'commit'

dsLightRag/Config/Config.py

@@ -100,3 +100,10 @@ LIBLIB_SECRETKEY="PUe8QTRG9i0G9EbpedHmIpLQ0FyxoYY9"
 XF_APPID="5b83f8d6"
 XF_APISECRET="604fa6cb9c5ab664a0d153fe0ccc6802"
 XF_APIKEY="5beb887923204000bfcb402046bb05a6"
+
+# 方舟模型专用API
+# https://console.volcengine.com/iam/keymanage/
+VOLC_ACCESSKEY="AKLTZjVlOGU1NzA1YWZkNDExMzkzYzY5YTNlOTRmMTMxODg"
+VOLC_SECRETKEY="WkdabU9UTXdNVEJpTmpWbE5HVTJZVGxtTnpWbU5XSTBaRGN5TW1NMk5tRQ=="
+# https://console.volcengine.com/ark/region:ark+cn-beijing/apiKey?apikey=%7B%7D
+VOLC_API_KEY= "f6150e6c-422a-4265-8b63-4d941b271220"

dsLightRag/JiMeng/Kit/FenJingTouGenerator.py

@@ -1,8 +1,9 @@
 import json
-import requests
 import logging
 
-from JiMeng.Kit.VolcanoConst import VOLCANO_API_KEY
+import requests
+
+from Config.Config import VOLC_API_KEY
 
 # 配置日志
 logger = logging.getLogger(__name__)
@@ -14,7 +15,7 @@ console_handler.setFormatter(formatter)
 logger.addHandler(console_handler)
 
 class FenJingTouGenerator:
-    def __init__(self, api_key=VOLCANO_API_KEY):
+    def __init__(self, api_key=VOLC_API_KEY):
         """
         初始化分镜头提示词生成器
         :param api_key: 火山引擎API密钥

dsLightRag/JiMeng/Kit/VolcanoConst.py

@@ -1,2 +0,0 @@
-# 火山API_KEY
-VOLCANO_API_KEY="f6150e6c-422a-4265-8b63-4d941b271220"

