WEB API independent

pilot/common/plugins.py

@@ -78,7 +78,6 @@ def load_native_plugins(cfg: Config):
     if not cfg.plugins_auto_load:
         print("not auto load_native_plugins")
         return
-
     def load_from_git(cfg: Config):
         print("async load_native_plugins")
         branch_name = cfg.plugins_git_branch
@@ -86,20 +85,16 @@ def load_native_plugins(cfg: Config):
         url = "https://github.com/csunny/{repo}/archive/{branch}.zip"
         try:
             session = requests.Session()
-            response = session.get(
+            response = session.get(url.format(repo=native_plugin_repo, branch=branch_name),
-                url.format(repo=native_plugin_repo, branch=branch_name),
+                                    headers={'Authorization': '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'})
-                headers={"Authorization": "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"},
-            )
 
             if response.status_code == 200:
                 plugins_path_path = Path(PLUGINS_DIR)
-                files = glob.glob(
+                files = glob.glob(os.path.join(plugins_path_path, f'{native_plugin_repo}*'))
-                    os.path.join(plugins_path_path, f"{native_plugin_repo}*")
-                )
                 for file in files:
                     os.remove(file)
                 now = datetime.datetime.now()
-                time_str = now.strftime("%Y%m%d%H%M%S")
+                time_str = now.strftime('%Y%m%d%H%M%S')
                 file_name = f"{plugins_path_path}/{native_plugin_repo}-{branch_name}-{time_str}.zip"
                 print(file_name)
                 with open(file_name, "wb") as f:
@@ -115,6 +110,7 @@ def load_native_plugins(cfg: Config):
     t.start()
 
 
+
 def scan_plugins(cfg: Config, debug: bool = False) -> List[AutoGPTPluginTemplate]:
     """Scan the plugins directory for plugins and loads them.
 

pilot/common/schema.py

@@ -7,3 +7,7 @@ class SeparatorStyle(Enum):
     TWO = "</s>"
     THREE = auto()
     FOUR = auto()
+
+class ExampleType(Enum):
+    ONE_SHOT = "one_shot"
+    FEW_SHOT = "few_shot"

pilot/configs/config.py

@@ -90,7 +90,7 @@ class Config(metaclass=Singleton):
         ### The associated configuration parameters of the plug-in control the loading and use of the plug-in
         self.plugins: List[AutoGPTPluginTemplate] = []
         self.plugins_openai = []
-        self.plugins_auto_load = os.getenv("AUTO_LOAD_PLUGIN", "True") == "True"
+        self.plugins_auto_load =  os.getenv("AUTO_LOAD_PLUGIN", "True") == "True"
 
         self.plugins_git_branch = os.getenv("PLUGINS_GIT_BRANCH", "plugin_dashboard")
 
@@ -150,8 +150,6 @@ class Config(metaclass=Singleton):
         self.MILVUS_USERNAME = os.getenv("MILVUS_USERNAME", None)
         self.MILVUS_PASSWORD = os.getenv("MILVUS_PASSWORD", None)
 
-        self.WEAVIATE_URL = os.getenv("WEAVIATE_URL", "http://127.0.0.1:8080")
-
         # QLoRA
         self.QLoRA = os.getenv("QUANTIZE_QLORA", "True")
 

pilot/configs/model_config.py

@@ -43,8 +43,6 @@ LLM_MODEL_CONFIG = {
     "guanaco-33b-merged": os.path.join(MODEL_PATH, "guanaco-33b-merged"),
     "falcon-40b": os.path.join(MODEL_PATH, "falcon-40b"),
     "gorilla-7b": os.path.join(MODEL_PATH, "gorilla-7b"),
-    # TODO Support baichuan-7b
-    # "baichuan-7b" : os.path.join(MODEL_PATH, "baichuan-7b"),
     "gptj-6b": os.path.join(MODEL_PATH, "ggml-gpt4all-j-v1.3-groovy.bin"),
     "proxyllm": "proxyllm",
 }

pilot/connections/rdbms/py_study/pd_study.py

@@ -6,6 +6,8 @@ import numpy as np
 from matplotlib.font_manager import FontProperties
 from pyecharts.charts import Bar
 from pyecharts import options as opts
+from test_cls_1 import TestBase,Test1
+from test_cls_2 import Test2
 
 CFG = Config()
 
@@ -56,20 +58,30 @@ CFG = Config()
 #
 
 
+
 if __name__ == "__main__":
 
-    def __extract_json(s):
+    # def __extract_json(s):
-        i = s.index("{")
+    #     i = s.index("{")
-        count = 1  # 当前所在嵌套深度，即还没闭合的'{'个数
+    #     count = 1  # 当前所在嵌套深度，即还没闭合的'{'个数
-        for j, c in enumerate(s[i + 1 :], start=i + 1):
+    #     for j, c in enumerate(s[i + 1 :], start=i + 1):
-            if c == "}":
+    #         if c == "}":
-                count -= 1
+    #             count -= 1
-            elif c == "{":
+    #         elif c == "{":
-                count += 1
+    #             count += 1
-            if count == 0:
+    #         if count == 0:
-                break
+    #             break
-        assert count == 0  # 检查是否找到最后一个'}'
+    #     assert count == 0  # 检查是否找到最后一个'}'
-        return s[i : j + 1]
+    #     return s[i : j + 1]
+    #
+    # ss = """here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:select u.city, count(*) as order_countfrom tran_order oleft join user u on o.user_id = u.idgroup by u.city;this will return the number of orders for each city that has at least one order. we can use this data to generate a histogram that shows the distribution of orders across different cities.here's the response in the required format:{ "thoughts": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:\n\nselect u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;", "speak": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities.", "command": { "name": "histogram-executor", "args": { "title": "distribution of user orders in different cities", "sql": "select u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;" } }}"""
+    # print(__extract_json(ss))
 
