debug chatbot' service

Author: lightislost

muagent/chat/agent_chat.py

@@ -174,7 +174,7 @@ def chat_iterator(message: Message, local_memory: Memory, isDetailed=False):
             result["related_nodes"] = related_nodes
 
             # logger.debug(f"{result['figures'].keys()}, isDetailed: {isDetailed}")
-            message_str = step_content
+            message_str = final_content
             if self.stream:
                 for token in message_str:
                     result["answer"] = token
@@ -238,8 +238,8 @@ def achat(
             custom_phase_configs, custom_chain_configs, custom_role_configs)
 
         # 
-        params = locals()
-        params.pop("self")
+        # params = locals()
+        # params.pop("self")
         # embed_config: EmbedConfig = EmbedConfig(**params)
         # llm_config: LLMConfig = LLMConfig(**params)
 
@@ -302,7 +302,7 @@ def chat_iterator(message: Message, local_memory: Memory, isDetailed=False):
             step_content = local_memory.to_str_messages(content_key='step_content', filter_roles=["human"])
             step_content = "\n\n".join([f"{v}" for parsed_output in local_memory.get_parserd_output_list() for k, v in parsed_output.items() if k not in ["Action Status", "human", "user"]])
             # logger.debug(f"{local_memory.get_parserd_output_list()}")
-            final_content = message.role_content
+            final_content = step_content or message.role_content
             result = {
                 "answer": "",
                 "db_docs": [str(doc) for doc in message.db_docs],
@@ -322,7 +322,7 @@ def chat_iterator(message: Message, local_memory: Memory, isDetailed=False):
             result["related_nodes"] = related_nodes
 
             # logger.debug(f"{result['figures'].keys()}, isDetailed: {isDetailed}")
-            message_str = step_content
+            message_str = final_content
             if self.stream:
                 for token in message_str:
                     result["answer"] = token

muagent/chat/base_chat.py

@@ -3,7 +3,7 @@
 import asyncio, json, os
 from typing import List, AsyncIterable
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.prompts.chat import ChatPromptTemplate
 
@@ -55,8 +55,8 @@ def chat(
             embed_config: EmbedConfig = Body({}, description="embedding_model config"),
             **kargs
             ):
-        params = locals()
-        params.pop("self", None)
+        # params = locals()
+        # params.pop("self", None)
         # llm_config: LLMConfig = LLMConfig(**params)
         # embed_config: EmbedConfig = EmbedConfig(**params)
         self.engine_name = engine_name if isinstance(engine_name, str) else engine_name.default
@@ -78,6 +78,7 @@ def _chat(self, query: str, history: List[History], llm_config: LLMConfig, embed
         def chat_iterator(query: str, history: List[History]):
             # model = getChatModel()
             model = getChatModelFromConfig(llm_config)
+            model = model.llm
 
             result, content = self.create_task(query, history, model, llm_config, embed_config, **kargs)
             logger.info('result={}'.format(result))
@@ -142,6 +143,7 @@ async def chat_iterator(query, history):
             callback = AsyncIteratorCallbackHandler()
             # model = getChatModel()
             model = getChatModelFromConfig(llm_config)
+            model = model.llm
 
             task, result = self.create_atask(query, history, model, llm_config, embed_config, callback)
             if self.stream:
@@ -166,7 +168,7 @@ def create_task(self, query: str, history: List[History], model, llm_config: LLM
         content = chain({"input": query})
         return {"answer": "", "docs": ""}, content
 
-    def create_atask(self, query, history, model, llm_config: LLMConfig, embed_config: EmbedConfig, callback: AsyncIteratorCallbackHandler):
+    def create_atask(self, query, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, callback: AsyncIteratorCallbackHandler):
         chat_prompt = ChatPromptTemplate.from_messages(
             [i.to_msg_tuple() for i in history] + [("human", "{input}")]
         )

muagent/chat/code_chat.py

@@ -11,7 +11,7 @@
 from typing import List
 from fastapi.responses import StreamingResponse
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.prompts.chat import ChatPromptTemplate
 
@@ -129,8 +129,8 @@ def chat(
             use_nh: bool =Body(True, description=""),
             **kargs
             ):
-        params = locals()
-        params.pop("self")
+        # params = locals()
+        # params.pop("self")
         # llm_config: LLMConfig = LLMConfig(**params)
         # embed_config: EmbedConfig = EmbedConfig(**params)
         self.engine_name = engine_name if isinstance(engine_name, str) else engine_name.default
@@ -151,6 +151,7 @@ def _chat(self, query: str, history: List[History], llm_config: LLMConfig, embed
         def chat_iterator(query: str, history: List[History]):
             # model = getChatModel()
             model = getChatModelFromConfig(llm_config)
+            model = model.llm
 
             result, content = self.create_task(query, history, model, llm_config, embed_config, local_graph_path, **kargs)
             # logger.info('result={}'.format(result))

muagent/chat/knowledge_chat.py

@@ -3,7 +3,7 @@
 from urllib.parse import urlencode
 from typing import List
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.prompts.chat import ChatPromptTemplate
 
