feat chat robot 接口迁移

app/api/api_chat_robot.py

@@ -0,0 +1,27 @@
+import logging
+import time
+from fastapi import APIRouter
+
+from app.schemas.chat_robot import ChatRobotModel
+from app.service.chat_robot.script.main import chat
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.post("/chat_robot")
+def chat_robot(request_data: ChatRobotModel):
+    try:
+        logger.info(f"chat_robot request item is : @@@@@@:{request_data}")
+        code = 200
+        message = "access"
+        start_time = time.time()
+        data = chat(post_data=request_data)
+        logger.info(f"chat_robot Run time is @@@@@@:{time.time() - start_time}")
+    except Exception as e:
+        code = 400
+        message = str(e)
+        data = str(e)
+        logger.warning(f"chat_robot Run Exception @@@@@@:{e}")
+    logger.info({"code": code, "message": message, "data": data})
+    return {"code": code, "message": message, "data": data}

app/api/api_prompt_generation.py

@@ -0,0 +1,28 @@
+import logging
+import time
+
+from fastapi import APIRouter
+
+from app.schemas.prompt_generation import PromptGenerationImageModel
+from app.service.prompt_generation.chatgpt_for_translation import translate_to_en
+
+router = APIRouter()
+logger = logging.getLogger()
+
+
+@router.post("/translateToEN")
+def prompt_generation(request_data: PromptGenerationImageModel):
+    try:
+        logger.info(f"prompt_translate to English request data :  @@@@@@:{request_data}")
+        code = 200
+        message = "access"
+        start_time = time.time()
+        data = translate_to_en(request_data.text)
+        logger.info(f"prompt_generation Run time is @@@@@@:{time.time() - start_time}")
+    except Exception as e:
+        code = 400
+        message = str(e)
+        data = str(e)
+        logger.warning(f"prompt_generation Run Exception @@@@@@:{e}")
+    logger.info({"code": code, "message": message, "data": data})
+    return {"code": code, "message": message, "data": data}

app/api/api_route.py

@@ -5,6 +5,9 @@ from app.api import api_super_resolution
 from app.api import api_generate_image
 from app.api import api_attribute_retrieve
 from app.api import api_design
+from app.api import api_chat_robot
+from app.api import api_prompt_generation
+
 
 router = APIRouter()
 
@@ -13,3 +16,5 @@ router.include_router(api_super_resolution.router, tags=["super_resolution"], pr
 router.include_router(api_generate_image.router, tags=["generate_image"], prefix="/api")
 router.include_router(api_attribute_retrieve.router, tags=["attribute_retrieve"], prefix="/api")
 router.include_router(api_design.router, tags=['design'], prefix="/api")
+router.include_router(api_chat_robot.router, tags=['chat_robot'], prefix="/api")
+router.include_router(api_prompt_generation.router, tags=['prompt_generation'], prefix="/api")

app/core/config.py

@@ -19,7 +19,7 @@ class Settings(BaseSettings):
     LOGGING_CONFIG_FILE = os.path.join(BASE_DIR, 'logging_env.py')
 
 
-DEBUG = False
+DEBUG = True
 if DEBUG:
     LOGS_PATH = "logs/"
     CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv"
@@ -61,6 +61,32 @@ MILVUS_PORT = "19530"
 MILVUS_TABLE_KEYPOINT = "keypoint_cache"
 MILVUS_TABLE_SEG = "seg_cache"
 
+# Mysql 配置
+DB_HOST = '18.167.251.121'  # 数据库主机地址
+# DB_PORT = int( 33006)
+DB_PORT = 33008  # 数据库端口
+DB_USERNAME = 'aida_con_python'  # 数据库用户名
+DB_PASSWORD = '123456'  # 数据库密码
+DB_NAME = 'aida'  # 数据库库名
+
+# openai
+os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c"
+OPENAI_STREAM = True
+BUFFER_THRESHOLD = 6  # must be even number
+SINGLE_TOKEN_THRESHOLD = 200
+TOKEN_THRESHOLD = 600
+OPENAI_TEMPERATURE = 0
+
+# OPENAI_API_KEY =  "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"
+OPENAI_API_KEY = "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"
+OPENAI_MODEL = "gpt-3.5-turbo-0613"
+OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613",
+                     "gpt-3.5-turbo-16k-0613",
+                     "gpt-4-0314",
+                     "gpt-4-32k-0314",
+                     "gpt-4-0613",
+                     "gpt-4-32k-0613", }
+
 # attribute service config
 ATT_TRITON_URL = "10.1.1.240:10000"
 

app/schemas/chat_robot.py

@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+
+
+class ChatRobotModel(BaseModel):
+    gender: str
+    message: str
+    session_id: str
+    user_id: int

app/schemas/prompt_generation.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+
+class PromptGenerationImageModel(BaseModel):
+    text: str

app/service/chat_robot/script/agents/__init__.py

@@ -0,0 +1,7 @@
+from .agent_executor import CustomAgentExecutor
+from .conversational_functions_agent import ConversationalFunctionsAgent
+
+__all__ = [
+    "CustomAgentExecutor",
+    "ConversationalFunctionsAgent"
+]

