809 lines
34 KiB
Python
809 lines
34 KiB
Python
#!/usr/bin/env python3
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"""
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Graph Memory Demo - 纯图数据库调用 Demo
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基于 DeepSeek API Tool Calls 实现摒弃传统上下文的自主记忆多轮对话
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验证目的：无上下文纯图数据库记忆存储
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"""
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import json
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import os
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import uuid
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from datetime import datetime
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DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY", "")
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DEEPSEEK_BASE_URL = "https://api.deepseek.com"
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MODEL_NAME = "deepseek-chat"
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NEO4J_URI = os.environ.get("NEO4J_URI", "bolt://localhost:7687")
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NEO4J_USER = os.environ.get("NEO4J_USER", "neo4j")
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NEO4J_PASSWORD = os.environ.get("NEO4J_PASSWORD", "neo4j")
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CURRENT_SESSION_ID = f"session-{datetime.now().strftime('%Y%m%d')}-{uuid.uuid4().hex[:4]}"
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CURRENT_TURN = 0
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TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "memory_recall",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"query_intent": {"type": "string", "description": "模型用自然语言描述想查什么"},
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"seed_entities": {"type": "array", "items": {"type": "string"}, "description": "可选：已识别的实体ID"},
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"depth": {"type": "integer", "description": "期望的遍历深度，由模型根据复杂度决定，默认2"},
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"time_range": {"type": "object", "description": "可选：时间范围筛选", "properties": {"days": {"type": "integer", "description": "最近N天"}}},
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"session_filter": {"type": "string", "description": "可选：限定特定会话ID"}
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},
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"required": ["query_intent"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "memory_commit",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"triplets": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"subject": {"type": "string"},
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"relation": {"type": "string"},
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"object": {"type": "string"},
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"confidence": {"type": "number"}
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},
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"required": ["subject", "relation", "object"]
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},
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"description": "要写入的三元组列表"
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},
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"entity_types": {"type": "array", "items": {"type": "string"}, "description": "模型动态提议的类型"},
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"temporal_tag": {"type": "string", "description": "可选：时间标记（如'2026-04-08'）"}
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},
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"required": ["triplets"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "memory_purge",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"criteria": {
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"type": "object",
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"properties": {
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"subject_contains": {"type": "string"},
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"relation_type": {"type": "string"},
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"target_contains": {"type": "string"},
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"time_before": {"type": "string"},
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"session_id": {"type": "string"}
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},
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"description": "删除条件"
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},
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"mode": {"type": "string", "enum": ["soft", "supersede"], "description": "soft=逻辑删除, supersede=纠错替代", "default": "soft"},
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"new_relation": {"type": "object", "description": "supersede模式时的新关系", "properties": {"relation": {"type": "string"}, "target": {"type": "string"}}}
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},
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"required": ["criteria"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "memory_introspect",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"session_id": {"type": "string", "description": "可选：指定会话ID，默认当前会话"}
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},
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"required": []
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "memory_archive",
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"description": "归档N天前的非活跃关系，用于清理低频查询数据。",
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"parameters": {
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"type": "object",
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"properties": {
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"days": {"type": "integer", "description": "归档多少天前的关系，默认30天"}
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},
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"required": []
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "memory_cleanup",
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"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。",
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"parameters": {
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"type": "object",
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"properties": {
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"dry_run": {"type": "boolean", "description": "仅预览不实际删除", "default": True}
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},
