refactor: rename to Graph Memory Demo, remove OpenClaw branding

2026-10-15 12:15:32 +00:00 · 2026-04-09 08:44:44 +08:00
parent d20870255b
commit 987768d835
6 changed files with 109 additions and 182 deletions
--- a/graph_memory_demo.py
+++ b/graph_memory_demo.py
@ -0,0 +1,714 @@
+#!/usr/bin/env python3
+"""
+Graph Memory Demo - 纯图数据库调用 Demo
+基于 DeepSeek API Tool Calls 实现摒弃传统上下文的自主记忆多轮对话
+验证目的：无上下文纯图数据库记忆存储
+"""
+
+import json
+import os
+import uuid
+from datetime import datetime
+
+DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY", "")
+DEEPSEEK_BASE_URL = "https://api.deepseek.com"
+MODEL_NAME = "deepseek-chat"
+
+NEO4J_URI = os.environ.get("NEO4J_URI", "bolt://localhost:7687")
+NEO4J_USER = os.environ.get("NEO4J_USER", "neo4j")
+NEO4J_PASSWORD = os.environ.get("NEO4J_PASSWORD", "neo4j")
+
+CURRENT_SESSION_ID = f"session-{datetime.now().strftime('%Y%m%d')}-{uuid.uuid4().hex[:4]}"
+CURRENT_TURN = 0
+
+TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_recall",
+            "description": "当你对当前输入中的实体、指代、或关系不确定时，调用此工具检索相关记忆。输入为自然语言查询意图，系统将返回相关子图。支持时间范围和多跳路径查询。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "query_intent": {"type": "string", "description": "模型用自然语言描述想查什么"},
+                    "seed_entities": {"type": "array", "items": {"type": "string"}, "description": "可选：已识别的实体ID"},
+                    "depth": {"type": "integer", "description": "期望的遍历深度，由模型根据复杂度决定，默认2"},
+                    "time_range": {"type": "object", "description": "可选：时间范围筛选", "properties": {"days": {"type": "integer", "description": "最近N天"}}},
+                    "session_filter": {"type": "string", "description": "可选：限定特定会话ID"}
+                },
+                "required": ["query_intent"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_commit",
+            "description": "当你认为当前对话包含对未来轮次有价值的信息时，将抽取的三元组写入图库。仅在信息具有跨轮次引用潜力时调用。支持批量写入。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "triplets": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "subject": {"type": "string"},
+                                "relation": {"type": "string"},
+                                "object": {"type": "string"},
+                                "confidence": {"type": "number"}
+                            },
+                            "required": ["subject", "relation", "object"]
+                        },
+                        "description": "要写入的三元组列表"
+                    },
+                    "entity_types": {"type": "array", "items": {"type": "string"}, "description": "模型动态提议的类型"},
+                    "temporal_tag": {"type": "string", "description": "可选：时间标记（如'2026-04-08'）"}
+                },
+                "required": ["triplets"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_purge",
+            "description": "当你发现记忆中的信息与当前认知矛盾，或用户明确要求更正时，删除指定关系。优先于memory_commit执行以维护一致性。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "criteria": {
+                        "type": "object",
+                        "properties": {
+                            "subject_contains": {"type": "string"},
+                            "relation_type": {"type": "string"},
+                            "target_contains": {"type": "string"},
+                            "time_before": {"type": "string"},
+                            "session_id": {"type": "string"}
+                        },
+                        "description": "删除条件"
+                    },
+                    "mode": {"type": "string", "enum": ["soft", "supersede"], "description": "soft=逻辑删除, supersede=纠错替代", "default": "soft"},
+                    "new_relation": {"type": "object", "description": "supersede模式时的新关系", "properties": {"relation": {"type": "string"}, "target": {"type": "string"}}}
+                },
+                "required": ["criteria"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_introspect",
+            "description": "检索当前对话会话的元数据：已讨论的实体、关系密度、记忆热点。用于自我监控信息缺口。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "session_id": {"type": "string", "description": "可选：指定会话ID，默认当前会话"}
+                },
+                "required": []
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_archive",
