️ Unexpected error during database initialization: {e}")
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raise
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# Create chunks table with embeddings
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self.conn.execute("""
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@@ -92,6 +126,8 @@ class MemoryStorage:
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""")
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# Create FTS5 virtual table for keyword search
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# Use default unicode61 tokenizer (stable and compatible)
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# For CJK support, we'll use LIKE queries as fallback
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self.conn.execute("""
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CREATE VIRTUAL TABLE IF NOT EXISTS chunks_fts USING fts5(
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text,
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@@ -261,13 +297,37 @@ class MemoryStorage:
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scopes: List[str] = None,
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limit: int = 10
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) -> List[SearchResult]:
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"""Keyword search using FTS5"""
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"""
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Keyword search using FTS5 + LIKE fallback
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Strategy:
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1. Try FTS5 search first (good for English and word-based languages)
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2. If no results and query contains CJK characters, use LIKE search
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"""
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if scopes is None:
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scopes = ["shared"]
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if user_id:
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scopes.append("user")
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# Build FTS query
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# Try FTS5 search first
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fts_results = self._search_fts5(query, user_id, scopes, limit)
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if fts_results:
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return fts_results
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# Fallback to LIKE search for CJK characters
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if MemoryStorage._contains_cjk(query):
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return self._search_like(query, user_id, scopes, limit)
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return []
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def _search_fts5(
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self,
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query: str,
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user_id: Optional[str],
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scopes: List[str],
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limit: int
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) -> List[SearchResult]:
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"""FTS5 full-text search"""
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fts_query = self._build_fts_query(query)
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if not fts_query:
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return []
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@@ -299,20 +359,83 @@ class MemoryStorage:
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"""
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params.append(limit)
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rows = self.conn.execute(sql_query, params).fetchall()
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try:
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rows = self.conn.execute(sql_query, params).fetchall()
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return [
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SearchResult(
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path=row['path'],
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start_line=row['start_line'],
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end_line=row['end_line'],
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score=self._bm25_rank_to_score(row['rank']),
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snippet=self._truncate_text(row['text'], 500),
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source=row['source'],
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user_id=row['user_id']
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)
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for row in rows
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]
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except Exception:
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return []
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def _search_like(
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self,
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query: str,
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user_id: Optional[str],
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scopes: List[str],
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limit: int
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) -> List[SearchResult]:
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"""LIKE-based search for CJK characters"""
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import re
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# Extract CJK words (2+ characters)
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cjk_words = re.findall(r'[\u4e00-\u9fff]{2,}', query)
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if not cjk_words:
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return []
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return [
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SearchResult(
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path=row['path'],
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start_line=row['start_line'],
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end_line=row['end_line'],
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score=self._bm25_rank_to_score(row['rank']),
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snippet=self._truncate_text(row['text'], 500),
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source=row['source'],
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user_id=row['user_id']
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)
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for row in rows
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]
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scope_placeholders = ','.join('?' * len(scopes))
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# Build LIKE conditions for each word
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like_conditions = []
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params = []
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for word in cjk_words:
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like_conditions.append("text LIKE ?")
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params.append(f'%{word}%')
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where_clause = ' OR '.join(like_conditions)
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params.extend(scopes)
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if user_id:
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sql_query = f"""
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SELECT * FROM chunks
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WHERE ({where_clause})
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AND scope IN ({scope_placeholders})
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AND (scope = 'shared' OR user_id = ?)
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LIMIT ?
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"""
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params.extend([user_id, limit])
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else:
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sql_query = f"""
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SELECT * FROM chunks
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WHERE ({where_clause})
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AND scope IN ({scope_placeholders})
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LIMIT ?
