[feature](coagent)<增加antflow兼容和增加coagent demo>

coagent/base_configs/env_config.py

@@ -26,9 +26,12 @@ JUPYTER_WORK_PATH = os.environ.get("JUPYTER_WORK_PATH", None) or os.path.join(ex
 WEB_CRAWL_PATH = os.environ.get("WEB_CRAWL_PATH", None) or os.path.join(executable_path, "knowledge_base")
 
 # NEBULA_DATA存储路径
-NELUBA_PATH = os.environ.get("NELUBA_PATH", None) or os.path.join(executable_path, "data/neluba_data")
+NEBULA_PATH = os.environ.get("NEBULA_PATH", None) or os.path.join(executable_path, "data/nebula_data")
 
-for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NELUBA_PATH]:
+# CHROMA 存储路径
+CHROMA_PERSISTENT_PATH = os.environ.get("CHROMA_PERSISTENT_PATH", None) or os.path.join(executable_path, "data/chroma_data")
+
+for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH]:
     if not os.path.exists(_path):
         os.makedirs(_path, exist_ok=True)
 
@@ -58,7 +61,8 @@ NEBULA_GRAPH_SERVER = {
 }
 
 # CHROMA CONFIG
-CHROMA_PERSISTENT_PATH = '/home/user/chatbot/data/chroma_data'
+# CHROMA_PERSISTENT_PATH = '/home/user/chatbot/data/chroma_data'
+# CHROMA_PERSISTENT_PATH = '/Users/bingxu/Desktop/工作/大模型/chatbot/codefuse-chatbot-antcode/data/chroma_data'
 
 
 # 默认向量库类型。可选：faiss, milvus, pg.

coagent/chat/base_chat.py

@@ -7,7 +7,7 @@ from langchain import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.prompts.chat import ChatPromptTemplate
 
-from coagent.llm_models import getChatModel, getChatModelFromConfig
+from coagent.llm_models import getChatModelFromConfig
 from coagent.chat.utils import History, wrap_done
 from coagent.llm_models.llm_config import LLMConfig, EmbedConfig
 # from configs.model_config import (llm_model_dict, LLM_MODEL, VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD)

coagent/chat/code_chat.py

@@ -22,7 +22,7 @@ from coagent.connector.configs.prompts import CODE_PROMPT_TEMPLATE
 from coagent.chat.utils import History, wrap_done
 from coagent.utils import BaseResponse
 from .base_chat import Chat
-from coagent.llm_models import getChatModel, getChatModelFromConfig
+from coagent.llm_models import getChatModelFromConfig
 
 from coagent.llm_models.llm_config import LLMConfig, EmbedConfig
 
@@ -67,6 +67,7 @@ class CodeChat(Chat):
                                 embed_model_path=embed_config.embed_model_path,
                                 embed_engine=embed_config.embed_engine,
                                 model_device=embed_config.model_device,
+                                embed_config=embed_config
                                 )
 
         context = codes_res['context']

coagent/codechat/code_analyzer/code_intepreter.py

@@ -12,7 +12,7 @@ from langchain.schema import (
 
 # from configs.model_config import CODE_INTERPERT_TEMPLATE
 from coagent.connector.configs.prompts import CODE_INTERPERT_TEMPLATE
-from coagent.llm_models.openai_model import getChatModel, getChatModelFromConfig
+from coagent.llm_models.openai_model import getChatModelFromConfig
 from coagent.llm_models.llm_config import LLMConfig
 
 
@@ -53,9 +53,15 @@ class CodeIntepreter:
             message = CODE_INTERPERT_TEMPLATE.format(code=code)
             messages.append(message)
 
+        try:
+            chat_ress = [chat_model(messages) for message in messages]
+        except:
             chat_ress = chat_model.batch(messages)
         for chat_res, code in zip(chat_ress, code_list):
+            try:
                 res[code] = chat_res.content
+            except:
+                res[code] = chat_res
         return res
 
 

coagent/codechat/code_crawler/dir_crawler.py

@@ -27,7 +27,7 @@ class DirCrawler:
         logger.info(java_file_list)
 
         for java_file in java_file_list:
-            with open(java_file) as f:
+            with open(java_file, encoding="utf-8") as f:
                 java_code = ''.join(f.readlines())
                 java_code_dict[java_file] = java_code
         return java_code_dict

coagent/codechat/code_search/code_search.py

@@ -5,6 +5,7 @@
 @time: 2023/11/21 下午2:35
 @desc:
 '''
+import json
 import time
 from loguru import logger
 from collections import defaultdict
@@ -15,7 +16,7 @@ from coagent.db_handler.vector_db_handler.chroma_handler import ChromaHandler
 from coagent.codechat.code_search.cypher_generator import CypherGenerator
 from coagent.codechat.code_search.tagger import Tagger
 from coagent.embeddings.get_embedding import get_embedding
-from coagent.llm_models.llm_config import LLMConfig
+from coagent.llm_models.llm_config import LLMConfig, EmbedConfig
 
 
 # from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL
@@ -29,7 +30,8 @@ MAX_DISTANCE = 1000
 
 
 class CodeSearch:
-    def __init__(self, llm_config: LLMConfig, nh: NebulaHandler, ch: ChromaHandler, limit: int = 3):
+    def __init__(self, llm_config: LLMConfig, nh: NebulaHandler, ch: ChromaHandler, limit: int = 3,
+                 local_graph_file_path: str = ''):
         '''
         init
         @param nh: NebulaHandler
@@ -37,7 +39,13 @@ class CodeSearch:
         @param limit: limit of result
         '''
         self.llm_config = llm_config
+
         self.nh = nh
+
+        if not self.nh:
+            with open(local_graph_file_path, 'r') as f:
+                self.graph = json.load(f)
+
         self.ch = ch
         self.limit = limit
 
@@ -51,7 +59,7 @@ class CodeSearch:
         tag_list = tagger.generate_tag_query(query)
         logger.info(f'query tag={tag_list}')
 
-        # get all verticex
+        # get all vertices
         vertex_list = self.nh.get_vertices().get('v', [])
         vertex_vid_list = [i.as_node().get_id().as_string() for i in vertex_list]
 
@@ -81,7 +89,7 @@ class CodeSearch:
         # get most prominent package tag
         package_score_dict = defaultdict(lambda: 0)
 
-        for vertex, score in vertex_score_dict.items():
+        for vertex, score in vertex_score_dict_final.items():
             if '#' in vertex:
                 # get class name first
                 cypher = f'''MATCH (v1:class)-[e:contain]->(v2) WHERE id(v2) == '{vertex}' RETURN id(v1) as id;'''
@@ -111,6 +119,53 @@ class CodeSearch:
         logger.info(f'ids={ids}')
         chroma_res = self.ch.get(ids=ids, include=['metadatas'])
 
+        for vertex, score in package_score_tuple:
+            index = chroma_res['result']['ids'].index(vertex)
+            code_text = chroma_res['result']['metadatas'][index]['code_text']
+            res.append({
+                "vertex": vertex,
+                "code_text": code_text}
+            )
+            if len(res) >= self.limit:
+                break
+        # logger.info(f'retrival code={res}')
+        return res
+
+    def search_by_tag_by_graph(self, query: str):
+        '''
+        search code by tag with graph
+        @param query:
+        @return:
+        '''
+        tagger = Tagger()
+        tag_list = tagger.generate_tag_query(query)
+        logger.info(f'query tag={tag_list}')
+
+        # loop to get package node
+        package_score_dict = {}
+        for code, structure in self.graph.items():
+            score = 0
+            for class_name in structure['class_name_list']:
+                for tag in tag_list:
+                    if tag.lower() in class_name.lower():
+                        score += 1
+
+            for func_name_list in structure['func_name_dict'].values():
+                for func_name in func_name_list:
+                    for tag in tag_list:
+                        if tag.lower() in func_name.lower():
+                            score += 1
+            package_score_dict[structure['pac_name']] = score
+
+        # get respective code
+        res = []
+        package_score_tuple = list(package_score_dict.items())
+        package_score_tuple.sort(key=lambda x: x[1], reverse=True)
+
+        ids = [i[0] for i in package_score_tuple]
+        logger.info(f'ids={ids}')
+        chroma_res = self.ch.get(ids=ids, include=['metadatas'])
+
         # logger.info(chroma_res)
         for vertex, score in package_score_tuple:
             index = chroma_res['result']['ids'].index(vertex)
@@ -121,23 +176,22 @@ class CodeSearch:
             )
             if len(res) >= self.limit:
                 break
-        logger.info(f'retrival code={res}')
+        # logger.info(f'retrival code={res}')
         return res
 
-    def search_by_desciption(self, query: str, engine: str, model_path: str = "text2vec-base-chinese", embedding_device: str = "cpu"):
+    def search_by_desciption(self, query: str, engine: str, model_path: str = "text2vec-base-chinese", embedding_device: str = "cpu", embed_config: EmbedConfig=None):
         '''
         search by perform sim search
         @param query:
         @return:
         '''
         query = query.replace(',', '，')
-        query_emb = get_embedding(engine=engine, text_list=[query], model_path=model_path, embedding_device= embedding_device,)
+        query_emb = get_embedding(engine=engine, text_list=[query], model_path=model_path, embedding_device= embedding_device, embed_config=embed_config)
         query_emb = query_emb[query]
 
         query_embeddings = [query_emb]
         query_result = self.ch.query(query_embeddings=query_embeddings, n_results=self.limit,
                                      include=['metadatas', 'distances'])
-        logger.debug(query_result)
 
         res = []
         for idx, distance in enumerate(query_result['result']['distances'][0]):

coagent/codechat/code_search/cypher_generator.py

@@ -8,7 +8,7 @@
 from langchain import PromptTemplate
 from loguru import logger
 
-from coagent.llm_models.openai_model import getChatModel, getChatModelFromConfig
+from coagent.llm_models.openai_model import getChatModelFromConfig
 from coagent.llm_models.llm_config import LLMConfig
 from coagent.utils.postprocess import replace_lt_gt
 from langchain.schema import (

coagent/codechat/codebase_handler/code_importer.py

@@ -6,11 +6,10 @@
 @desc:
 '''
 import time
+import json
+import os
 from loguru import logger
 
-# from configs.server_config import NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT
-# from configs.server_config import CHROMA_PERSISTENT_PATH
-# from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL
 from coagent.db_handler.graph_db_handler.nebula_handler import NebulaHandler
 from coagent.db_handler.vector_db_handler.chroma_handler import ChromaHandler
 from coagent.embeddings.get_embedding import get_embedding
@@ -18,12 +17,14 @@ from coagent.llm_models.llm_config import EmbedConfig
 
 
 class CodeImporter:
-    def __init__(self, codebase_name: str, embed_config: EmbedConfig, nh: NebulaHandler, ch: ChromaHandler):
+    def __init__(self, codebase_name: str, embed_config: EmbedConfig, nh: NebulaHandler, ch: ChromaHandler,
+                 local_graph_file_path: str):
         self.codebase_name = codebase_name
         # self.engine = engine
         self.embed_config: EmbedConfig = embed_config
         self.nh = nh
         self.ch = ch
+        self.local_graph_file_path = local_graph_file_path
 
     def import_code(self, static_analysis_res: dict, interpretation: dict, do_interpret: bool = True):
         '''
@@ -31,9 +32,14 @@ class CodeImporter:
         @return:
         '''
         static_analysis_res = self.filter_out_vertex(static_analysis_res, interpretation)
-        logger.info(f'static_analysis_res={static_analysis_res}')
 
+        if self.nh:
             self.analysis_res_to_graph(static_analysis_res)
+        else:
+            # persist to local dir
+            with open(self.local_graph_file_path, 'w') as f:
+                json.dump(static_analysis_res, f)
+
         self.interpretation_to_db(static_analysis_res, interpretation, do_interpret)
 
     def filter_out_vertex(self, static_analysis_res, interpretation):
@@ -114,12 +120,12 @@ class CodeImporter:
         # create vertex
         for tag_name, value_dict in vertex_value_dict.items():
             res = self.nh.insert_vertex(tag_name, value_dict)
-            logger.debug(res.error_msg())
+            # logger.debug(res.error_msg())
 
         # create edge
         for tag_name, value_dict in edge_value_dict.items():
             res = self.nh.insert_edge(tag_name, value_dict)
-            logger.debug(res.error_msg())
+            # logger.debug(res.error_msg())
 
         return
 
@@ -132,7 +138,7 @@ class CodeImporter:
         if do_interpret:
             logger.info('start get embedding for interpretion')
             interp_list = list(interpretation.values())
-            emb = get_embedding(engine=self.embed_config.embed_engine, text_list=interp_list, model_path=self.embed_config.embed_model_path, embedding_device= self.embed_config.model_device)
+            emb = get_embedding(engine=self.embed_config.embed_engine, text_list=interp_list, model_path=self.embed_config.embed_model_path, embedding_device= self.embed_config.model_device, embed_config=self.embed_config)
             logger.info('get embedding done')
         else:
             emb = {i: [0] for i in list(interpretation.values())}
@@ -161,7 +167,7 @@ class CodeImporter:
 
         # add documents to chroma
         res = self.ch.add_data(ids=ids, embeddings=embeddings, documents=documents, metadatas=metadatas)
-        logger.debug(res)
+        # logger.debug(res)
 
     def init_graph(self):
         '''
@@ -169,7 +175,7 @@ class CodeImporter:
         @return:
         '''
         res = self.nh.create_space(space_name=self.codebase_name, vid_type='FIXED_STRING(1024)')
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
         time.sleep(5)
 
         self.nh.set_space_name(self.codebase_name)
@@ -179,29 +185,29 @@ class CodeImporter:
         tag_name = 'package'
         prop_dict = {}
         res = self.nh.create_tag(tag_name, prop_dict)
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
 
         tag_name = 'class'
         prop_dict = {}
         res = self.nh.create_tag(tag_name, prop_dict)
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
 
         tag_name = 'method'
         prop_dict = {}
         res = self.nh.create_tag(tag_name, prop_dict)
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
 
         # create edge type
         edge_type_name = 'contain'
         prop_dict = {}
         res = self.nh.create_edge_type(edge_type_name, prop_dict)
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
 
         # create edge type
         edge_type_name = 'depend'
         prop_dict = {}
         res = self.nh.create_edge_type(edge_type_name, prop_dict)
-        logger.debug(res.error_msg())
+        # logger.debug(res.error_msg())
 
 
 if __name__ == '__main__':

coagent/codechat/codebase_handler/codebase_handler.py

@@ -5,16 +5,15 @@
 @time: 2023/11/21 下午2:25
 @desc:
 '''
+import os
 import time
+import json
+from typing import List
 from loguru import logger
 
-# from configs.server_config import NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT
-# from configs.server_config import CHROMA_PERSISTENT_PATH
-# from configs.model_config import EMBEDDING_ENGINE
-
 from coagent.base_configs.env_config import (
     NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT,
-    CHROMA_PERSISTENT_PATH
+    CHROMA_PERSISTENT_PATH, CB_ROOT_PATH
 )
 
 
@@ -35,7 +34,9 @@ class CodeBaseHandler:
             language: str = 'java', 
             crawl_type: str = 'ZIP',
             embed_config: EmbedConfig = EmbedConfig(),
-            llm_config: LLMConfig = LLMConfig()
+            llm_config: LLMConfig = LLMConfig(),
+            use_nh: bool = True,
+            local_graph_path: str = CB_ROOT_PATH
         ):
         self.codebase_name = codebase_name
         self.code_path = code_path
@@ -43,11 +44,28 @@ class CodeBaseHandler:
         self.crawl_type = crawl_type
         self.embed_config = embed_config
         self.llm_config = llm_config
+        self.local_graph_file_path = local_graph_path + os.sep + f'{self.codebase_name}_graph.json'
 
+        if use_nh:
+            try:
                 self.nh = NebulaHandler(host=NEBULA_HOST, port=NEBULA_PORT, username=NEBULA_USER,
                                         password=NEBULA_PASSWORD, space_name=codebase_name)
                 self.nh.add_host(NEBULA_HOST, NEBULA_STORAGED_PORT)
                 time.sleep(1)
+            except:
+                self.nh = None
+                try:
+                    with open(self.local_graph_file_path, 'r') as f:
+                        self.graph = json.load(f)
+                except:
+                    pass
+        elif local_graph_path:
+            self.nh = None
+            try:
+                with open(self.local_graph_file_path, 'r') as f:
+                    self.graph = json.load(f)
+            except:
+                pass
 
         self.ch = ChromaHandler(path=CHROMA_PERSISTENT_PATH, collection_name=codebase_name)
 
@@ -58,7 +76,8 @@ class CodeBaseHandler:
         '''
         # init graph to init tag and edge
         code_importer = CodeImporter(embed_config=self.embed_config, codebase_name=self.codebase_name,
-                                     nh=self.nh, ch=self.ch)
+                                     nh=self.nh, ch=self.ch, local_graph_file_path=self.local_graph_file_path)
+        if self.nh:
             code_importer.init_graph()
             time.sleep(5)
 
@@ -81,8 +100,12 @@ class CodeBaseHandler:
         logger.debug('update codebase done, rt={}'.format(time.time() - st2))
 
         # get KG info
+        if self.nh:
             stat = self.nh.get_stat()
             vertices_num, edges_num = stat['vertices'], stat['edges']
+        else:
+            vertices_num = 0
+            edges_num = 0
 
         # get chroma info
         file_num = self.ch.count()['result']
@@ -95,7 +118,11 @@ class CodeBaseHandler:
         @param codebase_name: name of codebase
         @return:
         '''
+        if self.nh:
             self.nh.drop_space(space_name=codebase_name)
+        elif self.local_graph_file_path and os.path.isfile(self.local_graph_file_path):
+            os.remove(self.local_graph_file_path)
+
         self.ch.delete_collection(collection_name=codebase_name)
 
     def crawl_code(self, zip_file=''):
@@ -124,9 +151,15 @@ class CodeBaseHandler:
         @param search_type: ['cypher', 'graph', 'vector']
         @return: 
         '''
+        if self.nh:
             assert search_type in ['cypher', 'tag', 'description']
+        else:
+            if search_type == 'tag':
+                search_type = 'tag_by_local_graph'
+            assert search_type in ['tag_by_local_graph', 'description']
 
-        code_search = CodeSearch(llm_config=self.llm_config, nh=self.nh, ch=self.ch, limit=limit)
+        code_search = CodeSearch(llm_config=self.llm_config, nh=self.nh, ch=self.ch, limit=limit,
+                                 local_graph_file_path=self.local_graph_file_path)
 
         if search_type == 'cypher':
             search_res = code_search.search_by_cypher(query=query)
@@ -134,7 +167,11 @@ class CodeBaseHandler:
             search_res = code_search.search_by_tag(query=query)
         elif search_type == 'description':
             search_res = code_search.search_by_desciption(
-                query=query, engine=self.embed_config.embed_engine, model_path=self.embed_config.embed_model_path, embedding_device=self.embed_config.model_device)
+                query=query, engine=self.embed_config.embed_engine, model_path=self.embed_config.embed_model_path, 
+                embedding_device=self.embed_config.model_device, embed_config=self.embed_config)
+        elif search_type == 'tag_by_local_graph':
+            search_res = code_search.search_by_tag_by_graph(query=query)
+
 
         context, related_vertice = self.format_search_res(search_res, search_type)
         return context, related_vertice
@@ -160,6 +197,12 @@ class CodeBaseHandler:
             for code in search_res:
                 context = context + code['code_text'] + '\n'
                 related_vertice.append(code['vertex'])
+        elif search_type == 'tag_by_local_graph':
+            context = ''
+            related_vertice = []
+            for code in search_res:
+                context = context + code['code_text'] + '\n'
+                related_vertice.append(code['vertex'])
         elif search_type == 'description':
             context = ''
             related_vertice = []
@@ -169,17 +212,63 @@ class CodeBaseHandler:
 
         return context, related_vertice
 
+    def search_vertices(self, vertex_type="class") -> List[str]:
+        '''
+        通过 method/class 来搜索所有的节点
+        '''
+        vertices = []
+        if self.nh:
+            vertices = self.nh.get_all_vertices()
+            vertices = [str(v.as_node().get_id()) for v in vertices["v"] if vertex_type in v.as_node().tags()]
+            # for v in vertices["v"]:
+            #     logger.debug(f"{v.as_node().get_id()}, {v.as_node().tags()}")
+        else:
+            if vertex_type == "class":
+                vertices = [str(class_name) for code, structure in self.graph.items() for class_name in structure['class_name_list']]
+            elif vertex_type == "method":
+                vertices = [
+                    str(methods_name)
+                    for code, structure in self.graph.items() 
+                    for methods_names in structure['func_name_dict'].values() 
+                    for methods_name in methods_names
+                ]
+        # logger.debug(vertices)
+        return vertices
+
 
 if __name__ == '__main__':
-    codebase_name = 'testing'
+    from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH
+    from configs.server_config import SANDBOX_SERVER
+
+    LLM_MODEL = "gpt-3.5-turbo"
+    llm_config = LLMConfig(
+        model_name=LLM_MODEL, model_device="cpu", api_key=os.environ["OPENAI_API_KEY"],
+        api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+    )
+    src_dir = '/Users/bingxu/Desktop/工作/大模型/chatbot/Codefuse-chatbot-antcode'
+    embed_config = EmbedConfig(
+        embed_engine="model", embed_model="text2vec-base-chinese",
+        embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+    )
+
+    codebase_name = 'client_local'
     code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
-    cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir')
+    use_nh = False
+    local_graph_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/Codefuse-chatbot-antcode/code_base'
+    CHROMA_PERSISTENT_PATH = '/Users/bingxu/Desktop/工作/大模型/chatbot/Codefuse-chatbot-antcode/data/chroma_data'
+
+    cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=local_graph_path,
+                          llm_config=llm_config, embed_config=embed_config)
+
+    # test import code
+    # cbh.import_code(do_interpret=True)
 
     # query = '使用不同的HTTP请求类型（GET、POST、DELETE等）来执行不同的操作'
     # query = '代码中一共有多少个类'
+    # query = 'remove 这个函数是用来做什么的'
+    query = '有没有函数是从字符串中删除指定字符串的功能'
 
-    query = 'intercept 函数作用是什么'
+    search_type = 'description'
-    search_type = 'graph'
     limit = 2
     res = cbh.search_code(query, search_type, limit)
     logger.debug(res)

coagent/connector/actions/__init__.py

@@ -0,0 +1,6 @@
+from .base_action import BaseAction
+
+
+__all__ = [
+    "BaseAction"
+]

coagent/connector/actions/base_action.py

@@ -0,0 +1,16 @@
+
+from langchain.schema import BaseRetriever, Document
+
+class BaseAction:
+
+
+    def __init__(self, ):
+        pass
+
+    def step(self, ):
+        pass
+
+    def astep(self, ):
+        pass
+
+    

coagent/connector/agents/base_agent.py

@@ -4,25 +4,25 @@ import re, os
 import copy
 from loguru import logger
 
+from langchain.schema import BaseRetriever
+
 from coagent.connector.schema import (
     Memory, Task, Role, Message, PromptField, LogVerboseEnum
 )
 from coagent.connector.memory_manager import BaseMemoryManager
-from coagent.connector.configs.prompts import BEGIN_PROMPT_INPUT
 from coagent.connector.message_process import MessageUtils
-from coagent.llm_models import getChatModel, getExtraModel, LLMConfig, getChatModelFromConfig, EmbedConfig
+from coagent.llm_models import getExtraModel, LLMConfig, getChatModelFromConfig, EmbedConfig
-from coagent.connector.prompt_manager import PromptManager
+from coagent.connector.prompt_manager.prompt_manager import PromptManager
 from coagent.connector.memory_manager import LocalMemoryManager
-from coagent.connector.utils import parse_section
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
-# from configs.model_config import JUPYTER_WORK_PATH
+
-# from configs.server_config import SANDBOX_SERVER
 
 class BaseAgent:
 
     def __init__(
             self, 
             role: Role,
-            prompt_config: [PromptField],
+            prompt_config: List[PromptField],
             prompt_manager_type: str = "PromptManager",
             task: Task = None,
             memory: Memory = None,
@@ -33,8 +33,11 @@ class BaseAgent:
             llm_config: LLMConfig = None,
             embed_config: EmbedConfig = None,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ):
         
@@ -43,7 +46,7 @@ class BaseAgent:
         self.sandbox_server = sandbox_server
         self.jupyter_work_path = jupyter_work_path
         self.kb_root_path = kb_root_path
-        self.message_utils = MessageUtils(role, sandbox_server, jupyter_work_path, embed_config, llm_config, kb_root_path, log_verbose)
+        self.message_utils = MessageUtils(role, sandbox_server, jupyter_work_path, embed_config, llm_config, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose)
         self.memory = self.init_history(memory)
         self.llm_config: LLMConfig = llm_config
         self.embed_config: EmbedConfig = embed_config
@@ -82,12 +85,8 @@ class BaseAgent:
                 llm_config=self.embed_config
             )
             memory_manager.append(query)
-            memory_pool = memory_manager.current_memory
+        memory_pool = memory_manager.get_memory_pool(query.user_name)
-        else:
-            memory_pool = memory_manager.current_memory
 
-
-        logger.debug(f"memory_pool: {memory_pool}")
         prompt = self.prompt_manager.generate_full_prompt(
             previous_agent_message=query_c, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, memory_pool=memory_pool)
         content = self.llm.predict(prompt)
@@ -99,6 +98,7 @@ class BaseAgent:
             logger.info(f"{self.role.role_name} content: {content}")
 
         output_message = Message(
+            user_name=query.user_name,
             role_name=self.role.role_name,
             role_type="assistant", #self.role.role_type,
             role_content=content,
@@ -151,9 +151,6 @@ class BaseAgent:
         self.memory = self.init_history()
     
     def create_llm_engine(self, llm_config: LLMConfig = None, temperature=0.2, stop=None):
-        if llm_config is None:
-            return getChatModel(temperature=temperature, stop=stop)
-        else:
         return getChatModelFromConfig(llm_config=llm_config)
     
     def registry_actions(self, actions):
@@ -212,171 +209,3 @@ class BaseAgent:
     
     def get_memory_str(self, content_key="role_content"):
         return "\n".join([": ".join(i) for i in self.memory.to_tuple_messages(content_key="step_content")])
-
-
-    def create_prompt(
-            self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_pool: Memory=None, prompt_mamnger=None) -> str:
-        '''
-        prompt engineer, contains role\task\tools\docs\memory
-        '''
-        # 
-        doc_infos = self.create_doc_prompt(query)
-        code_infos = self.create_codedoc_prompt(query)
-        # 
-        formatted_tools, tool_names, _ = self.create_tools_prompt(query)
-        task_prompt = self.create_task_prompt(query)
-        background_prompt = self.create_background_prompt(background, control_key="step_content")
-        history_prompt = self.create_history_prompt(history)
-        selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content")
-        
-        # extra_system_prompt = self.role.role_prompt
-
-        
-        prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
-        #
-        memory_pool_select_by_agent_key = self.select_memory_by_agent_key(memory_pool)
-        memory_pool_select_by_agent_key_context = '\n\n'.join([f"*{k}*\n{v}" for parsed_output in memory_pool_select_by_agent_key.get_parserd_output_list() for k, v in parsed_output.items() if k not in ['Action Status']])
-        
-        # input_query = query.input_query
-
-        # # logger.debug(f"{self.role.role_name}  extra_system_prompt: {self.role.role_prompt}")
-        # # logger.debug(f"{self.role.role_name}  input_query: {input_query}")
-        # # logger.debug(f"{self.role.role_name}  doc_infos: {doc_infos}")
-        # # logger.debug(f"{self.role.role_name}  tool_names: {tool_names}")
-        # if "**Context:**" in self.role.role_prompt:
-        #     # logger.debug(f"parsed_output_list: {query.parsed_output_list}")
-        #     # input_query =  "'''" + "\n".join([f"###{k}###\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k]) + "'''"
-        #     context =  "\n".join([f"*{k}*\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k])
-        #     # context = history_prompt or '""'
-        #     # logger.debug(f"parsed_output_list: {t}")
-        #     prompt += "\n" + QUERY_CONTEXT_PROMPT_INPUT.format(**{"context": context, "query": query.origin_query})
-        # else:
-        #     prompt += "\n" + PLAN_PROMPT_INPUT.format(**{"query": input_query})
-
-        task = query.task or self.task
-        if task_prompt is not None:
-            prompt += "\n" + task.task_prompt
-
-        DocInfos = ""
-        if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息":
-            DocInfos += f"\nDocument Information: {doc_infos}"
-
-        if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息":
-            DocInfos += f"\nCodeBase Infomation: {code_infos}"
-            
-        # if selfmemory_prompt:
-        #     prompt += "\n" + selfmemory_prompt
-
-        # if background_prompt:
-        #     prompt += "\n" + background_prompt
-
-        # if history_prompt:
-        #     prompt += "\n" + history_prompt
-
-        input_query = query.input_query
-
-        # logger.debug(f"{self.role.role_name}  extra_system_prompt: {self.role.role_prompt}")
-        # logger.debug(f"{self.role.role_name}  input_query: {input_query}")
-        # logger.debug(f"{self.role.role_name}  doc_infos: {doc_infos}")
-        # logger.debug(f"{self.role.role_name}  tool_names: {tool_names}")
-
-        # extra_system_prompt = self.role.role_prompt
-        input_keys = parse_section(self.role.role_prompt, 'Input Format')
-        prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
-        prompt += "\n" + BEGIN_PROMPT_INPUT
-        for input_key in input_keys:
-            if input_key == "Origin Query": 
-                prompt += "\n**Origin Query:**\n" + query.origin_query
-            elif input_key == "Context":
-                context =  "\n".join([f"*{k}*\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k])
-                if history:
-                    context = history_prompt + "\n" + context
-                if not context:
-                    context = "there is no context"
-
-                if self.focus_agents and memory_pool_select_by_agent_key_context:
-                    context = memory_pool_select_by_agent_key_context
-                prompt += "\n**Context:**\n" + context + "\n" + input_query
-            elif input_key == "DocInfos":
-                if DocInfos:
-                    prompt += "\n**DocInfos:**\n" + DocInfos
-                else:
-                    prompt += "\n**DocInfos:**\n" + "Empty"
-            elif input_key == "Question":
-                prompt += "\n**Question:**\n" + input_query
-
-        # if "**Context:**" in self.role.role_prompt:
-        #     # logger.debug(f"parsed_output_list: {query.parsed_output_list}")
-        #     # input_query =  "'''" + "\n".join([f"###{k}###\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k]) + "'''"
-        #     context =  "\n".join([f"*{k}*\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k])
-        #     if history:
-        #         context = history_prompt + "\n" + context
-
-        #     if not context:
-        #         context = "there is no context"
-
-        #     # logger.debug(f"parsed_output_list: {t}")
-        #     if "DocInfos" in prompt:
-        #         prompt += "\n" + QUERY_CONTEXT_DOC_PROMPT_INPUT.format(**{"context": context, "query": query.origin_query, "DocInfos": DocInfos})
-        #     else:
-        #         prompt += "\n" + QUERY_CONTEXT_PROMPT_INPUT.format(**{"context": context, "query": query.origin_query, "DocInfos": DocInfos})
-        # else:
-        #     prompt += "\n" + BASE_PROMPT_INPUT.format(**{"query": input_query})
-
-        # prompt = extra_system_prompt.format(**{"query": input_query, "doc_infos": doc_infos, "formatted_tools": formatted_tools, "tool_names": tool_names})
-        while "{{" in prompt or "}}" in prompt:
-            prompt = prompt.replace("{{", "{")
-            prompt = prompt.replace("}}", "}")
-
-        # logger.debug(f"{self.role.role_name}  prompt: {prompt}")
-        return prompt
-    
-    def create_doc_prompt(self, message: Message) -> str: 
-        ''''''
-        db_docs = message.db_docs
-        search_docs = message.search_docs
-        doc_infos = "\n".join([doc.get_snippet() for doc in db_docs] + [doc.get_snippet() for doc in search_docs])
-        return doc_infos or "不存在知识库辅助信息"
-    
-    def create_codedoc_prompt(self, message: Message) -> str: 
-        ''''''
-        code_docs = message.code_docs
-        doc_infos = "\n".join([doc.get_code() for doc in code_docs])
-        return doc_infos or "不存在代码库辅助信息"
-    
-    def create_tools_prompt(self, message: Message) -> str:
-        tools = message.tools
-        tool_strings = []
-        tools_descs = []
-        for tool in tools:
-            args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
-            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
-            tools_descs.append(f"{tool.name}: {tool.description}")
-        formatted_tools = "\n".join(tool_strings)
-        tools_desc_str = "\n".join(tools_descs)
-        tool_names = ", ".join([tool.name for tool in tools])
-        return formatted_tools, tool_names, tools_desc_str
-    
-    def create_task_prompt(self, message: Message) -> str:
-        task = message.task or self.task
-        return "\n任务目标: " + task.task_prompt if task is not None else None
-    
-    def create_background_prompt(self, background: Memory, control_key="role_content") -> str:
-        background_message = None if background is None else background.to_str_messages(content_key=control_key)
-        # logger.debug(f"background_message: {background_message}")
-        if background_message:
-            background_message = re.sub("}", "}}", re.sub("{", "{{", background_message))
-        return "\n背景信息: " + background_message if background_message else None
-    
-    def create_history_prompt(self, history: Memory, control_key="role_content") -> str:
-        history_message = None if history is None else history.to_str_messages(content_key=control_key)
-        if history_message:
-            history_message = re.sub("}", "}}", re.sub("{", "{{", history_message))
-        return "\n补充对话信息: " + history_message if history_message else None
-    
-    def create_selfmemory_prompt(self, selfmemory: Memory, control_key="role_content") -> str:
-        selfmemory_message = None if selfmemory is None else selfmemory.to_str_messages(content_key=control_key)
-        if selfmemory_message:
-            selfmemory_message = re.sub("}", "}}", re.sub("{", "{{", selfmemory_message))
-        return "\n补充自身对话信息: " + selfmemory_message if selfmemory_message else None
-    

coagent/connector/agents/executor_agent.py

@@ -2,14 +2,15 @@ from typing import List, Union
 import copy
 from loguru import logger
 
