Merge pull request #6 from codefuse-ai/pr_version_update

[bugfix]v0.0.4 chat and codechat bug

Author: lightislost

.gitignore

@@ -13,4 +13,5 @@ package.sh
 setup_test.py
 build
 *egg-info
-dist
+dist
+.ipynb_checkpoints

docs/overview/o1.muagent.md

@@ -21,7 +21,7 @@ aliases:
 本项目的Mutli-Agent框架汲取兼容了多个框架的优秀设计，比如metaGPT中的消息池（message pool）、autogen中的代理选择器（agent selector）等。
 
 <div align=center>
-  <img src="/docs/resources/muagent_framework.png" alt="图片" style="width: 500px;  height:auto;">
+  <img src="/docs/resources/muAgent_framework.png" alt="图片" style="width: 500px;  height:auto;">
 </div>
 
 
@@ -42,7 +42,7 @@ aliases:
 1. BaseAgent：提供基础问答、工具使用、代码执行的功能，根据Prompt格式实现 输入 => 输出
 
 <div align=center>
-  <img src="/docs/resources/baseagent.png" alt="图片" style="width: 500px;  height:auto;">
+  <img src="/docs/resources/BaseAgent.png" alt="图片" style="width: 500px;  height:auto;">
 </div>
 
 2. ReactAgent：提供标准React的功能，根据问题实现当前任务

examples/muagent_examples/baseGroup_example.py

@@ -70,7 +70,6 @@
 source_file = 'D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/jupyter_work/employee_data.csv'
 shutil.copy(source_file, JUPYTER_WORK_PATH)
 
-
 # round-1
 query_content = "确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
 query = Message(

muagent/chat/agent_chat.py

@@ -79,6 +79,7 @@ def chat(
             model_name: str = Body("", description="llm模型名称"),
             temperature: float = Body(0.2, description=""),
             chat_index: str = "",
+            local_graph_path: str = "",
             **kargs
             ) -> Message:
 
@@ -122,7 +123,8 @@ def chat(
             code_engine_name=code_engine_name,
             score_threshold=score_threshold, top_k=top_k,
             history_node_list=history_node_list,
-            tools=tools
+            tools=tools,
+            local_graph_path=local_graph_path
         )
         # history memory mangemant
         history = Memory(messages=[
@@ -223,6 +225,7 @@ def achat(
             model_name: str = Body("", description="llm模型名称"),
             temperature: float = Body(0.2, description=""),
             chat_index: str = "",
+            local_graph_path: str = "",
             **kargs
             ) -> Message:
 
@@ -264,7 +267,8 @@ def achat(
             cb_search_type=cb_search_type,
             score_threshold=score_threshold, top_k=top_k,
             history_node_list=history_node_list,
-            tools=tools
+            tools=tools,
+            local_graph_path=local_graph_path
         )
         # history memory mangemant
         history = Memory(messages=[
@@ -292,7 +296,8 @@ def achat(
 
         def chat_iterator(message: Message, local_memory: Memory, isDetailed=False):
             step_content = local_memory.to_str_messages(content_key='step_content', filter_roles=["human"])
-            step_content = "\n\n".join([f"{v}" for parsed_output in local_memory.get_parserd_output_list()[1:] for k, v in parsed_output.items() if k not in ["Action Status"]])
+            step_content = "\n\n".join([f"{v}" for parsed_output in local_memory.get_parserd_output_list() for k, v in parsed_output.items() if k not in ["Action Status", "human", "user"]])
+            # logger.debug(f"{local_memory.get_parserd_output_list()}")
             final_content = message.role_content
             result = {
                 "answer": "",
@@ -311,7 +316,7 @@ def chat_iterator(message: Message, local_memory: Memory, isDetailed=False):
                     if node not in has_nodes:
                         related_nodes.append(node)
             result["related_nodes"] = related_nodes
-            
+
             # logger.debug(f"{result['figures'].keys()}, isDetailed: {isDetailed}")
             message_str = step_content
             if self.stream:

muagent/chat/code_chat.py

@@ -53,7 +53,7 @@ def check_service_status(self) -> BaseResponse:
             return BaseResponse(code=404, msg=f"未找到代码库 {self.engine_name}")
         return BaseResponse(code=200, msg=f"找到代码库 {self.engine_name}")
 