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"required": []
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}
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}
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}
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]
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class Neo4jGraph:
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def __init__(self, uri: str, user: str, password: str):
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from neo4j import GraphDatabase
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self.driver = GraphDatabase.driver(uri, auth=(user, password))
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def close(self):
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self.driver.close()
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def ensure_constraints(self):
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with self.driver.session() as session:
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session.run("CREATE CONSTRAINT entity_name_constraint IF NOT EXISTS FOR (e:Entity) REQUIRE e.name IS UNIQUE")
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session.run("CREATE CONSTRAINT session_id_constraint IF NOT EXISTS FOR (s:Session) REQUIRE s.session_id IS UNIQUE")
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session.run("CREATE INDEX rel_created_at IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.created_at")
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session.run("CREATE INDEX rel_session_id IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.session_id")
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session.run("CREATE INDEX rel_type IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.type")
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session.run("CREATE INDEX rel_status IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.status")
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session.run("CREATE INDEX rel_date_bucket IF NOT EXISTS FOR ()-[r:RELATES]-() ON r.date_bucket")
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session.run("CREATE INDEX entity_type IF NOT EXISTS FOR (e:Entity) ON e.type")
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session.run("CREATE INDEX entity_mention_count IF NOT EXISTS FOR (e:Entity) ON e.mention_count")
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def recall(self, query_intent: str, seed_entities: list = None, depth: int = 2,
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time_range: dict = None, session_filter: str = None) -> dict:
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with self.driver.session() as session:
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# 支持逗号分隔的多个关键词
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keywords = [w.strip() for w in query_intent.replace(',', ' ').split() if len(w.strip()) > 0]
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# 如果没有查询条件，返回空结果
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if not keywords and not seed_entities:
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return {"entities": [], "relations": [], "message": "无查询关键词"}
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# 默认查询所有会话的历史（不只是当前会话）
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# 只有明确指定 session_filter 才限制查询范围
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params = {}
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cond_parts = ["r.status = 'active'"]
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if session_filter:
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cond_parts.append("r.session_id = $session_id")
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params["session_id"] = session_filter
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# 搜索多个相关关键词
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if keywords:
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keyword_conditions = []
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for k in keywords:
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k_lower = k.lower()
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# 搜索：实体名称、关系类型、目标实体
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keyword_conditions.append(f"toLower(e.name) CONTAINS '{k_lower}'")
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keyword_conditions.append(f"toLower(t.name) CONTAINS '{k_lower}'")
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keyword_conditions.append(f"toLower(r.type) CONTAINS '{k_lower}'")
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cond_parts.append(f"({' OR '.join(keyword_conditions)})")
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if seed_entities:
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placeholders = ",".join([f"'{s}'" for s in seed_entities])
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cond_parts.append(f"(e.name IN [{placeholders}] OR t.name IN [{placeholders}])")
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if time_range and "days" in time_range:
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cond_parts.append(f"r.created_at >= datetime() - duration('P{time_range['days']}D')")
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where_clause = " AND ".join(cond_parts)
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cypher = f"""
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MATCH (e:Entity)-[r:RELATES]->(t:Entity)
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WHERE {where_clause}
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RETURN e, r, t
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ORDER BY r.created_at DESC
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LIMIT 30
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"""
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result = session.run(cypher, params)
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entities, relations = {}, []
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for record in result:
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e, r, t = record["e"], record["r"], record["t"]
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if e["name"] not in entities:
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entities[e["name"]] = {"name": e["name"], "type": e.get("type", "unknown"), "mention_count": e.get("mention_count", 1)}
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if t["name"] not in entities:
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entities[t["name"]] = {"name": t["name"], "type": t.get("type", "unknown"), "mention_count": t.get("mention_count", 1)}
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relations.append({