+from langchain.schema import BaseRetriever
+
 from coagent.connector.schema import (
     Memory, Task, Env, Role, Message, ActionStatus, PromptField, LogVerboseEnum
 )
 from coagent.connector.memory_manager import BaseMemoryManager
-from coagent.connector.configs.prompts import BEGIN_PROMPT_INPUT
 from coagent.llm_models import LLMConfig, EmbedConfig
 from coagent.connector.memory_manager import LocalMemoryManager
-
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
 from .base_agent import BaseAgent
 
 
@@ -17,7 +18,7 @@ class ExecutorAgent(BaseAgent):
     def __init__(
             self, 
             role: Role,
-            prompt_config: [PromptField],
+            prompt_config: List[PromptField],
             prompt_manager_type: str= "PromptManager",
             task: Task = None,
             memory: Memory = None,
@@ -28,14 +29,17 @@ class ExecutorAgent(BaseAgent):
             llm_config: LLMConfig = None,
             embed_config: EmbedConfig = None,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ):
         
         super().__init__(role, prompt_config, prompt_manager_type, task, memory, chat_turn,
                          focus_agents, focus_message_keys, llm_config, embed_config, sandbox_server,
-                         jupyter_work_path, kb_root_path, log_verbose
+                         jupyter_work_path, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose
                          )
         self.do_all_task = True # run all tasks
 
@@ -45,6 +49,7 @@ class ExecutorAgent(BaseAgent):
         task_executor_memory = Memory(messages=[])
         # insert query
         output_message = Message(
+                user_name=query.user_name,
                 role_name=self.role.role_name,
                 role_type="assistant", #self.role.role_type,
                 role_content=query.input_query,
@@ -115,7 +120,7 @@ class ExecutorAgent(BaseAgent):
             history: Memory, background: Memory, memory_manager: BaseMemoryManager, 
             task_memory: Memory) -> Union[Message, Memory]:
         '''execute the llm predict by created prompt'''
-        memory_pool = memory_manager.current_memory
+        memory_pool = memory_manager.get_memory_pool(query.user_name)
         prompt = self.prompt_manager.generate_full_prompt(
             previous_agent_message=query, agent_long_term_memory=self_memory, ui_history=history, chain_summary_messages=background, memory_pool=memory_pool,
             task_memory=task_memory)

coagent/connector/agents/react_agent.py

@@ -3,23 +3,23 @@ import traceback
 import copy
 from loguru import logger
 
+from langchain.schema import BaseRetriever
+
 from coagent.connector.schema import (
     Memory, Task, Env, Role, Message, ActionStatus, PromptField, LogVerboseEnum
 )
 from coagent.connector.memory_manager import BaseMemoryManager
-from coagent.connector.configs.agent_config import REACT_PROMPT_INPUT
 from coagent.llm_models import LLMConfig, EmbedConfig
 from .base_agent import BaseAgent
 from coagent.connector.memory_manager import LocalMemoryManager
-
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
-from coagent.connector.prompt_manager import PromptManager
 
 
 class ReactAgent(BaseAgent):
     def __init__(
             self, 
             role: Role,
-            prompt_config: [PromptField],
+            prompt_config: List[PromptField],
             prompt_manager_type: str = "PromptManager",
             task: Task = None,
             memory: Memory = None,
@@ -30,14 +30,17 @@ class ReactAgent(BaseAgent):
             llm_config: LLMConfig = None,
             embed_config: EmbedConfig = None,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ):
         
         super().__init__(role, prompt_config, prompt_manager_type, task, memory, chat_turn, 
                          focus_agents, focus_message_keys, llm_config, embed_config, sandbox_server,
-                         jupyter_work_path, kb_root_path, log_verbose
+                         jupyter_work_path, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose
                          )
 
     def step(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager = None) -> Message:
@@ -52,6 +55,7 @@ class ReactAgent(BaseAgent):
         react_memory = Memory(messages=[])
         # insert query
         output_message = Message(
+                user_name=query.user_name,
                 role_name=self.role.role_name,
                 role_type="assistant", #self.role.role_type,
                 role_content=query.input_query,
@@ -84,9 +88,7 @@ class ReactAgent(BaseAgent):
                     llm_config=self.embed_config
                 )
                 memory_manager.append(query)
-                memory_pool = memory_manager.current_memory
+            memory_pool = memory_manager.get_memory_pool(query_c.user_name)
-            else:
-                memory_pool = memory_manager.current_memory
 
             prompt = self.prompt_manager.generate_full_prompt(
                 previous_agent_message=query_c, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, react_memory=react_memory, 
@@ -142,82 +144,4 @@ class ReactAgent(BaseAgent):
         title = f"<<<<{self.role.role_name}'s prompt>>>>"
         print("#"*len(title) + f"\n{title}\n"+ "#"*len(title)+ f"\n\n{prompt}\n\n")
 
-    # def create_prompt(
-    #         self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_manager: BaseMemoryManager= None, 
-    #         prompt_mamnger=None) -> str:
-    #     prompt_mamnger = PromptManager()
-    #     prompt_mamnger.register_standard_fields()
-        
-    #     # input_keys = parse_section(self.role.role_prompt, 'Agent Profile')
-        
-    #     data_dict = {
-    #         "agent_profile": extract_section(self.role.role_prompt, 'Agent Profile'),
-    #         "tool_information": query.tools,
-    #         "session_records": memory_manager,
-    #         "reference_documents": query,
-    #         "output_format": extract_section(self.role.role_prompt, 'Response Output Format'),
-    #         "response": "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()]),
-    #     }
-    #     # logger.debug(memory_pool)
-        
-    #     return prompt_mamnger.generate_full_prompt(data_dict)
-    
-    def create_prompt(
-            self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_pool: Memory= None, 
-            prompt_mamnger=None) -> str:
-        '''
-        role\task\tools\docs\memory
-        '''
-        # 
-        doc_infos = self.create_doc_prompt(query)
-        code_infos = self.create_codedoc_prompt(query)
-        # 
-        formatted_tools, tool_names, _ = self.create_tools_prompt(query)
-        task_prompt = self.create_task_prompt(query)
-        background_prompt = self.create_background_prompt(background)
-        history_prompt = self.create_history_prompt(history)
-        selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content")
-        # 
-        # extra_system_prompt = self.role.role_prompt
-        prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
-
-        # react 流程是自身迭代过程，另外二次触发的是需要作为历史对话信息
-        # input_query = react_memory.to_tuple_messages(content_key="step_content")
-        # # input_query = query.input_query + "\n" + "\n".join([f"{v}" for k, v in input_query if v])
-        # input_query = "\n".join([f"{v}" for k, v in input_query if v])
-        input_query = "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()])
-        # logger.debug(f"input_query: {input_query}")
-        
-        prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query})
-
-        task = query.task or self.task
-        # if task_prompt is not None:
-        #     prompt += "\n" + task.task_prompt
-
-        # if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息":
-        #     prompt += f"\n知识库信息: {doc_infos}"
-
-        # if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息":
-        #     prompt += f"\n代码库信息: {code_infos}"
-
-        # if background_prompt:
-        #     prompt += "\n" + background_prompt
-
-        # if history_prompt:
-        #     prompt += "\n" + history_prompt
-
-        # if selfmemory_prompt:
-        #     prompt += "\n" + selfmemory_prompt
-
-        # logger.debug(f"{self.role.role_name}  extra_system_prompt: {self.role.role_prompt}")
-        # logger.debug(f"{self.role.role_name}  input_query: {input_query}")
-        # logger.debug(f"{self.role.role_name}  doc_infos: {doc_infos}")
-        # logger.debug(f"{self.role.role_name}  tool_names: {tool_names}")
-        # prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query})
-
-        # prompt = extra_system_prompt.format(**{"query": input_query, "doc_infos": doc_infos, "formatted_tools": formatted_tools, "tool_names": tool_names})
-        while "{{" in prompt or "}}" in prompt:
-            prompt = prompt.replace("{{", "{")
-            prompt = prompt.replace("}}", "}")
-        return prompt
     

coagent/connector/agents/selector_agent.py

@@ -3,13 +3,15 @@ import copy
 import random
 from loguru import logger
 
+from langchain.schema import BaseRetriever
+
 from coagent.connector.schema import (
     Memory, Task, Role, Message, PromptField, LogVerboseEnum
 )
 from coagent.connector.memory_manager import BaseMemoryManager
-from coagent.connector.configs.prompts import BEGIN_PROMPT_INPUT
 from coagent.connector.memory_manager import LocalMemoryManager
 from coagent.llm_models import LLMConfig, EmbedConfig
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
 from .base_agent import BaseAgent
 
 
@@ -30,14 +32,17 @@ class SelectorAgent(BaseAgent):
             llm_config: LLMConfig = None,
             embed_config: EmbedConfig = None,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ):
         
         super().__init__(role, prompt_config, prompt_manager_type, task, memory, chat_turn, 
                          focus_agents, focus_message_keys, llm_config, embed_config, sandbox_server,
-                         jupyter_work_path, kb_root_path, log_verbose
+                         jupyter_work_path, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose
                          )
         self.group_agents = group_agents
 
@@ -56,9 +61,8 @@ class SelectorAgent(BaseAgent):
                 llm_config=self.embed_config
             )
             memory_manager.append(query)
-            memory_pool = memory_manager.current_memory
+        memory_pool = memory_manager.get_memory_pool(query_c.user_name)
-        else:
+
-            memory_pool = memory_manager.current_memory
         prompt = self.prompt_manager.generate_full_prompt(
                 previous_agent_message=query_c, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, react_memory=None, 
                 memory_pool=memory_pool, agents=self.group_agents)
@@ -90,6 +94,9 @@ class SelectorAgent(BaseAgent):
             for agent in self.group_agents:
                 if agent.role.role_name == select_message.parsed_output.get("Role", ""):
                     break
+            
+            # 把除了role以外的信息传给下一个agent
+            query_c.parsed_output.update({k:v for k,v in select_message.parsed_output.items() if k!="Role"})
             for output_message in agent.astep(query_c, history, background=background, memory_manager=memory_manager):
                 yield output_message or select_message
             # update self_memory
@@ -103,6 +110,7 @@ class SelectorAgent(BaseAgent):
             memory_manager.append(output_message)
 
             select_message.parsed_output = output_message.parsed_output
+            select_message.spec_parsed_output.update(output_message.spec_parsed_output)
             select_message.parsed_output_list.extend(output_message.parsed_output_list)
         yield select_message
 
@@ -115,76 +123,3 @@ class SelectorAgent(BaseAgent):
 
         for agent in self.group_agents:
             agent.pre_print(query=query, history=history, background=background, memory_manager=memory_manager)
-
-    # def create_prompt(
-    #         self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager=None, prompt_mamnger=None) -> str:
-    #     '''
-    #     role\task\tools\docs\memory
-    #     '''
-    #     # 
-    #     doc_infos = self.create_doc_prompt(query)
-    #     code_infos = self.create_codedoc_prompt(query)
-    #     # 
-    #     formatted_tools, tool_names, tools_descs = self.create_tools_prompt(query)
-    #     agent_names, agents = self.create_agent_names()
-    #     task_prompt = self.create_task_prompt(query)
-    #     background_prompt = self.create_background_prompt(background)
-    #     history_prompt = self.create_history_prompt(history)
-    #     selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content")
-        
-
-    #     DocInfos = ""
-    #     if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息":
-    #         DocInfos += f"\nDocument Information: {doc_infos}"
-
-    #     if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息":
-    #         DocInfos += f"\nCodeBase Infomation: {code_infos}"
-
-    #     input_query = query.input_query
-    #     logger.debug(f"{self.role.role_name}  input_query: {input_query}")
-    #     prompt = self.role.role_prompt.format(**{"agent_names": agent_names, "agents": agents, "formatted_tools": tools_descs, "tool_names": tool_names})
-    #     #
-    #     memory_pool_select_by_agent_key = self.select_memory_by_agent_key(memory_manager.current_memory)
-    #     memory_pool_select_by_agent_key_context = '\n\n'.join([f"*{k}*\n{v}" for parsed_output in memory_pool_select_by_agent_key.get_parserd_output_list() for k, v in parsed_output.items() if k not in ['Action Status']])
-
-    #     input_keys = parse_section(self.role.role_prompt, 'Input Format')
-    #     # 
-    #     prompt += "\n" + BEGIN_PROMPT_INPUT
-    #     for input_key in input_keys:
-    #         if input_key == "Origin Query": 
-    #             prompt += "\n**Origin Query:**\n" + query.origin_query
-    #         elif input_key == "Context":
-    #             context =  "\n".join([f"*{k}*\n{v}" for i in query.parsed_output_list for k,v in i.items() if "Action Status" !=k])
-    #             if history:
-    #                 context = history_prompt + "\n" + context
-    #             if not context:
-    #                 context = "there is no context"
-
-    #             if self.focus_agents and memory_pool_select_by_agent_key_context:
-    #                 context = memory_pool_select_by_agent_key_context
-    #             prompt += "\n**Context:**\n" + context + "\n" + input_query
-    #         elif input_key == "DocInfos":
-    #             prompt += "\n**DocInfos:**\n" + DocInfos
-    #         elif input_key == "Question":
-    #             prompt += "\n**Question:**\n" + input_query
-
-    #     while "{{" in prompt or "}}" in prompt:
-    #         prompt = prompt.replace("{{", "{")
-    #         prompt = prompt.replace("}}", "}")
-
-    #     # logger.debug(f"{self.role.role_name}  prompt: {prompt}")
-    #     return prompt
-    
-    # def create_agent_names(self):
-    #     random.shuffle(self.group_agents)
-    #     agent_names = ", ".join([f'{agent.role.role_name}' for agent in self.group_agents])
-    #     agent_descs = []
-    #     for agent in self.group_agents:
-    #         role_desc = agent.role.role_prompt.split("####")[1]
-    #         while "\n\n" in role_desc:
-    #             role_desc = role_desc.replace("\n\n", "\n")
-    #         role_desc = role_desc.replace("\n", ",")
-
-    #         agent_descs.append(f'"role name: {agent.role.role_name}\nrole description: {role_desc}"')
-
-    #     return agent_names, "\n".join(agent_descs)

coagent/connector/antflow/__init__.py

@@ -0,0 +1,7 @@
+from .flow import AgentFlow, PhaseFlow, ChainFlow
+
+
+
+__all__ = [
+    "AgentFlow", "PhaseFlow", "ChainFlow"
+]

coagent/connector/antflow/flow.py

@@ -0,0 +1,255 @@
+import importlib
+from typing import List, Union, Dict, Any
+from loguru import logger
+import os
+from langchain.embeddings.base import Embeddings
+from langchain.agents import Tool
+from langchain.llms.base import BaseLLM, LLM
+
+from coagent.retrieval.base_retrieval import IMRertrieval
+from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+from coagent.connector.phase import BasePhase
+from coagent.connector.agents import BaseAgent
+from coagent.connector.chains import BaseChain
+from coagent.connector.schema import Message, Role, PromptField, ChainConfig
+from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
+
+
+class AgentFlow:
+    def __init__(
+        self, 
+        role_name: str,
+        agent_type: str,
+        role_type: str = "assistant",
+        agent_index: int = 0,
+        role_prompt: str = "",
+        prompt_config: List[Dict[str, Any]] = [],
+        prompt_manager_type: str = "PromptManager",
+        chat_turn: int = 3,
+        focus_agents: List[str] = [],
+        focus_messages: List[str] = [],
+        embeddings: Embeddings = None,
+        llm: BaseLLM = None,
+        doc_retrieval: IMRertrieval = None,
+        code_retrieval: IMRertrieval = None,
+        search_retrieval: IMRertrieval = None,
+        **kwargs
+    ):
+        self.role_type = role_type
+        self.role_name = role_name
+        self.agent_type = agent_type
+        self.role_prompt = role_prompt
+        self.agent_index = agent_index
+
+        self.prompt_config = prompt_config
+        self.prompt_manager_type = prompt_manager_type
+
+        self.chat_turn = chat_turn
+        self.focus_agents = focus_agents
+        self.focus_messages = focus_messages
+
+        self.embeddings = embeddings
+        self.llm = llm
+        self.doc_retrieval = doc_retrieval
+        self.code_retrieval = code_retrieval
+        self.search_retrieval = search_retrieval
+        # self.build_config()
+        # self.build_agent()
+
+    def build_config(self, embeddings: Embeddings = None, llm: BaseLLM = None):
+        self.llm_config = LLMConfig(model_name="test", llm=self.llm or llm)
+        self.embed_config = EmbedConfig(embed_model="test", langchain_embeddings=self.embeddings or embeddings)
+
+    def build_agent(self, 
+                    embeddings: Embeddings = None, llm: BaseLLM = None,
+                    doc_retrieval: IMRertrieval = None, 
+                    code_retrieval: IMRertrieval = None, 
+                    search_retrieval: IMRertrieval = None,
+        ):
+        # 可注册个性化的agent，仅通过start_action和end_action来注册
+        # class ExtraAgent(BaseAgent):
+        #     def start_action_step(self, message: Message) -> Message:
+        #         pass
+
+        #     def end_action_step(self, message: Message) -> Message:
+        #         pass
+        # agent_module = importlib.import_module("coagent.connector.agents")
+        # setattr(agent_module, 'extraAgent', ExtraAgent)
+
+        # 可注册个性化的prompt组装方式，
+        # class CodeRetrievalPM(PromptManager):
+        #     def handle_code_packages(self, **kwargs) -> str:
+        #         if 'previous_agent_message' not in kwargs:
+        #             return ""
+        #         previous_agent_message: Message = kwargs['previous_agent_message']
+        #         # 由于两个agent共用了同一个manager，所以临时性处理
+        #         vertices = previous_agent_message.customed_kargs.get("RelatedVerticesRetrivalRes", {}).get("vertices", [])
+        #         return ", ".join([str(v) for v in vertices])
+
+        # prompt_manager_module = importlib.import_module("coagent.connector.prompt_manager")
+        # setattr(prompt_manager_module, 'CodeRetrievalPM', CodeRetrievalPM)
+        
+        # agent实例化
+        agent_module = importlib.import_module("coagent.connector.agents")
+        baseAgent: BaseAgent = getattr(agent_module, self.agent_type)
+        role = Role(
+            role_type=self.agent_type, role_name=self.role_name, 
+            agent_type=self.agent_type, role_prompt=self.role_prompt,
+        )
+
+        self.build_config(embeddings, llm)
+        self.agent = baseAgent(
+                    role=role, 
+                    prompt_config = [PromptField(**config) for config in self.prompt_config],
+                    prompt_manager_type=self.prompt_manager_type,
+                    chat_turn=self.chat_turn,
+                    focus_agents=self.focus_agents,
+                    focus_message_keys=self.focus_messages,
+                    llm_config=self.llm_config,
+                    embed_config=self.embed_config,
+                    doc_retrieval=doc_retrieval or self.doc_retrieval,
+                    code_retrieval=code_retrieval or self.code_retrieval,
+                    search_retrieval=search_retrieval or self.search_retrieval,
+                )
+        
+
+
+class ChainFlow:
+    def __init__(
+        self, 
+        chain_name: str,
+        chain_index: int = 0,
+        agent_flows: List[AgentFlow] = [],
+        chat_turn: int = 5,
+        do_checker: bool = False,
+        embeddings: Embeddings = None,
+        llm: BaseLLM = None,
+        doc_retrieval: IMRertrieval = None,
+        code_retrieval: IMRertrieval = None,
+        search_retrieval: IMRertrieval = None,
+        # chain_type: str = "BaseChain",
+        **kwargs
+    ):
+        self.agent_flows = sorted(agent_flows, key=lambda x:x.agent_index)
+        self.chat_turn = chat_turn
+        self.do_checker = do_checker
+        self.chain_name = chain_name
+        self.chain_index = chain_index
+        self.chain_type = "BaseChain"
+
+        self.embeddings = embeddings
+        self.llm = llm
+
+        self.doc_retrieval = doc_retrieval
+        self.code_retrieval = code_retrieval
+        self.search_retrieval = search_retrieval
+        # self.build_config()
+        # self.build_chain()
+
+    def build_config(self, embeddings: Embeddings = None, llm: BaseLLM = None):
+        self.llm_config = LLMConfig(model_name="test", llm=self.llm or llm)
+        self.embed_config = EmbedConfig(embed_model="test", langchain_embeddings=self.embeddings or embeddings)
+
+    def build_chain(self, 
+                    embeddings: Embeddings = None, llm: BaseLLM = None,
+                    doc_retrieval: IMRertrieval = None, 
+                    code_retrieval: IMRertrieval = None, 
+                    search_retrieval: IMRertrieval = None,
+        ):
+        # chain 实例化
+        chain_module = importlib.import_module("coagent.connector.chains")
+        baseChain: BaseChain = getattr(chain_module, self.chain_type)
+
+        agent_names = [agent_flow.role_name for agent_flow in self.agent_flows]
+        chain_config = ChainConfig(chain_name=self.chain_name, agents=agent_names, do_checker=self.do_checker, chat_turn=self.chat_turn)
+
+        # agent 实例化
+        self.build_config(embeddings, llm)
+        for agent_flow in self.agent_flows:
+            agent_flow.build_agent(embeddings, llm)
+
+        self.chain = baseChain(
+                chain_config,
+                [agent_flow.agent for agent_flow in self.agent_flows], 
+                embed_config=self.embed_config,
+                llm_config=self.llm_config,
+                doc_retrieval=doc_retrieval or self.doc_retrieval,
+                code_retrieval=code_retrieval or self.code_retrieval,
+                search_retrieval=search_retrieval or self.search_retrieval,
+                )
+
+class PhaseFlow:
+    def __init__(
+        self, 
+        phase_name: str,
+        chain_flows: List[ChainFlow],
+        embeddings: Embeddings = None,
+        llm: BaseLLM = None,
+        tools: List[Tool] = [],
+        doc_retrieval: IMRertrieval = None,
+        code_retrieval: IMRertrieval = None,
+        search_retrieval: IMRertrieval = None,
+        **kwargs
+    ):
+        self.phase_name = phase_name
+        self.chain_flows = sorted(chain_flows, key=lambda x:x.chain_index)
+        self.phase_type = "BasePhase"
+        self.tools = tools
+
+        self.embeddings = embeddings
+        self.llm = llm
+
+        self.doc_retrieval = doc_retrieval
+        self.code_retrieval = code_retrieval
+        self.search_retrieval = search_retrieval
+        # self.build_config()
+        self.build_phase()
+    
+    def __call__(self, params: dict) -> str:
+
+        # tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
+        # query_content = "帮我确认下127.0.0.1这个服务器的在10点是否存在异常，请帮我判断一下"
+        try:
+            logger.info(f"params: {params}")
+            query_content = params.get("query") or params.get("input")
+            search_type = params.get("search_type")
+            query = Message(
+                role_name="human", role_type="user", tools=self.tools,
+                role_content=query_content, input_query=query_content, origin_query=query_content,
+                cb_search_type=search_type,
+                )
+            # phase.pre_print(query)
+            output_message, output_memory = self.phase.step(query)
+            output_content = "\n\n".join((output_memory.to_str_messages(return_all=True, content_key="parsed_output_list").split("\n\n")[1:])) or output_message.role_content
+            return output_content
+        except Exception as e:
+            logger.exception(e)
+            return f"Error {e}"
+
+    def build_config(self, embeddings: Embeddings = None, llm: BaseLLM = None):
+        self.llm_config = LLMConfig(model_name="test", llm=self.llm or llm)
+        self.embed_config = EmbedConfig(embed_model="test", langchain_embeddings=self.embeddings or embeddings)
+
+    def build_phase(self, embeddings: Embeddings = None, llm: BaseLLM = None):
+        # phase 实例化
+        phase_module = importlib.import_module("coagent.connector.phase")
+        basePhase: BasePhase = getattr(phase_module, self.phase_type)
+
+        # chain 实例化
+        self.build_config(self.embeddings or embeddings, self.llm or llm)
+        os.environ["log_verbose"] = "2"
+        for chain_flow in self.chain_flows:
+            chain_flow.build_chain(
+                self.embeddings or embeddings, self.llm or llm,
+                self.doc_retrieval, self.code_retrieval, self.search_retrieval
+                )
+
+        self.phase: BasePhase = basePhase(
+                phase_name=self.phase_name,
+                chains=[chain_flow.chain for chain_flow in self.chain_flows],
+                embed_config=self.embed_config,
+                llm_config=self.llm_config,
+                doc_retrieval=self.doc_retrieval,
+                code_retrieval=self.code_retrieval,
+                search_retrieval=self.search_retrieval
+                )

coagent/connector/chains/base_chain.py

@@ -1,9 +1,10 @@
-from typing import List
+from typing import List, Tuple, Union
 from loguru import logger
 import copy, os
 
-from coagent.connector.agents import BaseAgent
+from langchain.schema import BaseRetriever
 
+from coagent.connector.agents import BaseAgent
 from coagent.connector.schema import (
     Memory, Role, Message, ActionStatus, ChainConfig,
     load_role_configs
@@ -11,31 +12,32 @@ from coagent.connector.schema import (
 from coagent.connector.memory_manager import BaseMemoryManager
 from coagent.connector.message_process import MessageUtils
 from coagent.llm_models.llm_config import LLMConfig, EmbedConfig
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
 from coagent.connector.configs.agent_config import AGETN_CONFIGS
 role_configs = load_role_configs(AGETN_CONFIGS)
 
-# from configs.model_config import JUPYTER_WORK_PATH
-# from configs.server_config import SANDBOX_SERVER
-
 
 class BaseChain:
     def __init__(
             self, 
-            # chainConfig: ChainConfig,
+            chainConfig: ChainConfig,
             agents: List[BaseAgent],
-            chat_turn: int = 1,
+            # chat_turn: int = 1,
-            do_checker: bool = False,
+            # do_checker: bool = False,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
             llm_config: LLMConfig = LLMConfig(),
             embed_config: EmbedConfig = None,
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ) -> None:
-        # self.chainConfig = chainConfig
+        self.chainConfig = chainConfig
         self.agents: List[BaseAgent] = agents
-        self.chat_turn = chat_turn
+        self.chat_turn = chainConfig.chat_turn
-        self.do_checker = do_checker
+        self.do_checker = chainConfig.do_checker
         self.sandbox_server = sandbox_server
         self.jupyter_work_path = jupyter_work_path
         self.llm_config = llm_config
@@ -45,9 +47,11 @@ class BaseChain:
             task = None, memory = None,
             llm_config=llm_config, embed_config=embed_config,
             sandbox_server=sandbox_server, jupyter_work_path=jupyter_work_path,
-            kb_root_path=kb_root_path
+            kb_root_path=kb_root_path,
+            doc_retrieval=doc_retrieval, code_retrieval=code_retrieval,
+            search_retrieval=search_retrieval
             )
-        self.messageUtils = MessageUtils(None, sandbox_server, self.jupyter_work_path, embed_config, llm_config, kb_root_path, log_verbose)
+        self.messageUtils = MessageUtils(None, sandbox_server, self.jupyter_work_path, embed_config, llm_config, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose)
         # all memory created by agent until instance deleted
         self.global_memory = Memory(messages=[])
 