-    def _process(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig):
+    def _process(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, local_graph_path=""):
         '''process'''
 
         codes_res = search_code(query=query, cb_name=self.engine_name, code_limit=self.code_limit,
@@ -67,7 +67,8 @@ def _process(self, query: str, history: List[History], model, llm_config: LLMCon
                                 embed_model_path=embed_config.embed_model_path,
                                 embed_engine=embed_config.embed_engine,
                                 model_device=embed_config.model_device,
-                                embed_config=embed_config
+                                embed_config=embed_config,
+                                local_graph_path=local_graph_path
                                 )
 
         context = codes_res['context']
@@ -115,6 +116,7 @@ def chat(
             model_name: str = Body("", ),
             temperature: float = Body(0.5, ),
             model_device: str = Body("", ),
+            local_graph_path: str=Body(", "),
             **kargs
             ):
         params = locals()
@@ -127,9 +129,9 @@ def chat(
         self.local_doc_url = local_doc_url if isinstance(local_doc_url, bool) else local_doc_url.default
         self.request = request
         self.cb_search_type = cb_search_type
-        return self._chat(query, history, llm_config, embed_config, **kargs)
+        return self._chat(query, history, llm_config, embed_config, local_graph_path, **kargs)
 
-    def _chat(self, query: str, history: List[History], llm_config: LLMConfig, embed_config: EmbedConfig, **kargs):
+    def _chat(self, query: str, history: List[History], llm_config: LLMConfig, embed_config: EmbedConfig, local_graph_path: str, **kargs):
         history = [History(**h) if isinstance(h, dict) else h for h in history]
 
         service_status = self.check_service_status()
@@ -140,7 +142,7 @@ def chat_iterator(query: str, history: List[History]):
             # model = getChatModel()
             model = getChatModelFromConfig(llm_config)
 
-            result, content = self.create_task(query, history, model, llm_config, embed_config, **kargs)
+            result, content = self.create_task(query, history, model, llm_config, embed_config, local_graph_path, **kargs)
             # logger.info('result={}'.format(result))
             # logger.info('content={}'.format(content))
 
@@ -156,9 +158,9 @@ def chat_iterator(query: str, history: List[History]):
         return StreamingResponse(chat_iterator(query, history),
                                  media_type="text/event-stream")
 
-    def create_task(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig):
+    def create_task(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, local_graph_path: str):
         '''构建 llm 生成任务'''
-        chain, context, result = self._process(query, history, model, llm_config, embed_config)
+        chain, context, result = self._process(query, history, model, llm_config, embed_config, local_graph_path)
         logger.info('chain={}'.format(chain))
         try:
             content = chain({"context": context, "question": query})

muagent/codechat/code_analyzer/code_intepreter.py

@@ -54,9 +54,11 @@ def get_intepretation_batch(self, code_list):
             messages.append(message)
 
         try:
-            chat_ress = [chat_model(messages) for message in messages]
-        except:
+            chat_ress = [chat_model.predict(message) for message in messages]
+        except Exception as e:
+            logger.exception(f"{e}")
             chat_ress = chat_model.batch(messages)
+
         for chat_res, code in zip(chat_ress, code_list):
             try:
                 res[code] = chat_res.content

muagent/connector/memory_manager.py

@@ -590,19 +590,23 @@ def append_tools(self, tool_information: dict, chat_index: str, nodeid: str, use
         }
 