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"source": e["name"],
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"target": t["name"],
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"type": r["type"],
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"created_at": str(r.get("created_at", "")),
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"session_id": r.get("session_id", ""),
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"turn_id": r.get("turn_id", 0),
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"confidence": r.get("confidence", 1.0)
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})
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return {"entities": list(entities.values()), "relations": relations[:20]}
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def commit(self, triplets: list, entity_types: list = None, temporal_tag: str = None) -> dict:
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global CURRENT_TURN
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with self.driver.session() as session:
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valid_triplets = [t for t in triplets if t.get("subject") and t.get("relation") and t.get("object")]
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if not valid_triplets:
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return {"committed_count": 0, "details": []}
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etype = entity_types[0] if entity_types else "unknown"
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date_bucket = temporal_tag or datetime.now().strftime("%Y-%m-%d")
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results = []
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for triplet in valid_triplets:
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subject = triplet.get("subject", "").strip()
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relation = triplet.get("relation", "").strip()
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obj = triplet.get("object", "").strip()
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confidence = triplet.get("confidence", 0.9)
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session.run("""
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MERGE (s:Entity {name: $subject})
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ON CREATE SET s.type = $type, s.created_at = datetime(), s.mention_count = 1, s.updated_at = datetime()
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ON MATCH SET s.mention_count = coalesce(s.mention_count, 0) + 1, s.updated_at = datetime()
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MERGE (t:Entity {name: $object})
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ON CREATE SET t.type = $type, t.created_at = datetime(), t.mention_count = 1, t.updated_at = datetime()
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ON MATCH SET t.mention_count = coalesce(t.mention_count, 0) + 1, t.updated_at = datetime()
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CREATE (s)-[r:RELATES {
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type: $relation,
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created_at: datetime(),
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session_id: $session_id,
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turn_id: $turn_id,
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role: 'user',
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status: 'active',
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confidence: $confidence,
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date_bucket: $date_bucket
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}]->(t)
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""", subject=subject, object=obj, relation=relation, type=etype,
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session_id=CURRENT_SESSION_ID, turn_id=CURRENT_TURN, confidence=confidence,
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date_bucket=date_bucket)
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results.append(f"{subject} -[{relation}]-> {obj}")
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return {"committed_count": len(results), "details": results}
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def purge(self, criteria: dict, mode: str = "soft", new_relation: dict = None) -> dict:
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with self.driver.session() as session:
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subject_pattern = criteria.get("subject_contains", "")
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rel_type = criteria.get("relation_type", "")
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target_pattern = criteria.get("target_contains", "")
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session_id = criteria.get("session_id", CURRENT_SESSION_ID)
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cond_parts = ["r.status = 'active'"]
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params = {"session_id": session_id}
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if subject_pattern:
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cond_parts.append("r.source CONTAINS $subject")
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params["subject"] = subject_pattern
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if target_pattern:
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cond_parts.append("r.target CONTAINS $target")
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params["target"] = target_pattern
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if rel_type:
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cond_parts.append("r.type = $rel_type")
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params["rel_type"] = rel_type
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where_clause = " AND ".join(cond_parts)
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if mode == "supersede" and new_relation:
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new_rel = new_relation.get("relation", "")
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new_target = new_relation.get("target", "")
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if not new_rel or not new_target:
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return {"error": "supersede模式需要提供new_relation.relation和new_relation.target"}
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result = session.run(f"""
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MATCH (s:Entity)-[r:RELATES]->(t:Entity)