app/service/chat_robot/script/agents/agent_executor.py

@@ -0,0 +1,132 @@
+import inspect
+import json
+import logging
+from typing import Any, Dict, List, Optional, Union, Tuple
+
+from langchain.agents import AgentExecutor
+from langchain.callbacks.manager import Callbacks, CallbackManager
+from langchain.load.dump import dumpd
+from langchain.schema import RUN_KEY, RunInfo
+from langchain_core.agents import AgentAction, AgentFinish
+
+
+class CustomAgentExecutor(AgentExecutor):
+    def __call__(
+            self,
+            inputs: Union[Dict[str, Any], Any],
+            return_only_outputs: bool = False,
+            callbacks: Callbacks = None,
+            session_key: str = "",
+            *,
+            tags: Optional[List[str]] = None,
+            include_run_info: bool = False,
+    ) -> Dict[str, Any]:
+        """Run the logic of this chain and add to output if desired.
+
+        Args:
+            inputs: Dictionary of inputs, or single input if chain expects
+                only one param.
+            return_only_outputs: boolean for whether to return only outputs in the
+                response. If True, only new keys generated by this chain will be
+                returned. If False, both input keys and new keys generated by this
+                chain will be returned. Defaults to False.
+            callbacks: Callbacks to use for this chain run. If not provided, will
+                use the callbacks provided to the chain.
+            include_run_info: Whether to include run info in the response. Defaults
+                to False.
+        """
+        inputs = self.prep_inputs(inputs, session_key)
+        callback_manager = CallbackManager.configure(
+            callbacks, self.callbacks, self.verbose, tags, self.tags
+        )
+        new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
+        run_manager = callback_manager.on_chain_start(
+            dumpd(self),
+            inputs,
+        )
+        try:
+            outputs = (
+                self._call(inputs, run_manager=run_manager)
+                if new_arg_supported
+                else self._call(inputs)
+            )
+        except (KeyboardInterrupt, Exception) as e:
+            logging.exception(e)
+            run_manager.on_chain_error(e)
+            raise e
+        run_manager.on_chain_end(outputs)
+        final_outputs: Dict[str, Any] = self.prep_outputs(
+            inputs, outputs, return_only_outputs, session_key
+        )
+        if include_run_info:
+            final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
+        return final_outputs
+
+    def prep_outputs(
+            self,
+            inputs: Dict[str, str],
+            outputs: Dict[str, str],
+            return_only_outputs: bool = False,
+            session_key: str = ""
+    ) -> Dict[str, str]:
+        """Validate and prep outputs."""
+        self._validate_outputs(outputs)
+        if self.memory is not None and outputs['need_record']:
+            self.memory.save_context(inputs, outputs, session_key)
+        if return_only_outputs:
+            return outputs
+        else:
+            return {**inputs, **outputs}
+
+    def prep_inputs(self, inputs: Union[Dict[str, Any], Any], session_key: str = "") -> Dict[str, str]:
+        """Validate and prep inputs."""
+        if not isinstance(inputs, dict):
+            _input_keys = set(self.input_keys)
+            if self.memory is not None:
+                # If there are multiple input keys, but some get set by memory so that
+                # only one is not set, we can still figure out which key it is.
+                _input_keys = _input_keys.difference(self.memory.memory_variables)
+            if len(_input_keys) != 1:
+                raise ValueError(
+                    f"A single string input was passed in, but this chain expects "
+                    f"multiple inputs ({_input_keys}). When a chain expects "
+                    f"multiple inputs, please call it by passing in a dictionary, "
+                    "eg `chain({'foo': 1, 'bar': 2})`"
+                )
+            inputs = {list(_input_keys)[0]: inputs}
+        if self.memory is not None:
+            external_context = self.memory.load_memory_variables(inputs, session_key)
+            inputs = dict(inputs, **external_context)
+        self._validate_inputs(inputs)
+        return inputs
+
+    def _get_tool_return(
+            self, next_step_output: Tuple[AgentAction, str]
+    ) -> Optional[AgentFinish]:
+        """Check if the tool is a returning tool."""
+        agent_action, observation = next_step_output
+        name_to_tool_map = {tool.name: tool for tool in self.tools}
+        return_value_key = "output"
+
+        if len(self.agent.return_values) > 0:
+            return_value_key = self.agent.return_values[0]
+
+        try:
+            observation_list = json.loads(observation)
+            if agent_action.tool == "sql_db_query" and isinstance(observation_list,
+                                                                  list) and observation_list.__len__() != 0:
+                return AgentFinish(
+                    {return_value_key: observation},
+                    "",
+                )
+        except:
+            pass
+
+        # Invalid tools won't be in the map, so we return False.
+        if agent_action.tool in name_to_tool_map:
+            if name_to_tool_map[agent_action.tool].return_direct:
+                return AgentFinish(
+                    {return_value_key: observation},
+                    "",
+                )
+        return None