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"""
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params.append(limit)
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try:
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rows = self.conn.execute(sql_query, params).fetchall()
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return [
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SearchResult(
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path=row['path'],
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start_line=row['start_line'],
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end_line=row['end_line'],
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score=0.5, # Fixed score for LIKE search
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snippet=self._truncate_text(row['text'], 500),
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source=row['source'],
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user_id=row['user_id']
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)
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for row in rows
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]
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except Exception:
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return []
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def delete_by_path(self, path: str):
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"""Delete all chunks from a file"""
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@@ -354,7 +477,19 @@ class MemoryStorage:
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def close(self):
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"""Close database connection"""
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if self.conn:
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self.conn.close()
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try:
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self.conn.commit() # Ensure all changes are committed
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self.conn.close()
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self.conn = None # Mark as closed
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except Exception as e:
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print(f"⚠️ Error closing database connection: {e}")
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def __del__(self):
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"""Destructor to ensure connection is closed"""
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try:
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self.close()
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except:
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pass # Ignore errors during cleanup
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# Helper methods
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@@ -390,14 +525,29 @@ class MemoryStorage:
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return dot_product / (norm1 * norm2)
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@staticmethod
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def _build_fts_query(raw_query: str) -> Optional[str]:
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"""Build FTS5 query from raw text"""
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def _contains_cjk(text: str) -> bool:
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"""Check if text contains CJK (Chinese/Japanese/Korean) characters"""
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import re
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tokens = re.findall(r'[A-Za-z0-9_\u4e00-\u9fff]+', raw_query)
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return bool(re.search(r'[\u4e00-\u9fff]', text))
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@staticmethod
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def _build_fts_query(raw_query: str) -> Optional[str]:
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"""
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Build FTS5 query from raw text
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Works best for English and word-based languages.
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For CJK characters, LIKE search will be used as fallback.
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"""
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import re
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# Extract words (primarily English words and numbers)
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tokens = re.findall(r'[A-Za-z0-9_]+', raw_query)
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if not tokens:
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return None
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# Quote tokens for exact matching
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quoted = [f'"{t}"' for t in tokens]
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return ' AND '.join(quoted)
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# Use OR for more flexible matching
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return ' OR '.join(quoted)
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@staticmethod
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def _bm25_rank_to_score(rank: float) -> float:
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@@ -1,10 +0,0 @@
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"""
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Memory tools for AgentMesh
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Provides memory_search and memory_get tools for agents
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"""
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from agent.memory.tools.memory_search import MemorySearchTool
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from agent.memory.tools.memory_get import MemoryGetTool
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__all__ = ['MemorySearchTool', 'MemoryGetTool']
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@@ -1,118 +0,0 @@
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"""
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Memory get tool
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Allows agents to read specific sections from memory files
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"""
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from typing import Dict, Any, Optional
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from pathlib import Path
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from agent.tools.base_tool import BaseTool
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from agent.memory.manager import MemoryManager
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class MemoryGetTool(BaseTool):
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"""Tool for reading memory file contents"""
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def __init__(self, memory_manager: MemoryManager):
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"""
