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"""
该文件中主要包含三个函数
不具备多线程能力的函数：
1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
具备多线程调用能力的函数
2. predict_no_ui_long_connection：
"""
import json
import os
import re
import time
import traceback
import requests
import random
from loguru import logger
# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件（ ） ， ，
from toolbox import get_conf , update_ui , is_any_api_key , select_api_key , what_keys , clip_history
from toolbox import trimmed_format_exc , is_the_upload_folder , read_one_api_model_name , log_chat
from toolbox import ChatBotWithCookies , have_any_recent_upload_image_files , encode_image
proxies , TIMEOUT_SECONDS , MAX_RETRY , API_ORG , AZURE_CFG_ARRAY = \
get_conf ( ' proxies ' , ' TIMEOUT_SECONDS ' , ' MAX_RETRY ' , ' API_ORG ' , ' AZURE_CFG_ARRAY ' )
timeout_bot_msg = ' [Local Message] Request timeout. Network error. Please check proxy settings in config.py. ' + \
' 网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。 '
def get_full_error ( chunk , stream_response ) :
"""
获取完整的从Openai返回的报错
"""
while True :
try :
chunk + = next ( stream_response )
except :
break
return chunk
def make_multimodal_input ( inputs , image_paths ) :
image_base64_array = [ ]
for image_path in image_paths :
path = os . path . abspath ( image_path )
base64 = encode_image ( path )
inputs = inputs + f ' <br/><br/><div align= " center " ><img src= " file= { path } " base64= " { base64 } " ></div> '
image_base64_array . append ( base64 )
return inputs , image_base64_array
def reverse_base64_from_input ( inputs ) :
# 定义一个正则表达式来匹配 Base64 字符串（假设格式为 base64="<Base64编码>"）
# pattern = re.compile(r'base64="([^"]+)"></div>')
pattern = re . compile ( r ' <br/><br/><div align= " center " ><img[^<>]+base64= " ([^ " ]+) " ></div> ' )
# 使用 findall 方法查找所有匹配的 Base64 字符串
base64_strings = pattern . findall ( inputs )
# 返回反转后的 Base64 字符串列表
return base64_strings
def contain_base64 ( inputs ) :
base64_strings = reverse_base64_from_input ( inputs )
return len ( base64_strings ) > 0
def append_image_if_contain_base64 ( inputs ) :
if not contain_base64 ( inputs ) :
return inputs
else :
image_base64_array = reverse_base64_from_input ( inputs )
pattern = re . compile ( r ' <br/><br/><div align= " center " ><img[^><]+></div> ' )
inputs = re . sub ( pattern , ' ' , inputs )
res = [ ]
res . append ( {
" type " : " text " ,
" text " : inputs
} )
for image_base64 in image_base64_array :
res . append ( {
" type " : " image_url " ,
" image_url " : {
" url " : f " data:image/jpeg;base64, { image_base64 } "
}
} )
return res
def remove_image_if_contain_base64 ( inputs ) :
if not contain_base64 ( inputs ) :
return inputs
else :
pattern = re . compile ( r ' <br/><br/><div align= " center " ><img[^><]+></div> ' )
inputs = re . sub ( pattern , ' ' , inputs )
return inputs
def decode_chunk ( chunk ) :
# 提前读取一些信息 （用于判断异常）
chunk_decoded = chunk . decode ( )
chunkjson = None
has_choices = False
choice_valid = False
has_content = False
has_role = False
try :
chunkjson = json . loads ( chunk_decoded [ 6 : ] )
has_choices = ' choices ' in chunkjson
if has_choices : choice_valid = ( len ( chunkjson [ ' choices ' ] ) > 0 )
if has_choices and choice_valid : has_content = ( " content " in chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] )
if has_content : has_content = ( chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] [ " content " ] is not None )
if has_choices and choice_valid : has_role = " role " in chunkjson [ ' choices ' ] [ 0 ] [ " delta " ]
except :
pass
