support claude api

config.py

@@ -71,7 +71,7 @@ MAX_RETRY = 2
 # 模型选择是 (注意: LLM_MODEL是默认选中的模型, 它*必须*被包含在AVAIL_LLM_MODELS列表中 )
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "moss", "newbing", "stack-claude"]
-# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "newbing-free", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
+# P.S. 其他可用的模型还包括 ["gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "claude-1-100k", "claude-2", "jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
 
 
 # ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
@@ -89,9 +89,11 @@ CONCURRENT_COUNT = 100
 # 是否在提交时自动清空输入框
 AUTO_CLEAR_TXT = False
 
+
 # 色彩主体，可选 ["Default", "Chuanhu-Small-and-Beautiful"]
 THEME = "Default"
 
+
 # 加一个live2d装饰
 ADD_WAIFU = False
 
@@ -131,3 +133,7 @@ put your new bing cookies here
 ENABLE_AUDIO = False
 ALIYUN_TOKEN=""    # 例如 f37f30e0f9934c34a992f6f64f7eba4f
 ALIYUN_APPKEY=""   # 例如 RoPlZrM88DnAFkZK
+
+
+# Claude API KEY
+ANTHROPIC_API_KEY = ""

request_llm/bridge_all.py

@@ -170,6 +170,29 @@ model_info = {
 
 AVAIL_LLM_MODELS, LLM_MODEL = get_conf("AVAIL_LLM_MODELS", "LLM_MODEL")
 AVAIL_LLM_MODELS = AVAIL_LLM_MODELS + [LLM_MODEL]
+if "claude-1-100k" in AVAIL_LLM_MODELS or "claude-2" in AVAIL_LLM_MODELS:
+    from .bridge_claude import predict_no_ui_long_connection as claude_noui
+    from .bridge_claude import predict as claude_ui
+    model_info.update({
+        "claude-1-100k": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": None,
+            "max_token": 8196,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
+    model_info.update({
+        "claude-2": {
+            "fn_with_ui": claude_ui,
+            "fn_without_ui": claude_noui,
+            "endpoint": None,
+            "max_token": 8196,
+            "tokenizer": tokenizer_gpt35,
+            "token_cnt": get_token_num_gpt35,
+        },
+    })
 if "jittorllms_rwkv" in AVAIL_LLM_MODELS:
     from .bridge_jittorllms_rwkv import predict_no_ui_long_connection as rwkv_noui
     from .bridge_jittorllms_rwkv import predict as rwkv_ui