-    ss = """here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:select u.city, count(*) as order_countfrom tran_order oleft join user u on o.user_id = u.idgroup by u.city;this will return the number of orders for each city that has at least one order. we can use this data to generate a histogram that shows the distribution of orders across different cities.here's the response in the required format:{ "thoughts": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities:\n\nselect u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;", "speak": "here's a sql statement that can be used to generate a histogram to analyze the distribution of user orders in different cities.", "command": { "name": "histogram-executor", "args": { "title": "distribution of user orders in different cities", "sql": "select u.city, count(*) as order_count\nfrom tran_order o\nleft join user u on o.user_id = u.id\ngroup by u.city;" } }}"""
+
-    print(__extract_json(ss))
+    test1 = Test1()
+    test2 = Test2()
+    test1.write()
+    test1.test()
+    test2.write()
+    test1.test()
+    test2.test()

pilot/connections/rdbms/py_study/test_cls_1.py

@@ -0,0 +1,12 @@
+from abc import ABC, abstractmethod
+from pydantic import BaseModel
+from test_cls_base import TestBase
+
+
+class Test1(TestBase):
+
+    def write(self):
+        self.test_values.append("x")
+        self.test_values.append("y")
+        self.test_values.append("g")
+

pilot/connections/rdbms/py_study/test_cls_2.py

@@ -0,0 +1,15 @@
+from abc import ABC, abstractmethod
+from pydantic import BaseModel
+from test_cls_base import TestBase
+from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
+
+class Test2(TestBase):
+    test_2_values:List = []
+
+    def write(self):
+        self.test_values.append(1)
+        self.test_values.append(2)
+        self.test_values.append(3)
+        self.test_2_values.append("x")
+        self.test_2_values.append("y")
+        self.test_2_values.append("z")

pilot/connections/rdbms/py_study/test_cls_base.py

@@ -0,0 +1,12 @@
+from abc import ABC, abstractmethod
+from pydantic import BaseModel
+from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
+
+
+class TestBase(BaseModel, ABC):
+    test_values: List = []
+
+
+    def test(self):
+        print(self.__class__.__name__ + ":" )
+        print(self.test_values)

pilot/model/adapter.py

@@ -32,14 +32,9 @@ class BaseLLMAdaper:
         return True
 
     def loader(self, model_path: str, from_pretrained_kwargs: dict):
-        tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
-            model_path, use_fast=False, trust_remote_code=True
-        )
         model = AutoModelForCausalLM.from_pretrained(
-            model_path,
+            model_path, low_cpu_mem_usage=True, **from_pretrained_kwargs
-            low_cpu_mem_usage=True,
-            trust_remote_code=True,
-            **from_pretrained_kwargs,
         )
         return model, tokenizer
 
@@ -62,7 +57,7 @@ def get_llm_model_adapter(model_path: str) -> BaseLLMAdaper:
     raise ValueError(f"Invalid model adapter for {model_path}")
 
 
-# TODO support cpu? for practice we support gpt4all or chatglm-6b-int4?
+# TODO support cpu? for practise we support gpt4all or chatglm-6b-int4?
 
 
 class VicunaLLMAdapater(BaseLLMAdaper):

pilot/model/llm_out/chatglm_llm.py

@@ -11,7 +11,7 @@ from pilot.conversation import ROLE_ASSISTANT, ROLE_USER
 def chatglm_generate_stream(
     model, tokenizer, params, device, context_len=2048, stream_interval=2
 ):
-    """Generate text using chatglm model's chat api"""
+    """Generate text using chatglm model's chat api_v1"""
     prompt = params["prompt"]
     temperature = float(params.get("temperature", 1.0))
     top_p = float(params.get("top_p", 1.0))

pilot/model/llm_utils.py

@@ -51,7 +51,7 @@ def create_chat_completion(
                 return message
 
         response = None
-        # TODO impl this use vicuna server api
+        # TODO impl this use vicuna server api_v1
 
 
 class Stream(transformers.StoppingCriteria):

pilot/out_parser/base.py

@@ -32,7 +32,7 @@ class BaseOutputParser(ABC):
     Output parsers help structure language model responses.
     """
 
-    def __init__(self, sep: str, is_stream_out: bool):
+    def __init__(self, sep: str, is_stream_out: bool = True):
         self.sep = sep
         self.is_stream_out = is_stream_out
 

pilot/prompts/example_base.py

@@ -0,0 +1,38 @@
+from abc import ABC, abstractmethod
+from pydantic import BaseModel
+from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
+
+from pilot.common.schema import ExampleType
+
+class ExampleSelector(BaseModel, ABC):
+    examples: List[List]
+    use_example: bool = False
+    type: str = ExampleType.ONE_SHOT.value
+
+    def examples(self, count: int = 2):
+        if ExampleType.ONE_SHOT.value == self.type:
+            return self.__one_show_context()
+        else:
+            return self.__few_shot_context(count)
+
+    def __few_shot_context(self, count: int = 2) -> List[List]:
+        """
+        Use 2 or more examples, default 2
+        Returns: example text
+        """
+        if self.use_example:
+            need_use = self.examples[:count]
+            return need_use
+        return None
+
+    def __one_show_context(self) -> List:
+        """
+         Use one examples
+        Returns:
+
+        """
+        if self.use_example:
+            need_use = self.examples[:1]
+            return need_use
+
+        return None