dsLightRag/Volcengine/VikingDBMemoryService.py

@@ -0,0 +1,195 @@
+"""
+pip install volcengine
+pip install --upgrade "volcengine-python-sdk[ark]"
+"""
+import json
+import threading
+
+from volcengine.ApiInfo import ApiInfo
+from volcengine.Credentials import Credentials
+from volcengine.base.Service import Service
+from volcengine.ServiceInfo import ServiceInfo
+from volcengine.auth.SignerV4 import SignerV4
+from volcengine.base.Request import Request
+
+
+class VikingDBMemoryException(Exception):
+    def __init__(self, code, request_id, message=None):
+        self.code = code
+        self.request_id = request_id
+        self.message = "{}, code:{}，request_id:{}".format(message, self.code, self.request_id)
+
+    def __str__(self):
+        return self.message
+
+
+class VikingDBMemoryService(Service):
+    _instance_lock = threading.Lock()
+
+    def __new__(cls, *args, **kwargs):
+        if not hasattr(VikingDBMemoryService, "_instance"):
+            with VikingDBMemoryService._instance_lock:
+                if not hasattr(VikingDBMemoryService, "_instance"):
+                    VikingDBMemoryService._instance = object.__new__(cls)
+        return VikingDBMemoryService._instance
+
+    def __init__(self, host="api-knowledgebase.mlp.cn-beijing.volces.com", region="cn-beijing", ak="", sk="",
+                 sts_token="", scheme='https',
+                 connection_timeout=30, socket_timeout=30):
+        self.service_info = VikingDBMemoryService.get_service_info(host, region, scheme, connection_timeout,
+                                                                   socket_timeout)
+        self.api_info = VikingDBMemoryService.get_api_info()
+        super(VikingDBMemoryService, self).__init__(self.service_info, self.api_info)
+        if ak:
+            self.set_ak(ak)
+        if sk:
+            self.set_sk(sk)
+        if sts_token:
+            self.set_session_token(session_token=sts_token)
+        try:
+            self.get_body("Ping", {}, json.dumps({}))
+        except Exception as e:
+            raise VikingDBMemoryException(1000028, "missed", "host or region is incorrect".format(str(e))) from None
+
+    def setHeader(self, header):
+        api_info = VikingDBMemoryService.get_api_info()
+        for key in api_info:
+            for item in header:
+                api_info[key].header[item] = header[item]
+        self.api_info = api_info
+
+    @staticmethod
+    def get_service_info(host, region, scheme, connection_timeout, socket_timeout):
+        service_info = ServiceInfo(host, {"Host": host},
+                                   Credentials('', '', 'air', region), connection_timeout, socket_timeout,
+                                   scheme=scheme)
+        return service_info
+
+    @staticmethod
+    def get_api_info():
+        api_info = {
+            "CreateCollection": ApiInfo("POST", "/api/memory/collection/create", {}, {},
+                                        {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+            "GetCollection": ApiInfo("POST", "/api/memory/collection/info", {}, {},
+                                     {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+            "DropCollection": ApiInfo("POST", "/api/memory/collection/delete", {}, {},
+                                      {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+            "UpdateCollection": ApiInfo("POST", "/api/memory/collection/update", {}, {},
+                                        {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+
+            "SearchMemory": ApiInfo("POST", "/api/memory/search", {}, {},
+                                    {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+            "AddSession": ApiInfo("POST", "/api/memory/session/add", {}, {},
+                                  {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+
+            "Ping": ApiInfo("GET", "/api/memory/ping", {}, {},
+                            {'Accept': 'application/json', 'Content-Type': 'application/json'}),
+        }
+        return api_info
+
+    def get_body(self, api, params, body):
+        if not (api in self.api_info):
+            raise Exception("no such api")
+        api_info = self.api_info[api]
+        r = self.prepare_request(api_info, params)
+        r.headers['Content-Type'] = 'application/json'
+        r.headers['Traffic-Source'] = 'SDK'
+        r.body = body
+