app/service/chat_robot/script/agents/conversational_functions_agent.py

@@ -0,0 +1,198 @@
+import json
+import re
+from json import JSONDecodeError
+from typing import List, Tuple, Any, Union
+from dataclasses import dataclass
+
+from langchain.callbacks.manager import Callbacks
+from langchain.agents import (
+    OpenAIFunctionsAgent,
+)
+from langchain.schema import (
+    AgentAction,
+    AgentFinish,
+    BaseMessage,
+    OutputParserException
+)
+from langchain.schema.messages import (
+    AIMessage,
+    FunctionMessage
+)
+from langchain.tools import BaseTool, StructuredTool
+# from langchain.tools.convert_to_openai import FunctionDescription
+from langchain.utils.openai_functions import FunctionDescription
+
+
+@dataclass
+class _FunctionsAgentAction(AgentAction):
+    """Add message_log to AgentAction class for the _FunctionAgentAction
+    """
+    message_log: List[BaseMessage]
+
+    def __init__(
+            self, tool: str, tool_input: Union[str, dict], log: str, **kwargs: Any
+    ):
+        """Override init to support instantiation by position for backward compat."""
+        super().__init__(tool=tool, tool_input=tool_input, log=log, **kwargs)
+
+
+def _convert_agent_action_to_messages(
+        agent_action: AgentAction, observation: str
+) -> List[BaseMessage]:
+    """Convert an agents action to a message.
+
+    This code is used to reconstruct the original AI message from the agents action.
+
+    Args:
+        agent_action: Agent action to convert.
+
+    Returns:
+        AIMessage that corresponds to the original tools invocation.
+    """
+    if isinstance(agent_action, _FunctionsAgentAction):
+        return agent_action.message_log + [
+            _create_function_message(agent_action, observation)
+        ]
+    else:
+        return [AIMessage(content=agent_action.log)]
+
+
+def _create_function_message(
+        agent_action: AgentAction, observation: str
+) -> FunctionMessage:
+    """Convert agents action and observation into a function message.
+    Args:
+        agent_action: the tools invocation request from the agents
+        observation: the result of the tools invocation
+    Returns:
+        FunctionMessage that corresponds to the original tools invocation
+    """
+    if not isinstance(observation, str):
+        try:
+            content = json.dumps(observation, ensure_ascii=False)
+        except Exception:
+            content = str(observation)
+    else:
+        content = observation
+    return FunctionMessage(
+        name=agent_action.tool,
+        content=content,
+    )
+
+
+def _format_intermediate_steps(
+        intermediate_steps: List[Tuple[AgentAction, str]],
+) -> List[BaseMessage]:
+    """Format intermediate steps.
+    Args:
+        intermediate_steps: Steps the LLM has taken to date, along with observations
+    Returns:
+        list of messages to send to the LLM for the next prediction
+    """
+    messages = []
+
+    for intermediate_step in intermediate_steps:
+        agent_action, observation = intermediate_step
+        messages.extend(_convert_agent_action_to_messages(agent_action, observation))
+
+    return messages
+
+
+def _format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
+    """Format tools into the OpenAI function API."""
+    parameters = {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": tool.param_description if hasattr(tool, 'param_description') else "",
+            },
+        },
+        "required": ["query"],
+    }
+
+    return {
+        "name": tool.name,
+        "description": tool.description,
+        "parameters": parameters,
+    }
+
+
+def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
+    if not isinstance(message, AIMessage):
+        raise TypeError(f"Expected an AI message but got {type(message)}")
+
+    function_call = message.additional_kwargs.get("function_call", {})
+
+    if function_call:
+        function_call = message.additional_kwargs["function_call"]
+        function_name = function_call["name"]
+        try:
+            _tool_input = json.loads(function_call["arguments"])
+        except JSONDecodeError:
+            raise OutputParserException(
+                f"Could not parse tools input: {function_call} because"
+                f"the `arguments` is not valid JSON."
+            )
+
+        if "query" in _tool_input:
+            tool_input = _tool_input["query"]
+        else:
+            tool_input = _tool_input
+
+        return _FunctionsAgentAction(
+            tool=function_name,
+            tool_input=tool_input,
+            log=f"\nInvoking: `{function_name}` with `{tool_input}`\n",
+            message_log=[message]
+        )
+
+    # pattern = r'\((.*?)\)'
+    # matches = re.findall(pattern, message.content)
+    # result = []
+    #
+    # for match in matches:
+    #     result.append(match)
+    #
+    # if result:
+    #     output = result
+    # else:
+    #     output = message.content
+
+    return AgentFinish(return_values={"output": message.content}, log=message.content)
+
+
+class ConversationalFunctionsAgent(OpenAIFunctionsAgent):
+    @property
+    def functions(self) -> List[dict]:
+        return [dict(_format_tool_to_openai_function(t)) for t in self.tools]
+
+    def plan(self,
+             intermediate_steps: List[Tuple[AgentAction, str]],
+             callbacks: Callbacks = None,
+             **kwargs: Any
+             ) -> Union[AgentAction, AgentFinish]:
+        """Decide how agents should move after receiving an input. The difference between
+        OpenAIFunctionsAgent lies in the '_parse_ai_message' function. We add an OutputParser
+        into it.
+
+        Args:
+            intermediate_steps: Steps the LLM has taken to date, along with observations
+            **kwargs: User inputs.
+            **kwargs: Including user's input string
+
+        Returns:
+            Action specifying what tools to use.
+        """
+        agent_scratchpad: List[BaseMessage] = _format_intermediate_steps(intermediate_steps)
+        selected_inputs = {
+            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
+        }
+        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
+        prompt = self.prompt.format_prompt(**full_inputs)
+        messages: List[BaseMessage] = prompt.to_messages()
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=callbacks
+        )
+        agent_decision = _parse_ai_message(predicted_message)
+        return agent_decision

app/service/chat_robot/script/callbacks/__init__.py

@@ -0,0 +1,6 @@
+from .openai_token_record_callback import OpenAITokenRecordCallbackHandler
+
+
+__all__ = [
+    'OpenAITokenRecordCallbackHandler'
+]