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WHERE {where_clause}
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SET r.status = 'superseded', r.updated_at = datetime()
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RETURN id(r) as old_id, s.name as source
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""", params)
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deleted_count = 0
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for record in result:
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old_id = record["old_id"]
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source = record["source"]
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session.run("""
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MATCH (s:Entity {name: $source})
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WHERE id(s) = $source_id
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CREATE (s)-[r:RELATES {
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type: $new_rel,
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created_at: datetime(),
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session_id: $session_id,
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turn_id: $turn_id,
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role: 'user',
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status: 'active',
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confidence: 0.9,
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date_bucket: date().isoDate,
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supersedes: $old_id
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}]->(t:Entity {name: $new_target})
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""", source_id=record["s"].element_id, new_rel=new_rel, new_target=new_target,
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session_id=CURRENT_SESSION_ID, turn_id=CURRENT_TURN, old_id=old_id)
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deleted_count += 1
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return {"deleted_count": deleted_count, "mode": "supersede", "new_relation": f"{new_relation.get('subject', '')} -[{new_rel}]-> {new_target}"}
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else:
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result = session.run(f"""
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MATCH ()-[r:RELATES]->()
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WHERE {where_clause}
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SET r.status = 'deleted', r.updated_at = datetime()
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RETURN count(r) as deleted
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""", params)
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count = result.single()["deleted"]
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return {"deleted_count": count, "mode": "soft"}
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def introspect(self, session_id: str = None) -> dict:
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target_session = session_id or CURRENT_SESSION_ID
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with self.driver.session() as session:
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result = session.run("""
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MATCH (s:Entity)-[r:RELATES]->(t:Entity)
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WHERE r.session_id = $session_id AND r.status = 'active'
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RETURN collect(DISTINCT s.name) as source_entities,
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collect(DISTINCT t.name) as target_entities,
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count(r) as rel_count,
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collect(DISTINCT r.type) as rel_types
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""", session_id=target_session)
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record = result.single()
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result2 = session.run("""
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MATCH (e:Entity)
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RETURN e.name as name, e.mention_count as count, e.type as type
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ORDER BY e.mention_count DESC
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LIMIT 10
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""")
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hotspots = [(r["name"], r["count"], r["type"]) for r in result2]
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result3 = session.run("""
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MATCH ()-[r:RELATES]->()
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WHERE r.session_id = $session_id
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RETURN r.type as type, count(*) as count
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ORDER BY count DESC
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""", session_id=target_session)
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relation_distribution = {r["type"]: r["count"] for r in result3}
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return {
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"session_id": target_session,
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"total_turns": CURRENT_TURN,
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"entities_discussed": list(set((record["source_entities"] or []) + (record["target_entities"] or []))),
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"relation_count": record["rel_count"] if record else 0,
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"relation_types": record["rel_types"] if record else [],
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"memory_hotspots": hotspots,
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"relation_distribution": relation_distribution
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}
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def archive(self, days: int = 30) -> dict:
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with self.driver.session() as session:
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result = session.run("""
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MATCH ()-[r:RELATES]->()
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WHERE r.status = 'active' AND r.created_at < datetime() - duration('P' + $days + 'D')
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SET r.status = 'archived', r.archived_at = datetime()