return chunk_decoded , chunkjson , has_choices , choice_valid , has_content , has_role
from functools import lru_cache
@lru_cache ( maxsize = 32 )
def verify_endpoint ( endpoint ) :
"""
检查endpoint是否可用
"""
if " 你亲手写的api名称 " in endpoint :
raise ValueError ( " Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为: " + endpoint )
return endpoint
def predict_no_ui_long_connection ( inputs : str , llm_kwargs : dict , history : list = [ ] , sys_prompt : str = " " , observe_window : list = None , console_slience : bool = False ) :
"""
发送至chatGPT， ， ，
inputs：
是本次问询的输入
sys_prompt:
系统静默prompt
llm_kwargs：
chatGPT的内部调优参数
history：
是之前的对话列表
observe_window = None：
用于负责跨越线程传递已经输出的部分， ， ：
"""
from request_llms . bridge_all import model_info
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
if model_info [ llm_kwargs [ ' llm_model ' ] ] . get ( ' openai_disable_stream ' , False ) : stream = False
else : stream = True
headers , payload = generate_payload ( inputs , llm_kwargs , history , system_prompt = sys_prompt , stream = stream )
retry = 0
while True :
try :
# make a POST request to the API endpoint, stream=False
endpoint = verify_endpoint ( model_info [ llm_kwargs [ ' llm_model ' ] ] [ ' endpoint ' ] )
response = requests . post ( endpoint , headers = headers , proxies = proxies ,
json = payload , stream = stream , timeout = TIMEOUT_SECONDS ) ; break
except requests . exceptions . ReadTimeout as e :
retry + = 1
traceback . print_exc ( )
if retry > MAX_RETRY : raise TimeoutError
if MAX_RETRY != 0 : logger . error ( f ' 请求超时，正在重试 ( { retry } / { MAX_RETRY } ) …… ' )
if not stream :
# 该分支仅适用于不支持stream的o1模型，
chunkjson = json . loads ( response . content . decode ( ) )
gpt_replying_buffer = chunkjson [ ' choices ' ] [ 0 ] [ " message " ] [ " content " ]
return gpt_replying_buffer
stream_response = response . iter_lines ( )
result = ' '
json_data = None
while True :
try : chunk = next ( stream_response )
except StopIteration :
break
except requests . exceptions . ConnectionError :
chunk = next ( stream_response ) # 失败了，重试一次？再失败就没办法了。
chunk_decoded , chunkjson , has_choices , choice_valid , has_content , has_role = decode_chunk ( chunk )
if len ( chunk_decoded ) == 0 : continue
if not chunk_decoded . startswith ( ' data: ' ) :
error_msg = get_full_error ( chunk , stream_response ) . decode ( )
if " reduce the length " in error_msg :
raise ConnectionAbortedError ( " OpenAI拒绝了请求: " + error_msg )
elif """ type " : " upstream_error " , " param " : " 307 """ in error_msg :
raise ConnectionAbortedError ( " 正常结束， ， ， " )
else :
raise RuntimeError ( " OpenAI拒绝了请求： " + error_msg )
if ( ' data: [DONE] ' in chunk_decoded ) : break # api2d 正常完成
# 提前读取一些信息 （用于判断异常）
if ( has_choices and not choice_valid ) or ( ' OPENROUTER PROCESSING ' in chunk_decoded ) :
# 一些垃圾第三方接口的出现这样的错误，
continue
json_data = chunkjson [ ' choices ' ] [ 0 ]
delta = json_data [ " delta " ]
if len ( delta ) == 0 : break
if ( not has_content ) and has_role : continue
if ( not has_content ) and ( not has_role ) : continue # raise RuntimeError("发现不标准的第三方接口："+delta)
if has_content : # has_role = True/False
result + = delta [ " content " ]
if not console_slience : print ( delta [ " content " ] , end = ' ' )
if observe_window is not None :
# 观测窗，把已经获取的数据显示出去
if len ( observe_window ) > = 1 :
observe_window [ 0 ] + = delta [ " content " ]
# 看门狗，如果超过期限没有喂狗，则终止
if len ( observe_window ) > = 2 :
if ( time . time ( ) - observe_window [ 1 ] ) > watch_dog_patience :
raise RuntimeError ( " 用户取消了程序。 " )
else : raise RuntimeError ( " 意外Json结构： " + delta )
if json_data and json_data [ ' finish_reason ' ] == ' content_filter ' :