+        SignerV4.sign(r, self.service_info.credentials)
+
+        url = r.build()
+        resp = self.session.get(url, headers=r.headers, data=r.body,
+                                timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout))
+        if resp.status_code == 200:
+            return json.dumps(resp.json())
+        else:
+            raise Exception(resp.text.encode("utf-8"))
+
+    def get_body_exception(self, api, params, body):
+        try:
+            res = self.get_body(api, params, body)
+        except Exception as e:
+            try:
+                res_json = json.loads(e.args[0].decode("utf-8"))
+            except:
+                raise VikingDBMemoryException(1000028, "missed", "json load res error, res:{}".format(str(e))) from None
+            code = res_json.get("code", 1000028)
+            request_id = res_json.get("request_id", 1000028)
+            message = res_json.get("message", None)
+
+            raise VikingDBMemoryException(code, request_id, message)
+
+        if res == '':
+            raise VikingDBMemoryException(1000028, "missed",
+                                          "empty response due to unknown error, please contact customer service") from None
+        return res
+
+    def get_exception(self, api, params):
+        try:
+            res = self.get(api, params)
+        except Exception as e:
+            try:
+                res_json = json.loads(e.args[0].decode("utf-8"))
+            except:
+                raise VikingDBMemoryException(1000028, "missed", "json load res error, res:{}".format(str(e))) from None
+            code = res_json.get("code", 1000028)
+            request_id = res_json.get("request_id", 1000028)
+            message = res_json.get("message", None)
+            raise VikingDBMemoryException(code, request_id, message)
+        if res == '':
+            raise VikingDBMemoryException(1000028, "missed",
+                                          "empty response due to unknown error, please contact customer service") from None
+        return res
+
+    def create_collection(self, collection_name, description="", custom_event_type_schemas=[],
+                          custom_entity_type_schemas=[], builtin_event_types=[], builtin_entity_types=[]):
+        params = {
+            "CollectionName": collection_name, "Description": description,
+            "CustomEventTypeSchemas": custom_event_type_schemas, "CustomEntityTypeSchemas": custom_entity_type_schemas,
+            "BuiltinEventTypes": builtin_event_types, "BuiltinEntityTypes": builtin_entity_types,
+        }
+        res = self.json("CreateCollection", {}, json.dumps(params))
+        return json.loads(res)
+
+    def get_collection(self, collection_name):
+        params = {"CollectionName": collection_name}
+        res = self.json("GetCollection", {}, json.dumps(params))
+        return json.loads(res)
+
+    def drop_collection(self, collection_name):
+        params = {"CollectionName": collection_name}
+        res = self.json("DropCollection", {}, json.dumps(params))
+        return json.loads(res)
+
+    def update_collection(self, collection_name, custom_event_type_schemas=[], custom_entity_type_schemas=[],
+                          builtin_event_types=[], builtin_entity_types=[]):
+        params = {
+            "CollectionName": collection_name,
+            "CustomEventTypeSchemas": custom_event_type_schemas, "CustomEntityTypeSchemas": custom_entity_type_schemas,
+            "BuiltinEventTypes": builtin_event_types, "BuiltinEntityTypes": builtin_entity_types,
+        }
+        res = self.json("UpdateCollection", {}, json.dumps(params))
+        return json.loads(res)
+
+    def search_memory(self, collection_name, query, filter, limit=10):
+        params = {
+            "collection_name": collection_name,
+            "query": query,
+            "limit": limit,
+            "filter": filter,
+        }
+        res = self.json("SearchMemory", {}, json.dumps(params))
+        return json.loads(res)
+
+    def add_session(self, collection_name, session_id, messages, metadata, entities=None):
+        params = {
+            "collection_name": collection_name,
+            "session_id": session_id,
+            "messages": messages,
+            "metadata": metadata,
+        }
+        if entities is not None:
+            params["entities"] = entities
+        res = self.json("AddSession", {}, json.dumps(params))
+        return json.loads(res)