@@ -62,13 +66,16 @@ class BaseChain:
         for agent in self.agents:
             agent.pre_print(query, history, background=background, memory_manager=memory_manager)
     
-    def astep(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager = None) -> Message:
+    def astep(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager = None) -> Tuple[Message, Memory]:
         '''execute chain'''
         local_memory = Memory(messages=[])
         input_message = copy.deepcopy(query)
         step_nums = copy.deepcopy(self.chat_turn)
         check_message = None
 
+        # if input_message not in memory_manager:
+        #     memory_manager.append(input_message)
+
         self.global_memory.append(input_message)
         # local_memory.append(input_message)
         while step_nums > 0:
@@ -78,7 +85,7 @@ class BaseChain:
                     yield output_message, local_memory + output_message
                 output_message = self.messageUtils.inherit_extrainfo(input_message, output_message)
                 # according the output to choose one action for code_content or tool_content
-                output_message = self.messageUtils.parser(output_message)
+                # output_message = self.messageUtils.parser(output_message)
                 yield output_message, local_memory + output_message
                 # output_message = self.step_router(output_message)
                 input_message = output_message

coagent/connector/configs/__init__.py

@@ -1,9 +1,10 @@
 from .agent_config import AGETN_CONFIGS
 from .chain_config import CHAIN_CONFIGS
 from .phase_config import PHASE_CONFIGS
-from .prompt_config import BASE_PROMPT_CONFIGS, EXECUTOR_PROMPT_CONFIGS, SELECTOR_PROMPT_CONFIGS, BASE_NOTOOLPROMPT_CONFIGS
+from .prompt_config import *
 
 __all__ = [
     "AGETN_CONFIGS", "CHAIN_CONFIGS", "PHASE_CONFIGS", 
-    "BASE_PROMPT_CONFIGS", "EXECUTOR_PROMPT_CONFIGS", "SELECTOR_PROMPT_CONFIGS", "BASE_NOTOOLPROMPT_CONFIGS"
+    "BASE_PROMPT_CONFIGS", "EXECUTOR_PROMPT_CONFIGS", "SELECTOR_PROMPT_CONFIGS", "BASE_NOTOOLPROMPT_CONFIGS",
+    "CODE2DOC_GROUP_PROMPT_CONFIGS", "CODE2DOC_PROMPT_CONFIGS", "CODE2TESTS_PROMPT_CONFIGS"
     ]

coagent/connector/configs/agent_config.py

@@ -1,19 +1,21 @@
 from enum import Enum
-from .prompts import (
+from .prompts import *
-    REACT_PROMPT_INPUT, CHECK_PROMPT_INPUT, EXECUTOR_PROMPT_INPUT, CONTEXT_PROMPT_INPUT, QUERY_CONTEXT_PROMPT_INPUT,PLAN_PROMPT_INPUT,
+# from .prompts import (
-    RECOGNIZE_INTENTION_PROMPT,
+#     REACT_PROMPT_INPUT, CHECK_PROMPT_INPUT, EXECUTOR_PROMPT_INPUT, CONTEXT_PROMPT_INPUT, QUERY_CONTEXT_PROMPT_INPUT,PLAN_PROMPT_INPUT,
-    CHECKER_TEMPLATE_PROMPT,
+#     RECOGNIZE_INTENTION_PROMPT,
-    CONV_SUMMARY_PROMPT,
+#     CHECKER_TEMPLATE_PROMPT,
-    QA_PROMPT, CODE_QA_PROMPT, QA_TEMPLATE_PROMPT,
+#     CONV_SUMMARY_PROMPT,
-    EXECUTOR_TEMPLATE_PROMPT,
+#     QA_PROMPT, CODE_QA_PROMPT, QA_TEMPLATE_PROMPT,
-    REFINE_TEMPLATE_PROMPT,
+#     EXECUTOR_TEMPLATE_PROMPT,
-    SELECTOR_AGENT_TEMPLATE_PROMPT,
+#     REFINE_TEMPLATE_PROMPT,
-    PLANNER_TEMPLATE_PROMPT, GENERAL_PLANNER_PROMPT, DATA_PLANNER_PROMPT, TOOL_PLANNER_PROMPT,
+#     SELECTOR_AGENT_TEMPLATE_PROMPT,
-    PRD_WRITER_METAGPT_PROMPT, DESIGN_WRITER_METAGPT_PROMPT, TASK_WRITER_METAGPT_PROMPT, CODE_WRITER_METAGPT_PROMPT,
+#     PLANNER_TEMPLATE_PROMPT, GENERAL_PLANNER_PROMPT, DATA_PLANNER_PROMPT, TOOL_PLANNER_PROMPT,
-    REACT_TEMPLATE_PROMPT,
+#     PRD_WRITER_METAGPT_PROMPT, DESIGN_WRITER_METAGPT_PROMPT, TASK_WRITER_METAGPT_PROMPT, CODE_WRITER_METAGPT_PROMPT,
-    REACT_TOOL_PROMPT, REACT_CODE_PROMPT, REACT_TOOL_AND_CODE_PLANNER_PROMPT, REACT_TOOL_AND_CODE_PROMPT
+#     REACT_TEMPLATE_PROMPT,
-)
+#     REACT_TOOL_PROMPT, REACT_CODE_PROMPT, REACT_TOOL_AND_CODE_PLANNER_PROMPT, REACT_TOOL_AND_CODE_PROMPT
-from .prompt_config import BASE_PROMPT_CONFIGS, EXECUTOR_PROMPT_CONFIGS, SELECTOR_PROMPT_CONFIGS, BASE_NOTOOLPROMPT_CONFIGS
+# )
+from .prompt_config import *
+# BASE_PROMPT_CONFIGS, EXECUTOR_PROMPT_CONFIGS, SELECTOR_PROMPT_CONFIGS, BASE_NOTOOLPROMPT_CONFIGS
 
 
 
@@ -261,4 +263,68 @@ AGETN_CONFIGS = {
         "focus_agents": ["metaGPT_DESIGN", "metaGPT_TASK"],
         "focus_message_keys": [],
     },
+    "class2Docer": {
+        "role": {
+            "role_prompt": Class2Doc_PROMPT,
+            "role_type": "assistant",
+            "role_name": "class2Docer",
+            "role_desc": "",
+            "agent_type": "CodeGenDocer"
+        },
+        "prompt_config": CODE2DOC_PROMPT_CONFIGS,
+        "prompt_manager_type": "Code2DocPM",
+        "chat_turn": 1,
+        "focus_agents": [],
+        "focus_message_keys": [],
+    },
+    "func2Docer": {
+        "role": {
+            "role_prompt": Func2Doc_PROMPT,
+            "role_type": "assistant",
+            "role_name": "func2Docer",
+            "role_desc": "",
+            "agent_type": "CodeGenDocer"
+        },
+        "prompt_config": CODE2DOC_PROMPT_CONFIGS,
+        "prompt_manager_type": "Code2DocPM",
+        "chat_turn": 1,
+        "focus_agents": [],
+        "focus_message_keys": [],
+    },
+    "code2DocsGrouper": {
+        "role": {
+            "role_prompt": Code2DocGroup_PROMPT,
+            "role_type": "assistant",
+            "role_name": "code2DocsGrouper",
+            "role_desc": "",
+            "agent_type": "SelectorAgent"
+        },
+        "prompt_config": CODE2DOC_GROUP_PROMPT_CONFIGS,
+        "group_agents": ["class2Docer", "func2Docer"],
+        "chat_turn": 1,
+    },
+    "Code2TestJudger": {
+        "role": {
+            "role_prompt": judgeCode2Tests_PROMPT,
+            "role_type": "assistant",
+            "role_name": "Code2TestJudger",
+            "role_desc": "",
+            "agent_type": "CodeRetrieval"
+        },
+        "prompt_config": CODE2TESTS_PROMPT_CONFIGS,
+        "prompt_manager_type": "CodeRetrievalPM",
+        "chat_turn": 1,
+    },
+    "code2Tests": {
+        "role": {
+            "role_prompt": code2Tests_PROMPT,
+            "role_type": "assistant",
+            "role_name": "code2Tests",
+            "role_desc": "",
+            "agent_type": "CodeRetrieval"
+        },
+        "prompt_config": CODE2TESTS_PROMPT_CONFIGS,
+        "prompt_manager_type": "CodeRetrievalPM",
+        "chat_turn": 1,
+    },
 }

coagent/connector/configs/chain_config.py

@@ -123,5 +123,21 @@ CHAIN_CONFIGS = {
         "chat_turn": 1,
         "do_checker": False,
         "chain_prompt": ""
+    },
+    "code2DocsGroupChain": {
+        "chain_name": "code2DocsGroupChain",
+        "chain_type": "BaseChain",
+        "agents": ["code2DocsGrouper"],
+        "chat_turn": 1,
+        "do_checker": False,
+        "chain_prompt": ""
+    },
+    "code2TestsChain": {
+        "chain_name": "code2TestsChain",
+        "chain_type": "BaseChain",
+        "agents": ["Code2TestJudger", "code2Tests"],
+        "chat_turn": 1,
+        "do_checker": False,
+        "chain_prompt": ""
     }
 }

coagent/connector/configs/phase_config.py

@@ -14,44 +14,24 @@ PHASE_CONFIGS = {
         "phase_name": "docChatPhase",
         "phase_type": "BasePhase",
         "chains": ["docChatChain"],
-        "do_summary": False,
-        "do_search": False,
         "do_doc_retrieval": True,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "searchChatPhase": {
         "phase_name": "searchChatPhase",
         "phase_type": "BasePhase",
         "chains": ["searchChatChain"],
-        "do_summary": False,
         "do_search": True,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "codeChatPhase": {
         "phase_name": "codeChatPhase",
         "phase_type": "BasePhase",
         "chains": ["codeChatChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
         "do_code_retrieval": True,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "toolReactPhase": {
         "phase_name": "toolReactPhase",
         "phase_type": "BasePhase",
         "chains": ["toolReactChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
         "do_using_tool": True
     },
     "codeReactPhase": {
@@ -59,55 +39,36 @@ PHASE_CONFIGS = {
         "phase_type": "BasePhase",
         # "chains": ["codePlannerChain", "codeReactChain"],
         "chains": ["planChain", "codeReactChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "codeToolReactPhase": {
         "phase_name": "codeToolReactPhase",
         "phase_type": "BasePhase",
         "chains": ["codeToolPlanChain", "codeToolReactChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
         "do_using_tool": True
     },
     "baseTaskPhase": {
         "phase_name": "baseTaskPhase",
         "phase_type": "BasePhase",
         "chains": ["planChain", "executorChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "metagpt_code_devlop": {
         "phase_name": "metagpt_code_devlop",
         "phase_type": "BasePhase",
         "chains": ["metagptChain",],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
     "baseGroupPhase": {
         "phase_name": "baseGroupPhase",
         "phase_type": "BasePhase",
         "chains": ["baseGroupChain"],
-        "do_summary": False,
-        "do_search": False,
-        "do_doc_retrieval": False,
-        "do_code_retrieval": False,
-        "do_tool_retrieval": False,
-        "do_using_tool": False
     },
+    "code2DocsGroup": {
+        "phase_name": "code2DocsGroup",
+        "phase_type": "BasePhase",
+        "chains": ["code2DocsGroupChain"],
+    },
+    "code2Tests": {
+        "phase_name": "code2Tests",
+        "phase_type": "BasePhase",
+        "chains": ["code2TestsChain"],
+    }
 }

coagent/connector/configs/prompt_config.py

@@ -41,3 +41,40 @@ SELECTOR_PROMPT_CONFIGS = [
     {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
     {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
     ]
+
+
+CODE2DOC_GROUP_PROMPT_CONFIGS = [
+    {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+    {"field_name": 'agent_infomation', "function_name": 'handle_agent_data', "is_context": False, "omit_if_empty": False},
+    # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
+    {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+    # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
+    {"field_name": 'session_records', "function_name": 'handle_session_records'},
+    {"field_name": 'Specific Objective', "function_name": 'handle_specific_objective'},
+    {"field_name": 'Code Snippet', "function_name": 'handle_code_snippet'},
+    {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+    {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+]
+
+CODE2DOC_PROMPT_CONFIGS = [
+    {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+    # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
+    {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+    # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
+    {"field_name": 'session_records', "function_name": 'handle_session_records'},
+    {"field_name": 'Specific Objective', "function_name": 'handle_specific_objective'},
+    {"field_name": 'Code Snippet', "function_name": 'handle_code_snippet'},
+    {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+    {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+]
+
+
+CODE2TESTS_PROMPT_CONFIGS = [
+    {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+    {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+    {"field_name": 'session_records', "function_name": 'handle_session_records'},
+    {"field_name": 'code_snippet', "function_name": 'handle_code_snippet'},
+    {"field_name": 'retrieval_codes', "function_name": 'handle_retrieval_codes', "description": ""},
+    {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+    {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+]

coagent/connector/configs/prompts/__init__.py

@@ -14,7 +14,8 @@ from .qa_template_prompt import QA_PROMPT, CODE_QA_PROMPT, QA_TEMPLATE_PROMPT, C
 
 from .executor_template_prompt import EXECUTOR_TEMPLATE_PROMPT
 from .refine_template_prompt import REFINE_TEMPLATE_PROMPT
-
+from .code2doc_template_prompt import Code2DocGroup_PROMPT, Class2Doc_PROMPT, Func2Doc_PROMPT
+from .code2test_template_prompt import code2Tests_PROMPT, judgeCode2Tests_PROMPT
 from .agent_selector_template_prompt import SELECTOR_AGENT_TEMPLATE_PROMPT
 
 from .react_template_prompt import REACT_TEMPLATE_PROMPT
@@ -37,5 +38,7 @@ __all__ = [
     "SELECTOR_AGENT_TEMPLATE_PROMPT",
     "PLANNER_TEMPLATE_PROMPT", "GENERAL_PLANNER_PROMPT", "DATA_PLANNER_PROMPT", "TOOL_PLANNER_PROMPT",
     "REACT_TEMPLATE_PROMPT", 
-    "REACT_CODE_PROMPT", "REACT_TOOL_PROMPT", "REACT_TOOL_AND_CODE_PROMPT", "REACT_TOOL_AND_CODE_PLANNER_PROMPT"
+    "REACT_CODE_PROMPT", "REACT_TOOL_PROMPT", "REACT_TOOL_AND_CODE_PROMPT", "REACT_TOOL_AND_CODE_PLANNER_PROMPT",
+    "Code2DocGroup_PROMPT", "Class2Doc_PROMPT", "Func2Doc_PROMPT",
+    "code2Tests_PROMPT", "judgeCode2Tests_PROMPT"
 ]

coagent/connector/configs/prompts/code2doc_template_prompt.py

@@ -0,0 +1,95 @@
+Code2DocGroup_PROMPT = """#### Agent Profile
+
+Your goal is to response according the Context Data's information with the role that will best facilitate a solution, taking into account all relevant context (Context) provided.
+
+When you need to select the appropriate role for handling a user's query, carefully read the provided role names, role descriptions and tool list.
+
+ATTENTION: response carefully referenced "Response Output Format" in format.
+
+#### Input Format
+
+#### Response Output Format
+
+**Code Path:** Extract the paths for the class/method/function that need to be addressed from the context
+
+**Role:** Select the role from agent names
+"""
+
+Class2Doc_PROMPT = """#### Agent Profile
+As an advanced code documentation generator, you are proficient in translating class definitions into comprehensive documentation with a focus on instantiation parameters. 
+Your specific task is to parse the given code snippet of a class, extract information regarding its instantiation parameters.
+
+ATTENTION: response carefully in "Response Output Format".
+
+#### Input Format
+
+**Code Snippet:** Provide the full class definition, including the constructor and any parameters it may require for instantiation.
+
+#### Response Output Format
+**Class Base:** Specify the base class or interface from which the current class extends, if any.
+
+**Class Description:** Offer a brief description of the class's purpose and functionality.
+
+**Init Parameters:** List each parameter from construct. For each parameter, provide:
+    - `param`: The parameter name
+    - `param_description`: A concise explanation of the parameter's purpose.
+    - `param_type`: The data type of the parameter, if explicitly defined.
+
+    ```json
+    [
+        {
+            "param": "parameter_name",
+            "param_description": "A brief description of what this parameter is used for.",
+            "param_type": "The data type of the parameter"
+        },
+        ...
+    ]
+    ```
+
+        
+    If no parameter for construct, return 
+    ```json
+    []
+    ```
+"""
+
+Func2Doc_PROMPT = """#### Agent Profile
+You are a high-level code documentation assistant, skilled at extracting information from function/method code into detailed and well-structured documentation.
+
+ATTENTION: response carefully in "Response Output Format".
+
+
+#### Input Format
+**Code Path:** Provide the code path of the function or method you wish to document. 
+This name will be used to identify and extract the relevant details from the code snippet provided.
+    
+**Code Snippet:** A segment of code that contains the function or method to be documented.
+
+#### Response Output Format
+
+**Class Description:** Offer a brief description of the method(function)'s purpose and functionality.
+
+**Parameters:** Extract parameter for the specific function/method Code from Code Snippet. For parameter, provide:
+    - `param`: The parameter name
+    - `param_description`: A concise explanation of the parameter's purpose.
+    - `param_type`: The data type of the parameter, if explicitly defined.
+    ```json
+    [
+        {
+            "param": "parameter_name",
+            "param_description": "A brief description of what this parameter is used for.",
+            "param_type": "The data type of the parameter"
+        },
+        ...
+    ]
+    ```
+
+    If no parameter for function/method, return 
+    ```json
+    []
+    ```
+
+**Return Value Description:** Describe what the function/method returns upon completion.
+
+**Return Type:** Indicate the type of data the function/method returns (e.g., string, integer, object, void).
+"""

coagent/connector/configs/prompts/code2test_template_prompt.py

@@ -0,0 +1,65 @@
+judgeCode2Tests_PROMPT = """#### Agent Profile
+When determining the necessity of writing test cases for a given code snippet, 
+it's essential to evaluate its interactions with dependent classes and methods (retrieved code snippets), 
+in addition to considering these critical factors:
+1. Functionality: If it implements a concrete function or logic, test cases are typically necessary to verify its correctness.
+2. Complexity: If the code is complex, especially if it contains multiple conditional statements, loops, exceptions handling, etc., 
+it's more likely to harbor bugs, and thus test cases should be written. 
+If the code involves complex algorithms or logic, then writing test cases can help ensure the accuracy of the logic and prevent errors during future refactoring.
+3. Criticality: If it's part of the critical path or affects core functionalities, then it needs to be tested. 
+Comprehensive test cases should be written for core business logic or key components of the system to ensure the correctness and stability of the functionality.
+4. Dependencies: If the code has external dependencies, integration testing may be necessary, or mocking these dependencies during unit testing might be required.
+5. User Input: If the code handles user input, especially from unregulated external sources, creating test cases to check input validation and handling is important.
+6. Frequent Changes: For code that requires regular updates or modifications, having the appropriate test cases ensures that changes do not break existing functionalities.
+
+#### Input Format
+
+**Code Snippet:** the initial Code or objective that the user wanted to achieve
+
+**Retrieval Code Snippets:** These are the associated code segments that the main Code Snippet depends on. 
+Examine these snippets to understand how they interact with the main snippet and to determine how they might affect the overall functionality.
+
+#### Response Output Format
+**Action Status:** Set to 'finished' or 'continued'. 
+If set to 'finished', the code snippet does not warrant the generation of a test case.
+If set to 'continued', the code snippet necessitates the creation of a test case.
+
+**REASON:** Justify the selection of 'finished' or 'continued', contemplating the decision through a step-by-step rationale.
+"""
+
+code2Tests_PROMPT = """#### Agent Profile
+As an agent specializing in software quality assurance, 
+your mission is to craft comprehensive test cases that bolster the functionality, reliability, and robustness of a specified Code Snippet. 
+This task is to be carried out with a keen understanding of the snippet's interactions with its dependent classes and methods—collectively referred to as Retrieval Code Snippets. 
+Analyze the details given below to grasp the code's intended purpose, its inherent complexity, and the context within which it operates. 
+Your constructed test cases must thoroughly examine the various factors influencing the code's quality and performance.
+
+ATTENTION: response carefully referenced "Response Output Format" in format.
+
+Each test case should include:
+1. clear description of the test purpose.
+2. The input values or conditions for the test.
+3. The expected outcome or assertion for the test.
+4. Appropriate tags (e.g., 'functional', 'integration', 'regression') that classify the type of test case.
+5. these test code should have package and import
+
+#### Input Format
+
+**Code Snippet:** the initial Code or objective that the user wanted to achieve
+
+**Retrieval Code Snippets:** These are the interrelated pieces of code sourced from the codebase, which support or influence the primary Code Snippet.
+
+#### Response Output Format
+**SaveFileName:** construct a local file name based on Question and Context, such as
+
+```java
+package/class.java
+```
+
+
+**Test Code:** generate the test code for the current Code Snippet.
+```java
+...
+```
+
+"""

coagent/connector/memory_manager.py

@@ -1,5 +1,5 @@
 from abc import abstractmethod, ABC
-from typing import List
+from typing import List, Dict
 import os, sys, copy, json
 from jieba.analyse import extract_tags
 from collections import Counter
@@ -10,12 +10,13 @@ from langchain.docstore.document import Document
 
 from .schema import Memory, Message
 from coagent.service.service_factory import KBServiceFactory
-from coagent.llm_models import getChatModel, getChatModelFromConfig
+from coagent.llm_models import getChatModelFromConfig
 from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
 from coagent.embeddings.utils import load_embeddings_from_path
 from coagent.utils.common_utils import save_to_json_file, read_json_file, addMinutesToTime
 from coagent.connector.configs.prompts import CONV_SUMMARY_PROMPT_SPEC
 from coagent.orm import table_init
+from coagent.base_configs.env_config import KB_ROOT_PATH
 # from configs.model_config import KB_ROOT_PATH, EMBEDDING_MODEL, EMBEDDING_DEVICE, SCORE_THRESHOLD
 # from configs.model_config import embedding_model_dict
 
@@ -70,16 +71,22 @@ class BaseMemoryManager(ABC):
         self.unique_name = unique_name
         self.memory_type = memory_type
         self.do_init = do_init
-        self.current_memory = Memory(messages=[])
+        # self.current_memory = Memory(messages=[])
-        self.recall_memory = Memory(messages=[])
+        # self.recall_memory = Memory(messages=[])
-        self.summary_memory = Memory(messages=[])
+        # self.summary_memory = Memory(messages=[])
+        self.current_memory_dict: Dict[str, Memory] = {}
+        self.recall_memory_dict: Dict[str, Memory] = {}
+        self.summary_memory_dict: Dict[str, Memory] = {}
         self.save_message_keys = [
             'chat_index', 'role_name', 'role_type', 'role_prompt', 'input_query', 'origin_query',
             'datetime', 'role_content', 'step_content', 'parsed_output', 'spec_parsed_output', 'parsed_output_list', 
             'task', 'db_docs', 'code_docs', 'search_docs', 'phase_name', 'chain_name', 'customed_kargs']
         self.init_vb()
 
-    def init_vb(self):
+    def re_init(self, do_init: bool=False):
+        self.init_vb()
+
+    def init_vb(self, do_init: bool=None):
         """
         Initializes the vb.
         """
@@ -135,13 +142,15 @@ class BaseMemoryManager(ABC):
         """
         pass
 
-    def save_to_vs(self, embed_model="", embed_device=""):
+    def save_to_vs(self, ):
         """
         Saves the memory to the vector space.
+        """
+        pass
 
-        Args:
+    def get_memory_pool(self, user_name: str, ):
-        - embed_model: A string representing the embedding model. Default is EMBEDDING_MODEL.
+        """
-        - embed_device: A string representing the embedding device. Default is EMBEDDING_DEVICE.
+        return memory_pool
         """
         pass
 
@@ -230,7 +239,7 @@ class LocalMemoryManager(BaseMemoryManager):
             unique_name: str = "default",
             memory_type: str = "recall",
             do_init: bool = False,
-            kb_root_path: str = "",
+            kb_root_path: str = KB_ROOT_PATH,
         ):
         self.user_name = user_name
         self.unique_name = unique_name
@@ -239,16 +248,22 @@ class LocalMemoryManager(BaseMemoryManager):
         self.kb_root_path = kb_root_path
         self.embed_config: EmbedConfig = embed_config
         self.llm_config: LLMConfig = llm_config
-        self.current_memory = Memory(messages=[])
+        # self.current_memory = Memory(messages=[])
-        self.recall_memory = Memory(messages=[])
+        # self.recall_memory = Memory(messages=[])
-        self.summary_memory = Memory(messages=[])
+        # self.summary_memory = Memory(messages=[])
+        self.current_memory_dict: Dict[str, Memory] = {}
+        self.recall_memory_dict: Dict[str, Memory] = {}
+        self.summary_memory_dict: Dict[str, Memory] = {}
         self.save_message_keys = [
             'chat_index', 'role_name', 'role_type', 'role_prompt', 'input_query', 'origin_query',
             'datetime', 'role_content', 'step_content', 'parsed_output', 'spec_parsed_output', 'parsed_output_list', 
             'task', 'db_docs', 'code_docs', 'search_docs', 'phase_name', 'chain_name', 'customed_kargs']
         self.init_vb()
 
-    def init_vb(self):
+    def re_init(self, do_init: bool=False):
+        self.init_vb(do_init)
+
+    def init_vb(self, do_init: bool=None):
         vb_name = f"{self.user_name}/{self.unique_name}/{self.memory_type}"
         # default to recreate a new vb
         table_init()
@@ -256,31 +271,37 @@ class LocalMemoryManager(BaseMemoryManager):
         if vb:
             status = vb.clear_vs()
 
-        if not self.do_init:
+        check_do_init = do_init if do_init else self.do_init
+        if not check_do_init:
             self.load(self.kb_root_path)
             self.save_to_vs()
 
     def append(self, message: Message):
-        self.recall_memory.append(message)
+        self.check_user_name(message.user_name)
+
+        uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
+        self.recall_memory_dict[uuid_name].append(message)
         # 
         if message.role_type == "summary":
-            self.summary_memory.append(message)
+            self.summary_memory_dict[uuid_name].append(message)
         else:
-            self.current_memory.append(message)
+            self.current_memory_dict[uuid_name].append(message)
 
         self.save(self.kb_root_path)
         self.save_new_to_vs([message])
 
-    def extend(self, memory: Memory):
+    # def extend(self, memory: Memory):
-        self.recall_memory.extend(memory)
+    #     self.recall_memory.extend(memory)
-        self.current_memory.extend(self.recall_memory.filter_by_role_type(["summary"]))
+    #     self.current_memory.extend(self.recall_memory.filter_by_role_type(["summary"]))
-        self.summary_memory.extend(self.recall_memory.select_by_role_type(["summary"]))
+    #     self.summary_memory.extend(self.recall_memory.select_by_role_type(["summary"]))
-        self.save(self.kb_root_path)
+    #     self.save(self.kb_root_path)
-        self.save_new_to_vs(memory.messages)
+    #     self.save_new_to_vs(memory.messages)
 
     def save(self, save_dir: str = "./"):
         file_path = os.path.join(save_dir, f"{self.user_name}/{self.unique_name}/{self.memory_type}/converation.jsonl")
-        memory_messages = self.recall_memory.dict()
+        uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
+
+        memory_messages = self.recall_memory_dict[uuid_name].dict()
         memory_messages = {k: [
                 {kkk: vvv for kkk, vvv in vv.items() if kkk in self.save_message_keys}
                 for vv in v ] 
@@ -291,18 +312,28 @@ class LocalMemoryManager(BaseMemoryManager):
 
     def load(self, load_dir: str = "./") -> Memory:
         file_path = os.path.join(load_dir, f"{self.user_name}/{self.unique_name}/{self.memory_type}/converation.jsonl")
+        uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
 
         if os.path.exists(file_path):
-            self.recall_memory = Memory(**read_json_file(file_path))
+            # self.recall_memory = Memory(**read_json_file(file_path))
-            self.current_memory = Memory(messages=self.recall_memory.filter_by_role_type(["summary"]))
+            # self.current_memory = Memory(messages=self.recall_memory.filter_by_role_type(["summary"]))
-            self.summary_memory = Memory(messages=self.recall_memory.select_by_role_type(["summary"]))
+            # self.summary_memory = Memory(messages=self.recall_memory.select_by_role_type(["summary"]))
+
+            recall_memory = Memory(**read_json_file(file_path))
+            self.recall_memory_dict[uuid_name] = recall_memory
+            self.current_memory_dict[uuid_name] = Memory(messages=recall_memory.filter_by_role_type(["summary"]))
+            self.summary_memory_dict[uuid_name] = Memory(messages=recall_memory.select_by_role_type(["summary"]))
+        else:
+            self.recall_memory_dict[uuid_name] = Memory(messages=[])
+            self.current_memory_dict[uuid_name] = Memory(messages=[])
+            self.summary_memory_dict[uuid_name] = Memory(messages=[])
 
     def save_new_to_vs(self, messages: List[Message]):
         if self.embed_config:
             vb_name = f"{self.user_name}/{self.unique_name}/{self.memory_type}"
             # default to faiss, todo: add new vstype
             vb = KBServiceFactory.get_service(vb_name, "faiss", self.embed_config, self.kb_root_path)
-            embeddings = load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device,)
+            embeddings = load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device, self.embed_config.langchain_embeddings)
             messages = [
                     {k: v for k, v in m.dict().items() if k in self.save_message_keys}
                     for m in messages] 
@@ -311,7 +342,10 @@ class LocalMemoryManager(BaseMemoryManager):
             vb.do_add_doc(docs, embeddings)
 
     def save_to_vs(self):
+        '''only after load'''
+        if self.embed_config:
             vb_name = f"{self.user_name}/{self.unique_name}/{self.memory_type}"
+            uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
             # default to recreate a new vb
             vb = KBServiceFactory.get_service_by_name(vb_name, self.embed_config, self.kb_root_path)
             if vb:
@@ -320,8 +354,8 @@ class LocalMemoryManager(BaseMemoryManager):
 