@@ -75,7 +75,7 @@ def _process(self, query: str, history: List[History], model, llm_config: LLMCon
         result = {"answer": "", "docs": source_documents}
         return chain, context, result
 
-    def create_task(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, ):
+    def create_task(self, query: str, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, **kargs):
         '''构建 llm 生成任务'''
         logger.debug(f"query: {query}, history: {history}")
         chain, context, result = self._process(query, history, model, llm_config, embed_config)

muagent/chat/llm_chat.py

@@ -1,7 +1,7 @@
 import asyncio
 from typing import List
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from langchain.prompts.chat import ChatPromptTemplate
 
@@ -31,7 +31,7 @@ def create_task(self, query: str, history: List[History], model, llm_config: LLM
         content = chain({"input": query})
         return {"answer": "", "docs": ""}, content
 
-    def create_atask(self, query, history, model, llm_config: LLMConfig, embed_config: EmbedConfig, callback: AsyncIteratorCallbackHandler):
+    def create_atask(self, query, history: List[History], model, llm_config: LLMConfig, embed_config: EmbedConfig, callback: AsyncIteratorCallbackHandler):
         chat_prompt = ChatPromptTemplate.from_messages(
             [i.to_msg_tuple() for i in history] + [("human", "{input}")]
         )

muagent/chat/search_chat.py

@@ -1,9 +1,9 @@
 import os, asyncio
 from typing import List, Optional, Dict
 
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
-from langchain_community.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
+from langchain.utilities import BingSearchAPIWrapper, DuckDuckGoSearchAPIWrapper
 from langchain.prompts.chat import ChatPromptTemplate
 from langchain_community.docstore.document import Document
 

muagent/connector/configs/prompts/qa_template_prompt.py

@@ -5,20 +5,18 @@
 Based on the information provided, please answer the origin query concisely and professionally. 
 Attention: Follow the input format and response output format
 
-#### Input Format
-
-**Origin Query:** the initial question or objective that the user wanted to achieve
-
-**Context:** the current status and history of the tasks to determine if Origin Query has been achieved.
-
-**DocInfos:**: the relevant doc information or code information, if this is empty, don't refer to this.
-
 #### Response Output Format
 **Action Status:** Set to 'Continued' or 'Stopped'.
 **Answer:** Response to the user's origin query based on Context and DocInfos. If DocInfos is empty, you can ignore it.
 If the answer cannot be derived from the given Context and DocInfos, please say 'The question cannot be answered based on the information provided' and do not add any fabricated elements to the answer.
 """
 
+# **Origin Query:** the initial question or objective that the user wanted to achieve
+
+# **Context:** the current status and history of the tasks to determine if Origin Query has been achieved.
+
+# **DocInfos:**: the relevant doc information or code information, if this is empty, don't refer to this.
+
 
 CODE_QA_PROMPT = """#### Agent Profile
 

muagent/llm_models/openai_model.py

@@ -47,7 +47,7 @@ def __init__(self, llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler
             VISIT_BIZ_LINE = os.environ.get("visit_biz_line")
             # zdatafront 提供的统一加密密钥
             aes_secret_key = os.environ.get("aes_secret_key")
-
+            # logger.debug(f"{VISIT_DOMAIN}, {VISIT_BIZ}, {VISIT_BIZ_LINE}, {aes_secret_key}")
             zdatafront_client = ZDataFrontClient(visit_domain=VISIT_DOMAIN, visit_biz=VISIT_BIZ, visit_biz_line=VISIT_BIZ_LINE, aes_secret_key=aes_secret_key)
             http_client = SyncProxyHttpClient(zdatafront_client=zdatafront_client, prefer_async=True)
         except Exception as e:
@@ -112,8 +112,7 @@ def __init__(self, llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler
             )
 
 
-def getChatModelFromConfig(llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler = None, ) -> Union[ChatOpenAI, LLM]:
-
+def getChatModelFromConfig(llm_config: LLMConfig, callBack: AsyncIteratorCallbackHandler = None, ) -> Union[ChatOpenAI, LLM, CustomLLMModel]:
     if llm_config and llm_config.llm and isinstance(llm_config.llm, LLM):
         return CustomLLMModel(llm=llm_config.llm)
     elif llm_config:

muagent/service/service_factory.py

@@ -145,5 +145,5 @@ def get_kb_doc_details(kb_name: str, kb_root_path) -> List[Dict]:
     for i, v in enumerate(result.values()):
         v['No'] = i + 1
         data.append(v)
-   
+
     return data