feat: 本地mem0记忆功能，通过mem0+milvus向量数据库实现

main/manager-api/src/main/resources/db/changelog/202506181501.sql

@@ -0,0 +1,12 @@
+INSERT INTO `ai_model_config` (`id`, `model_type`, `model_code`, `model_name`, `is_default`,
+                                                `is_enabled`, `config_json`, `doc_link`, `remark`, `sort`, `creator`,
+                                                `create_date`, `updater`, `update_date`)
+VALUES ('Memory_mem0_milvus', 'memory', 'mem0_milvus', 'Mem0AI本地记忆', 0, 1,
+        '{\"llm\": {\"config\": {\"model\": \"qwen-plus\", \"top_p\": 1, \"api_key\": \"<your-apikey>\", \"max_tokens\": 2000, \"temperature\": 0.2, \"openai_base_url\": \"https://dashscope.aliyuncs.com/compatible-mode/v1\"}, \"provider\": \"openai\"}, \"type\": \"mem0_milvus\", \"embedder\": {\"config\": {\"model\": \"text-embedding-v4\", \"api_key\": \"<your-apikey>\", \"openai_base_url\": \"https://dashscope.aliyuncs.com/compatible-mode/v1/\"}, \"provider\": \"openai\"}, \"vector_store\": {\"config\": {\"url\": \"http://127.0.0.1:19530\", \"collection_name\": \"mem0_collection\", \"embedding_model_dims\": 1024}, \"provider\": \"milvus\"}}',
+        'https://app.mem0.ai/dashboard/get-started', 'Mem0AI记忆配置说明：\n1. 配置llm/embedder/vector_store\n', 3, NULL,
+        NULL, 1, '2025-06-18 17:02:49');
+INSERT INTO `ai_model_provider` (`id`, `model_type`, `provider_code`, `name`, `fields`, `sort`,
+                                                  `creator`, `create_date`, `updater`, `update_date`)
+VALUES ('SYSTEM_Memory_mem0_milvus', 'Memory', 'mem0_milvus', 'mem0ai本地记忆',
+        '[{\"key\": \"llm\", \"type\": \"dict\", \"label\": \"llm\", \"default\": \"\", \"editing\": false, \"selected\": false}, {\"key\": \"embedder\", \"type\": \"dict\", \"label\": \"embedder\", \"default\": \"\", \"editing\": false, \"selected\": false}, {\"key\": \"vector_store\", \"type\": \"dict\", \"label\": \"vector_store\", \"default\": \"\", \"editing\": false, \"selected\": false}]',
+        0, 1, '2025-06-18 14:00:57', 1, '2025-06-18 14:00:57');

main/manager-api/src/main/resources/db/changelog/db.changelog-master.yaml

@@ -226,6 +226,13 @@ databaseChangeLog:
         - sqlFile:
             encoding: utf8
             path: classpath:db/changelog/202506191643.sql
+  - changeSet:
+      id: 202506181501
+      author: chan
+      changes:
+        - sqlFile:
+            encoding: utf8
+            path: classpath:db/changelog/202506181501.sql
   - changeSet:
       id: 202506251620
       author: Tink
@@ -239,4 +246,4 @@ databaseChangeLog:
       changes:
         - sqlFile:
             encoding: utf8
-            path: classpath:db/changelog/202506261637.sql
+            path: classpath:db/changelog/202506261637.sql