             # default to faiss, todo: add new vstype
             vb = KBServiceFactory.get_service(vb_name, "faiss", self.embed_config, self.kb_root_path)
-        embeddings = load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device,)
+            embeddings = load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device, self.embed_config.langchain_embeddings)
-        messages = self.recall_memory.dict()
+            messages = self.recall_memory_dict[uuid_name].dict()
             messages = [
                     {kkk: vvv for kkk, vvv in vv.items() if kkk in self.save_message_keys}
                     for k, v in messages.items() for vv in v] 
@@ -338,7 +372,12 @@ class LocalMemoryManager(BaseMemoryManager):
     #     docs =  vb.get_all_documents()
     #     print(docs)
 
-    def router_retrieval(self, text: str=None, datetime: str = None, n=5, top_k=5, retrieval_type: str = "embedding", **kwargs) -> List[Message]:
+    def get_memory_pool(self, user_name: str, ):
+        self.check_user_name(user_name)
+        uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
+        return self.recall_memory_dict[uuid_name]
+
+    def router_retrieval(self, user_name: str = "default", text: str=None, datetime: str = None, n=5, top_k=5, retrieval_type: str = "embedding", **kwargs) -> List[Message]:
         retrieval_func_dict = {
             "embedding": self.embedding_retrieval, "text": self.text_retrieval, "datetime": self.datetime_retrieval
             }
@@ -356,20 +395,22 @@ class LocalMemoryManager(BaseMemoryManager):
         # 
         return retrieval_func(**params)
         
-    def embedding_retrieval(self, text: str, top_k=1, score_threshold=1.0, **kwargs) -> List[Message]:
+    def embedding_retrieval(self, text: str, top_k=1, score_threshold=1.0, user_name: str = "default", **kwargs) -> List[Message]:
         if text is None: return []
-        vb_name = f"{self.user_name}/{self.unique_name}/{self.memory_type}"
+        vb_name = f"{user_name}/{self.unique_name}/{self.memory_type}"
         vb = KBServiceFactory.get_service(vb_name, "faiss", self.embed_config, self.kb_root_path)
         docs = vb.search_docs(text, top_k=top_k, score_threshold=score_threshold)
         return [Message(**doc.metadata) for doc, score in docs]
     
-    def text_retrieval(self, text: str, **kwargs)  -> List[Message]:
+    def text_retrieval(self, text: str, user_name: str = "default", **kwargs)  -> List[Message]:
         if text is None: return []
-        return self._text_retrieval_from_cache(self.recall_memory.messages, text, score_threshold=0.3, topK=5, **kwargs)
+        uuid_name = "_".join([user_name, self.unique_name, self.memory_type])
+        return self._text_retrieval_from_cache(self.recall_memory_dict[uuid_name].messages, text, score_threshold=0.3, topK=5, **kwargs)
 
-    def datetime_retrieval(self, datetime: str, text: str = None, n: int = 5, **kwargs) -> List[Message]:
+    def datetime_retrieval(self,  datetime: str, text: str = None, n: int = 5, user_name: str = "default", **kwargs) -> List[Message]:
         if datetime is None: return []
-        return self._datetime_retrieval_from_cache(self.recall_memory.messages, datetime, text, n, **kwargs)
+        uuid_name = "_".join([user_name, self.unique_name, self.memory_type])
+        return self._datetime_retrieval_from_cache(self.recall_memory_dict[uuid_name].messages, datetime, text, n, **kwargs)
     
     def _text_retrieval_from_cache(self, messages: List[Message], text: str = None, score_threshold=0.3, topK=5, tag_topK=5, **kwargs) -> List[Message]:
         keywords = extract_tags(text, topK=tag_topK)
@@ -428,3 +469,17 @@ class LocalMemoryManager(BaseMemoryManager):
         summary_message.parsed_output_list.append({"summary": content})
         newest_messages.insert(0, summary_message)
         return newest_messages
+    
+    def check_user_name(self, user_name: str):
+        # logger.debug(f"self.user_name is {self.user_name}")
+        if user_name != self.user_name:
+            self.user_name = user_name
+            self.init_vb()
+
+        uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
+        if uuid_name not in self.recall_memory_dict:
+            self.recall_memory_dict[uuid_name] = Memory(messages=[])
+            self.current_memory_dict[uuid_name] = Memory(messages=[])
+            self.summary_memory_dict[uuid_name] = Memory(messages=[])
+
+        # logger.debug(f"self.user_name is {self.user_name}")

coagent/connector/message_process.py

@@ -1,16 +1,19 @@
 import re, traceback, uuid, copy, json, os
+from typing import Union
 from loguru import logger
 
+from langchain.schema import BaseRetriever
 
-# from configs.server_config import SANDBOX_SERVER
-# from configs.model_config import JUPYTER_WORK_PATH
 from coagent.connector.schema import (
     Memory, Role, Message, ActionStatus, CodeDoc, Doc, LogVerboseEnum
 )
+from coagent.retrieval.base_retrieval import IMRertrieval
 from coagent.connector.memory_manager import BaseMemoryManager
 from coagent.tools import DDGSTool, DocRetrieval, CodeRetrieval
 from coagent.sandbox import PyCodeBox, CodeBoxResponse
 from coagent.llm_models.llm_config import LLMConfig, EmbedConfig
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH
+
 from .utils import parse_dict_to_dict, parse_text_to_dict
 
 
@@ -19,10 +22,13 @@ class MessageUtils:
             self, 
             role: Role = None,
             sandbox_server: dict = {},
-            jupyter_work_path: str = "./",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
             embed_config: EmbedConfig = None,
             llm_config: LLMConfig = None,
             kb_root_path: str = "",
+            doc_retrieval: Union[BaseRetriever, IMRertrieval] = None,
+            code_retrieval: IMRertrieval = None,
+            search_retrieval: IMRertrieval = None,
             log_verbose: str = "0"
         ) -> None:
         self.role = role
@@ -31,6 +37,9 @@ class MessageUtils:
         self.embed_config = embed_config
         self.llm_config = llm_config
         self.kb_root_path = kb_root_path
+        self.doc_retrieval = doc_retrieval
+        self.code_retrieval = code_retrieval
+        self.search_retrieval = search_retrieval
         self.codebox = PyCodeBox(
                     remote_url=self.sandbox_server.get("url", "http://127.0.0.1:5050"),
                     remote_ip=self.sandbox_server.get("host", "http://127.0.0.1"),
@@ -44,6 +53,7 @@ class MessageUtils:
         self.log_verbose = os.environ.get("log_verbose", "0") or log_verbose
     
     def inherit_extrainfo(self, input_message: Message, output_message: Message):
+        output_message.user_name = input_message.user_name
         output_message.db_docs = input_message.db_docs
         output_message.search_docs = input_message.search_docs
         output_message.code_docs = input_message.code_docs
@@ -116,18 +126,45 @@ class MessageUtils:
         knowledge_basename = message.doc_engine_name
         top_k = message.top_k
         score_threshold = message.score_threshold
+        if self.doc_retrieval:
+            if isinstance(self.doc_retrieval, BaseRetriever):
+                docs = self.doc_retrieval.get_relevant_documents(query)
+            else:
+                # docs = self.doc_retrieval.run(query, search_top=message.top_k, score_threshold=message.score_threshold,)
+                docs = self.doc_retrieval.run(query)
+            docs = [
+                {"index": idx, "snippet": doc.page_content, "title": doc.metadata.get("title_prefix", ""), "link": doc.metadata.get("url", "")}
+                for idx, doc in enumerate(docs)
+            ]
+            message.db_docs = [Doc(**doc) for doc in docs]
+        else:
             if knowledge_basename:
                 docs = DocRetrieval.run(query, knowledge_basename, top_k, score_threshold, self.embed_config, self.kb_root_path)
                 message.db_docs = [Doc(**doc) for doc in docs]
         return message
     
     def get_code_retrieval(self, message: Message) -> Message:
-        query = message.input_query
+        query = message.role_content
         code_engine_name = message.code_engine_name
         history_node_list = message.history_node_list
+
+        use_nh = message.use_nh
+        local_graph_path = message.local_graph_path
+
+        if self.code_retrieval:
+            code_docs = self.code_retrieval.run(
+                query, history_node_list=history_node_list, search_type=message.cb_search_type,
+                code_limit=1
+            )
+        else:
             code_docs = CodeRetrieval.run(code_engine_name, query, code_limit=message.top_k, history_node_list=history_node_list, search_type=message.cb_search_type,
-                                      llm_config=self.llm_config, embed_config=self.embed_config,)
+                                      llm_config=self.llm_config, embed_config=self.embed_config,
+                                      use_nh=use_nh, local_graph_path=local_graph_path)
+        
         message.code_docs = [CodeDoc(**doc) for doc in code_docs] 
+
+        # related_nodes = [doc.get_related_node() for idx, doc in enumerate(message.code_docs) if idx==0],
+        # history_node_list.extend([node[0] for node in related_nodes]) 
         return message
     
     def get_tool_retrieval(self, message: Message) -> Message:
@@ -160,6 +197,7 @@ class MessageUtils:
                     if code_answer.code_exe_type == "error" else f"The return information after executing the above code is {code_answer.code_exe_response}.\n"
         
         observation_message = Message(
+                user_name=message.user_name,
                 role_name="observation",
                 role_type="function", #self.role.role_type,
                 role_content="",
@@ -190,6 +228,7 @@ class MessageUtils:
     def tool_step(self, message: Message) -> Message:
         '''execute tool'''
         observation_message = Message(
+                user_name=message.user_name,
                 role_name="observation",
                 role_type="function", #self.role.role_type,
                 role_content="\n**Observation:** there is no tool can execute\n",
@@ -226,7 +265,7 @@ class MessageUtils:
         return message, observation_message
 
     def parser(self, message: Message) -> Message:
-        ''''''
+        '''parse llm output into dict'''
         content = message.role_content
         # parse start
         parsed_dict = parse_text_to_dict(content)

coagent/connector/phase/base_phase.py

@@ -5,6 +5,8 @@ import importlib
 import copy
 from loguru import logger
 
+from langchain.schema import BaseRetriever
+
 from coagent.connector.agents import BaseAgent
 from coagent.connector.chains import BaseChain
 from coagent.connector.schema import (
@@ -18,9 +20,6 @@ from coagent.connector.message_process import MessageUtils
 from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
 from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
 
-# from configs.model_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
-# from configs.server_config import SANDBOX_SERVER
-
 
 role_configs = load_role_configs(AGETN_CONFIGS)
 chain_configs = load_chain_configs(CHAIN_CONFIGS)
@@ -39,20 +38,24 @@ class BasePhase:
             kb_root_path: str = KB_ROOT_PATH,
             jupyter_work_path: str = JUPYTER_WORK_PATH,
             sandbox_server: dict = {},
-            embed_config: EmbedConfig = EmbedConfig(),
+            embed_config: EmbedConfig = None,
-            llm_config: LLMConfig = LLMConfig(),
+            llm_config: LLMConfig = None,
             task: Task = None,
             base_phase_config: Union[dict, str] = PHASE_CONFIGS,
             base_chain_config: Union[dict, str] = CHAIN_CONFIGS,
             base_role_config: Union[dict, str] = AGETN_CONFIGS,
+            chains: List[BaseChain] = [],
+            doc_retrieval: Union[BaseRetriever] = None,
+            code_retrieval = None,
+            search_retrieval = None,
             log_verbose: str = "0"
             ) -> None:
         # 
         self.phase_name = phase_name
         self.do_summary = False
-        self.do_search = False
+        self.do_search = search_retrieval is not None
-        self.do_code_retrieval = False
+        self.do_code_retrieval = code_retrieval is not None
-        self.do_doc_retrieval = False
+        self.do_doc_retrieval = doc_retrieval is not None
         self.do_tool_retrieval = False
         # memory_pool dont have specific order
         # self.memory_pool = Memory(messages=[])
@@ -62,12 +65,15 @@ class BasePhase:
         self.jupyter_work_path = jupyter_work_path
         self.kb_root_path = kb_root_path
         self.log_verbose = max(os.environ.get("log_verbose", "0"), log_verbose)
-        
+        # TODO透传
-        self.message_utils = MessageUtils(None, sandbox_server, jupyter_work_path, embed_config, llm_config, kb_root_path, log_verbose)
+        self.doc_retrieval = doc_retrieval
+        self.code_retrieval = code_retrieval
+        self.search_retrieval = search_retrieval
+        self.message_utils = MessageUtils(None, sandbox_server, jupyter_work_path, embed_config, llm_config, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose)
         self.global_memory = Memory(messages=[])
         self.phase_memory: List[Memory] = []
         # according phase name to init the phase contains
-        self.chains: List[BaseChain] = self.init_chains(
+        self.chains: List[BaseChain] = chains if chains else self.init_chains(
             phase_name,
             phase_config,
             task=task, 
@@ -90,7 +96,9 @@ class BasePhase:
             kb_root_path=kb_root_path
             )
 
-    def astep(self, query: Message, history: Memory = None) -> Tuple[Message, Memory]:
+    def astep(self, query: Message, history: Memory = None, reinit_memory=False) -> Tuple[Message, Memory]:
+        if reinit_memory:
+            self.memory_manager.re_init(reinit_memory)
         self.memory_manager.append(query)
         summary_message = None
         chain_message = Memory(messages=[])
@@ -139,8 +147,8 @@ class BasePhase:
         message.role_name = self.phase_name
         yield message, local_phase_memory
 
-    def step(self, query: Message, history: Memory = None) -> Tuple[Message, Memory]:
+    def step(self, query: Message, history: Memory = None, reinit_memory=False) -> Tuple[Message, Memory]:
-        for message, local_phase_memory in self.astep(query, history=history):
+        for message, local_phase_memory in self.astep(query, history=history, reinit_memory=reinit_memory):
             pass
         return message, local_phase_memory
 
@@ -194,6 +202,9 @@ class BasePhase:
                     sandbox_server=self.sandbox_server,
                     jupyter_work_path=self.jupyter_work_path,
                     kb_root_path=self.kb_root_path,
+                    doc_retrieval=self.doc_retrieval,
+                    code_retrieval=self.code_retrieval,
+                    search_retrieval=self.search_retrieval,
                     log_verbose=self.log_verbose
                 ) 
                 if agent_config.role.agent_type == "SelectorAgent":
@@ -205,7 +216,7 @@ class BasePhase:
                         group_base_agent = baseAgent(
                             role=group_agent_config.role, 
                             prompt_config = group_agent_config.prompt_config,
-                            prompt_manager_type=agent_config.prompt_manager_type,
+                            prompt_manager_type=group_agent_config.prompt_manager_type,
                             task = task,
                             memory = memory,
                             chat_turn=group_agent_config.chat_turn,
@@ -216,6 +227,9 @@ class BasePhase:
                             sandbox_server=self.sandbox_server,
                             jupyter_work_path=self.jupyter_work_path,
                             kb_root_path=self.kb_root_path,
+                            doc_retrieval=self.doc_retrieval,
+                            code_retrieval=self.code_retrieval,
+                            search_retrieval=self.search_retrieval,
                             log_verbose=self.log_verbose
                         ) 
                         base_agent.group_agents.append(group_base_agent)
@@ -223,13 +237,16 @@ class BasePhase:
                 agents.append(base_agent)
             
             chain_instance = BaseChain(
-                agents, chain_config.chat_turn, 
+                chain_config,
-                do_checker=chain_configs[chain_name].do_checker, 
+                agents, 
                 jupyter_work_path=self.jupyter_work_path,
                 sandbox_server=self.sandbox_server,
                 embed_config=self.embed_config,
                 llm_config=self.llm_config,
                 kb_root_path=self.kb_root_path,
+                doc_retrieval=self.doc_retrieval,
+                code_retrieval=self.code_retrieval,
+                search_retrieval=self.search_retrieval,
                 log_verbose=self.log_verbose
                 )
             chains.append(chain_instance)

coagent/connector/prompt_manager/__init__.py

@@ -0,0 +1,2 @@
+from .prompt_manager import PromptManager
+from .extend_manager import *

coagent/connector/prompt_manager/extend_manager.py

@@ -0,0 +1,45 @@
+
+from coagent.connector.schema import Message
+from .prompt_manager import PromptManager
+
+
+class Code2DocPM(PromptManager):
+    def handle_code_snippet(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message: Message = kwargs['previous_agent_message']
+        code_snippet = previous_agent_message.customed_kargs.get("Code Snippet", "")
+        current_vertex = previous_agent_message.customed_kargs.get("Current_Vertex", "")
+        instruction = "A segment of code that contains the function or method to be documented.\n"
+        return instruction + "\n" + f"name: {current_vertex}\n{code_snippet}"
+
+    def handle_specific_objective(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message: Message = kwargs['previous_agent_message']
+        specific_objective = previous_agent_message.parsed_output.get("Code Path")
+
+        instruction = "Provide the code path of the function or method you wish to document.\n"
+        s = instruction + f"\n{specific_objective}"
+        return s
+    
+
+class CodeRetrievalPM(PromptManager):
+    def handle_code_snippet(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message: Message = kwargs['previous_agent_message']
+        code_snippet = previous_agent_message.customed_kargs.get("Code Snippet", "")
+        current_vertex = previous_agent_message.customed_kargs.get("Current_Vertex", "")
+        instruction = "the initial Code or objective that the user wanted to achieve"
+        return instruction + "\n" + f"name: {current_vertex}\n{code_snippet}"
+
+    def handle_retrieval_codes(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message: Message = kwargs['previous_agent_message']
+        Retrieval_Codes = previous_agent_message.customed_kargs["Retrieval_Codes"]
+        Relative_vertex = previous_agent_message.customed_kargs["Relative_vertex"]
+        instruction = "the initial Code or objective that the user wanted to achieve"
+        s = instruction + "\n" + "\n".join([f"name: {vertext}\n{code}" for vertext, code in zip(Relative_vertex, Retrieval_Codes)])
+        return s

coagent/connector/prompt_manager/prompt_manager.py

@@ -0,0 +1,353 @@
+import random
+from textwrap import dedent
+import copy
+from loguru import logger
+
+from langchain.agents.tools import Tool
+
+from coagent.connector.schema import Memory, Message
+from coagent.connector.utils import extract_section, parse_section
+
+
+
+class PromptManager:
+    def __init__(self, role_prompt="", prompt_config=None, monitored_agents=[], monitored_fields=[]):
+        self.role_prompt = role_prompt
+        self.monitored_agents = monitored_agents
+        self.monitored_fields = monitored_fields
+        self.field_handlers = {}
+        self.context_handlers = {}
+        self.field_order = []  # 用于普通字段的顺序
+        self.context_order = []  # 单独维护上下文字段的顺序
+        self.field_descriptions = {}
+        self.omit_if_empty_flags = {}
+        self.context_title = "### Context Data\n\n"  
+        
+        self.prompt_config = prompt_config
+        if self.prompt_config:
+            self.register_fields_from_config()
+    
+    def register_field(self, field_name, function=None, title=None, description=None, is_context=True, omit_if_empty=True):
+        """
+        注册一个新的字段及其处理函数。
+        Args:
+            field_name (str): 字段名称。
+            function (callable): 处理字段数据的函数。
+            title (str, optional): 字段的自定义标题（可选）。
+            description (str, optional): 字段的描述（可选，可以是几句话）。
+            is_context (bool, optional): 指示该字段是否为上下文字段。
+            omit_if_empty (bool, optional): 如果数据为空，是否省略该字段。
+        """
+        if not function:
+            function = self.handle_custom_data
+        
+        # Register the handler function based on context flag
+        if is_context:
+            self.context_handlers[field_name] = function
+        else:
+            self.field_handlers[field_name] = function
+        
+        # Store the custom title if provided and adjust the title prefix based on context
+        title_prefix = "####" if is_context else "###"
+        if title is not None:
+            self.field_descriptions[field_name] = f"{title_prefix} {title}\n\n"
+        elif description is not None:
+            # If title is not provided but description is, use description as title
+            self.field_descriptions[field_name] = f"{title_prefix} {field_name.replace('_', ' ').title()}\n\n{description}\n\n"
+        else:
+            # If neither title nor description is provided, use the field name as title
+            self.field_descriptions[field_name] = f"{title_prefix} {field_name.replace('_', ' ').title()}\n\n"
+            
+        # Store the omit_if_empty flag for this field
+        self.omit_if_empty_flags[field_name] = omit_if_empty
+        
+        if is_context and field_name != 'context_placeholder':
+            self.context_handlers[field_name] = function
+            self.context_order.append(field_name)
+        else:
+            self.field_handlers[field_name] = function
+            self.field_order.append(field_name)
+    
+    def generate_full_prompt(self, **kwargs):
+        full_prompt = []
+        context_prompts = []  # 用于收集上下文内容
+        is_pre_print = kwargs.get("is_pre_print", False) # 用于强制打印所有prompt 字段信息，不管有没有空
+
+        # 先处理上下文字段
+        for field_name in self.context_order:
+            handler = self.context_handlers[field_name]
+            processed_prompt = handler(**kwargs)
+            # Check if the field should be omitted when empty
+            if self.omit_if_empty_flags.get(field_name, False) and not processed_prompt and not is_pre_print:
+                continue  # Skip this field
+            title_or_description = self.field_descriptions.get(field_name, f"#### {field_name.replace('_', ' ').title()}\n\n")
+            context_prompts.append(title_or_description + processed_prompt + '\n\n')
+
+        # 处理普通字段，同时查找 context_placeholder 的位置
+        for field_name in self.field_order:
+            if field_name == 'context_placeholder':
+                # 在 context_placeholder 的位置插入上下文数据
+                full_prompt.append(self.context_title)  # 添加上下文部分的大标题
+                full_prompt.extend(context_prompts)  # 添加收集的上下文内容
+            else:
+                handler = self.field_handlers[field_name]
+                processed_prompt = handler(**kwargs)
+                # Check if the field should be omitted when empty
+                if self.omit_if_empty_flags.get(field_name, False) and not processed_prompt and not is_pre_print:
+                    continue  # Skip this field
+                title_or_description = self.field_descriptions.get(field_name, f"### {field_name.replace('_', ' ').title()}\n\n")
+                full_prompt.append(title_or_description + processed_prompt + '\n\n')
+
+        # 返回完整的提示，移除尾部的空行
+        return ''.join(full_prompt).rstrip('\n')
+        
+    def pre_print(self, **kwargs):
+        kwargs.update({"is_pre_print": True})
+        prompt = self.generate_full_prompt(**kwargs)
+
+        input_keys = parse_section(self.role_prompt, 'Response Output Format')
+        llm_predict = "\n".join([f"**{k}:**" for k in input_keys])
+        return prompt + "\n\n" + "#"*19 + "\n<<<<LLM PREDICT>>>>\n" + "#"*19  + f"\n\n{llm_predict}\n"
+
+    def handle_custom_data(self, **kwargs):
+        return ""
+    
+    def handle_tool_data(self, **kwargs):
+        if 'previous_agent_message' not in kwargs:
+            return ""
+
+        previous_agent_message = kwargs.get('previous_agent_message')
+        tools: list[Tool] = previous_agent_message.tools
+        
+        if not tools:
+            return ""
+        
+        tool_strings = []
+        for tool in tools:
+            args_str = f'args: {str(tool.args)}' if tool.args_schema else ""
+            tool_strings.append(f"{tool.name}: {tool.description}, {args_str}")
+        formatted_tools = "\n".join(tool_strings)
+        
+        tool_names = ", ".join([tool.name for tool in tools])
+        
+        tool_prompt = dedent(f"""
+Below is a list of tools that are available for your use:
+{formatted_tools}
+
+valid "tool_name" value is:
+{tool_names}
+""")
+        
+        return tool_prompt
+
+    def handle_agent_data(self, **kwargs):
+        if 'agents' not in kwargs:
+            return ""
+        
+        agents = kwargs.get('agents')
+        random.shuffle(agents)
+        agent_names = ", ".join([f'{agent.role.role_name}' for agent in agents])
+        agent_descs = []
+        for agent in agents:
+            role_desc = agent.role.role_prompt.split("####")[1]
+            while "\n\n" in role_desc:
+                role_desc = role_desc.replace("\n\n", "\n")
+            role_desc = role_desc.replace("\n", ",")
+
+            agent_descs.append(f'"role name: {agent.role.role_name}\nrole description: {role_desc}"')
+
+        agents =  "\n".join(agent_descs)
+        agent_prompt = f'''
+        Please ensure your selection is one of the listed roles. Available roles for selection:
+        {agents}
+        Please ensure select the Role from agent names, such as {agent_names}'''
+
+        return dedent(agent_prompt)
+    
+    def handle_doc_info(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message: Message = kwargs.get('previous_agent_message')
+        db_docs = previous_agent_message.db_docs
+        search_docs = previous_agent_message.search_docs
+        code_cocs = previous_agent_message.code_docs
+        doc_infos = "\n".join([doc.get_snippet() for doc in db_docs] + [doc.get_snippet() for doc in search_docs] + 
+                              [doc.get_code() for doc in code_cocs])
+        return doc_infos
+    
+    def handle_session_records(self, **kwargs) -> str:
+
+        memory_pool: Memory = kwargs.get('memory_pool', Memory(messages=[]))
+        memory_pool = self.select_memory_by_agent_name(memory_pool)
+        memory_pool = self.select_memory_by_parsed_key(memory_pool)
+
+        return memory_pool.to_str_messages(content_key="parsed_output_list", with_tag=True)
+    
+    def handle_current_plan(self, **kwargs) -> str:
+        if 'previous_agent_message' not in kwargs:
+            return ""
+        previous_agent_message = kwargs['previous_agent_message']
+        return previous_agent_message.parsed_output.get("CURRENT_STEP", "")
+    
+    def handle_agent_profile(self, **kwargs) -> str:
+        return extract_section(self.role_prompt, 'Agent Profile')
+    
+    def handle_output_format(self, **kwargs) -> str:
+        return extract_section(self.role_prompt, 'Response Output Format')
+    
+    def handle_response(self, **kwargs) -> str:
+        if 'react_memory' not in kwargs:
+            return ""
+
+        react_memory = kwargs.get('react_memory', Memory(messages=[]))
+        if react_memory is None:
+            return ""
+        
+        return "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()])
+
+    def handle_task_records(self, **kwargs) -> str:
+        if 'task_memory' not in kwargs:
+            return ""
+
+        task_memory: Memory = kwargs.get('task_memory', Memory(messages=[]))
+        if task_memory is None:
+            return ""
+        
+        return "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items() if k not in ["CURRENT_STEP"]]) for _dict in task_memory.get_parserd_output()])
+    
+    def handle_previous_message(self, message: Message) -> str: 
+        pass
+    
+    def handle_message_by_role_name(self, message: Message) -> str: 
+        pass
+    
+    def handle_message_by_role_type(self, message: Message) -> str:
+        pass
+    
+    def handle_current_agent_react_message(self, message: Message) -> str:
+        pass
+    
+    def extract_codedoc_info_for_prompt(self, message: Message) -> str: 
+        code_docs = message.code_docs
+        doc_infos = "\n".join([doc.get_code() for doc in code_docs])
+        return doc_infos
+    
+    def select_memory_by_parsed_key(self, memory: Memory) -> Memory:
+        return Memory(
+            messages=[self.select_message_by_parsed_key(message) for message in memory.messages 
+                      if self.select_message_by_parsed_key(message) is not None]
+                      )
+
+    def select_memory_by_agent_name(self, memory: Memory) -> Memory:
+        return Memory(
+            messages=[self.select_message_by_agent_name(message) for message in memory.messages 
+                      if self.select_message_by_agent_name(message) is not None]
+                      )
+
+    def select_message_by_agent_name(self, message: Message) -> Message:
+        # assume we focus all agents
+        if self.monitored_agents == []:
+            return message
+        return None if message is None or message.role_name not in self.monitored_agents else self.select_message_by_parsed_key(message)
+    
+    def select_message_by_parsed_key(self, message: Message) -> Message:
+        # assume we focus all key contents
+        if message is None:
+            return message
+        
+        if self.monitored_fields == []:
+            return message
+        
+        message_c = copy.deepcopy(message)
+        message_c.parsed_output = {k: v for k,v in message_c.parsed_output.items() if k in self.monitored_fields}
+        message_c.parsed_output_list = [{k: v for k,v in parsed_output.items() if k in self.monitored_fields} for parsed_output in message_c.parsed_output_list]
+        return message_c
+    
+    def get_memory(self, content_key="role_content"):
+        return self.memory.to_tuple_messages(content_key="step_content")
+    
+    def get_memory_str(self, content_key="role_content"):
+        return "\n".join([": ".join(i) for i in self.memory.to_tuple_messages(content_key="step_content")])
+    
+    def register_fields_from_config(self):
+        
+        for prompt_field in self.prompt_config:
+            
+            function_name = prompt_field.function_name
+            # 检查function_name是否是self的一个方法
+            if function_name and hasattr(self, function_name):
+                function = getattr(self, function_name)
+            else:
+                function = self.handle_custom_data
+                
+            self.register_field(prompt_field.field_name, 
+                                function=function, 
+                                title=prompt_field.title,
+                                description=prompt_field.description,
+                                is_context=prompt_field.is_context,
+                                omit_if_empty=prompt_field.omit_if_empty)
+    
+    def register_standard_fields(self):
+        self.register_field('agent_profile', function=self.handle_agent_profile, is_context=False)
+        self.register_field('tool_information', function=self.handle_tool_data, is_context=False)
+        self.register_field('context_placeholder', is_context=True)  # 用于标记上下文数据部分的位置
+        self.register_field('reference_documents', function=self.handle_doc_info, is_context=True)
+        self.register_field('session_records', function=self.handle_session_records, is_context=True)
+        self.register_field('output_format', function=self.handle_output_format, title='Response Output Format', is_context=False)
+        self.register_field('response', function=self.handle_response, is_context=False, omit_if_empty=False)
+        
+    def register_executor_fields(self):
+        self.register_field('agent_profile', function=self.handle_agent_profile, is_context=False)
+        self.register_field('tool_information', function=self.handle_tool_data, is_context=False)
+        self.register_field('context_placeholder', is_context=True)  # 用于标记上下文数据部分的位置
+        self.register_field('reference_documents', function=self.handle_doc_info, is_context=True)
+        self.register_field('session_records', function=self.handle_session_records, is_context=True)
+        self.register_field('current_plan', function=self.handle_current_plan, is_context=True)
+        self.register_field('output_format', function=self.handle_output_format, title='Response Output Format', is_context=False)
+        self.register_field('response', function=self.handle_response, is_context=False, omit_if_empty=False)
+        
+    def register_fields_from_dict(self, fields_dict):
+        # 使用字典注册字段的函数
+        for field_name, field_config in fields_dict.items():
+            function_name = field_config.get('function', None)
+            title = field_config.get('title', None)
+            description = field_config.get('description', None)
+            is_context = field_config.get('is_context', True)
+            omit_if_empty = field_config.get('omit_if_empty', True)
+            
+            # 检查function_name是否是self的一个方法
+            if function_name and hasattr(self, function_name):
+                function = getattr(self, function_name)
+            else:
+                function = self.handle_custom_data
+            
+            # 调用已存在的register_field方法注册字段
+            self.register_field(field_name, function=function, title=title, description=description, is_context=is_context, omit_if_empty=omit_if_empty)
+
+
+
+def main():
+    manager = PromptManager()
+    manager.register_standard_fields()
+
+    manager.register_field('agents_work_progress', title=f"Agents' Work Progress", is_context=True)
+
+    # 创建数据字典
+    data_dict = {
+        "agent_profile": "这是代理配置文件...",
+        # "tool_list": "这是工具列表...",
+        "reference_documents": "这是参考文档...",
+        "session_records": "这是会话记录...",
+        "agents_work_progress": "这是代理工作进展...",
+        "output_format": "这是预期的输出格式...",
+        # "response": "这是生成或继续回应的指令...",
+        "response": "",
+        "test": 'xxxxx'
+        }
+
+    # 组合完整的提示
+    full_prompt = manager.generate_full_prompt(data_dict)
+    print(full_prompt)
+
+if __name__ == "__main__":
+    main()

coagent/connector/schema/general_schema.py

@@ -215,15 +215,15 @@ class Env(BaseModel):
 class Role(BaseModel):
     role_type: str
     role_name: str
-    role_desc: str
+    role_desc: str = ""
-    agent_type: str = ""
+    agent_type: str = "BaseAgent"
     role_prompt: str = ""
     template_prompt: str = ""
 
 
 class ChainConfig(BaseModel):
     chain_name: str
-    chain_type: str
+    chain_type: str = "BaseChain"
     agents: List[str]
     do_checker: bool = False
     chat_turn: int = 1

coagent/connector/schema/memory.py

@@ -132,6 +132,9 @@ class Memory(BaseModel):
         # return [parsed_output for message in self.messages for parsed_output in message.parsed_output_list[1:]]
         return [parsed_output for message in self.messages for parsed_output in message.parsed_output_list]
 