pilot/prompts/prompt_new.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Extra, Field, root_validator
 from pilot.common.formatting import formatter
 from pilot.out_parser.base import BaseOutputParser
 from pilot.common.schema import SeparatorStyle
-
+from pilot.prompts.example_base import ExampleSelector
 
 def jinja2_formatter(template: str, **kwargs: Any) -> str:
     """Format a template using jinja2."""
@@ -32,7 +32,6 @@ class PromptTemplate(BaseModel, ABC):
     input_variables: List[str]
     """A list of the names of the variables the prompt template expects."""
     template_scene: Optional[str]
-
     template_define: Optional[str]
     """this template define"""
     template: Optional[str]
@@ -46,6 +45,7 @@ class PromptTemplate(BaseModel, ABC):
     output_parser: BaseOutputParser = None
     """"""
     sep: str = SeparatorStyle.SINGLE.value
+    example: ExampleSelector = None
 
     class Config:
         """Configuration for this pydantic object."""

pilot/prompts/prompt_template.py

@@ -182,7 +182,7 @@ class BasePromptTemplate(BaseModel, ABC):
         Example:
         .. code-block:: python
 
-            prompt.save(file_path="path/prompt.yaml")
+            prompt.save(file_path="path/prompt.api_v1")
         """
         if self.partial_variables:
             raise ValueError("Cannot save prompt with partial variables.")
@@ -201,11 +201,11 @@ class BasePromptTemplate(BaseModel, ABC):
         if save_path.suffix == ".json":
             with open(file_path, "w") as f:
                 json.dump(prompt_dict, f, indent=4)
-        elif save_path.suffix == ".yaml":
+        elif save_path.suffix == ".api_v1":
             with open(file_path, "w") as f:
                 yaml.dump(prompt_dict, f, default_flow_style=False)
         else:
-            raise ValueError(f"{save_path} must be json or yaml")
+            raise ValueError(f"{save_path} must be json or api_v1")
 
 
 class StringPromptValue(PromptValue):

pilot/scene/base.py

@@ -10,3 +10,7 @@ class ChatScene(Enum):
     ChatUrlKnowledge = "chat_url_knowledge"
     InnerChatDBSummary = "inner_chat_db_summary"
     ChatNormal = "chat_normal"
+
+    @staticmethod
+    def is_valid_mode(mode):
+        return any(mode == item.value for item in ChatScene)

pilot/scene/base_chat.py

@@ -70,7 +70,7 @@ class BaseChat(ABC):
         self.current_user_input: str = current_user_input
         self.llm_model = CFG.LLM_MODEL
         ### can configurable storage methods
-        self.memory = MemHistoryMemory(chat_session_id)
+        self.memory = FileHistoryMemory(chat_session_id)
 
         ### load prompt template
         self.prompt_template: PromptTemplate = CFG.prompt_templates[
@@ -139,9 +139,7 @@ class BaseChat(ABC):
 
         self.skip_echo_len = len(payload.get("prompt").replace("</s>", " ")) + 11
         logger.info(f"Requert: \n{payload}")
-        ai_response_text = ""
         try:
-            show_info = ""
             response = requests.post(
                 urljoin(CFG.MODEL_SERVER, "generate_stream"),
                 headers=headers,
@@ -157,16 +155,10 @@ class BaseChat(ABC):
             #     show_info = resp_text_trunck
             #     yield resp_text_trunck + "▌"
 
-            self.current_message.add_ai_message(show_info)
-
         except Exception as e:
             print(traceback.format_exc())
             logger.error("model response parase faild！" + str(e))
-            self.current_message.add_view_message(
+            raise ValueError(str(e))
-                f"""<span style=\"color:red\">ERROR!</span>{str(e)}\n  {ai_response_text} """
-            )
-        ### 对话记录存储
-        self.memory.append(self.current_message)
 
     def nostream_call(self):
         payload = self.__call_base()
@@ -188,20 +180,6 @@ class BaseChat(ABC):
                 )
             )
 
-            #             ### MOCK
-            #             ai_response_text = """{
-            # "thoughts": "可以从users表和tran_order表联合查询，按城市和订单数量进行分组统计，并使用柱状图展示。",
-            # "reasoning": "为了分析用户在不同城市的分布情况，需要查询users表和tran_order表，使用LEFT JOIN将两个表联合起来。按照城市进行分组，统计每个城市的订单数量。使用柱状图展示可以直观地看出每个城市的订单数量，方便比较。",
-            # "speak": "根据您的分析目标，我查询了用户表和订单表，统计了每个城市的订单数量，并生成了柱状图展示。",
-            # "command": {
-            # "name": "histogram-executor",
-            # "args": {
-            # "title": "订单城市分布柱状图",
-            # "sql": "SELECT users.city, COUNT(tran_order.order_id) AS order_count FROM users LEFT JOIN tran_order ON users.user_name = tran_order.user_name GROUP BY users.city"
-            # }
-            # }
-            # }"""
-
             self.current_message.add_ai_message(ai_response_text)
             prompt_define_response = (
                 self.prompt_template.output_parser.parse_prompt_response(
@@ -293,6 +271,7 @@ class BaseChat(ABC):
 
         return text
 
+
     # 暂时为了兼容前端
     def current_ai_response(self) -> str:
         for message in self.current_message.messages:

pilot/scene/chat_execution/example.py

@@ -0,0 +1,9 @@
+from pilot.prompts.example_base import ExampleSelector
+
+## Two examples are defined by default
+EXAMPLES = [
+    [{"System": "123"},{"System":"xxx"},{"User":"xxx"},{"Assistant":"xxx"}],
+    [{"System": "123"},{"System":"xxx"},{"User":"xxx"},{"Assistant":"xxx"}]
+]
+
+example = ExampleSelector(examples=EXAMPLES, use_example=True)

pilot/scene/chat_execution/prompt.py

@@ -1,36 +1,31 @@
 import json
-import importlib
 from pilot.prompts.prompt_new import PromptTemplate
 from pilot.configs.config import Config
 from pilot.scene.base import ChatScene
-from pilot.common.schema import SeparatorStyle
+from pilot.common.schema import SeparatorStyle, ExampleType
 
 from pilot.scene.chat_execution.out_parser import PluginChatOutputParser
-
+from pilot.scene.chat_execution.example import example
 
 CFG = Config()
 
-PROMPT_SCENE_DEFINE = """You are an AI designed to solve the user's goals with given commands, please follow the prompts and constraints of the system's input for your answers."""
+# PROMPT_SCENE_DEFINE = """You are an AI designed to solve the user's goals with given commands, please follow the prompts and constraints of the system's input for your answers."""
+PROMPT_SCENE_DEFINE = "You are an AI designed to solve the user's goals with given commands, please follow the  constraints of the system's input for your answers."
 
-PROMPT_SUFFIX = """
+
+_DEFAULT_TEMPLATE = """
 Goals: 
     {input}
     
-"""
-
-_DEFAULT_TEMPLATE = """
 Constraints:
 0.Exclusively use the commands listed in double quotes e.g. "command name"
 {constraints}
     
 Commands:
 {commands_infos}
-"""
 
-
-PROMPT_RESPONSE = """
 Please response strictly according to the following json format:
-    {response}
+{response}
 Ensure the response is correct json and can be parsed by Python json.loads
 """
 
@@ -40,20 +35,22 @@ RESPONSE_FORMAT = {
     "command": {"name": "command name", "args": {"arg name": "value"}},
 }
 
+
+
+EXAMPLE_TYPE = ExampleType.ONE_SHOT
 PROMPT_SEP = SeparatorStyle.SINGLE.value
 ### Whether the model service is streaming output
-PROMPT_NEED_NEED_STREAM_OUT = False
+PROMPT_NEED_STREAM_OUT = False
 
 prompt = PromptTemplate(
     template_scene=ChatScene.ChatExecution.value,
     input_variables=["input", "constraints", "commands_infos", "response"],
     response_format=json.dumps(RESPONSE_FORMAT, indent=4),
     template_define=PROMPT_SCENE_DEFINE,
-    template=PROMPT_SUFFIX + _DEFAULT_TEMPLATE + PROMPT_RESPONSE,
+    template=_DEFAULT_TEMPLATE,
-    stream_out=PROMPT_NEED_NEED_STREAM_OUT,
+    stream_out=PROMPT_NEED_STREAM_OUT,
-    output_parser=PluginChatOutputParser(
+    output_parser=PluginChatOutputParser(sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT),
-        sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
+    example=example
-    ),
 )
 
 CFG.prompt_templates.update({prompt.template_scene: prompt})

pilot/scene/chat_execution/prompt_v2.py

@@ -0,0 +1 @@
+

pilot/scene/chat_knowledge/custom/chat.py

@@ -1,5 +1,3 @@
-from chromadb.errors import NoIndexException
-
 from pilot.scene.base_chat import BaseChat, logger, headers
 from pilot.scene.base import ChatScene
 from pilot.common.sql_database import Database
@@ -52,19 +50,12 @@ class ChatNewKnowledge(BaseChat):
         )
 
     def generate_input_values(self):
-        try:
+        docs = self.knowledge_embedding_client.similar_search(
-            docs = self.knowledge_embedding_client.similar_search(
+            self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
-                self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
+        )
-            )
+        context = [d.page_content for d in docs]
-            context = [d.page_content for d in docs]
+        context = context[:2000]
-            self.metadata = [d.metadata for d in docs]
+        input_values = {"context": context, "question": self.current_user_input}
-            context = context[:2000]
-            input_values = {"context": context, "question": self.current_user_input}
-        except NoIndexException:
-            raise ValueError(
-                f"you have no {self.knowledge_name} knowledge store, please upload your knowledge"
-            )
-
         return input_values
 
     def do_with_prompt_response(self, prompt_response):

pilot/scene/message.py

@@ -43,12 +43,29 @@ class OnceConversation:
 
     def add_ai_message(self, message: str) -> None:
         """Add an AI message to the store"""
+
         has_message = any(isinstance(instance, AIMessage) for instance in self.messages)
         if has_message:
-            raise ValueError("Already Have Ai message")
+            self.update_ai_message(message)
-        self.messages.append(AIMessage(content=message))
+        else:
+            self.messages.append(AIMessage(content=message))
         """  """
 
