new branch

src/blackbox/blackbox_factory.py

@@ -6,16 +6,16 @@ from .asr import ASR
 from .audio_to_text import AudioToText
 #from .emotion import Emotion
 from .blackbox import Blackbox
-from .text_to_audio import TextToAudio
+# from .text_to_audio import TextToAudio
-from .tesou import Tesou
+# from .tesou import Tesou
 from .fastchat import Fastchat
 from .g2e import G2E
 from .text_and_image import TextAndImage
-# from .chroma_query import ChromaQuery
-# from .chroma_upsert import ChromaUpsert
-# from .chroma_chat import ChromaChat
 from .melotts import MeloTTS
 from .vlms import VLMS
+from .chroma_query import ChromaQuery
+from .chroma_upsert import ChromaUpsert
+from .chroma_chat import ChromaChat
 from injector import inject, singleton
 
 @singleton

src/blackbox/chroma_chat.py

@@ -5,13 +5,17 @@ from fastapi.responses import JSONResponse
 from .blackbox import Blackbox
 
 from ..utils import chroma_setting
+from .fastchat import Fastchat
+from .chroma_query import ChromaQuery
+
 DEFAULT_COLLECTION_ID = "123"
 
-from injector import singleton
+from injector import singleton,inject
 @singleton
 class ChromaChat(Blackbox):
 
-    def __init__(self, fastchat, chroma_query):
+    @inject
+    def __init__(self, fastchat: Fastchat, chroma_query: ChromaQuery):
         self.fastchat = fastchat
         self.chroma_query = chroma_query
 
@@ -29,11 +33,13 @@ class ChromaChat(Blackbox):
             collection_id = DEFAULT_COLLECTION_ID
         else:
             collection_id = setting.ChromaSetting.collection_ids[0]
+        print("collection_id: ",collection_id)
         # query it
         chroma_result = self.chroma_query(question, collection_id)
+        print("chroma_result: ",type(chroma_result),chroma_result)
 
-        fast_question = "问题: "+ question + "。根据问题，总结以下内容：" + chroma_result
+        fast_question = "问题: "+ question + "。根据问题，总结以下内容和来源：" + chroma_result
-        response = self.fastchat(fast_question)
+        response = self.fastchat(model_name="Qwen1.5-14B-Chat", prompt=fast_question, template='回答限制50字', context=None)
 
         return response
 

src/blackbox/chroma_query.py

@@ -39,7 +39,7 @@ class ChromaQuery(Blackbox):
             n_results=3,    
         )
 
-        response = results["documents"] + results["metadatas"]
+        response = str(results["documents"] + results["metadatas"])
         return response
 
 

src/blackbox/chroma_upsert.py

@@ -59,8 +59,11 @@ class ChromaUpsert(Blackbox):
         else:
             collection_id = "123"
 
+        print("file: ",file)
+        print("file name: ",file.filename)
         if file is not None:
-            file_type = file.split(".")[-1]
+            file_type = file.filename.split(".")[-1]
+            print("file_type: ",file_type)
             if file_type == "pdf":
                 loader = PyPDFLoader(file)
             elif file_type == "txt":
@@ -102,16 +105,12 @@ class ChromaUpsert(Blackbox):
 
 
     async def fast_api_handler(self, request: Request) -> Response:
-        try:
+ 
-            data = await request.json()
+        user_collection_id = (await request.form()).get("collection_id")
-        except:
+        user_file = (await request.form()).get("file")
-            return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
+        user_string = (await request.form()).get("string")
-
+        user_context = (await request.form()).get("context")
-        user_collection_id = data.get("collection_id")
+        user_setting = (await request.form()).get("setting")
-        user_file = data.get("file")
-        user_string = data.get("string")
-        user_context = data.get("context")
-        user_setting = data.get("setting")
 
         if user_collection_id is None and user_setting["collections"] == []:
             return JSONResponse(content={"error": "The first creation requires a collection id"}, status_code=status.HTTP_400_BAD_REQUEST)
@@ -119,6 +118,17 @@ class ChromaUpsert(Blackbox):
         if user_file is None and user_string is None:
             return JSONResponse(content={"error": "file or string is required"}, status_code=status.HTTP_400_BAD_REQUEST)
         