+    def get_spec_parserd_output(self, ):
+        return [message.spec_parsed_output for message in self.messages]
+    
     def get_rolenames(self, ):
         ''''''
         return [message.role_name for message in self.messages]

coagent/connector/schema/message.py

@@ -7,6 +7,7 @@ from .general_schema import *
 
 class Message(BaseModel):
     chat_index: str = None
+    user_name: str = "default"
     role_name: str
     role_type: str
     role_prompt: str = None
@@ -53,6 +54,8 @@ class Message(BaseModel):
     cb_search_type: str = None
     search_engine_name: str = None 
     top_k: int = 3
+    use_nh: bool = True
+    local_graph_path: str = ''
     score_threshold: float = 1.0
     do_doc_retrieval: bool = False
     do_code_retrieval: bool = False

coagent/connector/utils.py

@@ -72,20 +72,25 @@ def parse_text_to_dict(text):
 def parse_dict_to_dict(parsed_dict) -> dict:
     code_pattern = r'```python\n(.*?)```'
     tool_pattern = r'```json\n(.*?)```'
+    java_pattern = r'```java\n(.*?)```'
     
-    pattern_dict = {"code": code_pattern, "json": tool_pattern}
+    pattern_dict = {"code": code_pattern, "json": tool_pattern, "java": java_pattern}
     spec_parsed_dict = copy.deepcopy(parsed_dict)
     for key, pattern in pattern_dict.items():
         for k, text in parsed_dict.items():
             # Search for the code block
-            if not isinstance(text, str): continue
+            if not isinstance(text, str): 
+                spec_parsed_dict[k] = text
+                continue
             _match = re.search(pattern, text, re.DOTALL)
             if _match:
                 # Add the code block to the dictionary
                 try:
                     spec_parsed_dict[key] = json.loads(_match.group(1).strip())
+                    spec_parsed_dict[k] = json.loads(_match.group(1).strip())
                 except:
                     spec_parsed_dict[key] = _match.group(1).strip()
+                    spec_parsed_dict[k] = _match.group(1).strip()
                 break
     return spec_parsed_dict
 

coagent/db_handler/graph_db_handler/nebula_handler.py

@@ -43,7 +43,7 @@ class NebulaHandler:
                 elif self.space_name:
                     cypher = f'USE {self.space_name};{cypher}'
 
-            logger.debug(cypher)
+            # logger.debug(cypher)
             resp = session.execute(cypher)
 
             if format_res:
@@ -247,6 +247,24 @@ class NebulaHandler:
         res = self.execute_cypher(cypher, self.space_name)
         return self.result_to_dict(res)
 
+    def get_all_vertices(self,):
+        '''
+        get all vertices
+        @return:
+        '''
+        cypher = "MATCH (v) RETURN v;"
+        res = self.execute_cypher(cypher, self.space_name)
+        return self.result_to_dict(res)
+    
+    def get_relative_vertices(self, vertice):
+        '''
+        get all vertices
+        @return:
+        '''
+        cypher = f'''MATCH (v1)--(v2) WHERE id(v1) == '{vertice}' RETURN id(v2) as id;'''
+        res = self.execute_cypher(cypher, self.space_name)
+        return self.result_to_dict(res)
+
     def result_to_dict(self, result) -> dict:
         """
         build list for each column, and transform to dataframe

coagent/embeddings/faiss_m.py

@@ -6,6 +6,7 @@ import os
 import pickle
 import uuid
 import warnings
+from enum import Enum
 from pathlib import Path
 from typing import (
     Any,
@@ -22,10 +23,22 @@ import numpy as np
 
 from langchain.docstore.base import AddableMixin, Docstore
 from langchain.docstore.document import Document
-from langchain.docstore.in_memory import InMemoryDocstore
+# from langchain.docstore.in_memory import InMemoryDocstore
+from .in_memory import InMemoryDocstore
 from langchain.embeddings.base import Embeddings
 from langchain.vectorstores.base import VectorStore
-from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance
+from langchain.vectorstores.utils import maximal_marginal_relevance
+
+
+class DistanceStrategy(str, Enum):
+    """Enumerator of the Distance strategies for calculating distances
+    between vectors."""
+
+    EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE"
+    MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT"
+    DOT_PRODUCT = "DOT_PRODUCT"
+    JACCARD = "JACCARD"
+    COSINE = "COSINE"
 
 
 def dependable_faiss_import(no_avx2: Optional[bool] = None) -> Any:
@@ -219,6 +232,9 @@ class FAISS(VectorStore):
         if self._normalize_L2:
             faiss.normalize_L2(vector)
         scores, indices = self.index.search(vector, k if filter is None else fetch_k)
+        # 经过normalize的结果会超出1
+        if self._normalize_L2:
+            scores = np.array([row / np.linalg.norm(row) if np.max(row) > 1 else row for row in scores])
         docs = []
         for j, i in enumerate(indices[0]):
             if i == -1:
@@ -565,7 +581,7 @@ class FAISS(VectorStore):
         vecstore = cls(
             embedding.embed_query,
             index,
-            InMemoryDocstore(),
+            InMemoryDocstore({}),
             {},
             normalize_L2=normalize_L2,
             distance_strategy=distance_strategy,

coagent/embeddings/get_embedding.py

@@ -10,13 +10,14 @@ from loguru import logger
 # from configs.model_config import EMBEDDING_MODEL
 from coagent.embeddings.openai_embedding import OpenAIEmbedding
 from coagent.embeddings.huggingface_embedding import HFEmbedding
-
+from coagent.llm_models.llm_config import EmbedConfig
 
 def get_embedding(
         engine: str, 
         text_list: list, 
         model_path: str = "text2vec-base-chinese",
         embedding_device: str = "cpu",
+        embed_config: EmbedConfig = None,
         ):
     '''
     get embedding
@@ -25,8 +26,12 @@ def get_embedding(
     @return:
     '''
     emb_res = {}
-
+    if embed_config and embed_config.langchain_embeddings:
-    if engine == 'openai':
+        emb_res = embed_config.langchain_embeddings.embed_documents(text_list)
+        emb_res = {
+            text_list[idx]: emb_res[idx] for idx in range(len(text_list))
+        }
+    elif engine == 'openai':
         oae = OpenAIEmbedding()
         emb_res = oae.get_emb(text_list)
     elif engine == 'model':

coagent/embeddings/in_memory.py

@@ -0,0 +1,49 @@
+"""Simple in memory docstore in the form of a dict."""
+from typing import Dict, List, Optional, Union
+
+from langchain.docstore.base import AddableMixin, Docstore
+from langchain.docstore.document import Document
+
+
+class InMemoryDocstore(Docstore, AddableMixin):
+    """Simple in memory docstore in the form of a dict."""
+
+    def __init__(self, _dict: Optional[Dict[str, Document]] = None):
+        """Initialize with dict."""
+        self._dict = _dict if _dict is not None else {}
+
+    def add(self, texts: Dict[str, Document]) -> None:
+        """Add texts to in memory dictionary.
+
+        Args:
+            texts: dictionary of id -> document.
+
+        Returns:
+            None
+        """
+        overlapping = set(texts).intersection(self._dict)
+        if overlapping:
+            raise ValueError(f"Tried to add ids that already exist: {overlapping}")
+        self._dict = {**self._dict, **texts}
+
+    def delete(self, ids: List) -> None:
+        """Deleting IDs from in memory dictionary."""
+        overlapping = set(ids).intersection(self._dict)
+        if not overlapping:
+            raise ValueError(f"Tried to delete ids that does not  exist: {ids}")
+        for _id in ids:
+            self._dict.pop(_id)
+
+    def search(self, search: str) -> Union[str, Document]:
+        """Search via direct lookup.
+
+        Args:
+            search: id of a document to search for.
+
+        Returns:
+            Document if found, else error message.
+        """
+        if search not in self._dict:
+            return f"ID {search} not found."
+        else:
+            return self._dict[search]

coagent/embeddings/utils.py

@@ -1,6 +1,8 @@
 import os
 from functools import lru_cache
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.embeddings.base import Embeddings
+
 # from configs.model_config import embedding_model_dict
 from loguru import logger
 
@@ -12,8 +14,11 @@ def load_embeddings(model: str, device: str, embedding_model_dict: dict):
     return embeddings
 
 
-@lru_cache(1)
+# @lru_cache(1)
-def load_embeddings_from_path(model_path: str, device: str):
+def load_embeddings_from_path(model_path: str, device: str, langchain_embeddings: Embeddings = None):
+    if langchain_embeddings:
+        return langchain_embeddings
+    
     embeddings = HuggingFaceEmbeddings(model_name=model_path,
                                        model_kwargs={'device': device})
     return embeddings

coagent/llm_models/__init__.py

@@ -1,8 +1,8 @@
-from .openai_model import getChatModel, getExtraModel, getChatModelFromConfig
+from .openai_model import getExtraModel, getChatModelFromConfig
 from .llm_config import LLMConfig, EmbedConfig
 
 
 __all__ = [
-    "getChatModel", "getExtraModel", "getChatModelFromConfig",
+    "getExtraModel", "getChatModelFromConfig",
     "LLMConfig", "EmbedConfig"
 ]

coagent/llm_models/llm_config.py

@@ -1,6 +1,9 @@
 from dataclasses import dataclass
 from typing import List, Union
 
+from langchain.embeddings.base import Embeddings
+from langchain.llms.base import LLM, BaseLLM
+
 
 
 @dataclass
@@ -12,7 +15,8 @@ class LLMConfig:
             stop: Union[List[str], str] = None,
             api_key: str = "",
             api_base_url: str = "",
-            model_device: str = "cpu",
+            model_device: str = "cpu", # unuse，will delete it
+            llm: LLM = None,
             **kwargs
         ):
 
@@ -21,7 +25,7 @@ class LLMConfig:
         self.stop: Union[List[str], str] = stop
         self.api_key: str = api_key
         self.api_base_url: str = api_base_url
-        self.model_device: str = model_device
+        self.llm: LLM = llm
         # 
         self.check_config()
 
@@ -42,6 +46,7 @@ class EmbedConfig:
             embed_model_path: str = "",
             embed_engine: str = "",
             model_device: str = "cpu",
+            langchain_embeddings: Embeddings = None,
             **kwargs
         ):
         self.embed_model: str = embed_model
@@ -51,6 +56,8 @@ class EmbedConfig:
         self.api_key: str = api_key
         self.api_base_url: str = api_base_url
         # 
+        self.langchain_embeddings = langchain_embeddings
+        # 
         self.check_config()
 
     def check_config(self, ):

coagent/llm_models/openai_model.py

@@ -1,38 +1,54 @@
 import os
+from typing import Union, Optional, List
+from loguru import logger
 
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.chat_models import ChatOpenAI
+from langchain.llms.base import LLM
 
 from .llm_config import LLMConfig
 # from configs.model_config import (llm_model_dict, LLM_MODEL)
 
 
-def getChatModel(callBack: AsyncIteratorCallbackHandler = None, temperature=0.3, stop=None):
+class CustomLLMModel:
-    if callBack is None:
+    
+    def __init__(self, llm: LLM):
+        self.llm: LLM = llm
+
+    def __call__(self, prompt: str,
+                  stop: Optional[List[str]] = None):
+        return self.llm(prompt, stop)
+
+    def _call(self, prompt: str,
+                  stop: Optional[List[str]] = None):
+        return self.llm(prompt, stop)
+    
+    def predict(self, prompt: str,
+                  stop: Optional[List[str]] = None):
+        return self.llm(prompt, stop)
+
+    def batch(self, prompts: str,
+                  stop: Optional[List[str]] = None):
+        return [self.llm(prompt, stop) for prompt in prompts]
+
+
+def getChatModelFromConfig(llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler = None, ) -> Union[ChatOpenAI, LLM]:
+    # logger.debug(f"llm type is {type(llm_config.llm)}")
+    if llm_config is None:
         model = ChatOpenAI(
             streaming=True,
             verbose=True,
-            openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
+            openai_api_key=os.environ.get("api_key"),
-            openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
+            openai_api_base=os.environ.get("api_base_url"),
-            model_name=LLM_MODEL,
+            model_name=os.environ.get("LLM_MODEL", "gpt-3.5-turbo"),
-            temperature=temperature,
+            temperature=os.environ.get("temperature", 0.5),
-            stop=stop
+            stop=os.environ.get("stop", ""),
-        )
-    else:
-        model = ChatOpenAI(
-            streaming=True,
-            verbose=True,
-            callBack=[callBack],
-            openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
-            openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-            model_name=LLM_MODEL,
-            temperature=temperature,
-            stop=stop
         )
         return model
 
+    if llm_config and llm_config.llm and isinstance(llm_config.llm, LLM):
+        return CustomLLMModel(llm=llm_config.llm)
     
-def getChatModelFromConfig(llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler = None, ):
     if callBack is None:
         model = ChatOpenAI(
                 streaming=True,

coagent/retrieval/__init__.py

@@ -0,0 +1,5 @@
+# from .base_retrieval import *
+
+# __all__ = [
+#     "IMRertrieval", "BaseDocRetrieval", "BaseCodeRetrieval", "BaseSearchRetrieval"
+# ]

coagent/retrieval/base_retrieval.py

@@ -0,0 +1,75 @@
+
+from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+from coagent.base_configs.env_config import KB_ROOT_PATH
+from coagent.tools import DocRetrieval, CodeRetrieval
+
+
+class IMRertrieval:
+
+    def __init__(self,):
+        '''
+        init your personal attributes
+        '''
+        pass
+
+    def run(self, ):
+        '''
+        execute interface, and can use init' attributes
+        '''
+        pass
+
+
+class BaseDocRetrieval(IMRertrieval):
+
+    def __init__(self, knowledge_base_name: str, search_top=5, score_threshold=1.0, embed_config: EmbedConfig=EmbedConfig(), kb_root_path: str=KB_ROOT_PATH):
+        self.knowledge_base_name = knowledge_base_name
+        self.search_top = search_top
+        self.score_threshold = score_threshold
+        self.embed_config = embed_config
+        self.kb_root_path = kb_root_path
+
+    def run(self, query: str, search_top=None, score_threshold=None, ):
+        docs = DocRetrieval.run(
+            query=query, knowledge_base_name=self.knowledge_base_name,
+            search_top=search_top or self.search_top,
+            score_threshold=score_threshold or self.score_threshold,
+            embed_config=self.embed_config,
+            kb_root_path=self.kb_root_path
+        )
+        return docs
+
+
+class BaseCodeRetrieval(IMRertrieval):
+
+    def __init__(self, code_base_name, embed_config: EmbedConfig, llm_config: LLMConfig, search_type = 'tag', code_limit = 1, local_graph_path: str=""):
+        self.code_base_name = code_base_name
+        self.embed_config = embed_config
+        self.llm_config = llm_config
+        self.search_type = search_type
+        self.code_limit = code_limit
+        self.use_nh: bool = False
+        self.local_graph_path: str = local_graph_path
+
+    def run(self, query,  history_node_list=[], search_type = None, code_limit=None):
+        code_docs = CodeRetrieval.run(
+            code_base_name=self.code_base_name,
+            query=query, 
+            history_node_list=history_node_list,
+            code_limit=code_limit or self.code_limit,
+            search_type=search_type or self.search_type,
+            llm_config=self.llm_config,
+            embed_config=self.embed_config,
+            use_nh=self.use_nh,
+            local_graph_path=self.local_graph_path
+            )
+        return code_docs
+
+
+
+class BaseSearchRetrieval(IMRertrieval):
+
+    def __init__(self, ):
+        pass
+
+    def run(self, ):
+        pass

coagent/retrieval/document_loaders/__init__.py

@@ -0,0 +1,6 @@
+from .json_loader import JSONLoader
+from .jsonl_loader import JSONLLoader
+
+__all__ = [
+    "JSONLoader", "JSONLLoader"
+]

coagent/retrieval/document_loaders/json_loader.py

@@ -0,0 +1,61 @@
+import json
+from pathlib import Path
+from typing import AnyStr, Callable, Dict, List, Optional, Union
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+
+from coagent.utils.common_utils import read_json_file
+
+
+class JSONLoader(BaseLoader):
+
+    def __init__(
+            self,
+            file_path: Union[str, Path],
+            schema_key: str = "all_text",
+            content_key: Optional[str] = None,
+            metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None,
+            text_content: bool = True,
+    ):
+        self.file_path = Path(file_path).resolve()
+        self.schema_key = schema_key
+        self._content_key = content_key
+        self._metadata_func = metadata_func
+        self._text_content = text_content
+
+    def load(self, ) -> List[Document]:
+        """Load and return documents from the JSON file."""
+        docs: List[Document] = []
+        datas = read_json_file(self.file_path)
+        self._parse(datas, docs)
+        return docs
+    
+    def _parse(self, datas: List, docs: List[Document]) -> None:
+        for idx, sample in enumerate(datas):
+            metadata = dict(
+                source=str(self.file_path),
+                seq_num=idx,
+            )
+            text = sample.get(self.schema_key, "")
+            docs.append(Document(page_content=text, metadata=metadata))
+
+    def load_and_split(
+        self, text_splitter: Optional[TextSplitter] = None
+    ) -> List[Document]:
+        """Load Documents and split into chunks. Chunks are returned as Documents.
+
+        Args:
+            text_splitter: TextSplitter instance to use for splitting documents.
+              Defaults to RecursiveCharacterTextSplitter.
+
+        Returns:
+            List of Documents.
+        """
+        if text_splitter is None:
+            _text_splitter: TextSplitter = RecursiveCharacterTextSplitter()
+        else:
+            _text_splitter = text_splitter
+        docs = self.load()
+        return _text_splitter.split_documents(docs)

coagent/retrieval/document_loaders/jsonl_loader.py

@@ -0,0 +1,62 @@
+import json
+from pathlib import Path
+from typing import AnyStr, Callable, Dict, List, Optional, Union
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+
+from coagent.utils.common_utils import read_jsonl_file
+
+
+class JSONLLoader(BaseLoader):
+
+    def __init__(
+            self,
+            file_path: Union[str, Path],
+            schema_key: str = "all_text",
+            content_key: Optional[str] = None,
+            metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None,
+            text_content: bool = True,
+    ):
+        self.file_path = Path(file_path).resolve()
+        self.schema_key = schema_key
+        self._content_key = content_key
+        self._metadata_func = metadata_func
+        self._text_content = text_content
+
+    def load(self, ) -> List[Document]:
+        """Load and return documents from the JSON file."""
+        docs: List[Document] = []
+        datas = read_jsonl_file(self.file_path)
+        self._parse(datas, docs)
+        return docs
+    
+    def _parse(self, datas: List, docs: List[Document]) -> None:
+        for idx, sample in enumerate(datas):
+            metadata = dict(
+                source=str(self.file_path),
+                seq_num=idx,
+            )
+            text = sample.get(self.schema_key, "")
+            docs.append(Document(page_content=text, metadata=metadata))
+
+    def load_and_split(
+        self, text_splitter: Optional[TextSplitter] = None
+    ) -> List[Document]:
+        """Load Documents and split into chunks. Chunks are returned as Documents.
+
+        Args:
+            text_splitter: TextSplitter instance to use for splitting documents.
+              Defaults to RecursiveCharacterTextSplitter.
+
+        Returns:
+            List of Documents.
+        """
+        if text_splitter is None:
+            _text_splitter: TextSplitter = RecursiveCharacterTextSplitter()
+        else:
+            _text_splitter = text_splitter
+
+        docs = self.load()
+        return _text_splitter.split_documents(docs)

coagent/retrieval/text_splitter/__init__.py

@@ -0,0 +1,3 @@
+from .langchain_splitter import LCTextSplitter
+
+__all__ = ["LCTextSplitter"]

coagent/retrieval/text_splitter/langchain_splitter.py

@@ -0,0 +1,77 @@
+import os
+import importlib
+from loguru import logger
+
+from langchain.document_loaders.base import BaseLoader
+from langchain.text_splitter import (
+    SpacyTextSplitter, RecursiveCharacterTextSplitter
+)
+
+# from configs.model_config import (
+#     CHUNK_SIZE,
+#     OVERLAP_SIZE,
+#     ZH_TITLE_ENHANCE
+# )
+from coagent.utils.path_utils import *
+
+
+
+class LCTextSplitter:
+    '''langchain textsplitter 执行file2text'''
+    def __init__(
+            self, filepath: str, text_splitter_name: str = None,
+            chunk_size: int = 500, 
+            overlap_size: int = 50
+    ):
+        self.filepath = filepath
+        self.ext = os.path.splitext(filepath)[-1].lower()
+        self.text_splitter_name = text_splitter_name
+        self.chunk_size = chunk_size
+        self.overlap_size = overlap_size
+        if self.ext not in SUPPORTED_EXTS:
+            raise ValueError(f"暂未支持的文件格式 {self.ext}")
+        self.document_loader_name = get_LoaderClass(self.ext)
+
+    def file2text(self, ):
+        loader = self._load_document()
+        text_splitter = self._load_text_splitter()
+        if self.document_loader_name in ["JSONLoader", "JSONLLoader"]:
+            # docs = loader.load()
+            docs = loader.load_and_split(text_splitter)
+            # logger.debug(f"please check your file can be loaded, docs.lens {len(docs)}")
+        else:
+            docs = loader.load_and_split(text_splitter)
+
+        return docs
+
+    def _load_document(self, ) -> BaseLoader:
+        DocumentLoader = EXT2LOADER_DICT[self.ext]
+        if self.document_loader_name == "UnstructuredFileLoader":
+            loader = DocumentLoader(self.filepath, autodetect_encoding=True)
+        else:
+            loader = DocumentLoader(self.filepath)
+        return loader
+    
+    def _load_text_splitter(self, ):
+        try:
+            if self.text_splitter_name is None:
+                text_splitter = SpacyTextSplitter(
+                    pipeline="zh_core_web_sm",
+                    chunk_size=self.chunk_size,
+                    chunk_overlap=self.overlap_size,
+                )
+                self.text_splitter_name = "SpacyTextSplitter"
+            # elif self.document_loader_name in ["JSONLoader", "JSONLLoader"]:
+            #     text_splitter = None
+            else:
+                text_splitter_module = importlib.import_module('langchain.text_splitter')
+                TextSplitter = getattr(text_splitter_module, self.text_splitter_name)
+                text_splitter = TextSplitter(
+                    chunk_size=self.chunk_size,
+                    chunk_overlap=self.overlap_size)
+        except Exception as e:
+            text_splitter = RecursiveCharacterTextSplitter(
+                chunk_size=self.chunk_size,
+                chunk_overlap=self.overlap_size,
+            )
+        return text_splitter

coagent/sandbox/pycodebox.py

@@ -32,8 +32,8 @@ class PyCodeBox(BaseBox):
         self.do_check_net = do_check_net
         self.use_stop = use_stop
         self.jupyter_work_path = jupyter_work_path
-        asyncio.run(self.astart())
+        # asyncio.run(self.astart())
-        # self.start()
+        self.start()
 
         # logger.info(f"""remote_url: {self.remote_url},
         #             remote_ip: {self.remote_ip},
@@ -199,13 +199,13 @@ class PyCodeBox(BaseBox):
         
     async def _aget_kernelid(self, ) -> None:
         headers = {"Authorization": f'Token {self.token}', 'token': self.token}
-        response = requests.get(f"{self.kernel_url}?token={self.token}", headers=headers)
+        # response = requests.get(f"{self.kernel_url}?token={self.token}", headers=headers)
         async with aiohttp.ClientSession() as session:
-            async with session.get(f"{self.kernel_url}?token={self.token}", headers=headers) as resp:
+            async with session.get(f"{self.kernel_url}?token={self.token}", headers=headers, timeout=270) as resp:
                 if len(await resp.json()) > 0:
                     self.kernel_id = (await resp.json())[0]["id"]
                 else:
-                    async with session.post(f"{self.kernel_url}?token={self.token}", headers=headers) as response:
+                    async with session.post(f"{self.kernel_url}?token={self.token}", headers=headers, timeout=270) as response:
                         self.kernel_id = (await response.json())["id"]
 
         # if len(response.json()) > 0:
@@ -220,41 +220,45 @@ class PyCodeBox(BaseBox):
             return False
 
         try:
-             response = requests.get(f"{self.kernel_url}?token={self.token}", timeout=270)
+             response = requests.get(f"{self.kernel_url}?token={self.token}", timeout=10)
              return response.status_code == 200
         except requests.exceptions.ConnectionError:
             return False
+        except requests.exceptions.ReadTimeout:
+            return False
 
     async def _acheck_connect(self, ) -> bool:
         if self.kernel_url == "":
             return False
         try:
             async with aiohttp.ClientSession() as session:
-                async with session.get(f"{self.kernel_url}?token={self.token}", timeout=270) as resp:
+                async with session.get(f"{self.kernel_url}?token={self.token}", timeout=10) as resp:
                     return resp.status == 200
         except aiohttp.ClientConnectorError:
-            pass
+            return False
         except aiohttp.ServerDisconnectedError:
-            pass
+            return False
 
     def  _check_port(self, ) -> bool:
         try:
-             response = requests.get(f"{self.remote_ip}:{self.remote_port}", timeout=270)
+             response = requests.get(f"{self.remote_ip}:{self.remote_port}", timeout=10)
              logger.warning(f"Port is conflict, please check your codebox's port {self.remote_port}")
              return response.status_code == 200
         except requests.exceptions.ConnectionError:
             return False
+        except requests.exceptions.ReadTimeout:
+            return False
         
     async def _acheck_port(self, ) -> bool:
         try:
             async with aiohttp.ClientSession() as session:
-                async with session.get(f"{self.remote_ip}:{self.remote_port}", timeout=270) as resp:
+                async with session.get(f"{self.remote_ip}:{self.remote_port}", timeout=10) as resp:
                     # logger.warning(f"Port is conflict, please check your codebox's port {self.remote_port}")
                     return resp.status == 200
         except aiohttp.ClientConnectorError:
-            pass
+            return False
         except aiohttp.ServerDisconnectedError:
-            pass
+            return False
 
     def _check_connect_success(self, retry_nums: int = 2) -> bool:
         if not self.do_check_net: return True
@@ -263,7 +267,7 @@ class PyCodeBox(BaseBox):
             try:
                 connect_status = self._check_connect()
                 if connect_status:
-                    logger.info(f"{self.remote_url} connection success")
+                    # logger.info(f"{self.remote_url} connection success")
                     return True
             except requests.exceptions.ConnectionError:
                 logger.info(f"{self.remote_url} connection fail")
@@ -301,10 +305,12 @@ class PyCodeBox(BaseBox):
         else:
             # TODO 自动检测本地接口
             port_status = self._check_port()
+            self.kernel_url = self.remote_url + "/api/kernels"
             connect_status = self._check_connect()
+            if os.environ.get("log_verbose", "0") >= "2":
                 logger.info(f"port_status: {port_status}, connect_status: {connect_status}")
             if port_status and not connect_status:
-                raise BaseException(f"Port is conflict, please check your codebox's port {self.remote_port}")
+                logger.error("Port is conflict, please check your codebox's port {self.remote_port}")
             
             if not connect_status:
                 self.jupyter = subprocess.Popen(
@@ -321,14 +327,32 @@ class PyCodeBox(BaseBox):
                     stdout=subprocess.PIPE,
                 )
 
