镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
Merge branch 'master' of github.com:binary-husky/chatgpt_academic
这个提交包含在：
binary-husky
@@ -43,7 +43,8 @@ AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024-
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"gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
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"gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
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"gemini-1.5-pro", "chatglm3", "chatglm4",
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"deepseek-chat", "deepseek-coder", "deepseek-reasoner"
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"deepseek-chat", "deepseek-coder", "deepseek-reasoner",
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"volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226",
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]
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EMBEDDING_MODEL = "text-embedding-3-small"
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@@ -267,6 +268,10 @@ MOONSHOT_API_KEY = ""
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YIMODEL_API_KEY = ""
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# 接入火山引擎的在线大模型)，api-key获取地址 https://console.volcengine.com/ark/region:ark+cn-beijing/endpoint
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ARK_API_KEY = "00000000-0000-0000-0000-000000000000" # 火山引擎 API KEY
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# 紫东太初大模型 https://ai-maas.wair.ac.cn
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TAICHU_API_KEY = ""
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@@ -80,6 +80,7 @@ ollama_endpoint = "http://localhost:11434/api/chat"
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yimodel_endpoint = "https://api.lingyiwanwu.com/v1/chat/completions"
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deepseekapi_endpoint = "https://api.deepseek.com/v1/chat/completions"
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grok_model_endpoint = "https://api.x.ai/v1/chat/completions"
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volcengine_endpoint = "https://ark.cn-beijing.volces.com/api/v3/chat/completions"
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if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
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azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
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@@ -102,6 +103,7 @@ if ollama_endpoint in API_URL_REDIRECT: ollama_endpoint = API_URL_REDIRECT[ollam
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if yimodel_endpoint in API_URL_REDIRECT: yimodel_endpoint = API_URL_REDIRECT[yimodel_endpoint]
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if deepseekapi_endpoint in API_URL_REDIRECT: deepseekapi_endpoint = API_URL_REDIRECT[deepseekapi_endpoint]
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if grok_model_endpoint in API_URL_REDIRECT: grok_model_endpoint = API_URL_REDIRECT[grok_model_endpoint]
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if volcengine_endpoint in API_URL_REDIRECT: volcengine_endpoint = API_URL_REDIRECT[volcengine_endpoint]
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# 获取tokenizer
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tokenizer_gpt35 = LazyloadTiktoken("gpt-3.5-turbo")
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@@ -1089,8 +1091,10 @@ if "deepseekcoder" in AVAIL_LLM_MODELS: # deepseekcoder
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})
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except:
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logger.error(trimmed_format_exc())
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# -=-=-=-=-=-=- 幻方-深度求索大模型在线API -=-=-=-=-=-=-
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if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS or "deepseek-reasoner" in AVAIL_LLM_MODELS:
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claude_models = ["deepseek-chat", "deepseek-coder", "deepseek-reasoner"]
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if any(item in claude_models for item in AVAIL_LLM_MODELS):
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try:
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deepseekapi_noui, deepseekapi_ui = get_predict_function(
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api_key_conf_name="DEEPSEEK_API_KEY", max_output_token=4096, disable_proxy=False
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@@ -1127,6 +1131,60 @@ if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS o
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})
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except:
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logger.error(trimmed_format_exc())
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# -=-=-=-=-=-=- 火山引擎 对齐支持 -=-=-=-=-=-=-
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for model in [m for m in AVAIL_LLM_MODELS if m.startswith("volcengine-")]:
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# 为了更灵活地接入volcengine多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["volcengine-deepseek-r1-250120(max_token=6666)"]
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# 其中
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# "volcengine-" 是前缀（必要）
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# "deepseek-r1-250120" 是模型名（必要）
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# "(max_token=6666)" 是配置（非必要）
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model_info_extend = model_info
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model_info_extend.update({
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"deepseek-r1-250120": {
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"max_token": 16384,
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"enable_reasoning": True,
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"can_multi_thread": True,
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"endpoint": volcengine_endpoint,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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"deepseek-v3-241226": {
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"max_token": 16384,
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"enable_reasoning": False,
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"can_multi_thread": True,
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"endpoint": volcengine_endpoint,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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},
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})
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try:
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origin_model_name, max_token_tmp = read_one_api_model_name(model)
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# 如果是已知模型，则尝试获取其信息
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original_model_info = model_info_extend.get(origin_model_name.replace("volcengine-", "", 1), None)
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except:
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logger.error(f"volcengine模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
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continue
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volcengine_noui, volcengine_ui = get_predict_function(api_key_conf_name="ARK_API_KEY", max_output_token=8192, disable_proxy=True, model_remove_prefix = ["volcengine-"])
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this_model_info = {
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"fn_with_ui": volcengine_ui,
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"fn_without_ui": volcengine_noui,
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"endpoint": volcengine_endpoint,
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"can_multi_thread": True,
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"max_token": 64000,
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"tokenizer": tokenizer_gpt35,
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"token_cnt": get_token_num_gpt35,
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}
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# 同步已知模型的其他信息
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attribute = "has_multimodal_capacity"
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if original_model_info is not None and original_model_info.get(attribute, None) is not None: this_model_info.update({attribute: original_model_info.get(attribute, None)})
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attribute = "enable_reasoning"
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if original_model_info is not None and original_model_info.get(attribute, None) is not None: this_model_info.update({attribute: original_model_info.get(attribute, None)})
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model_info.update({model: this_model_info})
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# -=-=-=-=-=-=- one-api 对齐支持 -=-=-=-=-=-=-
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for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
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# 为了更灵活地接入one-api多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["one-api-mixtral-8x7b(max_token=6666)"]
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@@ -57,7 +57,7 @@ def decode_chunk(chunk):
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finish_reason = chunk["error"]["code"]
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except:
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finish_reason = "API_ERROR"
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return response, reasoning_content, finish_reason
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return response, reasoning_content, finish_reason, str(chunk)
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try:
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if chunk["choices"][0]["delta"]["content"] is not None:
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@@ -122,7 +122,8 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
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def get_predict_function(
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api_key_conf_name,
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max_output_token,
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disable_proxy = False
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disable_proxy = False,
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model_remove_prefix = [],
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):
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"""
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为openai格式的API生成响应函数，其中传入参数：
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@@ -137,6 +138,16 @@ def get_predict_function(
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APIKEY = get_conf(api_key_conf_name)
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def remove_prefix(model_name):
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# 去除模型名字的前缀，输入 volcengine-deepseek-r1-250120 会返回 deepseek-r1-250120
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if not model_remove_prefix:
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return model_name
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model_without_prefix = model_name
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for prefix in model_remove_prefix:
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if model_without_prefix.startswith(prefix):
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model_without_prefix = model_without_prefix[len(prefix):]
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return model_without_prefix
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def predict_no_ui_long_connection(
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inputs,
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llm_kwargs,
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@@ -164,9 +175,11 @@ def get_predict_function(
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raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
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if inputs == "":
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inputs = "你好👋"
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headers, payload = generate_message(
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input=inputs,
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model=llm_kwargs["llm_model"],
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model=remove_prefix(llm_kwargs["llm_model"]),
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key=APIKEY,
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history=history,
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max_output_token=max_output_token,
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@@ -302,7 +315,7 @@ def get_predict_function(
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headers, payload = generate_message(
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input=inputs,
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model=llm_kwargs["llm_model"],
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model=remove_prefix(llm_kwargs["llm_model"]),
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key=APIKEY,
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history=history,
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max_output_token=max_output_token,
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