raise RuntimeError ( " 由于提问含不合规内容被Azure过滤。 " )
if json_data and json_data [ ' finish_reason ' ] == ' length ' :
raise ConnectionAbortedError ( " 正常结束， ， ， " )
return result
def predict ( inputs : str , llm_kwargs : dict , plugin_kwargs : dict , chatbot : ChatBotWithCookies ,
history : list = [ ] , system_prompt : str = ' ' , stream : bool = True , additional_fn : str = None ) :
"""
发送至chatGPT，
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是chatGPT的内部调优参数
history 是之前的对话列表（ ， ）
chatbot 为WebUI中显示的对话列表， ， ，
additional_fn代表点击的哪个按钮，
"""
from request_llms . bridge_all import model_info
if is_any_api_key ( inputs ) :
chatbot . _cookies [ ' api_key ' ] = inputs
chatbot . append ( ( " 输入已识别为openai的api_key " , what_keys ( inputs ) ) )
yield from update_ui ( chatbot = chatbot , history = history , msg = " api_key已导入 " ) # 刷新界面
return
elif not is_any_api_key ( chatbot . _cookies [ ' api_key ' ] ) :
chatbot . append ( ( inputs , " 缺少api_key。 \n \n 1. 临时解决方案： ， \n \n 2. 长效解决方案： " ) )
yield from update_ui ( chatbot = chatbot , history = history , msg = " 缺少api_key " ) # 刷新界面
return
user_input = inputs
if additional_fn is not None :
from core_functional import handle_core_functionality
inputs , history = handle_core_functionality ( additional_fn , inputs , history , chatbot )
# 多模态模型
has_multimodal_capacity = model_info [ llm_kwargs [ ' llm_model ' ] ] . get ( ' has_multimodal_capacity ' , False )
if has_multimodal_capacity :
has_recent_image_upload , image_paths = have_any_recent_upload_image_files ( chatbot , pop = True )
else :
has_recent_image_upload , image_paths = False , [ ]
if has_recent_image_upload :
_inputs , image_base64_array = make_multimodal_input ( inputs , image_paths )
else :
_inputs , image_base64_array = inputs , [ ]
chatbot . append ( ( _inputs , " " ) )
yield from update_ui ( chatbot = chatbot , history = history , msg = " 等待响应 " ) # 刷新界面
# 禁用stream的特殊模型处理
if model_info [ llm_kwargs [ ' llm_model ' ] ] . get ( ' openai_disable_stream ' , False ) : stream = False
else : stream = True
# check mis-behavior
if is_the_upload_folder ( user_input ) :
chatbot [ - 1 ] = ( inputs , f " [Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。 " )
yield from update_ui ( chatbot = chatbot , history = history , msg = " 正常 " ) # 刷新界面
time . sleep ( 2 )
try :
headers , payload = generate_payload ( inputs , llm_kwargs , history , system_prompt , image_base64_array , has_multimodal_capacity , stream )
except RuntimeError as e :
chatbot [ - 1 ] = ( inputs , f " 您提供的api-key不满足要求， { llm_kwargs [ ' llm_model ' ] } 的api-key。您可能选择了错误的模型或请求源。 " )
yield from update_ui ( chatbot = chatbot , history = history , msg = " api-key不满足要求 " ) # 刷新界面
return
# 检查endpoint是否合法
try :
endpoint = verify_endpoint ( model_info [ llm_kwargs [ ' llm_model ' ] ] [ ' endpoint ' ] )
except :
tb_str = ' ``` \n ' + trimmed_format_exc ( ) + ' ``` '
chatbot [ - 1 ] = ( inputs , tb_str )
yield from update_ui ( chatbot = chatbot , history = history , msg = " Endpoint不满足要求 " ) # 刷新界面
return
# 加入历史
if has_recent_image_upload :
history . extend ( [ _inputs , " " ] )
else :
history . extend ( [ inputs , " " ] )
retry = 0
while True :
try :
# make a POST request to the API endpoint, stream=True
response = requests . post ( endpoint , headers = headers , proxies = proxies ,
json = payload , stream = stream , timeout = TIMEOUT_SECONDS ) ; break
except :
retry + = 1
chatbot [ - 1 ] = ( ( chatbot [ - 1 ] [ 0 ] , timeout_bot_msg ) )
retry_msg = f " ，正在重试 ( { retry } / { MAX_RETRY } ) …… " if MAX_RETRY > 0 else " "