+            record = []
+            while True and self.jupyter and len(record)<100:
+                line = self.jupyter.stderr.readline()
+                try:
+                    content = line.decode("utf-8")
+                except:
+                    content = line.decode("gbk")
+                # logger.debug(content)
+                record.append(content)
+                if "control-c" in content.lower():
+                    break
+
             self.kernel_url = self.remote_url + "/api/kernels"
             self.do_check_net = True
             self._check_connect_success()
             self._get_kernelid()
-            # logger.debug(self.kernel_url.replace("http", "ws") + f"/{self.kernel_id}/channels?token={self.token}")
             self.wc_url = self.kernel_url.replace("http", "ws") + f"/{self.kernel_id}/channels?token={self.token}"
             headers = {"Authorization": f'Token {self.token}', 'token': self.token}
-            self.ws = create_connection(self.wc_url, headers=headers)
+            retry_nums = 3
+            while retry_nums>=0:
+                try:
+                    self.ws = create_connection(self.wc_url, headers=headers, timeout=5)
+                    break
+                except Exception as e:
+                    logger.error(f"create ws connection timeout {e}")
+                    retry_nums -= 1
 
     async def astart(self, ):
         '''判断是从外部service执行还是内部启动notebook执行'''
@@ -369,10 +393,16 @@ class PyCodeBox(BaseBox):
                     cwd=self.jupyter_work_path
                 )
 
-            while True and self.jupyter:
+            record = []
+            while True and self.jupyter and len(record)<100:
                 line = self.jupyter.stderr.readline()
-                # logger.debug(line.decode("gbk"))
+                try:
-                if "Control-C" in line.decode("gbk"):
+                    content = line.decode("utf-8")
+                except:
+                    content = line.decode("gbk")
+                # logger.debug(content)
+                record.append(content)
+                if "control-c" in content.lower():
                     break
             self.kernel_url = self.remote_url + "/api/kernels"
             self.do_check_net = True
@@ -380,7 +410,15 @@ class PyCodeBox(BaseBox):
             await self._aget_kernelid()
             self.wc_url = self.kernel_url.replace("http", "ws") + f"/{self.kernel_id}/channels?token={self.token}"
             headers = {"Authorization": f'Token {self.token}', 'token': self.token}
-            self.ws = create_connection(self.wc_url, headers=headers)
+
+            retry_nums = 3
+            while retry_nums>=0:
+                try:
+                    self.ws = create_connection(self.wc_url, headers=headers, timeout=5)
+                    break
+                except Exception as e:
+                    logger.error(f"create ws connection timeout {e}")
+                    retry_nums -= 1
 
     def status(self,) -> CodeBoxStatus:
         if not self.kernel_id:

coagent/service/base_service.py

@@ -17,7 +17,7 @@ from coagent.orm.commands import *
 from coagent.utils.path_utils import *
 from coagent.orm.utils import DocumentFile
 from coagent.embeddings.utils import load_embeddings, load_embeddings_from_path
-from coagent.text_splitter import LCTextSplitter
+from coagent.retrieval.text_splitter import LCTextSplitter
 from coagent.llm_models.llm_config import EmbedConfig
 
 
@@ -46,7 +46,7 @@ class KBService(ABC):
 
     def _load_embeddings(self) -> Embeddings:
         # return load_embeddings(self.embed_model, embed_device, embedding_model_dict)
-        return load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device)
+        return load_embeddings_from_path(self.embed_config.embed_model_path, self.embed_config.model_device, self.embed_config.langchain_embeddings)
 
     def create_kb(self):
         """

coagent/service/cb_api.py

@@ -20,9 +20,6 @@ from coagent.utils.path_utils import *
 from coagent.orm.commands import *
 from coagent.db_handler.graph_db_handler.nebula_handler import NebulaHandler
 from coagent.db_handler.vector_db_handler.chroma_handler import ChromaHandler
-# from configs.server_config import NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT
-# from configs.server_config import CHROMA_PERSISTENT_PATH
-
 from coagent.base_configs.env_config import (
     CB_ROOT_PATH, 
     NEBULA_HOST, NEBULA_PORT, NEBULA_USER, NEBULA_PASSWORD, NEBULA_STORAGED_PORT,
@@ -58,10 +55,11 @@ async def create_cb(zip_file,
                     model_name: bool = Body(..., examples=["samples"]),
                     temperature: bool = Body(..., examples=["samples"]),
                     model_device: bool = Body(..., examples=["samples"]),
+                    embed_config: EmbedConfig = None,
                     ) -> BaseResponse:
     logger.info('cb_name={}, zip_path={}, do_interpret={}'.format(cb_name, code_path, do_interpret))
 
-    embed_config: EmbedConfig = EmbedConfig(**locals())
+    embed_config: EmbedConfig = EmbedConfig(**locals()) if embed_config is None else embed_config
     llm_config: LLMConfig = LLMConfig(**locals())
 
     # Create selected knowledge base
@@ -101,9 +99,10 @@ async def delete_cb(
         model_name: bool = Body(..., examples=["samples"]),
         temperature: bool = Body(..., examples=["samples"]),
         model_device: bool = Body(..., examples=["samples"]),
+        embed_config: EmbedConfig = None,
         ) -> BaseResponse:
     logger.info('cb_name={}'.format(cb_name))
-    embed_config: EmbedConfig = EmbedConfig(**locals())
+    embed_config: EmbedConfig = EmbedConfig(**locals()) if embed_config is None else embed_config
     llm_config: LLMConfig = LLMConfig(**locals())
     # Create selected knowledge base
     if not validate_kb_name(cb_name):
@@ -143,18 +142,24 @@ def search_code(cb_name: str = Body(..., examples=["sofaboot"]),
                 model_name: bool = Body(..., examples=["samples"]),
                 temperature: bool = Body(..., examples=["samples"]),
                 model_device: bool = Body(..., examples=["samples"]),
+                use_nh: bool = True,
+                local_graph_path: str = '',
+                embed_config: EmbedConfig = None,
                 ) -> dict:
     
+    if os.environ.get("log_verbose", "0") >= "2":
+        logger.info(f'local_graph_path={local_graph_path}')
         logger.info('cb_name={}'.format(cb_name))
         logger.info('query={}'.format(query))
         logger.info('code_limit={}'.format(code_limit))
         logger.info('search_type={}'.format(search_type))
         logger.info('history_node_list={}'.format(history_node_list))
-    embed_config: EmbedConfig = EmbedConfig(**locals())
+    embed_config: EmbedConfig = EmbedConfig(**locals()) if embed_config is None else embed_config
     llm_config: LLMConfig = LLMConfig(**locals())
     try:
         # load codebase
-        cbh = CodeBaseHandler(codebase_name=cb_name, embed_config=embed_config, llm_config=llm_config)
+        cbh = CodeBaseHandler(codebase_name=cb_name, embed_config=embed_config, llm_config=llm_config,
+                              use_nh=use_nh, local_graph_path=local_graph_path)
 
         # search code
         context, related_vertices = cbh.search_code(query, search_type=search_type, limit=code_limit)
@@ -180,10 +185,12 @@ def search_related_vertices(cb_name: str = Body(..., examples=["sofaboot"]),
         nh = NebulaHandler(host=NEBULA_HOST, port=NEBULA_PORT, username=NEBULA_USER,
                            password=NEBULA_PASSWORD, space_name=cb_name)
         
+        if vertex.endswith(".java"):
+            cypher = f'''MATCH (v1)--(v2:package) WHERE id(v1) == '{vertex}' RETURN id(v2) as id;'''
+        else:
             cypher = f'''MATCH (v1)--(v2) WHERE id(v1) == '{vertex}' RETURN id(v2) as id;'''
-
+        # cypher = f'''MATCH (v1)--(v2) WHERE id(v1) == '{vertex}' RETURN v2;'''
         cypher_res = nh.execute_cypher(cypher=cypher, format_res=True)
-
         related_vertices = cypher_res.get('id', [])
         related_vertices = [i.as_string() for i in related_vertices]
 
@@ -200,8 +207,8 @@ def search_related_vertices(cb_name: str = Body(..., examples=["sofaboot"]),
 def search_code_by_vertex(cb_name: str = Body(..., examples=["sofaboot"]),
                             vertex: str = Body(..., examples=['***'])) -> dict:
 
-    logger.info('cb_name={}'.format(cb_name))
+    # logger.info('cb_name={}'.format(cb_name))
-    logger.info('vertex={}'.format(vertex))
+    # logger.info('vertex={}'.format(vertex))
 
     try:
         nh = NebulaHandler(host=NEBULA_HOST, port=NEBULA_PORT, username=NEBULA_USER,
@@ -233,7 +240,7 @@ def search_code_by_vertex(cb_name: str = Body(..., examples=["sofaboot"]),
         return res
     except Exception as e:
         logger.exception(e)
-        return {}
+        return {'code': ""}
 
 
 def cb_exists_api(cb_name: str = Body(..., examples=["sofaboot"])) -> bool:

coagent/service/faiss_db_service.py

@@ -8,17 +8,6 @@ from loguru import logger
 from langchain.embeddings.base import Embeddings
 from langchain.docstore.document import Document
 from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain.vectorstores.utils import DistanceStrategy
-
-# from configs.model_config import (
-#     KB_ROOT_PATH,
-#     CACHED_VS_NUM,
-#     EMBEDDING_MODEL,
-#     EMBEDDING_DEVICE,
-#     SCORE_THRESHOLD,
-#     FAISS_NORMALIZE_L2
-# )
-# from configs.model_config import embedding_model_dict
 
 from coagent.base_configs.env_config import (
     KB_ROOT_PATH,
@@ -52,15 +41,15 @@ def load_vector_store(
         tick: int = 0,  # tick will be changed by upload_doc etc. and make cache refreshed.
         kb_root_path: str = KB_ROOT_PATH,
 ):
-    print(f"loading vector store in '{knowledge_base_name}'.")
+    # print(f"loading vector store in '{knowledge_base_name}'.")
     vs_path = get_vs_path(knowledge_base_name, kb_root_path)
     if embeddings is None:
-        embeddings = load_embeddings_from_path(embed_config.embed_model_path, embed_config.model_device)
+        embeddings = load_embeddings_from_path(embed_config.embed_model_path, embed_config.model_device, embed_config.langchain_embeddings)
 
     if not os.path.exists(vs_path):
         os.makedirs(vs_path)
     
-    distance_strategy = DistanceStrategy.EUCLIDEAN_DISTANCE
+    distance_strategy = "EUCLIDEAN_DISTANCE"
     if "index.faiss" in os.listdir(vs_path):
         search_index = FAISS.load_local(vs_path, embeddings, normalize_L2=FAISS_NORMALIZE_L2, distance_strategy=distance_strategy)
     else:

coagent/tools/cb_query_tool.py

@@ -9,9 +9,7 @@ from pydantic import BaseModel, Field
 from loguru import logger
 
 from coagent.llm_models import LLMConfig, EmbedConfig
-
 from .base_tool import BaseToolModel
-
 from coagent.service.cb_api import search_code
 
 
@@ -29,7 +27,17 @@ class CodeRetrieval(BaseToolModel):
         code: str  = Field(..., description="检索代码")
 
     @classmethod
-    def run(cls, code_base_name, query, code_limit=1, history_node_list=[], search_type="tag", llm_config: LLMConfig=None, embed_config: EmbedConfig=None):
+    def run(cls,
+            code_base_name,
+            query,
+            code_limit=1,
+            history_node_list=[],
+            search_type="tag",
+            llm_config: LLMConfig=None,
+            embed_config: EmbedConfig=None,
+            use_nh: str=True,
+            local_graph_path: str=''
+            ):
         """excute your tool!"""
         
         search_type = {
@@ -45,7 +53,8 @@ class CodeRetrieval(BaseToolModel):
         codes = search_code(code_base_name, query, code_limit, search_type=search_type, history_node_list=history_node_list,
                             embed_engine=embed_config.embed_engine, embed_model=embed_config.embed_model, embed_model_path=embed_config.embed_model_path,
                             model_device=embed_config.model_device, model_name=llm_config.model_name, temperature=llm_config.temperature,
-                            api_base_url=llm_config.api_base_url, api_key=llm_config.api_key
+                            api_base_url=llm_config.api_base_url, api_key=llm_config.api_key, use_nh=use_nh,
+                            local_graph_path=local_graph_path, embed_config=embed_config
                             )
         return_codes = []
         context = codes['context']

coagent/tools/codechat_tools.py

@@ -5,6 +5,7 @@
 @time: 2023/12/14 上午10:24
 @desc:
 '''
+import os
 from pydantic import BaseModel, Field
 from loguru import logger
 
@@ -40,10 +41,9 @@ class CodeRetrievalSingle(BaseToolModel):
         vertex: str = Field(..., description="代码对应 id")
 
     @classmethod
-    def run(cls, code_base_name, query, embed_config: EmbedConfig, llm_config: LLMConfig, **kargs):
+    def run(cls, code_base_name, query, embed_config: EmbedConfig, llm_config: LLMConfig, search_type="description", **kargs):
         """excute your tool!"""
 
-        search_type = 'description'
         code_limit = 1
 
         # default
@@ -51,9 +51,10 @@ class CodeRetrievalSingle(BaseToolModel):
                             history_node_list=[],
                             embed_engine=embed_config.embed_engine, embed_model=embed_config.embed_model, embed_model_path=embed_config.embed_model_path,
                             model_device=embed_config.model_device, model_name=llm_config.model_name, temperature=llm_config.temperature,
-                            api_base_url=llm_config.api_base_url, api_key=llm_config.api_key
+                            api_base_url=llm_config.api_base_url, api_key=llm_config.api_key, embed_config=embed_config, use_nh=kargs.get("use_nh", True),
+                            local_graph_path=kargs.get("local_graph_path", "")
                             )
-
+        if os.environ.get("log_verbose", "0") >= "3":
             logger.debug(search_result)
         code = search_result['context']
         vertex = search_result['related_vertices'][0]
@@ -83,7 +84,7 @@ class RelatedVerticesRetrival(BaseToolModel):
     def run(cls, code_base_name: str, vertex: str, **kargs):
         """execute your tool!"""
         related_vertices = search_related_vertices(cb_name=code_base_name, vertex=vertex)
-        logger.debug(f"related_vertices: {related_vertices}")
+        # logger.debug(f"related_vertices: {related_vertices}")
 
         return related_vertices
 
@@ -110,6 +111,6 @@ class Vertex2Code(BaseToolModel):
         else:
             vertex = vertex.strip(' "')
 
-        logger.info(f'vertex={vertex}')
+        # logger.info(f'vertex={vertex}')
         res = search_code_by_vertex(cb_name=code_base_name, vertex=vertex)
         return res

coagent/tools/docs_retrieval.py

@@ -2,11 +2,7 @@ from pydantic import BaseModel, Field
 from loguru import logger
 
 from coagent.llm_models.llm_config import EmbedConfig
-
 from .base_tool import BaseToolModel
-
-
-
 from coagent.service.kb_api import search_docs
 
 

coagent/tools/duckduckgo_search.py

@@ -9,8 +9,10 @@ import numpy as np
 from loguru import logger
 
 from .base_tool import BaseToolModel
-
+try:
     from duckduckgo_search import DDGS
+except:
+    logger.warning("can't find duckduckgo_search, if you need it, please `pip install duckduckgo_search`")
 
 
 class DDGSTool(BaseToolModel):

coagent/utils/code2doc_util.py

@@ -0,0 +1,89 @@
+import json
+
+
+def class_info_decode(data):
+    '''解析class的相关信息'''
+    params_dict = {}
+
+    for i in data:
+        _params_dict = {}
+        for ii in i:
+            for k, v in ii.items():
+                if k=="origin_query": continue
+                
+                if k == "Code Path":
+                    _params_dict["code_path"] = v.split("#")[0]
+                    _params_dict["function_name"] = ".".join(v.split("#")[1:])
+                    
+                if k == "Class Description":
+                    _params_dict["ClassDescription"] = v
+
+                if k == "Class Base":
+                    _params_dict["ClassBase"] = v
+                    
+                if k=="Init Parameters":
+                    _params_dict["Parameters"] = v
+                    
+                    
+        code_path = _params_dict["code_path"]
+        params_dict.setdefault(code_path, []).append(_params_dict)
+        
+    return params_dict
+
+def method_info_decode(data):
+    params_dict = {}
+
+    for i in data:
+        _params_dict = {}
+        for ii in i:
+            for k, v in ii.items():
+                if k=="origin_query": continue
+                
+                if k == "Code Path":
+                    _params_dict["code_path"] = v.split("#")[0]
+                    _params_dict["function_name"] = ".".join(v.split("#")[1:])
+                    
+                if k == "Return Value Description":
+                    _params_dict["Returns"] = v
+                
+                if k == "Return Type":
+                    _params_dict["ReturnType"] = v
+                    
+                if k=="Parameters":
+                    _params_dict["Parameters"] = v
+                    
+                    
+        code_path = _params_dict["code_path"]
+        params_dict.setdefault(code_path, []).append(_params_dict)
+        
+    return params_dict
+
+def encode2md(data, md_format):
+    md_dict = {}
+    for code_path, params_list in data.items():
+        for params in params_list:
+            params["Parameters_text"] = "\n".join([f"{param['param']}({param['param_type']})-{param['param_description']}"  
+                                    for param in params["Parameters"]])
+    #         params.delete("Parameters")
+            text=md_format.format(**params)
+            md_dict.setdefault(code_path, []).append(text)
+    return md_dict
+
+
+method_text_md = '''> {function_name}
+
+| Column Name | Content |
+|-----------------|-----------------|
+| Parameters   | {Parameters_text} |
+| Returns   | {Returns} |
+| Return type   | {ReturnType} |
+'''
+
+class_text_md = '''> {code_path}
+
+Bases: {ClassBase}
+
+{ClassDescription}
+
+{Parameters_text}
+'''

coagent/utils/common_utils.py

@@ -7,7 +7,7 @@ from pathlib import Path
 from io import BytesIO
 from fastapi import Body, File, Form, Body, Query, UploadFile
 from tempfile import SpooledTemporaryFile
-
+import json
 
 
 DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
@@ -110,3 +110,5 @@ def get_uploadfile(file: Union[str, Path, bytes], filename=None) -> UploadFile:
     temp_file.write(file.read())
     temp_file.seek(0)
     return UploadFile(file=temp_file, filename=filename)
+
+

coagent/utils/path_utils.py

@@ -1,7 +1,7 @@
 import os
 from langchain.document_loaders import CSVLoader, PyPDFLoader, UnstructuredFileLoader, TextLoader, PythonLoader
 
-from coagent.document_loaders import JSONLLoader, JSONLoader
+from coagent.retrieval.document_loaders import JSONLLoader, JSONLoader
 # from configs.model_config import (
 #     embedding_model_dict,
 #     KB_ROOT_PATH,

configs/default_config.py

@@ -21,17 +21,20 @@ JUPYTER_WORK_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath
 # WEB_CRAWL存储路径
 WEB_CRAWL_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "knowledge_base")
 # NEBULA_DATA存储路径
-NELUBA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/neluba_data")
+NEBULA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/nebula_data")
 
-for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NELUBA_PATH]:
+# CHROMA 存储路径
+CHROMA_PERSISTENT_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/chroma_data")
+
+for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH]:
     if not os.path.exists(_path):
         os.makedirs(_path, exist_ok=True)
         
-# 
 path_envt_dict = {
     "LOG_PATH": LOG_PATH, "SOURCE_PATH": SOURCE_PATH, "KB_ROOT_PATH": KB_ROOT_PATH,
     "NLTK_DATA_PATH":NLTK_DATA_PATH, "JUPYTER_WORK_PATH": JUPYTER_WORK_PATH,
-    "WEB_CRAWL_PATH": WEB_CRAWL_PATH, "NELUBA_PATH": NELUBA_PATH
+    "WEB_CRAWL_PATH": WEB_CRAWL_PATH, "NEBULA_PATH": NEBULA_PATH,
+    "CHROMA_PERSISTENT_PATH": CHROMA_PERSISTENT_PATH
     }        
 for path_name, _path in path_envt_dict.items():
     os.environ[path_name] = _path

configs/model_config.py.example

@@ -33,7 +33,7 @@ except:
     pass
 
 # add your openai key
-OPENAI_API_BASE = "http://openai.com/v1/chat/completions"
+OPENAI_API_BASE = "https://api.openai.com/v1"
 os.environ["API_BASE_URL"] = OPENAI_API_BASE
 os.environ["OPENAI_API_KEY"] = "sk-xx"
 openai.api_key = "sk-xx"

configs/server_config.py.example

@@ -58,9 +58,6 @@ NEBULA_GRAPH_SERVER = {
     "docker_port": NEBULA_PORT
 }
 
-# chroma conf
-CHROMA_PERSISTENT_PATH = '/home/user/chatbot/data/chroma_data'
-
 # sandbox api server
 SANDBOX_CONTRAINER_NAME = "devopsgpt_sandbox"
 SANDBOX_IMAGE_NAME = "devopsgpt:py39"

examples/agent_examples/baseGroupPhase_example.py

@@ -15,11 +15,11 @@ from coagent.connector.schema import Message
 #
 tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
 # log-level，print prompt和llm predict
-os.environ["log_verbose"] = "0"
+os.environ["log_verbose"] = "2"
 
 phase_name = "baseGroupPhase"
 llm_config = LLMConfig(
-    model_name=LLM_MODEL, model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name=LLM_MODEL, api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/baseTaskPhase_example.py

@@ -17,7 +17,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "baseTaskPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/codeChatPhaseLocal_example.py

@@ -0,0 +1,135 @@
+# encoding: utf-8
+'''
+@author: 温进
+@file: codeChatPhaseLocal_example.py
+@time: 2024/1/31 下午4:32
+@desc:
+'''
+import os, sys, requests
+from concurrent.futures import ThreadPoolExecutor
+from tqdm import tqdm
+
+import requests
+from typing import List
+
+src_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+sys.path.append(src_dir)
+
+from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
+from configs.server_config import SANDBOX_SERVER
+from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
+from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+from coagent.connector.phase import BasePhase
+from coagent.connector.schema import Message, Memory
+from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
+
+
+
+# log-level，print prompt和llm predict
+os.environ["log_verbose"] = "1"
+
+llm_config = LLMConfig(
+    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"],
+    api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+    )
+embed_config = EmbedConfig(
+    embed_engine="model", embed_model="text2vec-base-chinese",
+    embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+    )
+
+
+# delete codebase
+codebase_name = 'client_local'
+code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+use_nh = True
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.delete_codebase(codebase_name=codebase_name)
+
+
+# initialize codebase
+codebase_name = 'client_local'
+code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+code_path = "/home/user/client"
+use_nh = True
+do_interpret = True
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.import_code(do_interpret=do_interpret)
+
+
+
+# chat with codebase
+phase_name = "codeChatPhase"
+phase = BasePhase(
+    phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
+    embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
+)
+
+# remove 这个函数是做什么的  => 基于标签
+# 有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码  => 基于描述
+# 有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串 => 基于描述
+
+## 需要启动容器中的nebula，采用use_nh=True来构建代码库，是可以通过cypher来查询
+# round-1
+# query_content = "代码一共有多少类"
+# query = Message(
+#     role_name="human", role_type="user",
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+#     )
+#
+# output_message1, _ = phase.step(query)
+# print(output_message1)
+
+# round-2
+# query_content = "代码库里有哪些函数，返回5个就行"
+# query = Message(
+#     role_name="human", role_type="user",
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+#     )
+# output_message2, _ = phase.step(query)
+# print(output_message2)
+
+
+# round-3
+query_content = "remove 这个函数是做什么的"
+query = Message(
+    role_name="user", role_type="human",
+    role_content=query_content, input_query=query_content, origin_query=query_content,
+    code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
+    use_nh=False, local_graph_path=CB_ROOT_PATH
+    )
+output_message3, output_memory3 = phase.step(query)
+print(output_memory3.to_str_messages(return_all=True, content_key="parsed_output_list"))
+
+#
+# # round-4
+query_content = "有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码"
+query = Message(
+    role_name="human", role_type="user",
+    role_content=query_content, input_query=query_content, origin_query=query_content,
+    code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="description",
+    use_nh=False, local_graph_path=CB_ROOT_PATH
+    )
+output_message4, output_memory4 = phase.step(query)
+print(output_memory4.to_str_messages(return_all=True, content_key="parsed_output_list"))
+
+
+# # round-5
+query_content = "有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串"
+query = Message(
+    role_name="human", role_type="user",
+    role_content=query_content, input_query=query_content, origin_query=query_content,
+    code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="description",
+    use_nh=False, local_graph_path=CB_ROOT_PATH
+    )
+output_message5, output_memory5 = phase.step(query)
+print(output_memory5.to_str_messages(return_all=True, content_key="parsed_output_list"))

examples/agent_examples/codeChatPhase_example.py

@@ -17,13 +17,14 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "codeChatPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(
     embed_engine="model", embed_model="text2vec-base-chinese",
     embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
     )
+
 phase = BasePhase(
     phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
     embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
@@ -35,25 +36,28 @@ phase = BasePhase(
 # 有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串 => 基于描述
 
 # round-1
-query_content = "代码一共有多少类"
+# query_content = "代码一共有多少类"
-query = Message(
+# query = Message(
-    role_name="human", role_type="user", 
+#     role_name="human", role_type="user",
-    role_content=query_content, input_query=query_content, origin_query=query_content,
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
-    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
-    )
+#     )
-
+#
-output_message1, _ = phase.step(query)
+# output_message1, _ = phase.step(query)
+# print(output_message1)
 
 # round-2
-query_content = "代码库里有哪些函数，返回5个就行"
+# query_content = "代码库里有哪些函数，返回5个就行"
-query = Message(
+# query = Message(
-    role_name="human", role_type="user", 
+#     role_name="human", role_type="user",
-    role_content=query_content, input_query=query_content, origin_query=query_content,
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
-    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
-    )
+#     )
-output_message2, _ = phase.step(query)
+# output_message2, _ = phase.step(query)
+# print(output_message2)
 
-# round-3
+#
+# # round-3
 query_content = "remove 这个函数是做什么的"
 query = Message(
     role_name="user", role_type="human",
@@ -61,24 +65,27 @@ query = Message(
     code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="tag"
     )
 output_message3, _ = phase.step(query)
+print(output_message3)
 