+        # data = await user_file.read()
+        # with open(f'./{data.filename}', 'wb') as f:
+        #     f.write(content)
+
+        loader = PyPDFLoader(f'./{user_file.filename}')
+        documents = loader.load()
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=0)
+        docs = text_splitter.split_documents(documents)
+        print("docs: ",docs)
+
+
         return JSONResponse(
             content={"response": self.processing(user_collection_id, user_file, user_string, user_context, user_setting)},
             status_code=status.HTTP_200_OK)

src/blackbox/fastchat.py

@@ -19,40 +19,68 @@ class Fastchat(Blackbox):
         return isinstance(data, list)
 
     # model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
-    def processing(self, model_name, prompt, template, context: list)  -> str:
+    def processing(self, model_name, prompt, template, context: list, temperature, top_p, top_k, n, max_tokens)  -> str:
         if context == None:
             context = []
         url = 'http://120.196.116.194:48892/v1/chat/completions'
 
-        # history可以为空列表，也可以是用户的对话历史
+        # context可以为空列表，也可以是用户的对话历史
-        # history = [
+        # context = [
         #     {
         #         "role": "user",
-        #         "content": "你吃饭了吗"
+        #         "content": "智能体核心思想"
         #     },
         #     {
         #         "role": "assistant", 
-        #         "content": "作为一个AI模型，我没有吃饭的需要，因为我并不具备实体形态。我专注于提供信息和帮助回答你的问题。你有什么需要帮助的吗？"
+        #         "content": "智能体的核心思想是将人工智能应用于问题求解者角色，它通过算法模拟人类决策过程，通过感知环境、学习、规划和执行行动，以实现特定任务或目标。其目标是通过自我适应和优化，实现高效问题解决。"
         #     },
         # ]
 
+        prompt_template = [
+           {"role": "system", "content": template},
+        ]
+
         fastchat_inputs={
             "model": model_name,
-            "messages": context + [
+            "messages": prompt_template + context + [
                 {
                     "role": "user",
-                    "content": template + prompt
+                    "content": prompt
                 }
-            ]
+            ],
+            "temperature": temperature,
+            "top_p": top_p,
+            "top_k": top_k,
+            "n": n,
+            "max_tokens": max_tokens,
+            "stream": False,
         }
 
+
+        # {
+        #     "model": "string",
+        #     "messages": "string",
+        #     "temperature": 0.7, #  between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
+        #     "top_p": 1, # 控制生成下一个单词的概率分布，即从所有可能的单词中，只选择概率最高的一部分作为候选单词
+        #     "top_k": -1, #  top-k 参数设置为 3意味着选择前三个tokens。
+        #     "n": 1, # How many chat completion choices to generate for each input message.
+        #     "max_tokens": 1024, # The maximum number of tokens to generate in the chat completion.
+        #     "stop": [
+        #         "string"
+        #     ],
+        #     "stream": False,
+        #     "presence_penalty": 0, # Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
+        #     "frequency_penalty": 0, # Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim.
+        #     "user": "string"
+        # }
+
         fastchat_response = requests.post(url, json=fastchat_inputs)
 
-        user_message = fastchat_inputs["messages"]
+        # user_message = fastchat_inputs["messages"]
-        context.append(user_message)
+        # context.append(user_message)
 
         assistant_message = fastchat_response.json()["choices"][0]["message"]
-        context.append(assistant_message)
+        # context.append(assistant_message)
 
         fastchat_content = assistant_message["content"]
 
@@ -68,17 +96,37 @@ class Fastchat(Blackbox):
         user_context = data.get("context")
         user_prompt = data.get("prompt")
         user_template = data.get("template")
+        user_temperature = data.get("temperature")
+        user_top_p = data.get("top_p")
+        user_top_k = data.get("top_k")
+        user_n = data.get("n")
+        user_max_tokens = data.get("max_tokens")
 
         if user_prompt is None:
             return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
 
-        if user_model_name is None or user_model_name.isspace():
+        if user_model_name is None or user_model_name.isspace() or user_model_name == "":
             user_model_name = "Qwen1.5-14B-Chat"
 
         if user_template is None or user_template.isspace():
-            # user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气，或者是使用我们提供的语气
+            # user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气
             user_template = ""
-        else:
+        
-            user_template = f"使用{user_template}的语气说话。"
+        if user_temperature is None or user_temperature == "":
+            user_temperature = 0.7
 