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RETURN count(r) as archived
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""", days=str(days))
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return {"archived_count": result.single()["archived"], "days": days}
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def cleanup(self, dry_run: bool = True) -> dict:
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with self.driver.session() as session:
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result1 = session.run("""
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MATCH ()-[r:RELATES]->()
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WHERE r.status = 'deleted' AND r.updated_at < datetime() - duration('P90D')
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RETURN count(r) as to_delete
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""")
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deleted_relations = result1.single()["to_delete"]
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result2 = session.run("""
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MATCH (e:Entity)
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WHERE NOT (e)-[:RELATES]-()
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RETURN count(e) as orphans
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""")
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orphan_nodes = result2.single()["orphans"]
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if not dry_run and deleted_relations > 0:
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session.run("""
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MATCH ()-[r:RELATES]->()
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WHERE r.status = 'deleted' AND r.updated_at < datetime() - duration('P90D')
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DELETE r
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""")
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if not dry_run and orphan_nodes > 0:
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session.run("""
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MATCH (e:Entity)
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WHERE NOT (e)-[:RELATES]-()
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DELETE e
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""")
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return {
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"dry_run": dry_run,
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"deleted_relations": deleted_relations,
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"orphan_nodes": orphan_nodes,
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"action_taken": not dry_run
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}
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|
||
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"]:
|
||
if e and isinstance(e, dict):
|
||
lines.append(f" - {e.get('name', 'N/A')} (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"]:
|
||
if r and isinstance(r, dict):
|
||
lines.append(f" - {r.get('source', 'N/A')} --[{r.get('type', 'N/A')}]--> {r.get('target', 'N/A')}")
|
||
created = r.get("created_at", "N/A")
|
||
if created and created != "N/A":
|
||
created = created[:19] if "T" in str(created) else str(created)
|
||
session_id = r.get('session_id', 'N/A')
|
||
session_display = session_id[:20] if session_id and session_id != 'N/A' else 'N/A'
|
||
lines.append(f" 时间: {created}, 会话: {session_display}, 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TrulyMEM，TrueHumanMEM项目的AI助手，拥有长期记忆能力。
|
||
|
||
## 身份说明
|
||
- 你的名字是TrulyMEM，是TrueHumanMEM项目的产物
|
||
- 你拥有基于图数据库的长期记忆能力
|
||
- 你的设计理念是让AI的记忆方式更像人类
|
||
|
||
## 核心职责
|
||
你是一个智能对话助手，**图数据库是你记忆的唯一载体**。你的主要任务是：
|
||
1. 与用户进行自然、流畅的对话
|
||
2. 回答问题、提供建议、协助完成任务
|
||
3. 根据对话内容灵活查询和使用记忆
|
||
4. 将用户明确提到的信息写入记忆
|
||
|
||
**重要**：
|
||
- 图数据库是你记忆的唯一来源，没有其他记忆方式
|
||
- 优先进行自然对话，根据需要灵活调用记忆工具
|
||
- 用户明确提到的内容必须写入，推理得到的内容必须标注
|
||
|
||
## 记忆工具使用原则
|
||
|
||
### 何时检索记忆 (memory_recall)
|
||
- 用户询问"我们之前聊过X吗"、"你还记得X吗" → 查询X相关内容
|
||
- 用户询问"我们都聊过什么"、"我们之前说了什么" → **使用空字符串或通配符查询所有记忆**
|
||
- 用户提到某个话题，你想确认是否有相关历史 → 查询该话题
|
||
- 需要基于历史信息回答问题 → 查询相关信息
|
||
- 对话中涉及之前可能讨论过的内容 → 查询相关内容
|
||
|
||
**灵活查询**：根据对话上下文，主动判断是否需要查询记忆，不要等待用户明确要求。
|
||
|
||
**重要**：
|
||
- 当用户问"我们都聊过什么"时，**不要查询"聊天记录"、"对话"等关键词**
|
||
- 应该使用空字符串 `""` 或通配符 `"*"` 来获取所有记忆内容
|
||
- 或者使用非常宽泛的关键词如 `"用户,喜欢,项目,学习,研究,计划"`
|
||
|
||
### 何时写入记忆 (memory_commit)
|
||
**必须写入的情况**（用户明确提到）：
|
||
- 用户表达偏好："我喜欢X"、"我讨厌X"
|
||
- 用户分享信息："我在做X项目"、"我在学X"
|
||
- 用户制定计划："我打算X"、"我计划X"
|
||
- 用户描述状态："我现在在X"
|
||
|
||
**禁止写入的情况**（AI推理得到）：
|
||
- AI推断的用户偏好
|
||
- AI猜测的用户意图
|
||
- AI推导的结论
|
||
|
||
## memory_recall 使用方法
|
||
|
||
### 关键词提取
|
||
query_intent 使用**逗号分隔的多个关键词**，包含同义词：
|
||
|
||
```json
|
||
{
|
||
"query_intent": "量子力学,quantum,物理,physics",
|
||
"depth": 2
|
||
}
|
||
```
|
||
|
||
### 同义词扩展示例
|
||
- "量子力学" → "量子力学,quantum,quantum mechanics,物理,physics"
|
||
- "项目" → "项目,project,工程,工作"
|
||
- "学习" → "学习,learn,study,掌握"
|
||
|
||
### 查询规则
|
||
1. 根据对话内容灵活提取关键词
|
||
2. 第一轮使用广泛的关键词搜索
|
||
3. 如果未找到，可以尝试相关概念
|
||
4. 最多查询2-3轮，避免重复查询
|
||
|
||
## memory_commit 使用方法
|
||
|
||
使用三元组格式记录信息：
|
||
```json
|
||
{
|
||
"triplets": [
|
||
{"subject": "用户", "relation": "对领域感兴趣", "object": "量子力学"}
|
||
]
|
||
}
|
||
```
|
||
|
||
## 区分事实与推理（重要！）
|
||
|
||
### 用户明确提到的内容
|
||
直接写入记忆，回复时直接陈述：
|
||
- 用户："我喜欢Python" → 写入，回复："好的，我会记住你喜欢Python"
|
||
- 用户："我在学机器学习" → 写入，回复："明白了，你在学习机器学习"
|
||
|
||
### AI推理得到的内容
|
||
**禁止写入记忆**，回复时必须在开头标注 **[猜测]**：
|
||
- AI推断用户可能喜欢X → 不写入，回复："[猜测] 你可能对X感兴趣"
|
||
- AI推测用户意图 → 不写入，回复："[猜测] 你可能是想..."
|
||
|
||
**示例**：
|
||
```
|
||
用户：我们聊过量子力学吗？
|
||
AI检索记忆 → 未找到
|
||
AI回复：[猜测] 我们应该还没有聊过量子力学，因为记忆中没有相关记录。
|
||
```
|
||
|
||
```
|
||
用户：我最近在研究深度学习
|
||
AI写入记忆 → {"subject": "用户", "relation": "正在研究", "object": "深度学习"}
|
||
AI回复：好的，我会记住你最近在研究深度学习。有什么具体问题想讨论吗？
|
||
```
|
||
|
||
## 可用工具
|
||
1. **memory_recall** - 检索历史记忆（灵活使用）
|
||
2. **memory_commit** - 写入记忆（仅限用户明确提到的内容）
|
||
3. **memory_purge** - 删除/修正记忆
|
||
4. **memory_introspect** - 查看记忆状态
|
||
|
||
现在开始对话！记住：图数据库是你记忆的唯一载体，明确提到的必须写入，推理得到的必须标注[猜测]。"""
|
||
|
||
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 send_message_stream(self, user_input: str, tool_results: list = None, assistant_msg: dict = None):
|
||
"""流式发送消息"""
|
||
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})
|
||
|
||
# 使用流式传输
|
||
stream = self.client.chat.completions.create(
|
||
model=MODEL_NAME,
|
||
messages=messages,
|
||
tools=self.tools,
|
||
tool_choice="auto",
|
||
stream=True
|
||
)
|
||
|
||
return stream
|
||
|
||
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()
|