app/service/chat_robot/script/callbacks/openai_token_record_callback.py

@@ -0,0 +1,46 @@
+"""Callback Handler that add on_chain_end function to record Token usage."""
+from typing import Any, Dict
+
+from langchain.callbacks import OpenAICallbackHandler
+from langchain.schema import LLMResult
+from langchain.callbacks.openai_info import standardize_model_name, MODEL_COST_PER_1K_TOKENS, get_openai_token_cost_for_model
+
+
+class OpenAITokenRecordCallbackHandler(OpenAICallbackHandler):
+    need_record: bool = True
+    response_type: str = "string"
+    """Callback Handler that tracks OpenAI info and write to redis after agent finish"""
+    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        """Collect token usage."""
+        if response.llm_output is None:
+            return None
+        self.successful_requests += 1
+        if "token_usage" not in response.llm_output:
+            return None
+        if "function_call" in response.generations[0][0].message.additional_kwargs:
+            if response.generations[0][0].message.additional_kwargs["function_call"]["name"] in ["sql_db_query", "sql_db_schema","tutorial_tool"]:
+                self.need_record = False
+            if response.generations[0][0].message.additional_kwargs["function_call"]["name"] == "sql_db_query":
+                self.response_type = "image"
+        token_usage = response.llm_output["token_usage"]
+        completion_tokens = token_usage.get("completion_tokens", 0)
+        prompt_tokens = token_usage.get("prompt_tokens", 0)
+        model_name = standardize_model_name(response.llm_output.get("model_name", ""))
+        if model_name in MODEL_COST_PER_1K_TOKENS:
+            completion_cost = get_openai_token_cost_for_model(
+                model_name, completion_tokens, is_completion=True
+            )
+            prompt_cost = get_openai_token_cost_for_model(model_name, prompt_tokens)
+            self.total_cost += prompt_cost + completion_cost
+        self.total_tokens += token_usage.get("total_tokens", 0)
+        self.prompt_tokens += prompt_tokens
+        self.completion_tokens += completion_tokens
+
+    def on_chain_end(self, outputs: Dict, **kwargs: Any) -> None:
+        """Write token usage to redis."""
+        outputs["total_tokens"] = self.total_tokens
+        outputs["total_cost"] = self.total_cost
+        outputs["prompt_tokens"] = self.prompt_tokens
+        outputs["completion_tokens"] = self.completion_tokens
+        outputs["need_record"] = self.need_record
+        outputs["response_type"] = self.response_type

app/service/chat_robot/script/database.py

@@ -0,0 +1,79 @@
+from typing import Optional, List
+import json
+
+from sqlalchemy import text
+# from langchain import SQLDatabase
+from langchain.utilities import SQLDatabase
+
+
+class CustomDatabase(SQLDatabase):
+    def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
+    # def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
+        connection = self._engine.connect()
+        all_table_names = self.get_usable_table_names()
+        if table_names is not None:
+            missing_tables = set(table_names).difference(all_table_names)
+            if missing_tables:
+                # raise ValueError(f"table_names {missing_tables} not found in database")
+                return f"Table {','.join(missing_tables)} can not be found in the database"
+            all_table_names = table_names
+        meta_tables = [
+            tbl
+            for tbl in self._metadata.sorted_tables
+            if tbl.name in set(all_table_names)
+        ]
+
+        tables = []
+        for table in meta_tables:
+            table_name = table.name
+            column_names = table.columns.keys()
+            table_info = f"Table: {table_name}\nColumns: \nID, \nimg_name\n"
+            for column_name in column_names:
+                if column_name not in ["ID", "img_name"]:
+                    query = text(f"SELECT DISTINCT {column_name} FROM {table_name}")
+                    result = connection.execute(query)
+                    enum_values: List[str] = [row[0] for row in result.fetchall()]
+                    column_info = f"{column_name}: {', '.join(enum_values)}\n"
+                    table_info += column_info
+
+            # table_info = f"Table: {table_name}\n"
+            #
+            # if self._sample_rows_in_table_info:
+            #     table_info += f"{self._get_sample_rows(table)}\n"
+            tables.append(table_info)
+        final_str = "\n\n".join(tables)
+        return final_str
+
+    def run(self, command: str, fetch: str = "all") -> str:
+        """Execute a SQL command and return a string representing the results.
+
+        If the statement returns rows, a string of the results is returned.
+        If the statement returns no rows, an empty string is returned.
+
+        """
+        with self._engine.begin() as connection:
+            if self._schema is not None:
+                if self.dialect == "snowflake":
+                    connection.exec_driver_sql(
+                        f"ALTER SESSION SET search_path='{self._schema}'"
+                    )
+                elif self.dialect == "bigquery":
+                    connection.exec_driver_sql(f"SET @@dataset_id='{self._schema}'")
+                else:
+                    connection.exec_driver_sql(f"SET search_path TO {self._schema}")
+            cursor = connection.execute(text(command))
+            if cursor.rowcount:
+                if fetch == "all":
+                    result = cursor.fetchall()
+                elif fetch == "one":
+                    result = cursor.fetchone()  # type: ignore
+                else:
+                    raise ValueError("Fetch parameter must be either 'one' or 'all'")
+
+                # Convert columns values to string to avoid issues with sqlalchmey
+                # trunacating text
+                if isinstance(result, list):
+                    return json.dumps([r[0] for r in result])
+
+                return json.dumps([result[0]])
+        return ""