+
+    def __update_ai_message(self, new_message:str)-> None:
+        """
+        stream out message update
+        Args:
+            new_message:
+
+        Returns:
+
+        """
+
+        for item in self.messages:
+            if item.type == "ai":
+                item.content = new_message
+
     def add_view_message(self, message: str) -> None:
         """Add an AI message to the store"""
 

pilot/server/api_v1/api_v1.py

@@ -0,0 +1,146 @@
+import uuid
+
+from fastapi import APIRouter, Request, Body, status
+
+from fastapi.responses import JSONResponse
+from fastapi.responses import StreamingResponse
+from fastapi.encoders import jsonable_encoder
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+from typing import List
+
+from pilot.server.api_v1.api_view_model import Result, ConversationVo, MessageVo
+from pilot.configs.config import Config
+from pilot.scene.base_chat import BaseChat
+from pilot.scene.base import ChatScene
+from pilot.scene.chat_factory import ChatFactory
+from pilot.configs.model_config import (LOGDIR)
+from pilot.utils import build_logger
+from pilot.scene.base_message import (BaseMessage)
+
+router = APIRouter()
+CFG = Config()
+CHAT_FACTORY = ChatFactory()
+logger = build_logger("api_v1", LOGDIR + "api_v1.log")
+
+
+async def validation_exception_handler(request: Request, exc: RequestValidationError):
+    message = ""
+    for error in exc.errors():
+        message += ".".join(error.get("loc")) + ":" + error.get("msg") + ";"
+    return Result.faild(message)
+
+
+@router.get('/v1/chat/dialogue/list', response_model=Result[List[ConversationVo]])
+async def dialogue_list(user_id: str):
+    #### TODO
+
+    conversations = [ConversationVo(conv_uid="123", chat_mode="user", select_param="test1", user_input="message[0]"),
+                     ConversationVo(conv_uid="123", chat_mode="user", select_param="test1", user_input="message[0]")]
+
+    return Result[ConversationVo].succ(conversations)
+
+
+@router.post('/v1/chat/dialogue/new', response_model=Result[str])
+async def dialogue_new(user_id: str):
+    unique_id = uuid.uuid1()
+    return Result.succ(unique_id)
+
+
+@router.post('/v1/chat/dialogue/delete')
+async def dialogue_delete(con_uid: str, user_id: str):
+    #### TODO
+    return Result.succ(None)
+
+
+@router.post('/v1/chat/completions', response_model=Result[MessageVo])
+async def chat_completions(dialogue: ConversationVo = Body()):
+    print(f"chat_completions:{dialogue.chat_mode},{dialogue.select_param}")
+
+    if not ChatScene.is_valid_mode(dialogue.chat_mode):
+        raise StopAsyncIteration(Result.faild("Unsupported Chat Mode," + dialogue.chat_mode + "!"))
+
+    chat_param = {
+        "chat_session_id": dialogue.conv_uid,
+        "user_input": dialogue.user_input,
+    }
+
+    if ChatScene.ChatWithDbExecute == dialogue.chat_mode:
+        chat_param.update("db_name", dialogue.select_param)
+    elif ChatScene.ChatWithDbQA == dialogue.chat_mode:
+        chat_param.update("db_name", dialogue.select_param)
+    elif ChatScene.ChatExecution == dialogue.chat_mode:
+        chat_param.update("plugin_selector", dialogue.select_param)
+    elif ChatScene.ChatNewKnowledge == dialogue.chat_mode:
+        chat_param.update("knowledge_name", dialogue.select_param)
+    elif ChatScene.ChatUrlKnowledge == dialogue.chat_mode:
+        chat_param.update("url", dialogue.select_param)
+
+    chat: BaseChat = CHAT_FACTORY.get_implementation(dialogue.chat_mode, **chat_param)
+    if not chat.prompt_template.stream_out:
+        return non_stream_response(chat)
+    else:
+        return stream_response(chat)
+
+
+def stream_generator(chat):
+    model_response = chat.stream_call()
+    for chunk in model_response.iter_lines(decode_unicode=False, delimiter=b"\0"):
+        if chunk:
+            msg = chat.prompt_template.output_parser.parse_model_stream_resp_ex(chunk, chat.skip_echo_len)
+            chat.current_message.add_ai_message(msg)
+            messageVos = [message2Vo(element) for element in chat.current_message.messages]
+            yield Result.succ(messageVos)
+def stream_response(chat):
+    logger.info("stream out start!")
+    api_response = StreamingResponse(stream_generator(chat), media_type="application/json")
+    return api_response
+
+def message2Vo(message:BaseMessage)->MessageVo:
+    vo:MessageVo = MessageVo()
+    vo.role = message.type
+    vo.role = message.content
+    vo.time_stamp = message.additional_kwargs.time_stamp if message.additional_kwargs["time_stamp"] else 0
+
+def non_stream_response(chat):
+    logger.info("not stream out, wait model response!")
+    chat.nostream_call()
+    messageVos = [message2Vo(element) for element in chat.current_message.messages]
+    return Result.succ(messageVos)
+
+
+@router.get('/v1/db/types', response_model=Result[str])
+async def db_types():
+    return Result.succ(["mysql", "duckdb"])
+
+
+@router.get('/v1/db/list', response_model=Result[str])
+async def db_list():
+    db = CFG.local_db
+    dbs = db.get_database_list()
+    return Result.succ(dbs)
+
+
+@router.get('/v1/knowledge/list')
+async def knowledge_list():
+    return ["test1", "test2"]
+
+
+@router.post('/v1/knowledge/add')
+async def knowledge_add():
+    return ["test1", "test2"]
+
+
+@router.post('/v1/knowledge/delete')
+async def knowledge_delete():
+    return ["test1", "test2"]
+
+
+@router.get('/v1/knowledge/types')
+async def knowledge_types():
+    return ["test1", "test2"]
+
+
+@router.get('/v1/knowledge/detail')
+async def knowledge_detail():
+    return ["test1", "test2"]