+            "description": "归档N天前的非活跃关系，用于清理低频查询数据。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "days": {"type": "integer", "description": "归档多少天前的关系，默认30天"}
+                },
+                "required": []
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "memory_cleanup",
+            "description": "物理清理已删除状态超过90天的关系和孤立节点。谨慎使用。",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "dry_run": {"type": "boolean", "description": "仅预览不实际删除", "default": True}
+                },
+                "required": []
+            }
+        }
+    }
+]
+
+
+class Neo4jGraph:
+    def __init__(self, uri: str, user: str, password: str):
+        from neo4j import GraphDatabase
+        self.driver = GraphDatabase.driver(uri, auth=(user, password))
+    
+    def close(self):
+        self.driver.close()
+    
+    def ensure_constraints(self):
+        with self.driver.session() as session:
+            session.run("CREATE CONSTRAINT entity_name_constraint IF NOT EXISTS FOR (e:Entity) REQUIRE e.name IS UNIQUE")
+            session.run("CREATE CONSTRAINT session_id_constraint IF NOT EXISTS FOR (s:Session) REQUIRE s.session_id IS UNIQUE")
+            
+            session.run("CREATE INDEX rel_created_at IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.created_at")
+            session.run("CREATE INDEX rel_session_id IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.session_id")
+            session.run("CREATE INDEX rel_type IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.type")
+            session.run("CREATE INDEX rel_status IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.status")
+            session.run("CREATE INDEX rel_date_bucket IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.date_bucket")
+            session.run("CREATE INDEX entity_type IF NOT EXISTS FOR (e:Entity) ON e.type")
+            session.run("CREATE INDEX entity_mention_count IF NOT EXISTS FOR (e:Entity) ON e.mention_count")
+    
+    def recall(self, query_intent: str, seed_entities: list = None, depth: int = 2, 
+               time_range: dict = None, session_filter: str = None) -> dict:
+        with self.driver.session() as session:
+            # 支持逗号分隔的多个关键词
+            keywords = [w.strip() for w in query_intent.replace(',', ' ').split() if len(w.strip()) > 0]
+            
+            # 如果没有查询条件，返回空结果
+            if not keywords and not seed_entities:
+                return {"entities": [], "relations": [], "message": "无查询关键词"}
+            
+            # 默认查询所有会话的历史（不只是当前会话）
+            # 只有明确指定 session_filter 才限制查询范围
+            params = {}
+            cond_parts = ["r.status = 'active'"]
+            
+            if session_filter:
+                cond_parts.append("r.session_id = $session_id")
+                params["session_id"] = session_filter
+            
+            # 搜索多个相关关键词
+            if keywords:
+                keyword_conditions = []
+                for k in keywords:
+                    k_lower = k.lower()
+                    # 搜索：实体名称、关系类型、目标实体
+                    keyword_conditions.append(f"toLower(e.name) CONTAINS '{k_lower}'")
+                    keyword_conditions.append(f"toLower(t.name) CONTAINS '{k_lower}'")
+                    keyword_conditions.append(f"toLower(r.type) CONTAINS '{k_lower}'")
+                cond_parts.append(f"({' OR '.join(keyword_conditions)})")
+            
+            if seed_entities:
+                placeholders = ",".join([f"'{s}'" for s in seed_entities])
+                cond_parts.append(f"(e.name IN [{placeholders}] OR t.name IN [{placeholders}])")
+            
+            if time_range and "days" in time_range:
+                cond_parts.append(f"r.created_at >= datetime() - duration('P{time_range['days']}D')")
+            
+            where_clause = " AND ".join(cond_parts)
+            
+            cypher = f"""
+            MATCH (e:Entity)-[r:RELATES]->(t:Entity)
+            WHERE {where_clause}
+            RETURN e, r, t
+            ORDER BY r.created_at DESC
+            LIMIT 30
+            """
+            
+            result = session.run(cypher, params)
+            entities, relations = {}, []
+            
