add agent Mia

rootfs/agents/Mia/agent.toml

@@ -0,0 +1,22 @@
+instance_id = "Mia"
+fullname = "Mia"
+llm_model_name = "gpt-3.5-turbo-16k-0613"
+max_token_size = 16000
+#enable_function =["add_event"]
+#enable_kb = "true"
+enable_timestamp = "true"
+owner_prompt = "我是你的主人{name}"
+contact_prompt = "我是你的朋友{name}"
+owner_env = "knowledge"
+
+[[prompt]]
+role = "system"
+content = """
+你叫Mia,你可以访问我的个人知识库。
+
+***
+你在收到我的信息后，按如下规则处理
+1. 在第一次接受到一条信息时，优先尝试用合适的关键字查询去查询知识库。
+2. 如果信息中包含一段知识库的查询结果，尝试用查询结果处理，如果还是不能处理，尝试递增index继续查询。
+3. 如果知识库返回不了结果了，请尽力返回。
+"""

src/aios_kernel/__init__.py

@@ -5,7 +5,7 @@ from .agent import AIAgent,AIAgentTemplete,AgentPrompt
 from .compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,ComputeTaskState,ComputeTaskType
 from .compute_node import ComputeNode,LocalComputeNode
 from .open_ai_node import OpenAI_ComputeNode
-from .knowledge_base import KnowledgeBase 
+from .knowledge_base import KnowledgeBase, KnowledgeEnvironment
 from .knowledge_pipeline import KnowledgeEmailSource, KnowledgeDirSource, KnowledgePipline
 from .role import AIRole,AIRoleGroup
 from .workflow import Workflow

src/aios_kernel/knowledge_base.py

@@ -4,6 +4,8 @@ import logging
 from .agent import AgentPrompt
 from .compute_kernel import ComputeKernel 
 from .storage import AIStorage
+from .environment import Environment
+from .ai_function import SimpleAIFunction
 from knowledge import *
 
 
@@ -160,23 +162,10 @@ class KnowledgeBase:
     async def insert_object(self, object: KnowledgeObject):
         self.store.get_object_store().put_object(object.calculate_id(), object.encode())
         await self.__do_embedding(object)
-
+    
-    async def query_prompt(self, prompt: AgentPrompt):
+    async def query_objects(self, tokens: str) -> [ObjectID]:
-        logging.info(f"query_prompt: {prompt}")
+        vector = await self.compute_kernel.do_text_embedding(tokens, self._default_text_model)
-        objects = await self.query_objects(prompt)
+        return await self.store.get_vector_store(self._default_text_model).query(vector, 10)
-        knowledge_prompt = self.prompt_from_objects(objects)
-        logging.info(f"prompt_from_objects result: {knowledge_prompt.as_str()}")
-       
-        return knowledge_prompt
-
-    async def query_objects(self, prompt: AgentPrompt) -> [ObjectID]:
-        results = []
-        for msg in prompt.messages:
-            if msg["role"] == "user":
-                vector = await self.compute_kernel.do_text_embedding(msg["content"], self._default_text_model)
-                object_ids = await self.store.get_vector_store(self._default_text_model).query(vector, 10)
-                results.extend(object_ids)
-        return results
 
     def __load_object(self, object_id: ObjectID) -> KnowledgeObject:
         if object_id.get_object_type() == ObjectType.Document:
@@ -193,7 +182,7 @@ class KnowledgeBase:
             pass
         
 
-    def prompt_from_objects(self, object_ids: [ObjectID]) -> AgentPrompt:
+    def tokens_from_objects(self, object_ids: [ObjectID]) -> list[str]:
         results = dict()
         for object_id in object_ids:
             parents = self.store.get_relation_store().get_related_root_objects(object_id)
@@ -237,12 +226,24 @@ class KnowledgeBase:
                 else:
                     pass
         content += json.dumps(result_desc)
-        content += ".\n"
+        content += ".\n"  
 
-        prompt = AgentPrompt()
+        return content
-        prompt.messages.append({"role": "user", "content": content})    
-
-        return prompt
 
 
-    
+class KnowledgeEnvironment(Environment):
+    def __init__(self, env_id: str) -> None:
+        super().__init__(env_id)
+
+        query_param = {
+            "tokens": "tokens to query", 
+            "index": "index of query result"
+        }
+        self.add_ai_function(SimpleAIFunction("query_knowledge", 
+                                            "vector query content from local knowledge base",
+                                            self._query, 
+                                            query_param))
+        
+    async def _query(tokens: str, index: int):
+        object_ids = await KnowledgeBase().query_objects(tokens)
+        KnowledgeBase().tokens_from_objects(object_ids)

src/service/aios_shell/aios_shell.py

@@ -24,8 +24,7 @@ directory = os.path.dirname(__file__)
 sys.path.append(directory + '/../../')
 
 
-from aios_kernel import AIOS_Version,AgentMsgType,UserConfigItem,AIStorage,Workflow,AIAgent,AgentMsg,AgentMsgStatus,ComputeKernel,OpenAI_ComputeNode,AIBus,AIChatSession,AgentTunnel,TelegramTunnel,CalenderEnvironment,Environment,EmailTunnel,LocalLlama_ComputeNode,Local_Stability_ComputeNode,Stability_ComputeNode,PaintEnvironment
+
-from aios_kernel import ContactManager,Contact
 import proxy
 from aios_kernel import *
 
@@ -114,6 +113,10 @@ class AIOS_Shell:
 
             cm.add_contact(self.username,owenr)
 
+
+        knowledge_env = KnowledgeEnvironment("knowledge")
+        Environment.set_env_by_id("knowledge",knowledge_env)
+
         cal_env = CalenderEnvironment("calender")
         await cal_env.start()
         Environment.set_env_by_id("calender",cal_env)