pilot/server/api_v1/api_view_model.py

@@ -0,0 +1,57 @@
+from pydantic import BaseModel, Field
+from typing import TypeVar, Union, List, Generic
+
+T = TypeVar('T')
+
+
+class Result(Generic[T], BaseModel):
+    success: bool
+    err_code: str
+    err_msg: str
+    data: List[T]
+
+    @classmethod
+    def succ(cls, data: List[T]):
+        return Result(True, None, None, data)
+
+    @classmethod
+    def faild(cls, msg):
+        return Result(True, "E000X", msg, None)
+
+    @classmethod
+    def faild(cls, code, msg):
+        return Result(True, code, msg, None)
+
+
+class ConversationVo(BaseModel):
+    """
+    dialogue_uid
+    """
+    conv_uid: str = Field(...,  description="dialogue uid")
+    """ 
+    user input 
+    """
+    user_input: str
+    """ 
+    the scene of chat 
+    """
+    chat_mode: str  = Field(..., description="the scene of chat ")
+    """
+    chat scene select param 
+    """
+    select_param: str
+
+
+class MessageVo(BaseModel):
+    """
+    role that sends out the current message 
+    """
+    role: str
+    """
+    current message 
+    """
+    context: str
+    """
+    time the current message was sent 
+    """
+    time_stamp: float

pilot/server/llmserver.py

@@ -5,7 +5,6 @@ import asyncio
 import json
 import os
 import sys
-import traceback
 
 import uvicorn
 from fastapi import BackgroundTasks, FastAPI, Request
@@ -76,10 +75,8 @@ class ModelWorker:
             ):
                 # Please do not open the output in production!
                 # The gpt4all thread shares stdout with the parent process,
-                # and opening it may affect the frontend output
+                # and opening it may affect the frontend output.
-                if not ("gptj" in CFG.LLM_MODEL or "guanaco" in CFG.LLM_MODEL):
+                # print("output: ", output)
-                    print("output: ", output)
-
                 ret = {
                     "text": output,
                     "error_code": 0,
@@ -90,13 +87,10 @@ class ModelWorker:
             ret = {"text": "**GPU OutOfMemory, Please Refresh.**", "error_code": 0}
             yield json.dumps(ret).encode() + b"\0"
         except Exception as e:
-            msg = "{}: {}".format(str(e), traceback.format_exc())
-
             ret = {
-                "text": f"**LLMServer Generate Error, Please CheckErrorInfo.**: {msg}",
+                "text": f"**LLMServer Generate Error, Please CheckErrorInfo.**: {e}",
                 "error_code": 0,
             }
-
             yield json.dumps(ret).encode() + b"\0"
 
     def get_embeddings(self, prompt):

pilot/server/webserver.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-import signal
 import threading
 import traceback
 import argparse
@@ -12,12 +11,10 @@ import uuid
 
 import gradio as gr
 
-
 ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.append(ROOT_PATH)
 
 from pilot.summary.db_summary_client import DBSummaryClient
-from pilot.commands.command_mange import CommandRegistry
 
 from pilot.scene.base_chat import BaseChat
 
@@ -25,8 +22,8 @@ from pilot.configs.config import Config
 from pilot.configs.model_config import (
     DATASETS_DIR,
     KNOWLEDGE_UPLOAD_ROOT_PATH,
-    LOGDIR,
     LLM_MODEL_CONFIG,
+    LOGDIR,
 )
 
 from pilot.conversation import (
@@ -35,7 +32,6 @@ from pilot.conversation import (
     chat_mode_title,
     default_conversation,
 )
-from pilot.common.plugins import scan_plugins, load_native_plugins
 
 from pilot.server.gradio_css import code_highlight_css
 from pilot.server.gradio_patch import Chatbot as grChatbot
@@ -49,6 +45,19 @@ from pilot.vector_store.extract_tovec import (
 from pilot.scene.base import ChatScene
 from pilot.scene.chat_factory import ChatFactory
 from pilot.language.translation_handler import get_lang_text
+from pilot.server.webserver_base import server_init
+
+
+import uvicorn
+from fastapi import BackgroundTasks, Request
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from fastapi import FastAPI, applications
+from fastapi.openapi.docs import get_swagger_ui_html
+from fastapi.exceptions import  RequestValidationError
+from fastapi.staticfiles import StaticFiles
+
+from pilot.server.api_v1.api_v1 import router as api_v1, validation_exception_handler
 
 # 加载插件
 CFG = Config()
@@ -95,6 +104,19 @@ knowledge_qa_type_list = [
     add_knowledge_base_dialogue,
 ]
 
+def swagger_monkey_patch(*args, **kwargs):
+    return get_swagger_ui_html(
+        *args, **kwargs,
+        swagger_js_url='https://cdn.bootcdn.net/ajax/libs/swagger-ui/4.10.3/swagger-ui-bundle.js',
+        swagger_css_url='https://cdn.bootcdn.net/ajax/libs/swagger-ui/4.10.3/swagger-ui.css'
+    )
+applications.get_swagger_ui_html = swagger_monkey_patch
+
+app = FastAPI()
+# app.mount("static", StaticFiles(directory="static"), name="static")
+app.include_router(api_v1)
+app.add_exception_handler(RequestValidationError, validation_exception_handler)
+
 
 def get_simlar(q):
     docsearch = knownledge_tovec_st(os.path.join(DATASETS_DIR, "plan.md"))
@@ -324,15 +346,14 @@ def http_bot(
             response = chat.stream_call()
             for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
                 if chunk:
-                    state.messages[-1][
+                    msg = chat.prompt_template.output_parser.parse_model_stream_resp_ex(chunk, chat.skip_echo_len)
-                        -1
+                    state.messages[-1][-1] =msg
-                    ] = chat.prompt_template.output_parser.parse_model_stream_resp_ex(
+                    chat.current_message.add_ai_message(msg)
-                        chunk, chat.skip_echo_len
-                    )
                     yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
+            chat.memory.append(chat.current_message)
         except Exception as e:
             print(traceback.format_exc())
-            state.messages[-1][-1] = "Error:" + str(e)
+            state.messages[-1][-1] = f"""<span style=\"color:red\">ERROR!</span>{str(e)} """
             yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 5
 