-# round-4
+#
-query_content = "有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码"
+# # round-4
-query = Message(
+# query_content = "有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码"
-    role_name="human", role_type="user", 
+# query = Message(
-    role_content=query_content, input_query=query_content, origin_query=query_content,
+#     role_name="human", role_type="user",
-    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="description"
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
-    )
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="description"
-output_message4, _ = phase.step(query)
+#     )
-
+# output_message4, _ = phase.step(query)
-
+# print(output_message4)
-# round-5
+#
-query_content = "有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串"
+# # round-5
-query = Message(
+# query_content = "有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串"
-    role_name="human", role_type="user", 
+# query = Message(
-    role_content=query_content, input_query=query_content, origin_query=query_content,
+#     role_name="human", role_type="user",
-    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="description"
+#     role_content=query_content, input_query=query_content, origin_query=query_content,
-    )
+#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="description"
-output_message5, output_memory5 = phase.step(query)
+#     )
-
+# output_message5, output_memory5 = phase.step(query)
-print(output_memory5.to_str_messages(return_all=True, content_key="parsed_output_list"))
+# print(output_message5)
+#
+# print(output_memory5.to_str_messages(return_all=True, content_key="parsed_output_list"))

examples/agent_examples/codeGenDoc_example.py

@@ -0,0 +1,507 @@
+import os, sys, json
+from loguru import logger
+src_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+sys.path.append(src_dir)
+
+from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
+from configs.server_config import SANDBOX_SERVER
+from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+
+from coagent.connector.phase import BasePhase
+from coagent.connector.agents import BaseAgent
+from coagent.connector.schema import Message
+from coagent.tools import CodeRetrievalSingle
+from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
+import importlib
+
+
+# 定义一个新的agent类
+class CodeGenDocer(BaseAgent):
+
+    def start_action_step(self, message: Message) -> Message:
+        '''do action before agent predict '''
+        # 根据问题获取代码片段和节点信息
+        action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, 
+                                              llm_config=self.llm_config, embed_config=self.embed_config, local_graph_path=message.local_graph_path, use_nh=message.use_nh,search_type="tag")
+        current_vertex = action_json['vertex']
+        message.customed_kargs["Code Snippet"] = action_json["code"]
+        message.customed_kargs['Current_Vertex'] = current_vertex
+        return message
+
+
+# add agent or prompt_manager class
+agent_module = importlib.import_module("coagent.connector.agents")
+setattr(agent_module, 'CodeGenDocer', CodeGenDocer)
+
+
+# log-level，print prompt和llm predict
+os.environ["log_verbose"] = "1"
+
+phase_name = "code2DocsGroup"
+llm_config = LLMConfig(
+    model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"], 
+    api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+)
+embed_config = EmbedConfig(
+    embed_engine="model", embed_model="text2vec-base-chinese", 
+    embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+    )
+
+# initialize codebase
+# delete codebase
+codebase_name = 'client_local'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+use_nh = False
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.delete_codebase(codebase_name=codebase_name)
+
+
+# load codebase
+codebase_name = 'client_local'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+use_nh = True
+do_interpret = True
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.import_code(do_interpret=do_interpret)
+
+# 根据前面的load过程进行初始化
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+phase = BasePhase(
+    phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
+    embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
+)
+
+for vertex_type in ["class", "method"]:
+    vertexes = cbh.search_vertices(vertex_type=vertex_type)
+    logger.info(f"vertexes={vertexes}")
+
+    # round-1
+    docs = []
+    for vertex in vertexes:
+        vertex = vertex.split("-")[0] # -为method的参数
+        query_content = f"为{vertex_type}节点 {vertex}生成文档"
+        query = Message(
+            role_name="human", role_type="user", 
+            role_content=query_content, input_query=query_content, origin_query=query_content,
+            code_engine_name="client_local", score_threshold=1.0, top_k=3, cb_search_type="tag", use_nh=use_nh,
+            local_graph_path=CB_ROOT_PATH,
+            )
+        output_message, output_memory = phase.step(query, reinit_memory=True)
+        # print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
+        docs.append(output_memory.get_spec_parserd_output())
+
+        os.makedirs(f"{CB_ROOT_PATH}/docs", exist_ok=True)
+        with open(f"{CB_ROOT_PATH}/docs/raw_{vertex_type}.json", "w") as f:
+            json.dump(docs, f)
+    
+
+# 下面把生成的文档信息转换成markdown文本
+from coagent.utils.code2doc_util import *
+import json
+with open(f"{CB_ROOT_PATH}/docs/raw_method.json", "r") as f:
+    method_raw_data = json.load(f)
+
+with open(f"{CB_ROOT_PATH}/docs/raw_class.json", "r") as f:
+    class_raw_data = json.load(f)
+    
+
+method_data = method_info_decode(method_raw_data)
+class_data = class_info_decode(class_raw_data)
+method_mds = encode2md(method_data, method_text_md)
+class_mds = encode2md(class_data, class_text_md)
+
+
+docs_dict = {}
+for k,v in class_mds.items():
+    method_textmds = method_mds.get(k, [])
+    for vv in v:
+        # 理论上只有一个
+        text_md = vv
+
+    for method_textmd in method_textmds:
+        text_md += "\n<br>" + method_textmd
+
+    docs_dict.setdefault(k, []).append(text_md)
+    
+    with open(f"{CB_ROOT_PATH}//docs/{k}.md", "w") as f:
+        f.write(text_md)
+    
+
+
+
+
+####################################
+######## 下面是完整的复现过程 ########
+####################################
+
+# import os, sys, requests
+# from loguru import logger
+# src_dir = os.path.join(
+#     os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+# )
+# sys.path.append(src_dir)
+
+# from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
+# from configs.server_config import SANDBOX_SERVER
+# from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
+# from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+
+# from coagent.connector.phase import BasePhase
+# from coagent.connector.agents import BaseAgent, SelectorAgent
+# from coagent.connector.chains import BaseChain
+# from coagent.connector.schema import (
+#     Message, Memory, load_role_configs, load_phase_configs, load_chain_configs, ActionStatus
+#     )
+# from coagent.connector.memory_manager import BaseMemoryManager
+# from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS, BASE_PROMPT_CONFIGS
+# from coagent.connector.prompt_manager.prompt_manager import PromptManager
+# from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
+
+# import importlib
+# from loguru import logger
+
+
+# from coagent.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
+
+
+# # update new agent configs
+# codeGenDocGroup_PROMPT = """#### Agent Profile
+
+# Your goal is to response according the Context Data's information with the role that will best facilitate a solution, taking into account all relevant context (Context) provided.
+
+# When you need to select the appropriate role for handling a user's query, carefully read the provided role names, role descriptions and tool list.
+
+# ATTENTION: response carefully referenced "Response Output Format" in format.
+
+# #### Input Format
+
+# #### Response Output Format
+
+# **Code Path:** Extract the paths for the class/method/function that need to be addressed from the context
+
+# **Role:** Select the role from agent names
+# """
+
+# classGenDoc_PROMPT = """#### Agent Profile
+# As an advanced code documentation generator, you are proficient in translating class definitions into comprehensive documentation with a focus on instantiation parameters. 
+# Your specific task is to parse the given code snippet of a class, extract information regarding its instantiation parameters.
+
+# ATTENTION: response carefully in "Response Output Format".
+
+# #### Input Format
+
+# **Code Snippet:** Provide the full class definition, including the constructor and any parameters it may require for instantiation.
+
+# #### Response Output Format
+# **Class Base:** Specify the base class or interface from which the current class extends, if any.
+
+# **Class Description:** Offer a brief description of the class's purpose and functionality.
+
+# **Init Parameters:** List each parameter from construct. For each parameter, provide:
+#     - `param`: The parameter name
+#     - `param_description`: A concise explanation of the parameter's purpose.
+#     - `param_type`: The data type of the parameter, if explicitly defined.
+
+#     ```json
+#     [
+#         {
+#             "param": "parameter_name",
+#             "param_description": "A brief description of what this parameter is used for.",
+#             "param_type": "The data type of the parameter"
+#         },
+#         ...
+#     ]
+#     ```
+
+        
+#     If no parameter for construct, return 
+#     ```json
+#     []
+#     ```
+# """
+
+# funcGenDoc_PROMPT = """#### Agent Profile
+# You are a high-level code documentation assistant, skilled at extracting information from function/method code into detailed and well-structured documentation.
+
+# ATTENTION: response carefully in "Response Output Format".
+
+
+# #### Input Format
+# **Code Path:** Provide the code path of the function or method you wish to document. 
+# This name will be used to identify and extract the relevant details from the code snippet provided.
+    
+# **Code Snippet:** A segment of code that contains the function or method to be documented.
+
+# #### Response Output Format
+
+# **Class Description:** Offer a brief description of the method(function)'s purpose and functionality.
+
+# **Parameters:** Extract parameter for the specific function/method Code from Code Snippet. For parameter, provide:
+#     - `param`: The parameter name
+#     - `param_description`: A concise explanation of the parameter's purpose.
+#     - `param_type`: The data type of the parameter, if explicitly defined.
+#     ```json
+#     [
+#         {
+#             "param": "parameter_name",
+#             "param_description": "A brief description of what this parameter is used for.",
+#             "param_type": "The data type of the parameter"
+#         },
+#         ...
+#     ]
+#     ```
+
+#     If no parameter for function/method, return 
+#     ```json
+#     []
+#     ```
+
+# **Return Value Description:** Describe what the function/method returns upon completion.
+
+# **Return Type:** Indicate the type of data the function/method returns (e.g., string, integer, object, void).
+# """
+
+# CODE_GENERATE_GROUP_PROMPT_CONFIGS = [
+#     {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+#     {"field_name": 'agent_infomation', "function_name": 'handle_agent_data', "is_context": False, "omit_if_empty": False},
+#     # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
+#     {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+#     # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
+#     {"field_name": 'session_records', "function_name": 'handle_session_records'},
+#     {"field_name": 'Specific Objective', "function_name": 'handle_specific_objective'},
+#     {"field_name": 'Code Snippet', "function_name": 'handle_code_snippet'},
+#     {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+#     {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+# ]
+
+# CODE_GENERATE_DOC_PROMPT_CONFIGS = [
+#     {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+#     # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
+#     {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+#     # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
+#     {"field_name": 'session_records', "function_name": 'handle_session_records'},
+#     {"field_name": 'Specific Objective', "function_name": 'handle_specific_objective'},
+#     {"field_name": 'Code Snippet', "function_name": 'handle_code_snippet'},
+#     {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+#     {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+# ]
+
+
+# class CodeGenDocPM(PromptManager):
+#     def handle_code_snippet(self, **kwargs) -> str:
+#         if 'previous_agent_message' not in kwargs:
+#             return ""
+#         previous_agent_message: Message = kwargs['previous_agent_message']
+#         code_snippet = previous_agent_message.customed_kargs.get("Code Snippet", "")
+#         current_vertex = previous_agent_message.customed_kargs.get("Current_Vertex", "")
+#         instruction = "A segment of code that contains the function or method to be documented.\n"
+#         return instruction + "\n" + f"name: {current_vertex}\n{code_snippet}"
+
+#     def handle_specific_objective(self, **kwargs) -> str:
+#         if 'previous_agent_message' not in kwargs:
+#             return ""
+#         previous_agent_message: Message = kwargs['previous_agent_message']
+#         specific_objective = previous_agent_message.parsed_output.get("Code Path")
+
+#         instruction = "Provide the code path of the function or method you wish to document.\n"
+#         s = instruction + f"\n{specific_objective}"
+#         return s
+
+
+# from coagent.tools import CodeRetrievalSingle
+
+# # 定义一个新的agent类
+# class CodeGenDocer(BaseAgent):
+
+#     def start_action_step(self, message: Message) -> Message:
+#         '''do action before agent predict '''
+#         # 根据问题获取代码片段和节点信息
+#         action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, 
+#                                               llm_config=self.llm_config, embed_config=self.embed_config, local_graph_path=message.local_graph_path, use_nh=message.use_nh,search_type="tag")
+#         current_vertex = action_json['vertex']
+#         message.customed_kargs["Code Snippet"] = action_json["code"]
+#         message.customed_kargs['Current_Vertex'] = current_vertex
+#         return message
+
+# # add agent or prompt_manager class
+# agent_module = importlib.import_module("coagent.connector.agents")
+# prompt_manager_module = importlib.import_module("coagent.connector.prompt_manager")
+
+# setattr(agent_module, 'CodeGenDocer', CodeGenDocer)
+# setattr(prompt_manager_module, 'CodeGenDocPM', CodeGenDocPM)
+
+
+
+
+# AGETN_CONFIGS.update({
+#     "classGenDoc": {
+#         "role": {
+#             "role_prompt": classGenDoc_PROMPT,
+#             "role_type": "assistant",
+#             "role_name": "classGenDoc",
+#             "role_desc": "",
+#             "agent_type": "CodeGenDocer"
+#         },
+#         "prompt_config": CODE_GENERATE_DOC_PROMPT_CONFIGS,
+#         "prompt_manager_type": "CodeGenDocPM",
+#         "chat_turn": 1,
+#         "focus_agents": [],
+#         "focus_message_keys": [],
+#     },
+#     "funcGenDoc": {
+#         "role": {
+#             "role_prompt": funcGenDoc_PROMPT,
+#             "role_type": "assistant",
+#             "role_name": "funcGenDoc",
+#             "role_desc": "",
+#             "agent_type": "CodeGenDocer"
+#         },
+#         "prompt_config": CODE_GENERATE_DOC_PROMPT_CONFIGS,
+#         "prompt_manager_type": "CodeGenDocPM",
+#         "chat_turn": 1,
+#         "focus_agents": [],
+#         "focus_message_keys": [],
+#     },
+#     "codeGenDocsGrouper": {
+#         "role": {
+#             "role_prompt": codeGenDocGroup_PROMPT,
+#             "role_type": "assistant",
+#             "role_name": "codeGenDocsGrouper",
+#             "role_desc": "",
+#             "agent_type": "SelectorAgent"
+#         },
+#         "prompt_config": CODE_GENERATE_GROUP_PROMPT_CONFIGS,
+#         "group_agents": ["classGenDoc", "funcGenDoc"],
+#         "chat_turn": 1,
+#     },
+# })
+# # update new chain configs
+# CHAIN_CONFIGS.update({
+#     "codeGenDocsGroupChain": {
+#         "chain_name": "codeGenDocsGroupChain",
+#         "chain_type": "BaseChain",
+#         "agents": ["codeGenDocsGrouper"],
+#         "chat_turn": 1,
+#         "do_checker": False,
+#         "chain_prompt": ""
+#     }
+# })
+
+# # update phase configs
+# PHASE_CONFIGS.update({
+#     "codeGenDocsGroup": {
+#         "phase_name": "codeGenDocsGroup",
+#         "phase_type": "BasePhase",
+#         "chains": ["codeGenDocsGroupChain"],
+#         "do_summary": False,
+#         "do_search": False,
+#         "do_doc_retrieval": False,
+#         "do_code_retrieval": False,
+#         "do_tool_retrieval": False,
+#     },
+# })
+
+
+# role_configs = load_role_configs(AGETN_CONFIGS)
+# chain_configs = load_chain_configs(CHAIN_CONFIGS)
+# phase_configs = load_phase_configs(PHASE_CONFIGS)
+
+# # log-level，print prompt和llm predict
+# os.environ["log_verbose"] = "1"
+
+# phase_name = "codeGenDocsGroup"
+# llm_config = LLMConfig(
+#     model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"], 
+#     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+# )
+# embed_config = EmbedConfig(
+#     embed_engine="model", embed_model="text2vec-base-chinese", 
+#     embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+#     )
+
+
+# # initialize codebase
+# # delete codebase
+# codebase_name = 'client_local'
+# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+# use_nh = False
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+# cbh.delete_codebase(codebase_name=codebase_name)
+
+
+# # load codebase
+# codebase_name = 'client_local'
+# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+# use_nh = False
+# do_interpret = True
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+# cbh.import_code(do_interpret=do_interpret)
+
+
+# phase = BasePhase(
+#     phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
+#     embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
+# )
+
+# for vertex_type in ["class", "method"]:
+#     vertexes = cbh.search_vertices(vertex_type=vertex_type)
+#     logger.info(f"vertexes={vertexes}")
+
+#     # round-1
+#     docs = []
+#     for vertex in vertexes:
+#         vertex = vertex.split("-")[0] # -为method的参数
+#         query_content = f"为{vertex_type}节点 {vertex}生成文档"
+#         query = Message(
+#             role_name="human", role_type="user", 
+#             role_content=query_content, input_query=query_content, origin_query=query_content,
+#             code_engine_name="client_local", score_threshold=1.0, top_k=3, cb_search_type="tag", use_nh=use_nh,
+#             local_graph_path=CB_ROOT_PATH,
+#             )
+#         output_message, output_memory = phase.step(query, reinit_memory=True)
+#         # print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
+#         docs.append(output_memory.get_spec_parserd_output())
+
+#         import json
+#         os.makedirs("/home/user/code_base/docs", exist_ok=True)
+#         with open(f"/home/user/code_base/docs/raw_{vertex_type}.json", "w") as f:
+#             json.dump(docs, f)
+
+
+# # 下面把生成的文档信息转换成markdown文本
+# from coagent.utils.code2doc_util import *
+
+# import json
+# with open(f"/home/user/code_base/docs/raw_method.json", "r") as f:
+#     method_raw_data = json.load(f)
+
+# with open(f"/home/user/code_base/docs/raw_class.json", "r") as f:
+#     class_raw_data = json.load(f)
+    
+
+# method_data = method_info_decode(method_raw_data)
+# class_data = class_info_decode(class_raw_data)
+# method_mds = encode2md(method_data, method_text_md)
+# class_mds = encode2md(class_data, class_text_md)
+
+# docs_dict = {}
+# for k,v in class_mds.items():
+#     method_textmds = method_mds.get(k, [])
+#     for vv in v:
+#         # 理论上只有一个
+#         text_md = vv
+
+#     for method_textmd in method_textmds:
+#         text_md += "\n<br>" + method_textmd
+
+#     docs_dict.setdefault(k, []).append(text_md)
+    
+#     with open(f"/home/user/code_base/docs/{k}.md", "w") as f:
+#         f.write(text_md)

examples/agent_examples/codeGenTestCases_example.py

@@ -0,0 +1,444 @@
+import os, sys, json
+from loguru import logger
+src_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+sys.path.append(src_dir)
+
+from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
+from configs.server_config import SANDBOX_SERVER
+from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+
+from coagent.connector.phase import BasePhase
+from coagent.connector.agents import BaseAgent
+from coagent.connector.schema import Message
+from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
+
+import importlib
+from loguru import logger
+
+
+from coagent.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
+
+# 定义一个新的agent类
+class CodeRetrieval(BaseAgent):
+
+    def start_action_step(self, message: Message) -> Message:
+        '''do action before agent predict '''
+        # 根据问题获取代码片段和节点信息
+        action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, llm_config=self.llm_config, embed_config=self.embed_config, search_type="tag", 
+                                              local_graph_path=message.local_graph_path, use_nh=message.use_nh)
+        current_vertex = action_json['vertex']
+        message.customed_kargs["Code Snippet"] = action_json["code"]
+        message.customed_kargs['Current_Vertex'] = current_vertex
+
+        # 获取邻近节点
+        action_json = RelatedVerticesRetrival.run(message.code_engine_name, message.customed_kargs['Current_Vertex'])
+        # 获取邻近节点所有代码
+        relative_vertex = []
+        retrieval_Codes = []
+        for vertex in action_json["vertices"]:
+            # 由于代码是文件级别，所以相同文件代码不再获取
+            # logger.debug(f"{current_vertex}, {vertex}")
+            current_vertex_name = current_vertex.replace("#", "").replace(".java", "" ) if current_vertex.endswith(".java") else current_vertex
+            if current_vertex_name.split("#")[0] == vertex.split("#")[0]: continue
+
+            action_json = Vertex2Code.run(message.code_engine_name, vertex)
+            if action_json["code"]:
+                retrieval_Codes.append(action_json["code"])
+                relative_vertex.append(vertex)
+        # 
+        message.customed_kargs["Retrieval_Codes"] = retrieval_Codes
+        message.customed_kargs["Relative_vertex"] = relative_vertex
+        return message
+
+
+# add agent or prompt_manager class
+agent_module = importlib.import_module("coagent.connector.agents")
+setattr(agent_module, 'CodeRetrieval', CodeRetrieval)
+
+
+
+llm_config = LLMConfig(
+    model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"], 
+    api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+)
+embed_config = EmbedConfig(
+    embed_engine="model", embed_model="text2vec-base-chinese", 
+    embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+    )
+
+## initialize codebase
+# delete codebase
+codebase_name = 'client_local'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+use_nh = False
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.delete_codebase(codebase_name=codebase_name)
+
+
+# load codebase
+codebase_name = 'client_local'
+code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+use_nh = True
+do_interpret = True
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+cbh.import_code(do_interpret=do_interpret)
+
+
+cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+                      llm_config=llm_config, embed_config=embed_config)
+vertexes = cbh.search_vertices(vertex_type="class")
+
+
+# log-level，print prompt和llm predict
+os.environ["log_verbose"] = "0"
+
+phase_name = "code2Tests"
+phase = BasePhase(
+    phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
+    embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
+)
+
+# round-1
+logger.debug(vertexes)
+test_cases = []
+for vertex in vertexes:
+    query_content = f"为{vertex}生成可执行的测例 "
+    query = Message(
+        role_name="human", role_type="user", 
+        role_content=query_content, input_query=query_content, origin_query=query_content,
+        code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
+        use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+        )
+    output_message, output_memory = phase.step(query, reinit_memory=True)
+    # print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
+    print(output_memory.get_spec_parserd_output())
+    values = output_memory.get_spec_parserd_output()
+    test_code = {k:v for i in values for k,v in i.items() if k in ["SaveFileName", "Test Code"]}
+    test_cases.append(test_code)
+
+    os.makedirs(f"{CB_ROOT_PATH}/tests", exist_ok=True)
+
+    with open(f"{CB_ROOT_PATH}/tests/{test_code['SaveFileName']}", "w") as f:
+        f.write(test_code["Test Code"])
+    break
+
+
+
+
+# import os, sys, json
+# from loguru import logger
+# src_dir = os.path.join(
+#     os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+# )
+# sys.path.append(src_dir)
+
+# from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH, CB_ROOT_PATH
+# from configs.server_config import SANDBOX_SERVER
+# from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
+# from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
+
+# from coagent.connector.phase import BasePhase
+# from coagent.connector.agents import BaseAgent
+# from coagent.connector.chains import BaseChain
+# from coagent.connector.schema import (
+#     Message, Memory, load_role_configs, load_phase_configs, load_chain_configs, ActionStatus
+#     )
+# from coagent.connector.memory_manager import BaseMemoryManager
+# from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS, BASE_PROMPT_CONFIGS
+# from coagent.connector.prompt_manager.prompt_manager import PromptManager
+# from coagent.codechat.codebase_handler.codebase_handler import CodeBaseHandler
+
+# import importlib
+# from loguru import logger
+
+# # 下面给定了一份代码片段，以及来自于它的依赖类、依赖方法相关的代码片段，你需要判断是否为这段指定代码片段生成测例。
+# # 理论上所有代码都需要写测例，但是受限于人的精力不可能覆盖所有代码
+# # 考虑以下因素进行裁剪：
+# # 功能性: 如果它实现的是一个具体的功能或逻辑，则通常需要编写测试用例以验证其正确性。
+# # 复杂性: 如果代码较为，尤其是包含多个条件判断、循环、异常处理等的代码，更可能隐藏bug，因此应该编写测试用例。如果代码涉及复杂的算法或者逻辑，那么编写测试用例可以帮助确保逻辑的正确性，并在未来的重构中防止引入错误。
+# # 关键性: 如果它是关键路径的一部分或影响到核心功能，那么它就需要被测试。对于核心业务逻辑或者系统的关键组件，应当编写全面的测试用例来确保功能的正确性和稳定性。
+# # 依赖性: 如果代码有外部依赖，可能需要编写集成测试或模拟这些依赖进行单元测试。
+# # 用户输入: 如果代码处理用户输入，尤其是来自外部的、非受控的输入，那么创建测试用例来检查输入验证和处理是很重要的。
+# # 频繁更改：对于经常需要更新或修改的代码，有相应的测试用例可以确保更改不会破坏现有功能。
+
+
+# # 代码公开或重用：如果代码将被公开或用于其他项目，编写测试用例可以提高代码的可信度和易用性。
+
+
+# # update new agent configs
+# judgeGenerateTests_PROMPT = """#### Agent Profile
+# When determining the necessity of writing test cases for a given code snippet, 
+# it's essential to evaluate its interactions with dependent classes and methods (retrieved code snippets), 
+# in addition to considering these critical factors:
+# 1. Functionality: If it implements a concrete function or logic, test cases are typically necessary to verify its correctness.
+# 2. Complexity: If the code is complex, especially if it contains multiple conditional statements, loops, exceptions handling, etc., 
+# it's more likely to harbor bugs, and thus test cases should be written. 
+# If the code involves complex algorithms or logic, then writing test cases can help ensure the accuracy of the logic and prevent errors during future refactoring.
+# 3. Criticality: If it's part of the critical path or affects core functionalities, then it needs to be tested. 
+# Comprehensive test cases should be written for core business logic or key components of the system to ensure the correctness and stability of the functionality.
+# 4. Dependencies: If the code has external dependencies, integration testing may be necessary, or mocking these dependencies during unit testing might be required.
+# 5. User Input: If the code handles user input, especially from unregulated external sources, creating test cases to check input validation and handling is important.
+# 6. Frequent Changes: For code that requires regular updates or modifications, having the appropriate test cases ensures that changes do not break existing functionalities.
+
+# #### Input Format
+
+# **Code Snippet:** the initial Code or objective that the user wanted to achieve
+
+# **Retrieval Code Snippets:** These are the associated code segments that the main Code Snippet depends on. 
+# Examine these snippets to understand how they interact with the main snippet and to determine how they might affect the overall functionality.
+
+# #### Response Output Format
+# **Action Status:** Set to 'finished' or 'continued'. 
+# If set to 'finished', the code snippet does not warrant the generation of a test case.
+# If set to 'continued', the code snippet necessitates the creation of a test case.
+
+# **REASON:** Justify the selection of 'finished' or 'continued', contemplating the decision through a step-by-step rationale.
+# """
+
+# generateTests_PROMPT = """#### Agent Profile
+# As an agent specializing in software quality assurance, 
+# your mission is to craft comprehensive test cases that bolster the functionality, reliability, and robustness of a specified Code Snippet. 
+# This task is to be carried out with a keen understanding of the snippet's interactions with its dependent classes and methods—collectively referred to as Retrieval Code Snippets. 
+# Analyze the details given below to grasp the code's intended purpose, its inherent complexity, and the context within which it operates. 
+# Your constructed test cases must thoroughly examine the various factors influencing the code's quality and performance.
+
+# ATTENTION: response carefully referenced "Response Output Format" in format.
+
+# Each test case should include:
+# 1. clear description of the test purpose.
+# 2. The input values or conditions for the test.
+# 3. The expected outcome or assertion for the test.
+# 4. Appropriate tags (e.g., 'functional', 'integration', 'regression') that classify the type of test case.
+# 5. these test code should have package and import
+
+# #### Input Format
+
+# **Code Snippet:** the initial Code or objective that the user wanted to achieve
+
+# **Retrieval Code Snippets:** These are the interrelated pieces of code sourced from the codebase, which support or influence the primary Code Snippet.
+
+# #### Response Output Format
+# **SaveFileName:** construct a local file name based on Question and Context, such as
+
+# ```java
+# package/class.java
+# ```
+
+
+# **Test Code:** generate the test code for the current Code Snippet.
+# ```java
+# ...
+# ```
+
+# """
+
+# CODE_GENERATE_TESTS_PROMPT_CONFIGS = [
+#     {"field_name": 'agent_profile', "function_name": 'handle_agent_profile', "is_context": False},
+#     # {"field_name": 'tool_information',"function_name": 'handle_tool_data', "is_context": False},
+#     {"field_name": 'context_placeholder', "function_name": '', "is_context": True},
+#     # {"field_name": 'reference_documents', "function_name": 'handle_doc_info'},
+#     {"field_name": 'session_records', "function_name": 'handle_session_records'},
+#     {"field_name": 'code_snippet', "function_name": 'handle_code_snippet'},
+#     {"field_name": 'retrieval_codes', "function_name": 'handle_retrieval_codes', "description": ""},
+#     {"field_name": 'output_format', "function_name": 'handle_output_format', 'title': 'Response Output Format', "is_context": False},
+#     {"field_name": 'begin!!!', "function_name": 'handle_response', "is_context": False, "omit_if_empty": False}
+# ]
+
+
+# class CodeRetrievalPM(PromptManager):
+#     def handle_code_snippet(self, **kwargs) -> str:
+#         if 'previous_agent_message' not in kwargs:
+#             return ""
+#         previous_agent_message: Message = kwargs['previous_agent_message']
+#         code_snippet = previous_agent_message.customed_kargs.get("Code Snippet", "")
+#         current_vertex = previous_agent_message.customed_kargs.get("Current_Vertex", "")
+#         instruction = "the initial Code or objective that the user wanted to achieve"
+#         return instruction + "\n" + f"name: {current_vertex}\n{code_snippet}"
+
+#     def handle_retrieval_codes(self, **kwargs) -> str:
+#         if 'previous_agent_message' not in kwargs:
+#             return ""
+#         previous_agent_message: Message = kwargs['previous_agent_message']
+#         Retrieval_Codes = previous_agent_message.customed_kargs["Retrieval_Codes"]
+#         Relative_vertex = previous_agent_message.customed_kargs["Relative_vertex"]
+#         instruction = "the initial Code or objective that the user wanted to achieve"
+#         s = instruction + "\n" + "\n".join([f"name: {vertext}\n{code}" for vertext, code in zip(Relative_vertex, Retrieval_Codes)])
+#         return s
+
+
+
+# AGETN_CONFIGS.update({
+#     "CodeJudger": {
+#         "role": {
+#             "role_prompt": judgeGenerateTests_PROMPT,
+#             "role_type": "assistant",
+#             "role_name": "CodeJudger",
+#             "role_desc": "",
+#             "agent_type": "CodeRetrieval"
+#             # "agent_type": "BaseAgent"
+#         },
+#         "prompt_config": CODE_GENERATE_TESTS_PROMPT_CONFIGS,
+#         "prompt_manager_type": "CodeRetrievalPM",
+#         "chat_turn": 1,
+#         "focus_agents": [],
+#         "focus_message_keys": [],
+#     },
+#     "generateTests": {
+#         "role": {
+#             "role_prompt": generateTests_PROMPT,
+#             "role_type": "assistant",
+#             "role_name": "generateTests",
+#             "role_desc": "",
+#             "agent_type": "CodeRetrieval"
+#             # "agent_type": "BaseAgent"
+#         },
+#         "prompt_config": CODE_GENERATE_TESTS_PROMPT_CONFIGS,
+#         "prompt_manager_type": "CodeRetrievalPM",
+#         "chat_turn": 1,
+#         "focus_agents": [],
+#         "focus_message_keys": [],
+#     },
+# })
+# # update new chain configs
+# CHAIN_CONFIGS.update({
+#     "codeRetrievalChain": {
+#         "chain_name": "codeRetrievalChain",
+#         "chain_type": "BaseChain",
+#         "agents": ["CodeJudger", "generateTests"],
+#         "chat_turn": 1,
+#         "do_checker": False,
+#         "chain_prompt": ""
+#     }
+# })
+
+# # update phase configs
+# PHASE_CONFIGS.update({
+#     "codeGenerateTests": {
+#         "phase_name": "codeGenerateTests",
+#         "phase_type": "BasePhase",
+#         "chains": ["codeRetrievalChain"],
+#         "do_summary": False,
+#         "do_search": False,
+#         "do_doc_retrieval": False,
+#         "do_code_retrieval": False,
+#         "do_tool_retrieval": False,
+#     },
+# })
+
+
+# role_configs = load_role_configs(AGETN_CONFIGS)
+# chain_configs = load_chain_configs(CHAIN_CONFIGS)
+# phase_configs = load_phase_configs(PHASE_CONFIGS)
+
+
+# from coagent.tools import CodeRetrievalSingle, RelatedVerticesRetrival, Vertex2Code
+
+# # 定义一个新的agent类
+# class CodeRetrieval(BaseAgent):
+
+#     def start_action_step(self, message: Message) -> Message:
+#         '''do action before agent predict '''
+#         # 根据问题获取代码片段和节点信息
+#         action_json = CodeRetrievalSingle.run(message.code_engine_name, message.origin_query, llm_config=self.llm_config, embed_config=self.embed_config, search_type="tag", 
+#                                               local_graph_path=message.local_graph_path, use_nh=message.use_nh)
+#         current_vertex = action_json['vertex']
+#         message.customed_kargs["Code Snippet"] = action_json["code"]
+#         message.customed_kargs['Current_Vertex'] = current_vertex
+
+#         # 获取邻近节点
+#         action_json = RelatedVerticesRetrival.run(message.code_engine_name, message.customed_kargs['Current_Vertex'])
+#         # 获取邻近节点所有代码
+#         relative_vertex = []
+#         retrieval_Codes = []
+#         for vertex in action_json["vertices"]:
+#             # 由于代码是文件级别，所以相同文件代码不再获取
+#             # logger.debug(f"{current_vertex}, {vertex}")
+#             current_vertex_name = current_vertex.replace("#", "").replace(".java", "" ) if current_vertex.endswith(".java") else current_vertex
+#             if current_vertex_name.split("#")[0] == vertex.split("#")[0]: continue
+
+#             action_json = Vertex2Code.run(message.code_engine_name, vertex)
+#             if action_json["code"]:
+#                 retrieval_Codes.append(action_json["code"])
+#                 relative_vertex.append(vertex)
+#         # 
+#         message.customed_kargs["Retrieval_Codes"] = retrieval_Codes
+#         message.customed_kargs["Relative_vertex"] = relative_vertex
+#         return message
+
+
+# # add agent or prompt_manager class
+# agent_module = importlib.import_module("coagent.connector.agents")
+# prompt_manager_module = importlib.import_module("coagent.connector.prompt_manager")
+
+# setattr(agent_module, 'CodeRetrieval', CodeRetrieval)
+# setattr(prompt_manager_module, 'CodeRetrievalPM', CodeRetrievalPM)
+
+
+# # log-level，print prompt和llm predict
+# os.environ["log_verbose"] = "0"
+
+# phase_name = "codeGenerateTests"
+# llm_config = LLMConfig(
+#     model_name="gpt-4", api_key=os.environ["OPENAI_API_KEY"], 
+#     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
+# )
+# embed_config = EmbedConfig(
+#     embed_engine="model", embed_model="text2vec-base-chinese", 
+#     embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
+#     )
+
+# ## initialize codebase
+# # delete codebase
+# codebase_name = 'client_local'
+# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+# use_nh = False
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+# cbh.delete_codebase(codebase_name=codebase_name)
+
+
+# # load codebase
+# codebase_name = 'client_local'
+# code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
+# use_nh = True
+# do_interpret = True
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+# cbh.import_code(do_interpret=do_interpret)
+
+
+# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#                       llm_config=llm_config, embed_config=embed_config)
+# vertexes = cbh.search_vertices(vertex_type="class")
+
+# phase = BasePhase(
+#     phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
+#     embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
+# )
+
+# # round-1
+# logger.debug(vertexes)
+# test_cases = []
+# for vertex in vertexes:
+#     query_content = f"为{vertex}生成可执行的测例 "
+#     query = Message(
+#         role_name="human", role_type="user", 
+#         role_content=query_content, input_query=query_content, origin_query=query_content,
+#         code_engine_name=codebase_name, score_threshold=1.0, top_k=3, cb_search_type="tag",
+#         use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+#         )
+#     output_message, output_memory = phase.step(query, reinit_memory=True)
+#     # print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
+#     print(output_memory.get_spec_parserd_output())
+#     values = output_memory.get_spec_parserd_output()
+#     test_code = {k:v for i in values for k,v in i.items() if k in ["SaveFileName", "Test Code"]}
+#     test_cases.append(test_code)
+
+#     os.makedirs(f"{CB_ROOT_PATH}/tests", exist_ok=True)
+
+#     with open(f"{CB_ROOT_PATH}/tests/{test_code['SaveFileName']}", "w") as f:
+#         f.write(test_code["Test Code"])

examples/agent_examples/codeReactPhase_example.py

@@ -17,7 +17,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "codeReactPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo",api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/codeRetrieval_example.py