-        return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context)}, status_code=status.HTTP_200_OK)
+        if user_top_p is None or user_top_p == "":
+            user_top_p = 1
+        
+        if user_top_k is None or user_top_k == "":
+            user_top_k = -1
+
+        if user_n is None or user_n == "":
+            user_n = 3
+        
+        if user_max_tokens is None or user_max_tokens == "":
+            user_max_tokens = 1024
+
+
+        return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context, 
+        user_temperature, user_top_p, user_top_k, user_n, user_max_tokens)}, status_code=status.HTTP_200_OK)

src/blackbox/modelscope.py

@@ -0,0 +1,145 @@
+from typing import Any, Coroutine
+
+from fastapi import Request, Response, status
+from fastapi.responses import JSONResponse
+from .blackbox import Blackbox
+
+import requests
+import json
+
+from modelscope_agent.agents import RolePlay
+from modelscope_agent.tools.base import BaseTool
+from modelscope_agent.tools import register_tool
+
+class Modelscope(Blackbox):
+    
+    def __call__(self, *args, **kwargs):
+        return self.processing(*args, **kwargs)
+
+    def valid(self, *args, **kwargs) -> bool:
+        data = args[0]
+        return isinstance(data, list)
+
+    # model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
+    def processing(self, model_name, prompt, template, context: list)  -> str:
+        if context == None:
+            context = []
+
+        @register_tool('ChromaQuery')
+        class AliyunRenewInstanceTool(BaseTool):
+            description = '查询chroma数据库中的数据'
+            name = 'ChromaQuery'
+            parameters: list = [{
+                'name': 'id',
+                'description': '用户的chroma id',
+                'required': True,
+                'type': 'string'
+            }, {
+                'name': 'query',
+                'description': '用户需要在chroma中查询的问题',
+                'required': True,
+                'type': 'string'
+            }]
+
+            def call(self, params: str, **kwargs):
+                params = self._verify_args(params)
+                id = params['id']
+                query = params['query']
+                query_data = {
+                    "chroma_query_data": {
+                        "id": id,
+                        "question": query
+                    }
+                }
+                url = "http://10.6.80.75:7003"
+                response = requests.post(f"{url}/api/chroma_query", json=query_data)
+                result = response.json()['response']
+                return str({'result': f'Chroma ID为{id}的用户，查询结果为{response}。'})
+
+    
+        @register_tool('WebSearch')
+        class WebSearchTool(BaseTool):
+            description = '查询网络中的内容'
+            name = 'WebSearch'
+            parameters: list = [ {
+                'name': 'search_term',
+                'description': '用户需要在Web中查询的问题',
+                'required': True,
+                'type': 'string'
+            }]
+
+            def call(self, params: str, **kwargs):
+                params = self._verify_args(params)
+                search_term = params['search_term']
+
+                api_key='9e51be0aaecb5a56fe2faead6e2c702fde92e62a'
+                headers = {
+                    'X-API-KEY': api_key,
+                    'Content-Type': 'application/json',
+                }
+                params = {
+                    'q': search_term
+                }
+                try:
+                    response = requests.post(
+                        f'https://google.serper.dev/search',
+                        headers=headers,
+                        params=params,
+                        timeout=5)
+                except Exception as e:
+                    return -1, str(e)
+
+                result = response.json()['answerBox']['snippet']
+
+                return str({'result': f'WebSearch查询结果为{search_term}{result}。'})
+
+
+        # define LLM
+        api_base_url = "http://120.196.116.194:48892/v1"
+        api_key= "EMPTY"
+        LLM_MODEL = model_name
+
+        llm_config = {
+            'model': LLM_MODEL, 
+            'model_server': 'openai',
+            'api_base':api_base_url,
+            'api_key': api_key
+            }
+
+        function_list = ['WebSearch', 'ChromaQuery']
+
+        bot = RolePlay(function_list=function_list,llm=llm_config, instruction=template)
+
+        response = bot.run(prompt, history=context, lang='zh')
+
+        text = ''
+        for chunk in response:
+            text += chunk
+
+        return text
+    
+
+    async def fast_api_handler(self, request: Request) -> Response:
+        try:
+            data = await request.json()
+        except:
+            return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
+
+        user_model_name = data.get("model_name")
+        user_context = data.get("context")
+        user_prompt = data.get("prompt")
+        user_template = data.get("template")
+
+        if user_prompt is None:
+            return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
+
+        if user_model_name is None or user_model_name.isspace():
+            user_model_name = "Qwen1.5-14B-Chat"
+
+        if user_template is None or user_template.isspace():
+            # user_template 是定义LLM的语气，例如template = "使用小丑的语气说话。"，user_template可以为空字串，或者是用户自定义的语气，或者是使用我们提供的语气
+            user_template = ""
+        else:
+            user_template = f"使用{user_template}的语气说话。"
+
+        return JSONResponse(content={"response": self.processing(user_model_name, user_prompt, user_template, user_context)}, status_code=status.HTTP_200_OK)