app/service/chat_robot/script/main.py

@@ -0,0 +1,114 @@
+import logging
+from loguru import logger
+from langchain.agents import Tool
+from langchain.utilities import SerpAPIWrapper
+from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
+from langchain.schema import SystemMessage, AIMessage
+from langchain.chat_models import ChatOpenAI
+from langchain.llms.openai import OpenAI
+from langchain.callbacks import FileCallbackHandler
+from app.service.chat_robot.script.agents import CustomAgentExecutor, ConversationalFunctionsAgent
+from app.service.chat_robot.script.callbacks import OpenAITokenRecordCallbackHandler
+from app.service.chat_robot.script.database import CustomDatabase
+from app.service.chat_robot.script.prompt import FASHION_CHAT_BOT_PREFIX, TOOLS_FUNCTIONS_SUFFIX
+from app.service.chat_robot.script.tools import (QuerySQLDataBaseTool, InfoSQLDatabaseTool, QuerySQLCheckerTool, ListSQLDatabaseTool)
+from app.service.chat_robot.script.memory import UserConversationBufferWindowMemory
+from app.service.chat_robot.script.tools.tutorial_tool import CustomTutorialTool
+from app.core.config import *
+
+import os
+
+# os.environ["http_proxy"] = "http://127.0.0.1:7890"
+# os.environ["https_proxy"] = "http://127.0.0.1:7890"
+# log callbacks
+logfile = "logs/chat_debug.log"
+logger.add(logfile, colorize=True, enqueue=True)
+log_handler = FileCallbackHandler(logfile)
+
+# Initiate our LLM 'gpt-3.5-turbo'
+llm = ChatOpenAI(temperature=0.1,
+                 openai_api_key=OPENAI_API_KEY,
+                 # callbacks=[OpenAICallbackHandler()]
+                 )
+
+search = SerpAPIWrapper()
+db = CustomDatabase.from_uri(f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/attribute_retrieval_V3',
+                             include_tables=['female_top', 'female_skirt', 'female_pants', 'female_dress',
+                                             'female_outwear', 'male_bottom', 'male_top', 'male_outwear'],
+                             engine_args={"pool_recycle": 7200})
+tools = [
+    Tool(
+        name="internet_search",
+        description="Can be used to perform Internet searches",
+        func=search.run
+    ),
+    QuerySQLDataBaseTool(db=db, return_direct=False),
+    InfoSQLDatabaseTool(db=db),
+    ListSQLDatabaseTool(db=db),
+    QuerySQLCheckerTool(db=db, llm=OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)),
+    Tool(
+        name="tutorial_tool",
+        description="Utilize this tool to retrieve specific statements related to user guidance tutorials."
+                    "Input is an empty string",
+        func=CustomTutorialTool(),
+        return_direct=True
+    )
+]
+
+messages = [
+    SystemMessage(content=FASHION_CHAT_BOT_PREFIX),
+    MessagesPlaceholder(variable_name="history"),
+    HumanMessagePromptTemplate.from_template(
+        "{input} "
+        "Question from a {gender}."
+    ),
+    AIMessage(content=TOOLS_FUNCTIONS_SUFFIX),
+    MessagesPlaceholder(variable_name="agent_scratchpad"),
+]
+
+prompt = ChatPromptTemplate(input_variables=["input", "gender", "agent_scratchpad", "history"], messages=messages)
+agent = ConversationalFunctionsAgent(
+    llm=llm,
+    tools=tools,
+    prompt=prompt
+)
+
+memory = UserConversationBufferWindowMemory.from_redis(
+    return_messages=True, k=2, input_key='input', output_key='output'
+)
+agent_executor = CustomAgentExecutor.from_agent_and_tools(
+    agent=agent,
+    tools=tools,
+    verbose=True,
+    memory=memory,
+)
+
+
+def chat(post_data):
+    user_id = post_data.user_id
+    session_id = post_data.session_id
+    input_message = post_data.message
+    gender = post_data.gender
+
+    final_outputs = agent_executor(
+        {"input": input_message, "gender": gender},
+        callbacks=[OpenAITokenRecordCallbackHandler(), log_handler],
+        session_key=f"buffer:{user_id}:{session_id}",
+    )
+    api_response = {
+        'user_id': user_id,
+        'session_id': session_id,
+        # 'message_id': message_id,
+        # 'create_time': created_time,
+        'input': final_outputs['input'],
+        # 'conversion': messages,
+        'output': final_outputs['output'],
+        # 'gpt_response_time': gpt_response_time,
+        'total_tokens': final_outputs['total_tokens'],
+        'total_cost': final_outputs['total_cost'],
+        'prompt_tokens': final_outputs['prompt_tokens'],
+        'completion_tokens': final_outputs['completion_tokens'],
+        'response_type': final_outputs['response_type']
+    }
+    logging.info(api_response)
+    return api_response

app/service/chat_robot/script/memory/__init__.py

@@ -0,0 +1,3 @@
+from .user_buffer_window import UserConversationBufferWindowMemory
+
+__all__ = ['UserConversationBufferWindowMemory']