+            for record in result:
+                e, r, t = record["e"], record["r"], record["t"]
+                if e["name"] not in entities:
+                    entities[e["name"]] = {"name": e["name"], "type": e.get("type", "unknown"), "mention_count": e.get("mention_count", 1)}
+                if t["name"] not in entities:
+                    entities[t["name"]] = {"name": t["name"], "type": t.get("type", "unknown"), "mention_count": t.get("mention_count", 1)}
+                
+                relations.append({
+                    "source": e["name"],
+                    "target": t["name"],
+                    "type": r["type"],
+                    "created_at": str(r.get("created_at", "")),
+                    "session_id": r.get("session_id", ""),
+                    "turn_id": r.get("turn_id", 0),
+                    "confidence": r.get("confidence", 1.0)
+                })
+            
+            return {"entities": list(entities.values()), "relations": relations[:20]}
+    
+    def commit(self, triplets: list, entity_types: list = None, temporal_tag: str = None) -> dict:
+        global CURRENT_TURN
+        with self.driver.session() as session:
+            valid_triplets = [t for t in triplets if t.get("subject") and t.get("relation") and t.get("object")]
+            
+            if not valid_triplets:
+                return {"committed_count": 0, "details": []}
+            
+            etype = entity_types[0] if entity_types else "unknown"
+            date_bucket = temporal_tag or datetime.now().strftime("%Y-%m-%d")
+            
+            results = []
+            for triplet in valid_triplets:
+                subject = triplet.get("subject", "").strip()
+                relation = triplet.get("relation", "").strip()
+                obj = triplet.get("object", "").strip()
+                confidence = triplet.get("confidence", 0.9)
+                
+                session.run("""
+                MERGE (s:Entity {name: $subject})
+                ON CREATE SET s.type = $type, s.created_at = datetime(), s.mention_count = 1, s.updated_at = datetime()
+                ON MATCH SET s.mention_count = coalesce(s.mention_count, 0) + 1, s.updated_at = datetime()
+                
+                MERGE (t:Entity {name: $object})
+                ON CREATE SET t.type = $type, t.created_at = datetime(), t.mention_count = 1, t.updated_at = datetime()
+                ON MATCH SET t.mention_count = coalesce(t.mention_count, 0) + 1, t.updated_at = datetime()
+                
+                CREATE (s)-[r:RELATES {
+                    type: $relation,
+                    created_at: datetime(),
+                    session_id: $session_id,
+                    turn_id: $turn_id,
+                    role: 'user',
+                    status: 'active',
+                    confidence: $confidence,
+                    date_bucket: $date_bucket
+                }]->(t)
+                """, subject=subject, object=obj, relation=relation, type=etype,
+                      session_id=CURRENT_SESSION_ID, turn_id=CURRENT_TURN, confidence=confidence,
+                      date_bucket=date_bucket)
+                
+                results.append(f"{subject} -[{relation}]-> {obj}")
+            
+            return {"committed_count": len(results), "details": results}
+    
+    def purge(self, criteria: dict, mode: str = "soft", new_relation: dict = None) -> dict:
+        with self.driver.session() as session:
+            subject_pattern = criteria.get("subject_contains", "")
+            rel_type = criteria.get("relation_type", "")
+            target_pattern = criteria.get("target_contains", "")
+            session_id = criteria.get("session_id", CURRENT_SESSION_ID)
+            
+            cond_parts = ["r.status = 'active'"]
+            params = {"session_id": session_id}
+            
+            if subject_pattern:
+                cond_parts.append("r.source CONTAINS $subject")
+                params["subject"] = subject_pattern
+            if target_pattern:
+                cond_parts.append("r.target CONTAINS $target")
+                params["target"] = target_pattern
+            if rel_type:
+                cond_parts.append("r.type = $rel_type")
+                params["rel_type"] = rel_type
+            
+            where_clause = " AND ".join(cond_parts)
+            
+            if mode == "supersede" and new_relation:
+                new_rel = new_relation.get("relation", "")
+                new_target = new_relation.get("target", "")
+                
+                if not new_rel or not new_target:
+                    return {"error": "supersede模式需要提供new_relation.relation和new_relation.target"}
+                
+                result = session.run(f"""
+                MATCH (s:Entity)-[r:RELATES]->(t:Entity)
+                WHERE {where_clause}
+                SET r.status = 'superseded', r.updated_at = datetime()
+                RETURN id(r) as old_id, s.name as source
+                """, params)
+                
+                deleted_count = 0
+                for record in result:
+                    old_id = record["old_id"]
+                    source = record["source"]
+                    
+                    session.run("""
+                    MATCH (s:Entity {name: $source})
+                    WHERE id(s) = $source_id
+                    CREATE (s)-[r:RELATES {
+                        type: $new_rel,
+                        created_at: datetime(),
+                        session_id: $session_id,
+                        turn_id: $turn_id,
+                        role: 'user',
+                        status: 'active',
+                        confidence: 0.9,
+                        date_bucket: date().isoDate,
+                        supersedes: $old_id
+                    }]->(t:Entity {name: $new_target})
+                    """, source_id=record["s"].element_id, new_rel=new_rel, new_target=new_target,
+                          session_id=CURRENT_SESSION_ID, turn_id=CURRENT_TURN, old_id=old_id)
+                    deleted_count += 1
+                
+                return {"deleted_count": deleted_count, "mode": "supersede", "new_relation": f"{new_relation.get('subject', '')} -[{new_rel}]-> {new_target}"}
+            else:
+                result = session.run(f"""
+                MATCH ()-[r:RELATES]->()
+                WHERE {where_clause}
+                SET r.status = 'deleted', r.updated_at = datetime()
+                RETURN count(r) as deleted
+                """, params)
+                count = result.single()["deleted"]
+                
+                return {"deleted_count": count, "mode": "soft"}
+    
+    def introspect(self, session_id: str = None) -> dict:
+        target_session = session_id or CURRENT_SESSION_ID
+        
+        with self.driver.session() as session:
+            result = session.run("""
+            MATCH (s:Entity)-[r:RELATES]->(t:Entity)
+            WHERE r.session_id = $session_id AND r.status = 'active'
+            RETURN collect(DISTINCT s.name) as source_entities,
+                   collect(DISTINCT t.name) as target_entities,
+                   count(r) as rel_count,
+                   collect(DISTINCT r.type) as rel_types
+            """, session_id=target_session)
+            record = result.single()
+            
+            result2 = session.run("""
+            MATCH (e:Entity)
+            RETURN e.name as name, e.mention_count as count, e.type as type
+            ORDER BY e.mention_count DESC
+            LIMIT 10
+            """)
+            hotspots = [(r["name"], r["count"], r["type"]) for r in result2]
+            
+            result3 = session.run("""
+            MATCH ()-[r:RELATES]->()
+            WHERE r.session_id = $session_id
+            RETURN r.type as type, count(*) as count
+            ORDER BY count DESC
+            """, session_id=target_session)
+            relation_distribution = {r["type"]: r["count"] for r in result3}
+            
+            return {
+                "session_id": target_session,
+                "total_turns": CURRENT_TURN,
+                "entities_discussed": list(set((record["source_entities"] or []) + (record["target_entities"] or []))),
+                "relation_count": record["rel_count"] if record else 0,