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Initialize memory get tool
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Args:
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memory_manager: MemoryManager instance
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"""
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super().__init__()
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self.memory_manager = memory_manager
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self._name = "memory_get"
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self._description = (
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"Read specific memory file content by path and line range. "
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"Use after memory_search to get full context from historical memory files."
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)
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@property
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def name(self) -> str:
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return self._name
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@property
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def description(self) -> str:
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return self._description
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@property
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def parameters(self) -> Dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"path": {
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"type": "string",
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"description": "Relative path to the memory file (e.g., 'MEMORY.md', 'memory/2024-01-29.md')"
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},
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"start_line": {
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"type": "integer",
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"description": "Starting line number (optional, default: 1)",
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"default": 1
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},
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"num_lines": {
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"type": "integer",
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"description": "Number of lines to read (optional, reads all if not specified)"
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}
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},
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"required": ["path"]
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}
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async def execute(self, **kwargs) -> str:
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"""
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Execute memory file read
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Args:
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path: File path
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start_line: Start line
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num_lines: Number of lines
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Returns:
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File content
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"""
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path = kwargs.get("path")
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start_line = kwargs.get("start_line", 1)
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num_lines = kwargs.get("num_lines")
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if not path:
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return "Error: path parameter is required"
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try:
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workspace_dir = self.memory_manager.config.get_workspace()
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file_path = workspace_dir / path
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if not file_path.exists():
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return f"Error: File not found: {path}"
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content = file_path.read_text()
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lines = content.split('\n')
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# Handle line range
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if start_line < 1:
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start_line = 1
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start_idx = start_line - 1
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if num_lines:
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end_idx = start_idx + num_lines
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selected_lines = lines[start_idx:end_idx]
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else:
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selected_lines = lines[start_idx:]
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result = '\n'.join(selected_lines)
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# Add metadata
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total_lines = len(lines)
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shown_lines = len(selected_lines)
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output = [
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f"File: {path}",
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f"Lines: {start_line}-{start_line + shown_lines - 1} (total: {total_lines})",
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"",
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result
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]
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return '\n'.join(output)
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except Exception as e:
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return f"Error reading memory file: {str(e)}"
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@@ -1,106 +0,0 @@
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"""
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Memory search tool
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Allows agents to search their memory using semantic and keyword search
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"""
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from typing import Dict, Any, Optional
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from agent.tools.base_tool import BaseTool
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from agent.memory.manager import MemoryManager
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class MemorySearchTool(BaseTool):
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"""Tool for searching agent memory"""
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def __init__(self, memory_manager: MemoryManager, user_id: Optional[str] = None):
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"""
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Initialize memory search tool