app/service/chat_robot/script/memory/user_buffer_window.py

@@ -0,0 +1,93 @@
+import logging
+from typing import Any, Dict, List, Tuple
+import json
+
+import redis
+from redis import Redis
+from langchain.memory import RedisChatMessageHistory
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.schema.messages import BaseMessage, get_buffer_string, HumanMessage, AIMessage
+from langchain.schema.messages import _message_to_dict, messages_from_dict
+from langchain.memory.utils import get_prompt_input_key
+
+from app.core.config import *
+
+
+class UserConversationBufferWindowMemory(BaseChatMemory):
+    """Buffer for storing conversation memory."""
+
+    redis_client: Redis
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    memory_key: str = "history"  #: :meta private:
+    k: int = 5
+
+    @classmethod
+    def from_redis(
+            cls,
+            host: str = REDIS_HOST,
+            port: int = REDIS_PORT,
+            db: int = 3,
+            **kwargs
+    ):
+        redis_client = Redis(host=host, port=port, db=db)
+        try:
+            response = redis_client.ping()
+            if response:
+                print("Connect to redis server successfully.")
+                logging.info("Connect to redis server successfully.")
+            else:
+                print("Fail to connect to redis server")
+                logging.info("Fail to connect to redis server")
+        except redis.RedisError as e:
+            logging.info(f"Error occurs when connecting to redis server: {str(e)}")
+        return cls(redis_client=redis_client, **kwargs)
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any], key: str = "") -> Dict[str, str]:
+        """Return history buffer."""
+        _items: Any = self.redis_client.lrange(key, 0, self.k * 2) if self.k > 0 else []
+        items = [json.loads(m.decode("utf-8")) for m in _items[::-1]]
+        buffer = messages_from_dict(items)
+        if not self.return_messages:
+            buffer = get_buffer_string(
+                buffer,
+                human_prefix=self.human_prefix,
+                ai_prefix=self.ai_prefix,
+            )
+        return {self.memory_key: buffer}
+
+    def _get_input_output(
+            self, inputs: Dict[str, Any], outputs: Dict[str, str]
+    ) -> Tuple[str, str]:
+        if self.input_key is None:
+            prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
+        else:
+            prompt_input_key = self.input_key
+        if self.output_key is None:
+            if len(outputs) != 1:
+                raise ValueError(f"One output key expected, got {outputs.keys()}")
+            output_key = list(outputs.keys())[0]
+        else:
+            output_key = self.output_key
+        return inputs[prompt_input_key], outputs[output_key]
+
+    def add_message(self, key: str, message: BaseMessage) -> None:
+        self.redis_client.lpush(key, json.dumps(_message_to_dict(message)))
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str], key: str = "") -> None:
+        """Save context from this conversation to buffer."""
+        input_str, output_str = self._get_input_output(inputs, outputs)
+        self.add_message(key, HumanMessage(content=input_str))
+        self.add_message(key, AIMessage(content=output_str))
+
+    # def clear(self, key) -> None:
+    #     """Clear memory contents."""
+    #     self.redis_client.delete(key)

app/service/chat_robot/script/prompt.py

@@ -0,0 +1,52 @@
+FASHION_CHAT_BOT_PREFIX = """
+You are a helpful assistant for fashion designers. You can chat with the users or answer their query as much as you can.
+The most crucial aspect is to accurately determine whether the user's inquiry requires a internet search or querying the database.
+Remember your answer should be very precise and the final output answer should not exceed 20 words.
+
+You may encounter the following types of questions:
+1) If the query related to clothing retrieval, you are an agent designed to interact with a SQL database.
+Given an input question, create a syntactically correct mysql query to run, always fetching random data from tables.
+Unless the user specifies a specific number of examples they wish to obtain,always limit your query to at most 4 results.
+Never query for all the columns from a specific table, only ask for the relevant columns given the question.
+You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.
+DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
+If the question does not seem related to the database, just return "I don't know" as the answer.
+
+2) If the query related to current events, you should use internet_search to seek help from the internet.
+
+3) If the query is just casual conversation, engage in the conversation as a fashion designer assistant.
+
+Be careful to use the tools, since you are actually a chat bot. Tools can only be used when essential.
+"""
+
+TOOL_SELECT_SUFFIX = """
+Prior to proceeding, it is essential to carefully assess the question and select the appropriate tools or approach accordingly. 
+For database-related questions, use SQL tools to identify relevant tables and query their schemas.
+The use of online resources should be limited to inquiry pertaining to current subjects.
+"""
+
+SQL_FUNCTIONS_SUFFIX = """ 
+For database-related questions, use SQL tools to identify relevant tables and query their schemas.
+"""
+
+INTERNET_SEARCH_SUFFIX = """
+If the question should be answered using internet search tools, I should seek help from the internet.
+"""
+
+ANSWER_FORMAT_SUFFIX = """
+My final answer are limited to 20 words and be as much precise as possible.
+"""
+
+TOOLS_FUNCTIONS_SUFFIX = (
+    "If the input involves clothing queries,"
+    "I should look at the tables in the database to see what I can query. Then I should query the schema of the most relevant tables."
+    "All SQL statements must use 'ORDER BY RAND()', for example:"
+    "Example Input 1: 'SELECT img_name FROM skirt WHERE opening_type = 'Button' ORDER BY RAND() LIMIT 1'"
+    "Example Input 2: 'SELECT img_name FROM top WHERE sleeve_length = 'Long' AND type = 'Blouse' ORDER BY RAND() LIMIT 2'"
+    "If the input does not involve clothing queries, "
+    "I should engage in conversation as an assistant or search from internet with internet_search tool."
+    "If the database query returns no results, please respond directly with: 'Apologies, I couldn't find any images that match your description. Could you please give me more details about the clothing you're searching for?'"
+    "Upon mentioning words related to 'tutorial' in the input, I should use tutorial_tool "
+)
+
+TUTORIAL_TOOL_RETURN = "Commencing the systematic tutorial guide now."