         for k, v in tool_map.items():
-            message = Message(
-                chat_index=chat_index,
-                message_index= f"{nodeid}-{uuid.uuid4()}",
-                user_name=user_name,
-                role_name = v["role_name"], # agent 名字，
-                role_type = v["role_type"], # agent 类型，默认assistant，可选observation
-                ## llm output
-                role_content = tool_information[k], # 输入
-                customed_kargs = {
-                    **{kk: vv for kk, vv in tool_information.items() 
-                        if kk in v.get("customed_keys", [])}
-                } # 存储docs、tool等信息
-            )
+            try:
+                message = Message(
+                    chat_index=chat_index,
+                    #message_index= f"{nodeid}-{uuid.uuid4()}",
+                    message_index= f"{nodeid}-{k}",
+                    user_name=user_name,
+                    role_name = v["role_name"], # agent 名字，
+                    role_type = v["role_type"], # agent 类型，默认assistant，可选observation
+                    ## llm output
+                    role_content = tool_information[k], # 输入
+                    customed_kargs = {
+                        **{kk: vv for kk, vv in tool_information.items() 
+                            if kk in v.get("customed_keys", [])}
+                    } # 存储docs、tool等信息
+                )
+            except:
+                pass
             self.append(message)
 
     def get_memory_pool(self, chat_index: str = "") -> Memory:
@@ -802,12 +806,16 @@ def tbasedoc2Memory(self, r_docs) -> Memory:
         for doc in r_docs.docs:
             tbase_message = {}
             for k, v in doc.__dict__.items():
+                if k in ["role_content", "input_query"]:
+                    tbase_message[k] = v
+                    continue
                 try:
                     v = json.loads(v)
                 except:
                     pass
 
                 tbase_message[k] = v
+
             message = Message(**tbase_message)
             memory.append(message)
 

muagent/service/cb_api.py

@@ -56,6 +56,7 @@ async def create_cb(zip_file,
                     temperature: bool = Body(..., examples=["samples"]),
                     model_device: bool = Body(..., examples=["samples"]),
                     embed_config: EmbedConfig = None,
+                    local_graph_path: str = '',
                     ) -> BaseResponse:
     logger.info('cb_name={}, zip_path={}, do_interpret={}'.format(cb_name, code_path, do_interpret))
 
@@ -74,7 +75,7 @@ async def create_cb(zip_file,
 
     try:
         logger.info('start build code base')
-        cbh = CodeBaseHandler(cb_name, code_path, embed_config=embed_config, llm_config=llm_config)
+        cbh = CodeBaseHandler(cb_name, code_path, embed_config=embed_config, llm_config=llm_config, local_graph_path=local_graph_path)
         vertices_num, edge_num, file_num = cbh.import_code(zip_file=zip_file, do_interpret=do_interpret)
         logger.info('build code base done')
 
@@ -100,6 +101,7 @@ async def delete_cb(
         temperature: bool = Body(..., examples=["samples"]),
         model_device: bool = Body(..., examples=["samples"]),
         embed_config: EmbedConfig = None,
+        local_graph_path: str="",
         ) -> BaseResponse:
     logger.info('cb_name={}'.format(cb_name))
     embed_config: EmbedConfig = EmbedConfig(**locals()) if embed_config is None else embed_config
@@ -119,7 +121,7 @@ async def delete_cb(
             shutil.rmtree(CB_ROOT_PATH + os.sep + cb_name)
 
             # delete from codebase
-            cbh = CodeBaseHandler(cb_name, embed_config=embed_config, llm_config=llm_config)
+            cbh = CodeBaseHandler(cb_name, embed_config=embed_config, llm_config=llm_config, local_graph_path=local_graph_path)
             cbh.delete_codebase(codebase_name=cb_name)
 
         except Exception as e:

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="codefuse-muagent",
-    version="0.0.2",
+    version="0.0.4",
     author="shanshi",
     author_email="[email protected]",
     description="A multi-agent framework that facilitates the rapid construction of collaborative teams of agents.",