+                "relation_types": record["rel_types"] if record else [],
+                "memory_hotspots": hotspots,
+                "relation_distribution": relation_distribution
+            }
+    
+    def archive(self, days: int = 30) -> dict:
+        with self.driver.session() as session:
+            result = session.run("""
+            MATCH ()-[r:RELATES]->()
+            WHERE r.status = 'active' AND r.created_at < datetime() - duration('P' + $days + 'D')
+            SET r.status = 'archived', r.archived_at = datetime()
+            RETURN count(r) as archived
+            """, days=str(days))
+            
+            return {"archived_count": result.single()["archived"], "days": days}
+    
+    def cleanup(self, dry_run: bool = True) -> dict:
+        with self.driver.session() as session:
+            result1 = session.run("""
+            MATCH ()-[r:RELATES]->()
+            WHERE r.status = 'deleted' AND r.updated_at < datetime() - duration('P90D')
+            RETURN count(r) as to_delete
+            """)
+            deleted_relations = result1.single()["to_delete"]
+            
+            result2 = session.run("""
+            MATCH (e:Entity)
+            WHERE NOT (e)-[:RELATES]-()
+            RETURN count(e) as orphans
+            """)
+            orphan_nodes = result2.single()["orphans"]
+            
+            if not dry_run and deleted_relations > 0:
+                session.run("""
+                MATCH ()-[r:RELATES]->()
+                WHERE r.status = 'deleted' AND r.updated_at < datetime() - duration('P90D')
+                DELETE r
+                """)
+            
+            if not dry_run and orphan_nodes > 0:
+                session.run("""
+                MATCH (e:Entity)
+                WHERE NOT (e)-[:RELATES]-()
+                DELETE e
+                """)
+            
+            return {
+                "dry_run": dry_run,
+                "deleted_relations": deleted_relations,
+                "orphan_nodes": orphan_nodes,
+                "action_taken": not dry_run
+            }
+
+
+def execute_tool(graph: Neo4jGraph, tool_name: str, arguments: dict) -> str:
+    print(f"\n[工具调用] {tool_name}")
+    print(f"[参数] {json.dumps(arguments, ensure_ascii=False, indent=2)}")
+    
+    try:
+        if tool_name == "memory_recall":
+            result = graph.recall(
+                query_intent=arguments.get("query_intent", ""),
+                seed_entities=arguments.get("seed_entities"),
+                depth=arguments.get("depth", 2),
+                time_range=arguments.get("time_range"),
+                session_filter=arguments.get("session_filter")
+            )
+            return format_recall_result(result)
+        
+        elif tool_name == "memory_commit":
+            result = graph.commit(
+                triplets=arguments.get("triplets", []),
+                entity_types=arguments.get("entity_types"),
+                temporal_tag=arguments.get("temporal_tag")
+            )
+            return json.dumps(result, ensure_ascii=False, default=str)
+        
+        elif tool_name == "memory_purge":
+            result = graph.purge(
+                criteria=arguments.get("criteria", {}),
+                mode=arguments.get("mode", "soft"),
+                new_relation=arguments.get("new_relation")
+            )
+            return json.dumps(result, ensure_ascii=False, default=str)
+        
+        elif tool_name == "memory_introspect":
+            result = graph.introspect(session_id=arguments.get("session_id"))
+            return json.dumps(result, ensure_ascii=False, default=str)
+        
+        elif tool_name == "memory_archive":
+            result = graph.archive(days=arguments.get("days", 30))
+            return json.dumps(result, ensure_ascii=False, default=str)
+        
+        elif tool_name == "memory_cleanup":
+            result = graph.cleanup(dry_run=arguments.get("dry_run", True))
+            return json.dumps(result, ensure_ascii=False, default=str)
+        
+        return f"未知工具: {tool_name}"
+    
+    except Exception as e:
+        return f"工具执行错误: {str(e)}"
+
+
+def format_recall_result(result: dict) -> str:
+    lines = ["===== 图数据库检索结果 ====="]
+    
+    if result.get("entities"):
+        lines.append(f"\n相关实体 ({len(result['entities'])} 个):")
+        for e in result["entities"]:
+            lines.append(f"  - {e['name']} (类型: {e.get('type', 'unknown')}, 提及: {e.get('mention_count', 1)}次)")
+    
+    if result.get("relations"):
+        lines.append(f"\n相关关系 ({len(result['relations'])} 条):")
+        for r in result["relations"]:
+            lines.append(f"  - {r['source']} --[{r['type']}]--> {r['target']}")
+            created = r.get("created_at", "N/A")
+            if created and created != "N/A":
+                created = created[:19] if "T" in str(created) else str(created)
+            lines.append(f"    时间: {created}, 会话: {r.get('session_id', 'N/A')[:20]}, 轮次: {r.get('turn_id', 0)}, 置信度: {r.get('confidence', 1.0)}")
+    
+    if not result.get("entities") and not result.get("relations"):
+        lines.append("\n(未找到相关记忆)")
+    
+    lines.append("=" * 35)
+    return "\n".join(lines)
+
+
+class GraphMemoryClient:
+    def __init__(self, api_key: str, base_url: str, graph: Neo4jGraph):
+        from openai import OpenAI
+        self.client = OpenAI(api_key=api_key, base_url=base_url)
+        self.graph = graph
+        self.tools = TOOLS
+        self.system_prompt = self._build_system_prompt()
+    
+    def _build_system_prompt(self) -> str:
+        return """你是图数据库记忆助手。
+
+## 核心职责
+你是用户的长期记忆助手。每次对话后，你**必须**主动决定是否需要将关键信息写入记忆图库。
+
+## 重要规则
+1. **只查询一次**：每轮对话**最多调用一次** memory_recall，不要反复查询。
+2. **不要重复查询**：如果一次 memory_recall 返回"未找到相关记忆"，**不要再继续查询**，直接告诉用户。
+3. **提取关键词而非整句**：
+   - ❌ 错误: "查找用户提到的鸿蒙工具链条和今天饮食相关的记忆"
+   - ✅ 正确: "鸿蒙" 或 "鸿蒙,工具链,用户"
+4. **区分事实与猜测**：当基于记忆检索结果回复时，**必须**使用"应该"标注你的推理。
+   - ✅ 正确: "根据记忆，你的鸿蒙工具链**应该**在 opt 目录下"
+   - ❌ 错误: "你的鸿蒙工具链在 opt 目录下"（没有标注"应该"）
+   - 原因：数据库中的记录可能不完整或已过期，你需要标注这是**推断**而非**确认**的事实
+
+## memory_recall 正确用法
+query_intent 应该是**逗号分隔的多个关键词**，并包含**同义词/近义词**：
+```json
+{
+  "query_intent": "鸿蒙,harmony,工具链,toolchain,开发环境,IDE",  // 包含同义词
+  "depth": 1
+}
+```
+- 搜索：实体名、关系类型、目标实体
+- **必须包含同义词**：如 "鸿蒙" 的同义词 "harmony"、"openharmony"
+- **必须包含近义词**：如 "工具链" 的 "sdk"、"toolchain"
+
+调用 recall 前，思考：用户问的词有哪些同义词？全部列出来用逗号分隔。
+
+## 重要规则：必须写入记忆的情况
+当用户提到以下内容时，你**必须**调用 memory_commit 写入记忆：
+1. 用户的**偏好**（"我喜欢X"）
+2. 用户的**项目**（"我在做X项目"）
+3. 用户的**学习内容**（"我在学Python"）
+4. 讨论的**主题**（"量子力学"）
+
+## 可用工具
+1. **memory_recall** - 检索历史记忆
+   - query_intent: 支持逗号分隔的多关键词，如 "鸿蒙,工具链"
+   - 会同时搜索：实体名、关系类型、目标实体
+2. **memory_commit** - 写入记忆（三元组格式）
+3. **memory_purge** - 修正/删除记忆
+4. **memory_introspect** - 查看会话状态
+
+## 错误策略（禁止）
+- ❌ query_intent 使用完整句子
+- ❌ 多次调用 memory_recall
+- ❌ 超过2次 tool calls 后还继续
+
+现在开始对话!"""
+
+    def send_message(self, user_input: str, tool_results: list = None, assistant_msg: dict = None) -> dict:
+        global CURRENT_TURN
+        
+        messages = [{"role": "system", "content": self.system_prompt}]
+        
+        # 添加之前的 assistant 消息（包含 tool_calls）
+        if assistant_msg:
+            messages.append(assistant_msg)
+        
+        # 添加之前的工具结果
+        if tool_results:
+            messages.extend(tool_results)
+        
+        # 添加当前用户输入
+        messages.append({"role": "user", "content": user_input})
+        
+        response = self.client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=messages,
+            tools=self.tools,
+            tool_choice="auto"
+        )
+        
+        return response
+    
+    def chat_loop(self):
+        global CURRENT_TURN
+        
+        print("\n" + "=" * 60)
+        print("Graph Memory Demo - 纯图数据库对话 (Neo4j)")
+        print("=" * 60)
+        print(f"会话ID: {CURRENT_SESSION_ID}")
+        print(f"Neo4j: {NEO4J_URI}")
+        print("输入 quit/exit 退出")
+        print("=" * 60 + "\n")
+        
+        while True:
+            try:
+                user_input = input("\n[你] ").strip()
+                if not user_input:
+                    continue
+                if user_input.lower() in ["quit", "exit", "退出"]:
+                    print("\n[系统] 再见！")
+                    break
+                
+                CURRENT_TURN += 1
+                print(f"\n[轮次 {CURRENT_TURN}] 发送请求...")