yield from update_ui ( chatbot = chatbot , history = history , msg = " 请求超时 " + retry_msg ) # 刷新界面
if retry > MAX_RETRY : raise TimeoutError
if not stream :
# 该分支仅适用于不支持stream的o1模型，
yield from handle_o1_model_special ( response , inputs , llm_kwargs , chatbot , history )
return
if stream :
gpt_replying_buffer = " "
is_head_of_the_stream = True
stream_response = response . iter_lines ( )
while True :
try :
chunk = next ( stream_response )
except StopIteration :
# 非OpenAI官方接口的出现这样的报错，
chunk_decoded = chunk . decode ( )
error_msg = chunk_decoded
# 首先排除一个one-api没有done数据包的第三方Bug情形
if len ( gpt_replying_buffer . strip ( ) ) > 0 and len ( error_msg ) == 0 :
yield from update_ui ( chatbot = chatbot , history = history , msg = " 检测到有缺陷的非OpenAI官方接口， " )
break
# 其他情况，直接返回报错
chatbot , history = handle_error ( inputs , llm_kwargs , chatbot , history , chunk_decoded , error_msg )
yield from update_ui ( chatbot = chatbot , history = history , msg = " 非OpenAI官方接口返回了错误: " + chunk . decode ( ) ) # 刷新界面
return
# 提前读取一些信息 （用于判断异常）
chunk_decoded , chunkjson , has_choices , choice_valid , has_content , has_role = decode_chunk ( chunk )
if is_head_of_the_stream and ( r ' " object " : " error " ' not in chunk_decoded ) and ( r " content " not in chunk_decoded ) :
# 数据流的第一帧不携带content
is_head_of_the_stream = False ; continue
if chunk :
try :
if ( has_choices and not choice_valid ) or ( ' OPENROUTER PROCESSING ' in chunk_decoded ) :
# 一些垃圾第三方接口的出现这样的错误, 或者OPENROUTER的特殊处理,因为OPENROUTER的数据流未连接到模型时会出现OPENROUTER PROCESSING
continue
if ( ' data: [DONE] ' not in chunk_decoded ) and len ( chunk_decoded ) > 0 and ( chunkjson is None ) :
# 传递进来一些奇怪的东西
raise ValueError ( f ' 无法读取以下数据，请检查配置。 \n \n { chunk_decoded } ' )
# 前者是API2D的结束条件，
if ( ' data: [DONE] ' in chunk_decoded ) or ( len ( chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] ) == 0 ) :
# 判定为数据流的结束，
log_chat ( llm_model = llm_kwargs [ " llm_model " ] , input_str = inputs , output_str = gpt_replying_buffer )
break
# 处理数据流的主体
status_text = f " finish_reason: { chunkjson [ ' choices ' ] [ 0 ] . get ( ' finish_reason ' , ' null ' ) } "
# 如果这里抛出异常， ，
if has_content :
# 正常情况
gpt_replying_buffer = gpt_replying_buffer + chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] [ " content " ]
elif has_role :
# 一些第三方接口的出现这样的错误，兼容一下吧
continue
else :
# 至此已经超出了正常接口应该进入的范围，一些垃圾第三方接口会出现这样的错误
if chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] [ " content " ] is None : continue # 一些垃圾第三方接口出现这样的错误，兼容一下吧
gpt_replying_buffer = gpt_replying_buffer + chunkjson [ ' choices ' ] [ 0 ] [ " delta " ] [ " content " ]
history [ - 1 ] = gpt_replying_buffer
chatbot [ - 1 ] = ( history [ - 2 ] , history [ - 1 ] )
yield from update_ui ( chatbot = chatbot , history = history , msg = status_text ) # 刷新界面
except Exception as e :
yield from update_ui ( chatbot = chatbot , history = history , msg = " Json解析不合常规 " ) # 刷新界面
chunk = get_full_error ( chunk , stream_response )
chunk_decoded = chunk . decode ( )
error_msg = chunk_decoded
chatbot , history = handle_error ( inputs , llm_kwargs , chatbot , history , chunk_decoded , error_msg )
yield from update_ui ( chatbot = chatbot , history = history , msg = " Json解析异常 " + error_msg ) # 刷新界面
logger . error ( error_msg )
return
return # return from stream-branch
def handle_o1_model_special ( response , inputs , llm_kwargs , chatbot , history ) :
try :
chunkjson = json . loads ( response . content . decode ( ) )
gpt_replying_buffer = chunkjson [ ' choices ' ] [ 0 ] [ " message " ] [ " content " ]
log_chat ( llm_model = llm_kwargs [ " llm_model " ] , input_str = inputs , output_str = gpt_replying_buffer )