app/service/chat_robot/script/tools/__init__.py

@@ -0,0 +1,10 @@
+from .sql_tools import (
+    QuerySQLDataBaseTool,
+    InfoSQLDatabaseTool,
+    ListSQLDatabaseTool,
+    QuerySQLCheckerTool
+)
+
+__all__ = [
+    "QuerySQLCheckerTool", "InfoSQLDatabaseTool", "ListSQLDatabaseTool", "QuerySQLDataBaseTool"
+]

app/service/chat_robot/script/tools/sql_tools.py

@@ -0,0 +1,183 @@
+# flake8: noqa
+"""Tools for interacting with a SQL database."""
+from typing import Any, Dict, Optional
+
+from pydantic import BaseModel, Extra, Field, root_validator
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import (
+    AsyncCallbackManagerForToolRun,
+    CallbackManagerForToolRun,
+)
+from langchain.chains.llm import LLMChain
+from langchain.prompts import PromptTemplate
+# from langchain.sql_database import SQLDatabase
+from langchain.utilities import SQLDatabase
+from langchain.tools.base import BaseTool
+from langchain.tools.sql_database.prompt import QUERY_CHECKER
+
+
+class BaseSQLDatabaseTool(BaseModel):
+    """Base tools for interacting with a SQL database."""
+
+    db: SQLDatabase = Field(exclude=True)
+    param_description: str = ""
+
+    # Override BaseTool.Config to appease mypy
+    # See https://github.com/pydantic/pydantic/issues/4173
+    class Config(BaseTool.Config):
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+        extra = Extra.forbid
+
+
+class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for querying a SQL database."""
+
+    name = "sql_db_query"
+    # description = """
+    # Before use this tool, another tool named sql_db_schema must be used first to find the schema of interested tables.
+    # This tool is designed exclusively for generating SELECT queries to retrieve clothing's img_name randomly from a MySQL database.
+    # You should always use ‘order by rand()’ to randomly select data.
+    # If the query is not correct, an error message will be returned.
+    # If an error is returned, rewrite the query, check the query, and try again.
+    # Always limit your query to at most 4 results.
+    # Never query for all the columns from a specific table, only ask for the relevant columns given the question.
+    # You MUST double check your query before executing it.
+    # DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.
+    # """
+
+    description: str = (
+        "The input of this tool is a detailed and correct SQL select query statement, "
+        "and the output is the result of the database, and it can only return up to 4 results."
+        "If the query is not correct, an error message will be returned."
+        "If an error is returned, rewrite the query, check the query, and try again."
+        "If you encounter an issue with Unknown column 'xxxx' in 'field list' or Table 'attribute_retrieval.xxxx' doesn't exist,"
+        "use sql_db_schema to query the correct table fields."
+
+        "Example Input: 'SELECT img_name FROM skirt WHERE opening_type = 'Button' ORDER BY RAND() "
+        "LIMIT 1'"
+        "Example Input 2: 'SELECT img_name FROM top WHERE sleeve_length = 'Long' AND type = 'Blouse' "
+        "order by rand() LIMIT 2'"
+                        )
+
+    def _run(
+            self,
+            query: str,
+            run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Execute the query, return the results or an error message."""
+        result = self.db.run_no_throw(query)
+        return result
+
+    async def _arun(
+            self,
+            query: str,
+            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        raise NotImplementedError("QuerySqlDbTool does not support async")
+
+
+class InfoSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for getting metadata about a SQL database."""
+
+    name = "sql_db_schema"
+    # description = """
+    # The database contains information of lots of fashion items, such as item name, their fashion attributes.
+    # There are five tables covering five fashion categories: top, pants, dress, skirt, and outwear.
+    # Find the most relevant tables with the query, and output the schema of these tables.
+    # """
+
+    description: str = (
+        "Input to this tool is a comma-separated list of tables, output is the schema and sample rows for those tables."
+        "There are eight tables covering eight fashion categories: female_top, female_pants, female_dress,"
+        "female_skirt, female_outwear, male_bottom, male_top, and male_outwear."
+        
+        "Example Input: 'female_outwear, male_top'"
+        )
+
+    def _run(
+            self,
+            table_names: str,
+            run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get the schema for tables in a comma-separated list."""
+        return self.db.get_table_info_no_throw(table_names.split(", "))
+
+    async def _arun(
+            self,
+            table_name: str,
+            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        raise NotImplementedError("SchemaSqlDbTool does not support async")
+
+
+class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
+    """Tool for getting tables names."""
+
+    name = "sql_db_list_tables"
+    description = "Input is an empty string, output is a comma separated list of tables in the database."
+
+    def _run(
+            self,
+            tool_input: str = "",
+            run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Get the schema for a specific table."""
+        return ", ".join(self.db.get_usable_table_names())
+
+    async def _arun(
+            self,
+            tool_input: str = "",
+            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        raise NotImplementedError("ListTablesSqlDbTool does not support async")
+
+
+class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
+    """Use an LLM to check if a query is correct.
+    Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
+
+    template: str = QUERY_CHECKER
+    llm: BaseLanguageModel
+    llm_chain: LLMChain = Field(init=False)
+    name = "sql_db_query_checker"
+    description = (
+        "Use this tools to double check if your query is correct before executing it."
+        "Always use this tools before executing a query with sql_db_query!"
+    )
+
+    @root_validator(pre=True)
+    def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        if "llm_chain" not in values:
+            values["llm_chain"] = LLMChain(
+                llm=values.get("llm"),
+                prompt=PromptTemplate(
+                    template=QUERY_CHECKER,
+                    input_variables=["query", "dialect"]
+                ),
+            )
+
+        if values["llm_chain"].prompt.input_variables != ["dialect", "query"]:
+            # if values["llm_chain"].prompt.input_variables != ["query", "dialect"]:
+            raise ValueError(
+                "LLM chain for QueryCheckerTool must have input variables ['query', 'dialect']"
+            )
+
+        return values
+
+    def _run(
+            self,
+            query: str,
+            run_manager: Optional[CallbackManagerForToolRun] = None,
+    ) -> str:
+        """Use the LLM to check the query."""
+        return self.llm_chain.predict(query=query, dialect=self.db.dialect)
+
+    async def _arun(
+            self,
+            query: str,
+            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> str:
+        return await self.llm_chain.apredict(query=query, dialect=self.db.dialect)

