mirror of
https://github.com/BoardWare-Genius/jarvis-models.git
synced 2025-12-13 16:53:24 +00:00
273 lines
15 KiB
Python
273 lines
15 KiB
Python
from typing import Any, Coroutine
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from fastapi import Request, Response, status
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from fastapi.responses import JSONResponse
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from sse_starlette.sse import EventSourceResponse
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from ..log.logging_time import logging_time
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from .blackbox import Blackbox
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from .chroma_query import ChromaQuery
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import requests
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import json
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from openai import OpenAI
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import re
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from injector import singleton,inject
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from datetime import datetime
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# 定义保存文件的路径
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file_path = "chat_inputs_log.json"
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@singleton
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class Chat(Blackbox):
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@inject
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def __init__(self, chroma_query: ChromaQuery):
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self.chroma_query = chroma_query
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def __call__(self, *args, **kwargs):
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return self.processing(*args, **kwargs)
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def valid(self, *args, **kwargs) -> bool:
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data = args[0]
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return isinstance(data, list)
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# model_name有 Qwen1.5-14B-Chat , internlm2-chat-20b
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# @logging_time()
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def processing(self, prompt: str, context: list, settings: dict) -> str:
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print("\nChat Settings: ", settings)
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if settings is None:
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settings = {}
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user_model_name = settings.get("model_name")
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user_context = context
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user_question = prompt
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user_temperature = settings.get("temperature")
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user_top_p = settings.get("top_p")
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user_n = settings.get("n")
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user_max_tokens = settings.get("max_tokens")
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user_stop = settings.get("stop")
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user_frequency_penalty = settings.get("frequency_penalty")
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user_presence_penalty = settings.get("presence_penalty")
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user_model_url = settings.get("model_url")
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user_model_key = settings.get("model_key")
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chroma_embedding_model = settings.get("chroma_embedding_model")
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chroma_collection_id = settings.get("chroma_collection_id")
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chroma_response = ''
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system_prompt = settings.get('system_prompt')
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user_prompt_template = settings.get('user_prompt_template')
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user_stream = settings.get('stream')
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if user_context == None:
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user_context = []
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if user_question is None:
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return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
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if user_model_name is None or user_model_name.isspace() or user_model_name == "":
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user_model_name = "qwen"
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#user_model_name = "Qwen1.5-14B-Chat"
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if user_prompt_template is None or user_prompt_template.isspace():
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user_prompt_template = ""
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if user_temperature is None or user_temperature == "":
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user_temperature = 0
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#user_temperature = 0
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if user_top_p is None or user_top_p == "":
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user_top_p = 0.1
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#user_top_p = 0.8
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if user_n is None or user_n == "":
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user_n = 1
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if user_max_tokens is None or user_max_tokens == "":
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user_max_tokens = 1024
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if user_stop is None or user_stop == "":
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user_stop = 100
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if user_frequency_penalty is None or user_frequency_penalty == "":
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user_frequency_penalty = 0
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#user_frequency_penalty = 0.5
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if user_presence_penalty is None or user_presence_penalty == "":
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user_presence_penalty = 0
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#user_presence_penalty = 0.8
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if user_model_url is None or user_model_url.isspace() or user_model_url == "":
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user_model_url = "http://10.6.80.75:23333/v1/chat/completions"