@@ -18,8 +18,7 @@ from coagent.connector.schema import (
     )
 from coagent.connector.memory_manager import BaseMemoryManager
 from coagent.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS, BASE_PROMPT_CONFIGS
-from coagent.connector.utils import parse_section
+from coagent.connector.prompt_manager.prompt_manager import PromptManager
-from coagent.connector.prompt_manager import PromptManager
 import importlib
 
 from loguru import logger
@@ -230,7 +229,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "codeRetrievalPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(
@@ -246,7 +245,7 @@ query_content = "UtilsTest 这个类中测试了哪些函数,测试的函数代
 query = Message(
     role_name="human", role_type="user", 
     role_content=query_content, input_query=query_content, origin_query=query_content,
-    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="tag"
+    code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="tag"
     )
 
 

examples/agent_examples/codeToolReactPhase_example.py

@@ -24,7 +24,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "codeToolReactPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo-0613", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo-0613", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.7
     )
 embed_config = EmbedConfig(

examples/agent_examples/docChatPhase_example.py

@@ -17,7 +17,7 @@ from coagent.connector.schema import Message, Memory
 
 tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo",api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/metagpt_phase_example.py

@@ -18,7 +18,7 @@ os.environ["log_verbose"] = "0"
 
 phase_name = "metagpt_code_devlop"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/searchChatPhase_example.py

@@ -20,7 +20,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "searchChatPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/agent_examples/toolReactPhase_example.py

@@ -18,7 +18,7 @@ os.environ["log_verbose"] = "2"
 
 phase_name = "toolReactPhase"
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo",api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

examples/api.py

@@ -152,8 +152,8 @@ def create_app():
 
     app.post("/code_base/code_base_chat",
             tags=["Code Base Management"],
-             summary="删除 code_base"
+            summary="code_base 对话"
-             )(delete_cb)
+            )(search_code)
 
     app.get("/code_base/list_code_bases",
             tags=["Code Base Management"],

examples/auto_examples/auto_feedback_from_code_execution.py

@@ -117,7 +117,7 @@ PHASE_CONFIGS.update({
 
 
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 

examples/start.py

@@ -98,12 +98,6 @@ def start_docker(client, script_shs, ports, image_name, container_name, mounts=N
 network_name ='my_network'
 
 def start_sandbox_service(network_name ='my_network'):
-    # networks = client.networks.list()
-    # if any([network_name==i.attrs["Name"] for i in networks]):
-    #     network = client.networks.get(network_name)
-    # else:
-    #     network = client.networks.create('my_network', driver='bridge')
-
     mount = Mount(
             type='bind',
             source=os.path.join(src_dir, "jupyter_work"),
@@ -114,6 +108,12 @@ def start_sandbox_service(network_name ='my_network'):
     # 沙盒的启动与服务的启动是独立的
     if SANDBOX_SERVER["do_remote"]:
         client = docker.from_env()
+        networks = client.networks.list()
+        if any([network_name==i.attrs["Name"] for i in networks]):
+            network = client.networks.get(network_name)
+        else:
+            network = client.networks.create('my_network', driver='bridge')
+
         # 启动容器
         logger.info("start container sandbox service")
         JUPYTER_WORK_PATH = "/home/user/chatbot/jupyter_work"
@@ -150,7 +150,7 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
         client = docker.from_env()
         logger.info("start container service")
         check_process("api.py", do_stop=True)
-        check_process("sdfile_api.py", do_stop=True)
+        check_process("llm_api.py", do_stop=True)
         check_process("sdfile_api.py", do_stop=True)
         check_process("webui.py", do_stop=True)
         mount = Mount(
@@ -159,27 +159,28 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
             target='/home/user/chatbot/',
             read_only=False  # 如果需要只读访问，将此选项设置为True
         )
-        mount_database = Mount(
+        # mount_database = Mount(
-            type='bind',
+        #     type='bind',
-            source=os.path.join(src_dir, "knowledge_base"),
+        #     source=os.path.join(src_dir, "knowledge_base"),
-            target='/home/user/knowledge_base/',
+        #     target='/home/user/knowledge_base/',
-            read_only=False  # 如果需要只读访问，将此选项设置为True
+        #     read_only=False  # 如果需要只读访问，将此选项设置为True
-        )
+        # )
-        mount_code_database = Mount(
+        # mount_code_database = Mount(
-            type='bind',
+        #     type='bind',
-            source=os.path.join(src_dir, "code_base"),
+        #     source=os.path.join(src_dir, "code_base"),
-            target='/home/user/code_base/',
+        #     target='/home/user/code_base/',
-            read_only=False  # 如果需要只读访问，将此选项设置为True
+        #     read_only=False  # 如果需要只读访问，将此选项设置为True
-        )
+        # )
         ports={
                 f"{API_SERVER['docker_port']}/tcp": f"{API_SERVER['port']}/tcp", 
                 f"{WEBUI_SERVER['docker_port']}/tcp": f"{WEBUI_SERVER['port']}/tcp",
                 f"{SDFILE_API_SERVER['docker_port']}/tcp": f"{SDFILE_API_SERVER['port']}/tcp",
                 f"{NEBULA_GRAPH_SERVER['docker_port']}/tcp": f"{NEBULA_GRAPH_SERVER['port']}/tcp"
                 }
-        mounts = [mount, mount_database, mount_code_database]
+        # mounts = [mount, mount_database, mount_code_database]
+        mounts = [mount]
         script_shs = [
-            "mkdir -p /home/user/logs",
+            "mkdir -p /home/user/chatbot/logs",
             '''
             if [ -d "/home/user/chatbot/data/nebula_data/data/meta" ]; then
                 cp -r /home/user/chatbot/data/nebula_data/data /usr/local/nebula/
@@ -197,12 +198,12 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
             "pip install jieba",
             "pip install duckduckgo-search",
 
-            "nohup python chatbot/examples/sdfile_api.py > /home/user/logs/sdfile_api.log 2>&1 &",
+            "nohup python chatbot/examples/sdfile_api.py > /home/user/chatbot/logs/sdfile_api.log 2>&1 &",
             f"export DUCKDUCKGO_PROXY=socks5://host.docker.internal:13659 && export SANDBOX_HOST={sandbox_host} &&\
-                nohup python chatbot/examples/api.py > /home/user/logs/api.log 2>&1 &",
+                nohup python chatbot/examples/api.py > /home/user/chatbot/logs/api.log 2>&1 &",
             "nohup python chatbot/examples/llm_api.py > /home/user/llm.log  2>&1 &",
             f"export DUCKDUCKGO_PROXY=socks5://host.docker.internal:13659 && export SANDBOX_HOST={sandbox_host} &&\
-                cd chatbot/examples && nohup streamlit run webui.py > /home/user/logs/start_webui.log 2>&1 &"
+                cd chatbot/examples && nohup streamlit run webui.py > /home/user/chatbot/logs/start_webui.log 2>&1 &"
             ]
         if check_docker(client, CONTRAINER_NAME, do_stop=True):
             container = start_docker(client, script_shs, ports, IMAGE_NAME, CONTRAINER_NAME, mounts, network=network_name)
@@ -212,12 +213,9 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
         # 关闭之前启动的docker 服务
         # check_docker(client, CONTRAINER_NAME, do_stop=True, )
 
-        # api_sh = "nohup python ../coagent/service/api.py > ../logs/api.log 2>&1 &"
         api_sh = "nohup python api.py > ../logs/api.log 2>&1 &"
-        # sdfile_sh = "nohup python ../coagent/service/sdfile_api.py > ../logs/sdfile_api.log 2>&1 &"
         sdfile_sh = "nohup python sdfile_api.py > ../logs/sdfile_api.log 2>&1 &"
         notebook_sh = f"nohup jupyter-notebook --NotebookApp.token=mytoken --port={SANDBOX_SERVER['port']} --allow-root --ip=0.0.0.0 --notebook-dir={JUPYTER_WORK_PATH} --no-browser --ServerApp.disable_check_xsrf=True > ../logs/sandbox.log 2>&1  &"
-        # llm_sh = "nohup python ../coagent/service/llm_api.py > ../logs/llm_api.log 2>&1 &"
         llm_sh = "nohup python llm_api.py > ../logs/llm_api.log 2>&1 &"
         webui_sh = "streamlit run webui.py" if USE_TTY else "streamlit run webui.py"
 

examples/webui/code.py

@@ -22,7 +22,7 @@ from coagent.service.service_factory import get_cb_details, get_cb_details_by_cb
 from coagent.orm import table_init
 
 
-from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict
+from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict,llm_model_dict
 # SENTENCE_SIZE = 100
 
 cell_renderer = JsCode("""function(params) {if(params.value==true){return '✓'}else{return '×'}}""")
@@ -117,6 +117,8 @@ def code_page(api: ApiRequest):
                     embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
                     embedding_device=EMBEDDING_DEVICE,
                     llm_model=LLM_MODEL,
+                    api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                 )
                 st.toast(ret.get("msg", " "))
                 st.session_state["selected_cb_name"] = cb_name
@@ -153,6 +155,8 @@ def code_page(api: ApiRequest):
                     embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
                     embedding_device=EMBEDDING_DEVICE,
                     llm_model=LLM_MODEL,
+                    api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                     )
             st.toast(ret.get("msg", "删除成功"))
             time.sleep(0.05)

examples/webui/dialogue.py

@@ -11,7 +11,7 @@ from coagent.chat.search_chat import SEARCH_ENGINES
 from coagent.connector import PHASE_LIST, PHASE_CONFIGS
 from coagent.service.service_factory import get_cb_details_by_cb_name
 
-from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL, embedding_model_dict, EMBEDDING_ENGINE, KB_ROOT_PATH
+from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL, embedding_model_dict, EMBEDDING_ENGINE, KB_ROOT_PATH, llm_model_dict
 chat_box = ChatBox(
     assistant_avatar="../sources/imgs/devops-chatbot2.png"
 )
@@ -174,7 +174,7 @@ def dialogue_page(api: ApiRequest):
             is_detailed = st.toggle(webui_configs["dialogue"]["phase_toggle_detailed_name"], False)
             tool_using_on = st.toggle(
                 webui_configs["dialogue"]["phase_toggle_doToolUsing"], 
-                PHASE_CONFIGS[choose_phase]["do_using_tool"])
+                PHASE_CONFIGS[choose_phase].get("do_using_tool", False))
             tool_selects = []
             if tool_using_on:
                 with st.expander("工具军火库", True):
@@ -183,7 +183,7 @@ def dialogue_page(api: ApiRequest):
                         TOOL_SETS, ["WeatherInfo"])
             
             search_on = st.toggle(webui_configs["dialogue"]["phase_toggle_doSearch"], 
-                                  PHASE_CONFIGS[choose_phase]["do_search"])
+                                  PHASE_CONFIGS[choose_phase].get("do_search", False))
             search_engine, top_k = None, 3
             if search_on:
                 with st.expander(webui_configs["dialogue"]["expander_search_name"], True):
@@ -195,7 +195,8 @@ def dialogue_page(api: ApiRequest):
 
             doc_retrieval_on = st.toggle(
                 webui_configs["dialogue"]["phase_toggle_doDocRetrieval"], 
-                  PHASE_CONFIGS[choose_phase]["do_doc_retrieval"])
+                  PHASE_CONFIGS[choose_phase].get("do_doc_retrieval", False)
+            )
             selected_kb, top_k, score_threshold = None, 3, 1.0
             if doc_retrieval_on:
                 with st.expander(webui_configs["dialogue"]["kbase_expander_name"], True):
@@ -215,7 +216,7 @@ def dialogue_page(api: ApiRequest):
                     
             code_retrieval_on = st.toggle(
                 webui_configs["dialogue"]["phase_toggle_doCodeRetrieval"], 
-                  PHASE_CONFIGS[choose_phase]["do_code_retrieval"])
+                  PHASE_CONFIGS[choose_phase].get("do_code_retrieval", False))
             selected_cb, top_k = None, 1
             cb_search_type = "tag"
             if code_retrieval_on:
@@ -296,7 +297,8 @@ def dialogue_page(api: ApiRequest):
             r = api.chat_chat(
                 prompt, history, no_remote_api=True, 
                 embed_model=EMBEDDING_MODEL, embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE,
+                model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE,api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                 llm_model=LLM_MODEL)
             for t in r:
                 if error_msg := check_error_msg(t):  # check whether error occured
@@ -362,6 +364,8 @@ def dialogue_page(api: ApiRequest):
                 "embed_engine": EMBEDDING_ENGINE,
                 "kb_root_path": KB_ROOT_PATH,
                 "model_name": LLM_MODEL,
+                "api_key": llm_model_dict[LLM_MODEL]["api_key"],
+                "api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],
             }
             text = ""
             d = {"docs": []}
@@ -405,7 +409,10 @@ def dialogue_page(api: ApiRequest):
                 api.knowledge_base_chat(
                     prompt, selected_kb, kb_top_k, score_threshold, history,
                     embed_model=EMBEDDING_MODEL, embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                    model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL)
+                    model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
+                    api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
+                    )
                     ):
                 if error_msg := check_error_msg(d): # check whether error occured
                     st.error(error_msg)
@@ -415,11 +422,7 @@ def dialogue_page(api: ApiRequest):
                 # chat_box.update_msg("知识库匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
             chat_box.update_msg(text, element_index=0, streaming=False)  # 更新最终的字符串，去除光标
             chat_box.update_msg("{webui_configs['chat']['chatbox_doc_result']}: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
-            # # 判断是否存在代码, 并提高编辑功能，执行功能
+
-            # code_text = api.codebox.decode_code_from_text(text)
-            # GLOBAL_EXE_CODE_TEXT = code_text
-            # if code_text and code_exec_on:
-            #     codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
         elif dialogue_mode == webui_configs["dialogue"]["mode"][2]:
             logger.info('prompt={}'.format(prompt))
             logger.info('history={}'.format(history))
@@ -438,7 +441,9 @@ def dialogue_page(api: ApiRequest):
                                                              cb_search_type=cb_search_type,
                                                              no_remote_api=True, embed_model=EMBEDDING_MODEL, 
                                                              embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                                                             embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL
+                                                             embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
+                                                             api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                                                             api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                                                              )):
                 if error_msg := check_error_msg(d):
                     st.error(error_msg)
@@ -448,6 +453,7 @@ def dialogue_page(api: ApiRequest):
                     chat_box.update_msg(text, element_index=0)
 
             # postprocess
+            logger.debug(f"d={d}")
             text = replace_lt_gt(text)
             chat_box.update_msg(text, element_index=0, streaming=False)  # 更新最终的字符串，去除光标
             logger.debug('text={}'.format(text))
@@ -467,7 +473,9 @@ def dialogue_page(api: ApiRequest):
                 api.search_engine_chat(
                     prompt, search_engine, se_top_k, history, embed_model=EMBEDDING_MODEL, 
                     embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                    model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL)
+                    model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
+                    pi_key=llm_model_dict[LLM_MODEL]["api_key"],
+                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
                     ):
                 if error_msg := check_error_msg(d): # check whether error occured
                     st.error(error_msg)
@@ -477,56 +485,11 @@ def dialogue_page(api: ApiRequest):
                 # chat_box.update_msg("搜索匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False)
             chat_box.update_msg(text, element_index=0, streaming=False)  # 更新最终的字符串，去除光标
             chat_box.update_msg(f"{webui_configs['chat']['chatbox_search_result']}: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
-            # # 判断是否存在代码, 并提高编辑功能，执行功能
-            # code_text = api.codebox.decode_code_from_text(text)
-            # GLOBAL_EXE_CODE_TEXT = code_text
-            # if code_text and code_exec_on:
-            #     codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
 
         # 将上传文件清空
         st.session_state["interpreter_file_key"] += 1
         st.experimental_rerun()
 
-    # if code_interpreter_on:
-    #     with st.expander(webui_configs['sandbox']['expander_code_name'], False):
-    #         code_part = st.text_area(
-    #             webui_configs['sandbox']['textArea_code_name'], code_text, key="code_text")
-    #         cols = st.columns(2)
-    #         if cols[0].button(
-    #             webui_configs['sandbox']['button_modify_code_name'],
-    #             use_container_width=True,
-    #         ):
-    #             code_text = code_part
-    #             GLOBAL_EXE_CODE_TEXT = code_text
-    #             st.toast(webui_configs['sandbox']['text_modify_code'])
-
-    #         if cols[1].button(
-    #             webui_configs['sandbox']['button_exec_code_name'],
-    #             use_container_width=True
-    #         ):
-    #             if code_text:
-    #                 codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
-    #                 st.toast(webui_configs['sandbox']['text_execing_code'],)
-    #             else:
-    #                 st.toast(webui_configs['sandbox']['text_error_exec_code'],)
-
-    # #TODO 这段信息会被记录到history里
-    # if codebox_res is not None and codebox_res.code_exe_status != 200:
-    #     st.toast(f"{codebox_res.code_exe_response}")
-
-    # if codebox_res is not None and codebox_res.code_exe_status == 200:
-    #     st.toast(f"codebox_chat {codebox_res}")
-    #     chat_box.ai_say(Markdown(code_text, in_expander=True, title="code interpreter", unsafe_allow_html=True), )
-    #     if codebox_res.code_exe_type == "image/png":
-    #         base_text = f"```\n{code_text}\n```\n\n"
-    #         img_html = "<img src='data:image/png;base64,{}' class='img-fluid'>".format(
-    #             codebox_res.code_exe_response
-    #         )
-    #         chat_box.update_msg(img_html, streaming=False, state="complete")
-    #     else:
-    #         chat_box.update_msg('```\n'+code_text+'\n```'+"\n\n"+'```\n'+codebox_res.code_exe_response+'\n```', 
-    #                             streaming=False, state="complete")
-
     now = datetime.now()
     with st.sidebar:
 

examples/webui/document.py

@@ -14,7 +14,8 @@ from coagent.orm import table_init
 
 from configs.model_config import (
     KB_ROOT_PATH, kbs_config, DEFAULT_VS_TYPE, WEB_CRAWL_PATH,
-    EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict
+    EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict,
+    llm_model_dict
 )
 
 # SENTENCE_SIZE = 100
@@ -136,6 +137,8 @@ def knowledge_page(
                     embed_engine=EMBEDDING_ENGINE,
                     embedding_device= EMBEDDING_DEVICE,
                     embed_model_path=embedding_model_dict[embed_model],
+                    api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                 )
                 st.toast(ret.get("msg", " "))
                 st.session_state["selected_kb_name"] = kb_name
@@ -160,7 +163,10 @@ def knowledge_page(
             data = [{"file": f, "knowledge_base_name": kb, "not_refresh_vs_cache": True, "embed_model": EMBEDDING_MODEL,
             "embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
             "model_device": EMBEDDING_DEVICE,
-            "embed_engine": EMBEDDING_ENGINE}
+            "embed_engine": EMBEDDING_ENGINE,
+            "api_key": llm_model_dict[LLM_MODEL]["api_key"],
+            "api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],
+            }
                     for f in files]
             data[-1]["not_refresh_vs_cache"]=False
             for k in data:
@@ -210,7 +216,9 @@ def knowledge_page(
                          "embed_model": EMBEDDING_MODEL,
                         "embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
                         "model_device": EMBEDDING_DEVICE,
-                        "embed_engine": EMBEDDING_ENGINE}]
+                        "embed_engine": EMBEDDING_ENGINE,
+                        "api_key": llm_model_dict[LLM_MODEL]["api_key"],
+                        "api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],}]
                 for k in data:
                     ret = api.upload_kb_doc(**k)
                     logger.info(ret)
@@ -297,7 +305,9 @@ def knowledge_page(
                     api.update_kb_doc(kb, row["file_name"], 
                                       embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                       embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                                      model_device=EMBEDDING_DEVICE
+                                      model_device=EMBEDDING_DEVICE,
+                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                                       )
                 st.experimental_rerun()
 
@@ -311,7 +321,9 @@ def knowledge_page(
                     api.delete_kb_doc(kb, row["file_name"],
                                       embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                       embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                                      model_device=EMBEDDING_DEVICE)
+                                      model_device=EMBEDDING_DEVICE,
+                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
                 st.experimental_rerun()
 
             if cols[3].button(
@@ -323,7 +335,9 @@ def knowledge_page(
                     ret = api.delete_kb_doc(kb, row["file_name"], True,
                                       embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                       embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
-                                      model_device=EMBEDDING_DEVICE)
+                                      model_device=EMBEDDING_DEVICE,
+                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
                     st.toast(ret.get("msg", " "))
                 st.experimental_rerun()
 
@@ -344,6 +358,8 @@ def knowledge_page(
                 for d in api.recreate_vector_store(
                     kb, vs_type=default_vs_type, embed_model=embedding_model, embedding_device=EMBEDDING_DEVICE,
                       embed_model_path=embedding_model_dict["embedding_model"], embed_engine=EMBEDDING_ENGINE,
+                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
+                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                     ):
                     if msg := check_error_msg(d):
                         st.toast(msg)

examples/webui/utils.py

@@ -299,7 +299,9 @@ class ApiRequest:
         stream: bool = True,
         no_remote_api: bool = None,
         embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
-        llm_model: str ="", temperature: float= 0.2
+        llm_model: str ="", temperature: float= 0.2,
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/chat/chat接口
@@ -311,8 +313,8 @@ class ApiRequest:
             "query": query,
             "history": history,
             "stream": stream,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -339,7 +341,9 @@ class ApiRequest:
         stream: bool = True,
         no_remote_api: bool = None,
         embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
-        llm_model: str ="", temperature: float= 0.2
+        llm_model: str ="", temperature: float= 0.2,
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/chat/knowledge_base_chat接口
@@ -355,8 +359,8 @@ class ApiRequest:
             "history": history,
             "stream": stream,
             "local_doc_url": no_remote_api,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -386,7 +390,10 @@ class ApiRequest:
         stream: bool = True,
         no_remote_api: bool = None,
         embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
-        llm_model: str ="", temperature: float= 0.2
+        llm_model: str ="", temperature: float= 0.2,
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
+
     ):
         '''
         对应api.py/chat/search_engine_chat接口
@@ -400,8 +407,8 @@ class ApiRequest:
             "top_k": top_k,
             "history": history,
             "stream": stream,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -432,7 +439,9 @@ class ApiRequest:
         stream: bool = True,
         no_remote_api: bool = None,
         embed_model: str="", embed_model_path: str="", model_device: str="", embed_engine: str="",
-        llm_model: str ="", temperature: float= 0.2
+        llm_model: str ="", temperature: float= 0.2,
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/chat/knowledge_base_chat接口
@@ -458,8 +467,8 @@ class ApiRequest:
             "cb_search_type": cb_search_type,
             "stream": stream,
             "local_doc_url": no_remote_api,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -510,6 +519,8 @@ class ApiRequest:
         embed_model: str="", embed_model_path: str="", 
         model_device: str="", embed_engine: str="",
         temperature: float=0.2, model_name:str ="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/chat/chat接口
@@ -541,8 +552,8 @@ class ApiRequest:
             "isDetailed": isDetailed,
             "upload_file": upload_file,
             "kb_root_path": kb_root_path,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -588,6 +599,8 @@ class ApiRequest:
         embed_model: str="", embed_model_path: str="", 
         model_device: str="", embed_engine: str="",
         temperature: float=0.2, model_name: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/chat/chat接口
@@ -620,8 +633,8 @@ class ApiRequest:
             "isDetailed": isDetailed,
             "upload_file": upload_file,
             "kb_root_path": kb_root_path,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -694,7 +707,9 @@ class ApiRequest:
         no_remote_api: bool = None,
         kb_root_path: str =KB_ROOT_PATH,
         embed_model: str="", embed_model_path: str="", 
-        embedding_device: str="", embed_engine: str=""
+        embedding_device: str="", embed_engine: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/knowledge_base/create_knowledge_base接口
@@ -706,8 +721,8 @@ class ApiRequest:
             "knowledge_base_name": knowledge_base_name,
             "vector_store_type": vector_store_type,
             "kb_root_path": kb_root_path,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "model_device": embedding_device,
@@ -781,7 +796,9 @@ class ApiRequest:
         no_remote_api: bool = None,
         kb_root_path: str = KB_ROOT_PATH,
         embed_model: str="", embed_model_path: str="", 
-        model_device: str="", embed_engine: str=""
+        model_device: str="", embed_engine: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/knowledge_base/upload_docs接口
@@ -810,8 +827,8 @@ class ApiRequest:
                 override,
                 not_refresh_vs_cache,
                 kb_root_path=kb_root_path,
-                api_key=os.environ["OPENAI_API_KEY"],
+                api_key=api_key,
-                api_base_url=os.environ["API_BASE_URL"],
+                api_base_url=api_base_url,
                 embed_model=embed_model,
                 embed_model_path=embed_model_path,
                 model_device=model_device,
@@ -839,7 +856,9 @@ class ApiRequest:
         no_remote_api: bool = None,
         kb_root_path: str = KB_ROOT_PATH,
         embed_model: str="", embed_model_path: str="", 
-        model_device: str="", embed_engine: str=""
+        model_device: str="", embed_engine: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/knowledge_base/delete_doc接口
@@ -853,8 +872,8 @@ class ApiRequest:
             "delete_content": delete_content,
             "not_refresh_vs_cache": not_refresh_vs_cache,
             "kb_root_path": kb_root_path,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "model_device": model_device,
@@ -878,7 +897,9 @@ class ApiRequest:
         not_refresh_vs_cache: bool = False,
         no_remote_api: bool = None,
         embed_model: str="", embed_model_path: str="", 
-        model_device: str="", embed_engine: str=""
+        model_device: str="", embed_engine: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/knowledge_base/update_doc接口
@@ -889,8 +910,8 @@ class ApiRequest:
         if no_remote_api:
             response = run_async(update_doc(
                 knowledge_base_name, file_name, not_refresh_vs_cache, kb_root_path=KB_ROOT_PATH,
-                                api_key=os.environ["OPENAI_API_KEY"],
+                                api_key=api_key,
-                api_base_url=os.environ["API_BASE_URL"],
+                api_base_url=api_base_url,
                 embed_model=embed_model,
                 embed_model_path=embed_model_path,
                 model_device=model_device,
@@ -915,7 +936,9 @@ class ApiRequest:
         no_remote_api: bool = None,
         kb_root_path: str =KB_ROOT_PATH,
         embed_model: str="", embed_model_path: str="", 
-        embedding_device: str="", embed_engine: str=""
+        embedding_device: str="", embed_engine: str="",
+        api_key: str=os.environ["OPENAI_API_KEY"],
+        api_base_url: str = os.environ["API_BASE_URL"],
     ):
         '''
         对应api.py/knowledge_base/recreate_vector_store接口
@@ -928,8 +951,8 @@ class ApiRequest:
             "allow_empty_kb": allow_empty_kb,
             "vs_type": vs_type,
             "kb_root_path": kb_root_path,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "model_device": embedding_device,
@@ -1041,7 +1064,9 @@ class ApiRequest:
     # code base 相关操作
     def create_code_base(self, cb_name, zip_file, do_interpret: bool, no_remote_api: bool = None,
                          embed_model: str="", embed_model_path: str="", embedding_device: str="", embed_engine: str="",
-                         llm_model: str ="", temperature: float= 0.2
+                         llm_model: str ="", temperature: float= 0.2,
+                         api_key: str=os.environ["OPENAI_API_KEY"],
+                         api_base_url: str = os.environ["API_BASE_URL"],
                          ):
         '''
         创建 code_base
@@ -1067,8 +1092,8 @@ class ApiRequest:
             "cb_name": cb_name,
             "code_path": raw_code_path,
             "do_interpret": do_interpret,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,
@@ -1091,7 +1116,9 @@ class ApiRequest:
 
     def delete_code_base(self, cb_name: str, no_remote_api: bool = None,
                          embed_model: str="", embed_model_path: str="", embedding_device: str="", embed_engine: str="",
-                         llm_model: str ="", temperature: float= 0.2
+                         llm_model: str ="", temperature: float= 0.2,
+                         api_key: str=os.environ["OPENAI_API_KEY"],
+                         api_base_url: str = os.environ["API_BASE_URL"],
                          ):
         '''
         删除 code_base
@@ -1102,8 +1129,8 @@ class ApiRequest:
             no_remote_api = self.no_remote_api
         data = {
             "cb_name": cb_name,
-            "api_key": os.environ["OPENAI_API_KEY"],
+            "api_key": api_key,
-            "api_base_url": os.environ["API_BASE_URL"],
+            "api_base_url": api_base_url,
             "embed_model": embed_model,
             "embed_model_path": embed_model_path,
             "embed_engine": embed_engine,