app/service/chat_robot/script/tools/tutorial_tool.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+from langchain.tools.base import BaseTool
+
+from app.service.chat_robot.script.prompt import TUTORIAL_TOOL_RETURN
+
+
+# 处理系统引导教程相关的输入
+class CustomTutorialTool(BaseTool):
+    name = "tutorial_tool"
+
+    description = ("Utilize this tool to retrieve specific statements related to user guidance tutorials."
+                   "Input is an empty string")
+
+    def _run(self, tool_input, **kwargs: Any) -> str:
+        return TUTORIAL_TOOL_RETURN
+
+    async def _arun(self, tool_input, **kwargs: Any) -> str:
+        raise NotImplementedError("CustomTutorialTool does not support async")

app/service/chat_robot/script/utils/__init__.py

@@ -0,0 +1 @@
+from .logger import Logger

app/service/chat_robot/script/utils/logger.py

@@ -0,0 +1,26 @@
+import logging
+from logging import handlers
+
+
+class Logger(object):
+    level_relations = {
+        'debug': logging.DEBUG,
+        'info': logging.INFO,
+        'warning': logging.WARNING,
+        'error': logging.ERROR,
+        'crit': logging.CRITICAL
+    }
+
+    def __init__(self, filename, level='info', when='D', backCount=3,
+                 fmt='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'):
+        self.logger = logging.getLogger(filename)
+        format_str = logging.Formatter(fmt)  # set log format
+        self.logger.setLevel(self.level_relations.get(level))  # set log level
+        sh = logging.StreamHandler()  # output to terminal
+        sh.setFormatter(format_str)  # set format for terminal log
+        th = handlers.TimedRotatingFileHandler(filename=filename, when=when, backupCount=backCount,
+                                               encoding='utf-8')  # log into file
+
+        th.setFormatter(format_str)  # set format for file log
+        self.logger.addHandler(sh)  # output to terminal
+        self.logger.addHandler(th)  # output to file

app/service/prompt_generation/chatgpt_for_translation.py

@@ -0,0 +1,70 @@
+import os
+
+from langchain.chains import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain_core.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, \
+    PromptTemplate
+
+from app.core.config import OPENAI_MODEL, OPENAI_API_KEY
+
+# os.environ["http_proxy"] = "http://127.0.0.1:7890"
+# os.environ["https_proxy"] = "http://127.0.0.1:7890"
+
+
+llm = ChatOpenAI(model_name=OPENAI_MODEL,
+                 openai_api_key=OPENAI_API_KEY,
+                 temperature=0)
+
+
+def translate_to_en(text):
+    template = (
+        """You are a translation expert, proficient in various languages.
+        And can translate various languages into English.
+        Please translate to grammatically correct English regardless of the input language.
+        If the input is in English, check for grammatical errors. If there are no errors, simply output the sentence.
+        If there are grammatical errors, correct them and then output the sentence."""
+    )
+    system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+
+    # 待翻译文本由 Human 角色输入
+    human_template = "User input : {text}"
+    human_message_prompt = HumanMessagePromptTemplate.from_template(input_variables=["text"], template=human_template)
+
+    # 使用 System 和 Human 角色的提示模板构造 ChatPromptTemplate
+    chat_prompt_template = ChatPromptTemplate.from_messages(
+        [system_message_prompt, human_message_prompt]
+    )
+    translate_chain = LLMChain(llm=llm, prompt=chat_prompt_template)
+
+    template = (
+        """
+        Input sentence:
+        {translate}
+        1. Based on the input,adjust the input sentence to make it more suitable for prompts for generating images,
+        ensuring all key nouns or adjectives related to the image are retained.
+        2. Simplify complex sentence structures and clarify ambiguous expressions.
+        3. Only Output the adjusted English sentence.
+
+        Output :
+        """
+    )
+    # "Based on the input sentence, extract key adjectives and nouns.Only Output extracted key words."
+    # 1. Check if the input sentence contains any grammatical errors. If there are errors, please correct them before proceeding.
+
+    prompt_template = PromptTemplate(input_variables=["translate"], template=template)
+    prompt_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+    from langchain.chains import SimpleSequentialChain
+    overall_chain = SimpleSequentialChain(chains=[translate_chain, prompt_chain], verbose=True)
+
+    response = overall_chain.run(text)
+    return response
+
+
+def main():
+    """Main function"""
+    translate_to_en("生成一件运动风格的夹克，带有拉链和口袋，适合休闲穿着")
+
+
+if __name__ == "__main__":
+    main()