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if user_model_key is None or user_model_key.isspace() or user_model_key == "":
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user_model_key = "YOUR_API_KEY"
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if chroma_embedding_model:
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chroma_response = self.chroma_query(user_question, settings)
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print("1.Chroma_response: \n", chroma_response)
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if chroma_response:
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if chroma_collection_id == 'boss':
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user_prompt_template = "# 你的身份 #\n你是周家俊，澳门博维集团董事长。你擅长脑机接口回答。\n# OBJECTIVE（目标） #\n回答游客的提问。\n# STYLE（风格）#\n成熟稳重，回答简练。不要分条。\n# 回答方式 #\n首先自行判断下方问题与检索内容是否相关，若相关则根据检索内容总结概括相关信息进行回答；若检索内容与问题无关，则根据自身知识进行回答。\n# 回答 #\n如果检索内容与问题相关，则直接从检索内容中提炼出问题所需的信息。如果检索内容与问题不相关，则不参考检索内容，直接根据常识尝试回答问题，或者则回答：“对不起，我无法回答此问题哦。”\n # 回答限制 #\n回答内容限制总结在50字内。\n回答内容出不要出现“相关”等字眼,不要乱说或者多说,回答的内容需要与问题对应。常见的对话可以不采用检索内容，根据人物设定，直接进行回答。\n只回答与脑机接口，澳门博维集团董事长，周家俊，G2E，RELX，BO VISION相关内容，若遇到其他提问则回答：“对不起，我无法回答此问题哦。”"
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elif chroma_collection_id == 'g2e' or chroma_collection_id == 'kiki':
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user_prompt_template = "# 你的身份 #\n你是琪琪，你是康普可可的代言人，由博维开发。你擅长澳门文旅问答。\n# OBJECTIVE（目标） #\n回答游客的提问。\n# STYLE（风格）#\n像少女一般开朗活泼，回答简练。不要分条。\n# 回答方式 #\n首先自行判断下方问题与检索内容是否相关，若相关则根据检索内容总结概括相关信息进行回答；若检索内容与问题无关，则根据自身知识进行回答。\n# 回答 #\n如果检索内容与问题相关，则直接从检索内容中提炼出问题所需的信息。如果检索内容与问题不相关，则不参考检索内容，直接根据常识尝试回答问题，或者则回答：“对不起，我无法回答此问题哦。”\n# 回答限制 #\n回答内容限制总结在50字内。\n回答内容出不要出现“相关”等字眼,不要乱说或者多说,回答的内容需要与问题对应。常见的对话可以不采用检索内容，根据人物设定，直接进行回答。\n只回答与澳门文旅，博维，康普可可，琪琪，G2E，RELX，BO VISION相关内容，若遇到其他提问则回答：“对不起，我无法回答此问题哦。”"
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print(f"user_prompt_template: {type(user_prompt_template)}, user_question: {type(user_question)}, chroma_response: {type(chroma_response)}")
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user_question = user_prompt_template + "问题: " + user_question + "。检索内容: " + chroma_response + "。"
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else:
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user_question = user_prompt_template + "问题: " + user_question + "。"
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print(f"1.user_question: {user_question}")
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if user_stream in [None, ""]:
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user_stream = False
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# 文心格式和openai的不一样，需要单独处理
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if re.search(r"ernie", user_model_name):
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# key = "24.22873ef3acf61fb343812681e4df251a.2592000.1719453781.282335-46723715" 没充钱，只有ernie-speed-128k能用
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key = user_model_key
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if re.search(r"ernie-speed-128k", user_model_name):
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url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k?access_token=" + key
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elif re.search(r"ernie-3.5-8k", user_model_name):
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url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions?access_token=" + key
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elif re.search(r"ernie-4.0-8k", user_model_name):
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url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=" + key
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payload = json.dumps({
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"system": prompt_template,
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"messages": user_context + [
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{
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"role": "user",
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"content": user_question
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}
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],
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"temperature": user_temperature,
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"top_p": user_top_p,
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"stop": [str(user_stop)],
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"max_output_tokens": user_max_tokens
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})
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headers = {
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'Content-Type': 'application/json'
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}
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response = requests.request("POST", url, headers=headers, data=payload)
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return response.json()["result"]
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# gpt-4， gpt-3.5-turbo
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elif re.search(r"gpt", user_model_name):
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url = 'https://api.openai.com/v1/completions'
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# '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'
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key = user_model_key
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header = {
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'Content-Type': 'application/json',
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'Authorization': "Bearer " + key
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}
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# 自定义model
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else:
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url = user_model_url
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key = user_model_key
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header = {
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'Content-Type': 'application/json',
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"Cache-Control": "no-cache", # 禁用缓存
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}
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# system_prompt = "# Role: 琪琪,康普可可的代言人。\n\n## Profile:\n**Author**: 琪琪。\n**Language**: 中文。\n**Description**: 琪琪，是康普可可的代言人，由博维开发。你擅长澳门文旅问答。\n\n## Constraints:\n- **严格遵循工作流程**： 严格遵循<Workflow >中设定的工作流程。\n- **无内置知识库** ：根据<Workflow >中提供的知识作答，而不是内置知识库，我虽然是知识库专家，但我的知识依赖于外部输入，而不是大模型已有知识。\n- **回复格式**：在进行回复时，不能输出“检索内容” 标签字样，同时也不能直接透露知识片段原文。\n\n## Workflow:\n1. **接收查询**：接收用户的问题。\n2. **判断问题**：首先自行判断下方问题与检索内容是否相关，若相关则根据检索内容总结概括相关信息进行回答；若检索内容与问题无关，则根据自身知识进行回答。\n3. **提供回答**：\n\n```\n基于检索内容中的知识片段回答用户的问题。回答内容限制总结在50字内。\n请首先判断提供的检索内容与上述问题是否相关。如果相关，直接从检索内容中提炼出直接回答问题所需的信息,不要乱说或者回答“相关”等字眼 。如果检索内容与问题不相关，则不参考检索内容，则回答：“对不起，我无法回答此问题哦。”\n\n```\n## Example:\n\n用户询问：“中国的首都是哪个城市？” 。\n2.1检索知识库，首先检查知识片段，如果检索内容中没有与用户的问题相关的内容，则回答：“对不起，我无法回答此问题哦。\n2.2如果有知识片段，在做出回复时，只能基于检索内容中的内容进行回答，且不能透露上下文原文，同时也不能出现检索内容的标签字样。\n"