 
@@ -632,7 +653,7 @@ def knowledge_embedding_store(vs_id, files):
         )
         knowledge_embedding_client = KnowledgeEmbedding(
             file_path=os.path.join(KNOWLEDGE_UPLOAD_ROOT_PATH, vs_id, filename),
-            model_name=LLM_MODEL_CONFIG[CFG.EMBEDDING_MODEL],
+            model_name=LLM_MODEL_CONFIG["text2vec"],
             vector_store_config={
                 "vector_store_name": vector_store_name["vs_name"],
                 "vector_store_path": KNOWLEDGE_UPLOAD_ROOT_PATH,
@@ -657,48 +678,36 @@ def signal_handler(sig, frame):
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
+    parser.add_argument("--model_list_mode", type=str, default="once", choices=["once", "reload"])
+    parser.add_argument('-new', '--new', action='store_true', help='enable new http mode')
+
+    # old version server config
     parser.add_argument("--host", type=str, default="0.0.0.0")
     parser.add_argument("--port", type=int, default=CFG.WEB_SERVER_PORT)
     parser.add_argument("--concurrency-count", type=int, default=10)
-    parser.add_argument(
-        "--model-list-mode", type=str, default="once", choices=["once", "reload"]
-    )
     parser.add_argument("--share", default=False, action="store_true")
 
+
+    # init server config
     args = parser.parse_args()
-    logger.info(f"args: {args}")
+    server_init(args)
+
+    if args.new:
+        import uvicorn
+        uvicorn.run(app, host="0.0.0.0", port=5000)
+    else:
+        ### Compatibility mode starts the old version server by default
+        demo = build_webdemo()
+        demo.queue(
+            concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False
+        ).launch(
+            server_name=args.host,
+            server_port=args.port,
+            share=args.share,
+            max_threads=200,
+        )
 
-    # init config
-    cfg = Config()
 
-    load_native_plugins(cfg)
-    dbs = cfg.local_db.get_database_list()
-    signal.signal(signal.SIGINT, signal_handler)
-    async_db_summery()
-    cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
 
-    # Loader plugins and commands
-    command_categories = [
-        "pilot.commands.built_in.audio_text",
-        "pilot.commands.built_in.image_gen",
-    ]
-    # exclude commands
-    command_categories = [
-        x for x in command_categories if x not in cfg.disabled_command_categories
-    ]
-    command_registry = CommandRegistry()
-    for command_category in command_categories:
-        command_registry.import_commands(command_category)
 
-    cfg.command_registry = command_registry
 
-    logger.info(args)
-    demo = build_webdemo()
-    demo.queue(
-        concurrency_count=args.concurrency_count, status_update_rate=10, api_open=False
-    ).launch(
-        server_name=args.host,
-        server_port=args.port,
-        share=args.share,
-        max_threads=200,
-    )

pilot/server/webserver_base.py

@@ -0,0 +1,60 @@
+import signal
+import os
+import threading
+import traceback
+import sys
+
+from pilot.summary.db_summary_client import DBSummaryClient
+from pilot.commands.command_mange import CommandRegistry
+from pilot.configs.config import Config
+from pilot.configs.model_config import (
+    DATASETS_DIR,
+    KNOWLEDGE_UPLOAD_ROOT_PATH,
+    LLM_MODEL_CONFIG,
+    LOGDIR,
+)
+from pilot.common.plugins import scan_plugins, load_native_plugins
+from pilot.utils import build_logger
+
+ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(ROOT_PATH)
+
+logger = build_logger("webserver", LOGDIR + "webserver.log")
+
+
+def signal_handler(sig, frame):
+    print("in order to avoid chroma db atexit problem")
+    os._exit(0)
+
+
+def async_db_summery():
+    client = DBSummaryClient()
+    thread = threading.Thread(target=client.init_db_summary)
+    thread.start()
+
+
+def server_init(args):
+    logger.info(f"args: {args}")
+
+    # init config
+    cfg = Config()
+
+    load_native_plugins(cfg)
+    signal.signal(signal.SIGINT, signal_handler)
+    async_db_summery()
+    cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode))
+
+    # Loader plugins and commands
+    command_categories = [
+        "pilot.commands.built_in.audio_text",
+        "pilot.commands.built_in.image_gen",
+    ]
+    # exclude commands
+    command_categories = [
+        x for x in command_categories if x not in cfg.disabled_command_categories
+    ]
+    command_registry = CommandRegistry()
+    for command_category in command_categories:
+        command_registry.import_commands(command_category)
+
+    cfg.command_registry = command_registry

pilot/speech/eleven_labs.py

@@ -35,7 +35,7 @@ class ElevenLabsSpeech(VoiceBase):
         }
         self._headers = {
             "Content-Type": "application/json",
-            "xi-api-key": cfg.elevenlabs_api_key,
+            "xi-api_v1-key": cfg.elevenlabs_api_key,
         }
         self._voices = default_voices.copy()
         if cfg.elevenlabs_voice_1_id in voice_options:

pilot/vector_store/connector.py

@@ -1,13 +1,12 @@
 from pilot.vector_store.chroma_store import ChromaStore
 
-from pilot.vector_store.milvus_store import MilvusStore
+# from pilot.vector_store.milvus_store import MilvusStore
-from pilot.vector_store.weaviate_store import WeaviateStore
 