history [ - 1 ] = gpt_replying_buffer
chatbot [ - 1 ] = ( history [ - 2 ] , history [ - 1 ] )
yield from update_ui ( chatbot = chatbot , history = history ) # 刷新界面
except Exception as e :
yield from update_ui ( chatbot = chatbot , history = history , msg = " Json解析异常 " + response . text ) # 刷新界面
def handle_error ( inputs , llm_kwargs , chatbot , history , chunk_decoded , error_msg ) :
from request_llms . bridge_all import model_info
openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup '
if " reduce the length " in error_msg :
if len ( history ) > = 2 : history [ - 1 ] = " " ; history [ - 2 ] = " " # 清除当前溢出的输入：
history = clip_history ( inputs = inputs , history = history , tokenizer = model_info [ llm_kwargs [ ' llm_model ' ] ] [ ' tokenizer ' ] ,
max_token_limit = ( model_info [ llm_kwargs [ ' llm_model ' ] ] [ ' max_token ' ] ) ) # history至少释放二分之一
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.) " )
elif " does not exist " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , f " [Local Message] Model { llm_kwargs [ ' llm_model ' ] } does not exist. 模型不存在, 或者您没有获得体验资格. " )
elif " Incorrect API key " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website )
elif " exceeded your current quota " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务. " + openai_website )
elif " account is not active " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务. " + openai_website )
elif " associated with a deactivated account " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务. " + openai_website )
elif " API key has been deactivated " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务. " + openai_website )
elif " bad forward key " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] Bad forward key. API2D账户额度不足. " )
elif " Not enough point " in error_msg :
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , " [Local Message] Not enough point. API2D账户点数不足. " )
else :
from toolbox import regular_txt_to_markdown
tb_str = ' ``` \n ' + trimmed_format_exc ( ) + ' ``` '
chatbot [ - 1 ] = ( chatbot [ - 1 ] [ 0 ] , f " [Local Message] 异常 \n \n { tb_str } \n \n { regular_txt_to_markdown ( chunk_decoded ) } " )
return chatbot , history
def generate_payload ( inputs : str , llm_kwargs : dict , history : list , system_prompt : str , image_base64_array : list = [ ] , has_multimodal_capacity : bool = False , stream : bool = True ) :
"""
整合所有信息， ， ，
"""
from request_llms . bridge_all import model_info
if not is_any_api_key ( llm_kwargs [ ' api_key ' ] ) :
raise AssertionError ( " 你提供了错误的API_KEY。 \n \n 1. 临时解决方案： ， \n \n 2. 长效解决方案： " )
if llm_kwargs [ ' llm_model ' ] . startswith ( ' vllm- ' ) :
api_key = ' no-api-key '
else :
api_key = select_api_key ( llm_kwargs [ ' api_key ' ] , llm_kwargs [ ' llm_model ' ] )
headers = {
" Content-Type " : " application/json " ,
" Authorization " : f " Bearer { api_key } "
}
if API_ORG . startswith ( ' org- ' ) : headers . update ( { " OpenAI-Organization " : API_ORG } )
if llm_kwargs [ ' llm_model ' ] . startswith ( ' azure- ' ) :
headers . update ( { " api-key " : api_key } )
if llm_kwargs [ ' llm_model ' ] in AZURE_CFG_ARRAY . keys ( ) :
azure_api_key_unshared = AZURE_CFG_ARRAY [ llm_kwargs [ ' llm_model ' ] ] [ " AZURE_API_KEY " ]
headers . update ( { " api-key " : azure_api_key_unshared } )
if has_multimodal_capacity :
# 当以下条件满足时，启用多模态能力：
# 1. 模型本身是多模态模型（ ）
# 2. 输入包含图像（ ）
# 3. 历史输入包含图像（ any([contain_base64(h) for h in history]) ）
enable_multimodal_capacity = ( len ( image_base64_array ) > 0 ) or any ( [ contain_base64 ( h ) for h in history ] )