dsLightRag/Volcengine/chat.py

@@ -0,0 +1,211 @@
+import json
+import os
+import time
+from dotenv import load_dotenv
+from volcenginesdkarkruntime import Ark
+
+from Config.Config import VOLC_ACCESSKEY, VOLC_SECRETKEY, VOLC_API_KEY
+from VikingDBMemoryService import VikingDBMemoryService
+
+
+def initialize_services():
+    load_dotenv()
+    ak = VOLC_ACCESSKEY
+    sk = VOLC_SECRETKEY
+    ark_api_key = VOLC_API_KEY
+
+    if not all([ak, sk, ark_api_key]):
+        raise ValueError("必须在环境变量中设置 VOLC_ACCESSKEY, VOLC_SECRETKEY, 和 ARK_API_KEY。")
+
+    memory_service = VikingDBMemoryService(ak=ak, sk=sk)
+    llm_client = Ark(
+        base_url="https://ark.cn-beijing.volces.com/api/v3",
+        api_key=ark_api_key,
+    )
+    return memory_service, llm_client
+
+
+def ensure_collection_exists(memory_service, collection_name):
+    """检查记忆集合是否存在，如果不存在则创建。"""
+    try:
+        memory_service.get_collection(collection_name)
+        print(f"记忆集合 '{collection_name}' 已存在。")
+    except Exception as e:
+        error_message = str(e)
+        if "collection not exist" in error_message:
+            print(f"记忆集合 '{collection_name}' 未找到，正在创建...")
+            try:
+                memory_service.create_collection(
+                    collection_name=collection_name,
+                    description="中文情感陪伴场景测试",
+                    builtin_event_types=["sys_event_v1", "sys_profile_collect_v1"],
+                    builtin_entity_types=["sys_profile_v1"]
+                )
+                print(f"记忆集合 '{collection_name}' 创建成功。")
+                print("等待集合准备就绪...")
+            except Exception as create_e:
+                print(f"创建集合失败: {create_e}")
+                raise
+        else:
+            print(f"检查集合时出错: {e}")
+            raise
+
+
+def search_relevant_memories(memory_service, collection_name, user_id, query):
+    """搜索与用户查询相关的记忆，并在索引构建中时重试。"""
+    print(f"正在搜索与 '{query}' 相关的记忆...")
+    retry_attempt = 0
+    while True:
+        try:
+            filter_params = {
+                "user_id": [user_id],
+                "memory_type": ["sys_event_v1", "sys_profile_v1"]
+            }
+            response = memory_service.search_memory(
+                collection_name=collection_name,
+                query=query,
+                filter=filter_params,
+                limit=3
+            )
+
+            memories = []
+            if response.get('data', {}).get('count', 0) > 0:
+                for result in response['data']['result_list']:
+                    if 'memory_info' in result and result['memory_info']:
+                        memories.append({
+                            'memory_info': result['memory_info'],
+                            'score': result['score']
+                        })
+
+            if memories:
+                if retry_attempt > 0:
+                    print("重试后搜索成功。")
+                print(f"找到 {len(memories)} 条相关记忆:")
+                for i, memory in enumerate(memories, 1):
+                    print(
+                        f"  {i}. (相关度: {memory['score']:.3f}): {json.dumps(memory['memory_info'], ensure_ascii=False, indent=2)}")
+            else:
+                print("未找到相关记忆。")
+            return memories
+
+        except Exception as e:
+            error_message = str(e)
+            if "1000023" in error_message:
+                retry_attempt += 1
+                print(f"记忆索引正在构建中。将在60秒后重试... (尝试次数 {retry_attempt})")
+                time.sleep(60)
+            else:
+                print(f"搜索记忆时出错 (不可重试): {e}")
+                return []
+
+
+def handle_conversation_turn(memory_service, llm_client, collection_name, user_id, user_message, conversation_history):
+    """处理一轮对话，包括记忆搜索和LLM响应。"""
+    print("\n" + "=" * 60)
+    print(f"用户: {user_message}")
+
+    relevant_memories = search_relevant_memories(memory_service, collection_name, user_id, user_message)
+
+    system_prompt = "你是一个富有同情心、善于倾听的AI伙伴，拥有长期记忆能力。你的目标是为用户提供情感支持和温暖的陪伴。"
+    if relevant_memories:
+        memory_context = "\n".join(
+            [f"- {json.dumps(mem['memory_info'], ensure_ascii=False)}" for mem in relevant_memories])
+        system_prompt += f"\n\n这是我们过去的一些对话记忆，请参考：\n{memory_context}\n\n请利用这些信息来更好地理解和回应用户。"
+
+    print("AI正在思考...")
+
+    try:
+        messages = [{"role": "system", "content": system_prompt}] + conversation_history + [
+            {"role": "user", "content": user_message}]
+        completion = llm_client.chat.completions.create(
+            model="doubao-seed-1-6-flash-250715",
+            messages=messages
+        )
+        assistant_reply = completion.choices[0].message.content
+    except Exception as e:
+        print(f"LLM调用失败: {e}")
+        assistant_reply = "抱歉，我现在有点混乱，无法回应。我们可以稍后再聊吗？"
+
+    print(f"伙伴: {assistant_reply}")
+
+    conversation_history.extend([
+        {"role": "user", "content": user_message},
+        {"role": "assistant", "content": assistant_reply}
+    ])
+    return assistant_reply
+
+
+def archive_conversation(memory_service, collection_name, user_id, assistant_id, conversation_history, topic_name):
+    """将对话历史归档到记忆数据库。"""
+    if not conversation_history:
+        print("没有对话可以归档。")
+        return False
+
+    print(f"\n正在归档关于 '{topic_name}' 的对话...")
+    session_id = f"{topic_name}_{int(time.time())}"
+    metadata = {
+        "default_user_id": user_id,
+        "default_assistant_id": assistant_id,
+        "time": int(time.time() * 1000)
+    }
+
+    try:
+        memory_service.add_session(
+            collection_name=collection_name,
+            session_id=session_id,
+            messages=conversation_history,
+            metadata=metadata
+        )
+        print(f"对话已成功归档，会话ID: {session_id}")
+        print("正在等待记忆索引更新...")
+        return True
+    except Exception as e:
+        print(f"归档对话失败: {e}")
+        return False
+
+
+def main():
+    print("开始端到端记忆测试...")
+
+    try:
+        memory_service, llm_client = initialize_services()
+        collection_name = "emotional_support"
+        user_id = "xiaoming"
+        assistant_id = "assistant"
+        ensure_collection_exists(memory_service, collection_name)
+    except Exception as e:
+        print(f"初始化失败: {e}")
+        return
+
+    print("\n--- 阶段 1: 初始对话 ---")
+    initial_conversation_history = []
+    handle_conversation_turn(
+        memory_service, llm_client, collection_name, user_id,
+        "你好，我是小明，今年18岁，但压力好大。",
+        initial_conversation_history
+    )
+    handle_conversation_turn(
+        memory_service, llm_client, collection_name, user_id,
+        "马上就要高考了，家里人的期待好高。",
+        initial_conversation_history
+    )
+
+    print("\n--- 阶段 2: 归档记忆 ---")
+    archive_conversation(
+        memory_service, collection_name, user_id, assistant_id,
+        initial_conversation_history, "study_stress_discussion"
+    )
+
+    print("\n--- 阶段 3: 验证记忆 ---")
+    verification_conversation_history = []
+    handle_conversation_turn(
+        memory_service, llm_client, collection_name, user_id,
+        "我最近很焦虑，不知道该怎么办。",
+        verification_conversation_history
+    )
+
+    print("\n端到端记忆测试完成！")
+
+
+if __name__ == "__main__":
+    main()