main/xiaozhi-server/config.yaml

@@ -239,7 +239,33 @@ Memory:
     # 如果这里不填，则会默认使用selected_module.LLM的模型作为意图识别的思考模型
     # 如果你的不想使用selected_module.LLM记忆存储，这里最好使用独立的LLM作为意图识别，例如使用免费的ChatGLMLLM
     llm: ChatGLMLLM
-
+  mem0_milvus:
+    # 本地 mem0+milvus 记忆功能
+    type: mem0_milvus
+    llm:
+      provider: openai
+      config:
+        model: qwen-plus
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
+        # https://bailian.console.aliyun.com/?tab=model#/api-key
+        api_key: 阿里云百炼平台api_key
+        temperature: 0.2
+        max_tokens: 2000
+        top_p: 1.0
+    embedder:
+      provider: openai
+      config:
+        model: text-embedding-v4
+        api_key: 阿里云百炼平台api_key
+        openai_base_url: https://dashscope.aliyuncs.com/compatible-mode/v1/
+    vector_store:
+      #可以使用容器本地部署 milvus 向量数据库
+      provider: milvus
+      config:
+        # 使用你的 milvus 向量数据库地址
+        url: http://127.0.0.1:19530
+        collection_name: mem0_collection
+        embedding_model_dims: 1024
 ASR:
   FunASR:
     type: fun_local

main/xiaozhi-server/core/providers/memory/mem0_milvus/mem0_milvus.py

@@ -0,0 +1,78 @@
+from ..base import MemoryProviderBase, logger
+from mem0 import Memory
+
+TAG = __name__
+
+
+class MemoryProvider(MemoryProviderBase):
+    def __init__(self, config, summary_memory=None):
+        super().__init__(config)
+
+        try:
+            self.client = Memory.from_config(self.config)
+            self.use_mem0local = True
+            logger.bind(tag=TAG).info("成功使用Mem0_milvus服务")
+        except Exception as e:
+            logger.bind(tag=TAG).error(f"Mem0配置错误: {str(e)}")
+            self.use_mem0local = False
+
+    async def save_memory(self, msgs):
+        if not self.use_mem0local:
+            return None
+        if len(msgs) < 2:
+            return None
+
+        try:
+            # Format the content as a message list for mem0
+            messages = [
+                {"role": message.role, "content": message.content}
+                for message in msgs if message.role != "system"
+            ]
+            result = self.client.add(messages, user_id=self.role_id)
+            logger.bind(tag=TAG).info(f"Save memory result: {result}")
+        except Exception as e:
+            print(e)
+            logger.bind(tag=TAG).error(f"保存记忆失败: {str(e)}")
+            return None
+
+    async def query_memory(self, query: str) -> str:
+        if not self.use_mem0local:
+            return ""
+        try:
+            results = self.client.search(
+                query,
+                user_id=self.role_id
+            )
+            logger.bind(tag=TAG).info(f"get memory result: {results}")
+            if not results or 'results' not in results:
+                return ""
+
+            # Format each memory entry with its update time up to minutes
+            memories = []
+            for entry in results['results']:
+                timestamp = entry.get('updated_at', '') or entry.get('created_at', '')
+                if timestamp:
+                    try:
+                        # Parse and reformat the timestamp
+                        dt = timestamp.split('.')[0]  # Remove milliseconds
+                        formatted_time = dt.replace('T', ' ')
+                    except:
+                        formatted_time = timestamp
+                memory = entry.get('memory', '')
+                if timestamp and memory:
+                    # Store tuple of (timestamp, formatted_string) for sorting
+                    memories.append((timestamp, f"[{formatted_time}] {memory}"))
+
+            # Sort by timestamp in descending order (newest first)
+            memories.sort(key=lambda x: x[0], reverse=True)
+
+            # Extract only the formatted strings
+            memories_str = "\n".join(f"- {memory[1]}" for memory in memories)
+            logger.bind(tag=TAG).debug(f"Query results: {memories_str}")
+            return memories_str
+        except Exception as e:
+            if "collection not found" in e:
+                return ""
+            else:
+                logger.bind(tag=TAG).error(f"查询记忆失败: {str(e)}")
+            return ""

main/xiaozhi-server/requirements.txt

@@ -33,4 +33,5 @@ markitdown==0.1.1
 mcp-proxy==0.8.0
 PyJWT==2.8.0
 psutil==7.0.0
-portalocker==2.10.1
+portalocker==2.10.1
+pymilvus==2.5.11