+                
+                # 用于累积工具结果
+                tool_results = []
+                last_assistant_msg = None
+                
+                # 首次请求
+                response = self.send_message(user_input, [])
+                message = response.choices[0].message
+                
+                # 处理工具调用循环 - 持续处理直到没有新的 tool_calls
+                while message.tool_calls:
+                    # 打印模型响应（如果有）
+                    if message.content:
+                        print(f"\n[模型] {message.content}")
+                    
+                    # 保存包含 tool_calls 的 assistant 消息（只保留最后一个）
+                    last_assistant_msg = {
+                        "role": "assistant",
+                        "content": message.content,
+                        "type": "message",
+                        "tool_calls": [{"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}} for tc in message.tool_calls]
+                    }
+                    
+                    # 执行当前轮的所有工具调用
+                    current_tool_results = []
+                    for tool_call in message.tool_calls:
+                        tool_name = tool_call.function.name
+                        tool_args = json.loads(tool_call.function.arguments)
+                        tool_id = tool_call.id
+                        
+                        result = execute_tool(self.graph, tool_name, tool_args)
+                        print(f"\n[工具结果] {result}")
+                        
+                        current_tool_results.append({
+                            "role": "tool",
+                            "tool_call_id": tool_id,
+                            "content": result
+                        })
+                    
+                    # 继续调用 - 只发送当前轮的 assistant 消息和工具结果
+                    messages = [
+                        {"role": "system", "content": self.system_prompt},
+                        last_assistant_msg,
+                    ]
+                    messages.extend(current_tool_results)
+                    messages.append({"role": "user", "content": user_input})
+                    
+                    response = self.client.chat.completions.create(
+                        model=MODEL_NAME,
+                        messages=messages,
+                        tools=self.tools
+                    )
+                    message = response.choices[0].message
+                
+                # 最终回复
+                final_content = message.content or "(无回复)"
+                print(f"\n[模型] {final_content}")
+                
+            except KeyboardInterrupt:
+                print("\n\n[系统] 中断退出")
+                break
+            except Exception as e:
+                print(f"\n[错误] {str(e)}")
+
+
+def main():
+    print("Graph Memory Demo - 纯图数据库调用的多轮对话")
+    print("-" * 40)
+    
+    if not DEEPSEEK_API_KEY:
+        print("错误: 请设置 DEEPSEEK_API_KEY 环境变量")
+        print("  export DEEPSEEK_API_KEY='your-actual-key'")
+        return
+    
+    print(f"Neo4j 配置: {NEO4J_URI}")
+    print(f"Neo4j 用户: {NEO4J_USER}")
+    print(f"Neo4j 密码: {NEO4J_PASSWORD[:4] if NEO4J_PASSWORD else 'None'}***")
+    print(f"DeepSeek API: {DEEPSEEK_API_KEY[:8]}...")
+    print()
+    
+    try:
+        graph = Neo4jGraph(NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD)
+        graph.ensure_constraints()
+        print("[Info] Neo4j 连接成功\n")
+    except Exception as e:
+        print(f"[Error] Neo4j 连接失败: {e}")
+        print("请确保 Neo4j 已启动，或运行 scripts/ 下的安装脚本")
+        return
+    
+    client = GraphMemoryClient(DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL, graph)
+    client.chat_loop()
+    graph.close()
+
+
+if __name__ == "__main__":
+    main()