else :
enable_multimodal_capacity = False
conversation_cnt = len ( history ) / / 2
openai_disable_system_prompt = model_info [ llm_kwargs [ ' llm_model ' ] ] . get ( ' openai_disable_system_prompt ' , False )
if openai_disable_system_prompt :
messages = [ { " role " : " user " , " content " : system_prompt } ]
else :
messages = [ { " role " : " system " , " content " : system_prompt } ]
if not enable_multimodal_capacity :
# 不使用多模态能力
if conversation_cnt :
for index in range ( 0 , 2 * conversation_cnt , 2 ) :
what_i_have_asked = { }
what_i_have_asked [ " role " ] = " user "
what_i_have_asked [ " content " ] = remove_image_if_contain_base64 ( history [ index ] )
what_gpt_answer = { }
what_gpt_answer [ " role " ] = " assistant "
what_gpt_answer [ " content " ] = remove_image_if_contain_base64 ( history [ index + 1 ] )
if what_i_have_asked [ " content " ] != " " :
if what_gpt_answer [ " content " ] == " " : continue
if what_gpt_answer [ " content " ] == timeout_bot_msg : continue
messages . append ( what_i_have_asked )
messages . append ( what_gpt_answer )
else :
messages [ - 1 ] [ ' content ' ] = what_gpt_answer [ ' content ' ]
what_i_ask_now = { }
what_i_ask_now [ " role " ] = " user "
what_i_ask_now [ " content " ] = inputs
messages . append ( what_i_ask_now )
else :
# 多模态能力
if conversation_cnt :
for index in range ( 0 , 2 * conversation_cnt , 2 ) :
what_i_have_asked = { }
what_i_have_asked [ " role " ] = " user "
what_i_have_asked [ " content " ] = append_image_if_contain_base64 ( history [ index ] )
what_gpt_answer = { }
what_gpt_answer [ " role " ] = " assistant "
what_gpt_answer [ " content " ] = append_image_if_contain_base64 ( history [ index + 1 ] )
if what_i_have_asked [ " content " ] != " " :
if what_gpt_answer [ " content " ] == " " : continue
if what_gpt_answer [ " content " ] == timeout_bot_msg : continue
messages . append ( what_i_have_asked )
messages . append ( what_gpt_answer )
else :
messages [ - 1 ] [ ' content ' ] = what_gpt_answer [ ' content ' ]
what_i_ask_now = { }
what_i_ask_now [ " role " ] = " user "
what_i_ask_now [ " content " ] = [ ]
what_i_ask_now [ " content " ] . append ( {
" type " : " text " ,
" text " : inputs
} )
for image_base64 in image_base64_array :
what_i_ask_now [ " content " ] . append ( {
" type " : " image_url " ,
" image_url " : {
" url " : f " data:image/jpeg;base64, { image_base64 } "
}
} )
messages . append ( what_i_ask_now )
model = llm_kwargs [ ' llm_model ' ]
if llm_kwargs [ ' llm_model ' ] . startswith ( ' api2d- ' ) :
model = llm_kwargs [ ' llm_model ' ] [ len ( ' api2d- ' ) : ]
if llm_kwargs [ ' llm_model ' ] . startswith ( ' one-api- ' ) :
model = llm_kwargs [ ' llm_model ' ] [ len ( ' one-api- ' ) : ]
model , _ = read_one_api_model_name ( model )
if llm_kwargs [ ' llm_model ' ] . startswith ( ' vllm- ' ) :
model = llm_kwargs [ ' llm_model ' ] [ len ( ' vllm- ' ) : ]
model , _ = read_one_api_model_name ( model )
if llm_kwargs [ ' llm_model ' ] . startswith ( ' openrouter- ' ) :
model = llm_kwargs [ ' llm_model ' ] [ len ( ' openrouter- ' ) : ]
model = read_one_api_model_name ( model )
if model == " gpt-3.5-random " : # 随机选择, 绕过openai访问频率限制
model = random . choice ( [
" gpt-3.5-turbo " ,
" gpt-3.5-turbo-16k " ,
" gpt-3.5-turbo-1106 " ,
" gpt-3.5-turbo-0613 " ,
" gpt-3.5-turbo-16k-0613 " ,
" gpt-3.5-turbo-0301 " ,
] )
payload = {
" model " : model ,
" messages " : messages ,
" temperature " : llm_kwargs [ ' temperature ' ] , # 1.0,
" top_p " : llm_kwargs [ ' top_p ' ] , # 1.0,
" n " : 1 ,
" stream " : stream ,
}
return headers , payload
binary-husky