-connector = {"Chroma": ChromaStore, "Milvus": MilvusStore, "Weaviate": WeaviateStore}
+connector = {"Chroma": ChromaStore, "Milvus": None}
 
 
 class VectorStoreConnector:
-    """vector store connector, can connect different vector db provided load document api and similar search api."""
+    """vector store connector, can connect different vector db provided load document api_v1 and similar search api_v1."""
 
     def __init__(self, vector_store_type, ctx: {}) -> None:
         """initialize vector store connector."""

pilot/vector_store/weaviate_store.py

@@ -1,146 +0,0 @@
-import os
-import json
-import weaviate
-from langchain.schema import Document
-from langchain.vectorstores import Weaviate
-from weaviate.exceptions import WeaviateBaseError
-
-from pilot.configs.config import Config
-from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH
-from pilot.logs import logger
-from pilot.vector_store.vector_store_base import VectorStoreBase
-
-CFG = Config()
-
-
-class WeaviateStore(VectorStoreBase):
-    """Weaviate database"""
-
-    def __init__(self, ctx: dict) -> None:
-        """Initialize with Weaviate client."""
-        try:
-            import weaviate
-        except ImportError:
-            raise ValueError(
-                "Could not import weaviate python package. "
-                "Please install it with `pip install weaviate-client`."
-            )
-
-        self.ctx = ctx
-        self.weaviate_url = CFG.WEAVIATE_URL
-        self.embedding = ctx.get("embeddings", None)
-        self.vector_name = ctx["vector_store_name"]
-        self.persist_dir = os.path.join(
-            KNOWLEDGE_UPLOAD_ROOT_PATH, self.vector_name + ".vectordb"
-        )
-
-        self.vector_store_client = weaviate.Client(self.weaviate_url)
-
-    def similar_search(self, text: str, topk: int) -> None:
-        """Perform similar search in Weaviate"""
-        logger.info("Weaviate similar search")
-        # nearText = {
-        #     "concepts": [text],
-        #     "distance": 0.75,  # prior to v1.14 use "certainty" instead of "distance"
-        # }
-        # vector = self.embedding.embed_query(text)
-        response = (
-            self.vector_store_client.query.get(
-                self.vector_name, ["metadata", "page_content"]
-            )
-            # .with_near_vector({"vector": vector})
-            .with_limit(topk).do()
-        )
-        res = response["data"]["Get"][list(response["data"]["Get"].keys())[0]]
-        docs = []
-        for r in res:
-            docs.append(
-                Document(
-                    page_content=r["page_content"],
-                    metadata={"metadata": r["metadata"]},
-                )
-            )
-        return docs
-
-    def vector_name_exists(self) -> bool:
-        """Check if a vector name exists for a given class in Weaviate.
-        Returns:
-            bool: True if the vector name exists, False otherwise.
-        """
-        try:
-            if self.vector_store_client.schema.get(self.vector_name):
-                return True
-            return False
-        except WeaviateBaseError as e:
-            logger.error("vector_name_exists error", e.message)
-            return False
-
-    def _default_schema(self) -> None:
-        """
-        Create the schema for Weaviate with a Document class containing metadata and text properties.
-        """
-
-        schema = {
-            "classes": [
-                {
-                    "class": self.vector_name,
-                    "description": "A document with metadata and text",
-                    # "moduleConfig": {
-                    #     "text2vec-transformers": {
-                    #         "poolingStrategy": "masked_mean",
-                    #         "vectorizeClassName": False,
-                    #     }
-                    # },
-                    "properties": [
-                        {
-                            "dataType": ["text"],
-                            # "moduleConfig": {
-                            #     "text2vec-transformers": {
-                            #         "skip": False,
-                            #         "vectorizePropertyName": False,
-                            #     }
-                            # },
-                            "description": "Metadata of the document",
-                            "name": "metadata",
-                        },
-                        {
-                            "dataType": ["text"],
-                            # "moduleConfig": {
-                            #     "text2vec-transformers": {
-                            #         "skip": False,
-                            #         "vectorizePropertyName": False,
-                            #     }
-                            # },
-                            "description": "Text content of the document",
-                            "name": "page_content",
-                        },
-                    ],
-                    # "vectorizer": "text2vec-transformers",
-                }
-            ]
-        }
-
-        # Create the schema in Weaviate
-        self.vector_store_client.schema.create(schema)
-
-    def load_document(self, documents: list) -> None:
-        """Load documents into Weaviate"""
-        logger.info("Weaviate load document")
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
-
-        # Import data
-        with self.vector_store_client.batch as batch:
-            batch.batch_size = 100
-
-            # Batch import all documents
-            for i in range(len(texts)):
-                properties = {
-                    "metadata": metadatas[i]["source"],
-                    "page_content": texts[i],
-                }
-
-                self.vector_store_client.batch.add_data_object(
-                    data_object=properties, class_name=self.vector_name
-                )
-            self.vector_store_client.batch.flush()