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Args:
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memory_manager: MemoryManager instance
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user_id: Optional user ID for scoped search
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"""
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super().__init__()
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self.memory_manager = memory_manager
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self.user_id = user_id
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self._name = "memory_search"
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self._description = (
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"Search historical memory files (beyond today/yesterday) using semantic and keyword search. "
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"Recent context (MEMORY.md + today + yesterday) is already loaded. "
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"Use this ONLY for older dates, specific past events, or when current context lacks needed info."
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)
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||||
@property
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||||
def name(self) -> str:
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||||
return self._name
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||||
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||||
@property
|
||||
def description(self) -> str:
|
||||
return self._description
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||||
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||||
@property
|
||||
def parameters(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "Search query (can be natural language question or keywords)"
|
||||
},
|
||||
"max_results": {
|
||||
"type": "integer",
|
||||
"description": "Maximum number of results to return (default: 10)",
|
||||
"default": 10
|
||||
},
|
||||
"min_score": {
|
||||
"type": "number",
|
||||
"description": "Minimum relevance score (0-1, default: 0.3)",
|
||||
"default": 0.3
|
||||
}
|
||||
},
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||||
"required": ["query"]
|
||||
}
|
||||
|
||||
async def execute(self, **kwargs) -> str:
|
||||
"""
|
||||
Execute memory search
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
max_results: Maximum results
|
||||
min_score: Minimum score
|
||||
|
||||
Returns:
|
||||
Formatted search results
|
||||
"""
|
||||
query = kwargs.get("query")
|
||||
max_results = kwargs.get("max_results", 10)
|
||||
min_score = kwargs.get("min_score", 0.3)
|
||||
|
||||
if not query:
|
||||
return "Error: query parameter is required"
|
||||
|
||||
try:
|
||||
results = await self.memory_manager.search(
|
||||
query=query,
|
||||
user_id=self.user_id,
|
||||
max_results=max_results,
|
||||
min_score=min_score,
|
||||
include_shared=True
|
||||
)
|
||||
|
||||
if not results:
|
||||
return f"No relevant memories found for query: {query}"
|
||||
|
||||
# Format results
|
||||
output = [f"Found {len(results)} relevant memories:\n"]
|
||||
|
||||
for i, result in enumerate(results, 1):
|
||||
output.append(f"\n{i}. {result.path} (lines {result.start_line}-{result.end_line})")
|
||||
output.append(f" Score: {result.score:.3f}")
|
||||
output.append(f" Snippet: {result.snippet}")
|
||||
|
||||
return "\n".join(output)
|
||||
|
||||
except Exception as e:
|
||||
return f"Error searching memory: {str(e)}"
|
||||
@@ -46,6 +46,7 @@ class Edit(BaseTool):
|
||||
def __init__(self, config: dict = None):
|
||||
self.config = config or {}
|
||||
self.cwd = self.config.get("cwd", os.getcwd())
|
||||
self.memory_manager = self.config.get("memory_manager", None)
|
||||
|
||||
def execute(self, args: Dict[str, Any]) -> ToolResult:
|
||||
"""
|
||||
@@ -141,6 +142,14 @@ class Edit(BaseTool):
|
||||
"first_changed_line": diff_result['first_changed_line']
|
||||
}
|
||||
|
||||
# Notify memory manager if file is in memory directory
|
||||
if self.memory_manager and "memory/" in path:
|
||||
try:
|
||||
self.memory_manager.mark_dirty()
|
||||
except Exception as e:
|
||||
# Don't fail the edit if memory notification fails
|
||||
pass
|
||||
|
||||
return ToolResult.success(result)
|
||||
|
||||
except UnicodeDecodeError:
|
||||
|
||||
@@ -22,7 +22,7 @@ class MemoryGetTool(BaseTool):
|
||||
"properties": {
|
||||
"path": {
|
||||
"type": "string",
|
||||
"description": "Relative path to the memory file (e.g., 'MEMORY.md', 'memory/2024-01-29.md')"
|
||||
"description": "Relative path to the memory file (e.g., 'memory/MEMORY.md', 'memory/2024-01-29.md')"
|
||||
},
|
||||
"start_line": {
|
||||
"type": "integer",
|
||||
@@ -68,6 +68,11 @@ class MemoryGetTool(BaseTool):
|
||||
|
||||
try:
|
||||
workspace_dir = self.memory_manager.config.get_workspace()
|
||||
|
||||
# Auto-prepend memory/ if not present and not absolute path
|
||||
if not path.startswith('memory/') and not path.startswith('/'):
|
||||
path = f'memory/{path}'
|
||||
|
||||
file_path = workspace_dir / path
|
||||
|
||||
if not file_path.exists():
|
||||
|
||||
@@ -30,8 +30,8 @@ class MemorySearchTool(BaseTool):
|
||||
},
|
||||
"min_score": {
|
||||
"type": "number",
|
||||
"description": "Minimum relevance score (0-1, default: 0.3)",
|
||||
"default": 0.3
|
||||
"description": "Minimum relevance score (0-1, default: 0.1)",
|
||||
"default": 0.1
|
||||
}
|
||||
},
|
||||
"required": ["query"]
|
||||
@@ -64,7 +64,7 @@ class MemorySearchTool(BaseTool):
|
||||
|
||||
query = args.get("query")
|
||||
max_results = args.get("max_results", 10)
|
||||
min_score = args.get("min_score", 0.3)
|
||||
min_score = args.get("min_score", 0.1)
|
||||
|
||||
if not query:
|
||||
return ToolResult.fail("Error: query parameter is required")
|
||||
|
||||
@@ -34,6 +34,7 @@ class Write(BaseTool):
|
||||
def __init__(self, config: dict = None):
|
||||
self.config = config or {}
|
||||
self.cwd = self.config.get("cwd", os.getcwd())
|
||||
self.memory_manager = self.config.get("memory_manager", None)
|
||||
|
||||
def execute(self, args: Dict[str, Any]) -> ToolResult:
|
||||
"""
|
||||
@@ -64,6 +65,10 @@ class Write(BaseTool):
|
||||
# Get bytes written
|
||||
bytes_written = len(content.encode('utf-8'))
|
||||
|
||||
# Auto-sync to memory database if this is a memory file
|
||||
if self.memory_manager and 'memory/' in path:
|
||||
self.memory_manager.mark_dirty()
|
||||
|
||||
result = {
|
||||
"message": f"Successfully wrote {bytes_written} bytes to {path}",
|
||||
"path": path,
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
# 2026-01-29 记录
|
||||
|
||||
## 老王的重要决定
|
||||
- 今天老王告诉我他决定要学AI了，这是一个重要的决策
|
||||
- 这可能会是他学习和职业发展的一个转折点
|
||||
@@ -1,21 +0,0 @@
|
||||
# Memory
|
||||
|
||||
Long-term curated memories and preferences.
|
||||
|
||||
## 用户信息
|
||||
- 用户名：老王
|
||||
|
||||
## 用户信息
|
||||
- 用户名：老王
|
||||
|
||||
## 用户偏好
|
||||
- 喜欢吃红烧肉
|
||||
- 爱打篮球
|
||||
|
||||
## 重要决策
|
||||
- 决定要学习AI（2026-01-29）
|
||||
|
||||
## Notes
|
||||
|
||||
- Important decisions and facts go here
|
||||
- This is your long-term knowledge base
|
||||
Reference in New Issue
Block a user