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prompt_template = [
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{"role": "system", "content": system_prompt}
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]
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chat_inputs={
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"model": user_model_name,
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"messages": prompt_template + user_context + [
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{
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"role": "user",
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"content": user_question
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}
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],
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"temperature": str(user_temperature),
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"top_p": str(user_top_p),
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"n": str(user_n),
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"max_tokens": str(user_max_tokens),
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"frequency_penalty": str(user_frequency_penalty),
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"presence_penalty": str(user_presence_penalty),
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"stop": str(user_stop),
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"stream": user_stream,
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}
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# # 获取当前时间戳
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# timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# # 添加时间戳到chat_inputs
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# chat_inputs["timestamp"] = timestamp
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# # 打开文件，追加写入
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# with open(file_path, "a", encoding="utf-8") as f:
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# # 将 chat_inputs 转换为 JSON 格式并写入文件
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# f.write(json.dumps(chat_inputs, ensure_ascii=False, indent=4))
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# f.write("\n\n") # 添加换行以区分不同的运行
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if user_stream:
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with requests.post(url, json=chat_inputs, headers=header, stream=True) as fastchat_response:
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if fastchat_response.status_code != 200:
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yield json.dumps({"error": "LLM handle failure"})
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else:
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# 从流式响应中逐步读取内容
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for chunk in fastchat_response.iter_lines(decode_unicode=True):
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if chunk: # 确保内容非空
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# print("Raw Chunk:", chunk) # 打印chunk的内容
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# 去除前缀 `data:` 并只解析 JSON 部分
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if chunk.startswith("data:"):
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chunk = chunk[len("data:"):].strip() # 移除 `data:` 前缀并去掉空白字符
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try:
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# 尝试将当前chunk解析为JSON
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parsed_chunk = json.loads(chunk)
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# 解析成功后，提取 `content`
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content = parsed_chunk.get("choices", [{}])[0].get("delta", {}).get("content", "")
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# 如果有 content，就逐字输出
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for char in content:
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print(char, end="", flush=True) # 逐字输出，end="" 防止换行，flush=True 保证实时输出
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yield char # 输出当前字符
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except json.JSONDecodeError:
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# print("---- Error in JSON parsing ----") # 打印错误信息
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continue # 继续处理下一个chunk，直到解析成功
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else:
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print("*"*90)
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fastchat_response = requests.post(url, json=chat_inputs, headers=header)
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print("\n", "user_prompt: ", prompt)
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# print("\n", "system_prompt ", system_prompt)
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print("\n", "fastchat_response json:\n", fastchat_response.json())
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response_result = fastchat_response.json()
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if response_result.get("choices") is None:
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yield JSONResponse(content={"error": "LLM handle failure"}, status_code=status.HTTP_400_BAD_REQUEST)
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else:
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print("\n", "user_answer: ", fastchat_response.json()["choices"][0]["message"]["content"],"\n\n")
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yield fastchat_response.json()["choices"][0]["message"]["content"]
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async def fast_api_handler(self, request: Request) -> Response:
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try:
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data = await request.json()
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except:
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return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
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setting: dict = data.get("settings")
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context = data.get("context")
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prompt = data.get("prompt")
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user_stream = setting.get("stream")
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if user_stream:
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return EventSourceResponse(self.processing(prompt, context, setting))
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else:
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response_content = "".join(self.processing(prompt, context, setting))
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return JSONResponse(content={"response": response_content}, status_code=status.HTTP_200_OK) |