request_llm/bridge_claude.py

@@ -0,0 +1,231 @@
+# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
+
+"""
+    该文件中主要包含2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：在实验过程中发现调用predict_no_ui处理长文档时，和openai的连接容易断掉，这个函数用stream的方式解决这个问题，同样支持多线程
+"""
+
+import os
+import json
+import time
+import gradio as gr
+import logging
+import traceback
+import requests
+import importlib
+
+# 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（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, trimmed_format_exc, ProxyNetworkActivate
+proxies, TIMEOUT_SECONDS, MAX_RETRY, ANTHROPIC_API_KEY = \
+    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'ANTHROPIC_API_KEY')
+
+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 predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
+    """
+    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    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，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    from anthropic import Anthropic
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+    prompt = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+    retry = 0
+    if len(ANTHROPIC_API_KEY) == 0:
+        raise RuntimeError("没有设置ANTHROPIC_API_KEY选项")
+
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            from .bridge_all import model_info
+            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            # with ProxyNetworkActivate()
+            stream = anthropic.completions.create(
+                prompt=prompt,
+                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
+                model=llm_kwargs['llm_model'],
+                stream=True,
+                temperature = llm_kwargs['temperature']
+            )
+            break
+        except Exception as e:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+    result = ''
+    try: 
+        for completion in stream:
+            result += completion.completion
+            if not console_slience: print(completion.completion, 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] += completion.completion
+                # 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。")
+    except Exception as e:
+        traceback.print_exc()
+
+    return result
+
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+    发送至chatGPT，流式获取输出。
+    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（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
+    additional_fn代表点击的哪个按钮，按钮见functional.py
+    """
+    from anthropic import Anthropic
+    if len(ANTHROPIC_API_KEY) == 0:
+        chatbot.append((inputs, "没有设置ANTHROPIC_API_KEY"))
+        yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+        return
+    
+    if additional_fn is not None:
+        import core_functional
+        importlib.reload(core_functional)    # 热更新prompt
+        core_functional = core_functional.get_core_functions()
+        if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs)  # 获取预处理函数（如果有的话）
+        inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
+
+    raw_input = inputs
+    logging.info(f'[raw_input] {raw_input}')
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    try:
+        prompt = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
+    except RuntimeError as e:
+        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{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
+
+    history.append(inputs); history.append("")
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            from .bridge_all import model_info
+            anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+            # endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
+            # with ProxyNetworkActivate()
+            stream = anthropic.completions.create(
+                prompt=prompt,
+                max_tokens_to_sample=4096,       # The maximum number of tokens to generate before stopping.
+                model=llm_kwargs['llm_model'],
+                stream=True,
+                temperature = llm_kwargs['temperature']
+            )
+            
+            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
+
+    gpt_replying_buffer = ""
+    
+    for completion in stream:
+        try:
+            gpt_replying_buffer = gpt_replying_buffer + completion.completion
+            history[-1] = gpt_replying_buffer
+            chatbot[-1] = (history[-2], history[-1])
+            yield from update_ui(chatbot=chatbot, history=history, msg='正常') # 刷新界面
+
+        except Exception as e:
+            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}")
+            yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + tb_str) # 刷新界面
+            return
+        
+
+
+
+# https://github.com/jtsang4/claude-to-chatgpt/blob/main/claude_to_chatgpt/adapter.py
+def convert_messages_to_prompt(messages):
+    prompt = ""
+    role_map = {
+        "system": "Human",
+        "user": "Human",
+        "assistant": "Assistant",
+    }
+    for message in messages:
+        role = message["role"]
+        content = message["content"]
+        transformed_role = role_map[role]
+        prompt += f"\n\n{transformed_role.capitalize()}: {content}"
+    prompt += "\n\nAssistant: "
+    return prompt
+
+def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+
+    conversation_cnt = len(history) // 2
+
+    messages = [{"role": "system", "content": system_prompt}]
+    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"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = 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)
+    prompt = convert_messages_to_prompt(messages)
+
+    return prompt
+
+

request_llm/test_llms.py

@@ -10,10 +10,11 @@ def validate_path():
     
 validate_path() # validate path so you can run from base directory
 if __name__ == "__main__":
-    from request_llm.bridge_newbingfree import predict_no_ui_long_connection
+    # from request_llm.bridge_newbingfree import predict_no_ui_long_connection
     # from request_llm.bridge_moss import predict_no_ui_long_connection
     # from request_llm.bridge_jittorllms_pangualpha import predict_no_ui_long_connection
     # from request_llm.bridge_jittorllms_llama import predict_no_ui_long_connection
+    from request_llm.bridge_claude import predict_no_ui_long_connection
 
     llm_kwargs = {
         'max_length': 512,
@@ -28,17 +29,6 @@ if __name__ == "__main__":
     print('final result:', result)
 
 
-    result = predict_no_ui_long_connection(inputs="what is a hero?", 
-                                        llm_kwargs=llm_kwargs,
-                                        history=["hello world"],
-                                        sys_prompt="")
-    print('final result:', result)
-
-    result = predict_no_ui_long_connection(inputs="如何理解传奇?", 
-                                        llm_kwargs=llm_kwargs,
-                                        history=[],
-                                        sys_prompt="")
-    print('final result:', result)
 
     # # print(result)
     # from multiprocessing import Process, Pipe
@@ -56,7 +46,6 @@ if __name__ == "__main__":
     #             os.chdir(root_dir_assume + '/request_llm/jittorllms')
     #             sys.path.append(root_dir_assume + '/request_llm/jittorllms')
     #         validate_path() # validate path so you can run from base directory
-
     #         jittorllms_model = None
     #         import types
     #         try:
@@ -70,7 +59,6 @@ if __name__ == "__main__":
     #         except:
     #             # self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
     #             raise RuntimeError("不能正常加载jittorllms的参数！")
-            
     # x = GetGLMHandle()
     # x.start()
 

requirements.txt

@@ -9,6 +9,7 @@ prompt_toolkit
 latex2mathml
 python-docx
 mdtex2html
+anthropic
 colorama
 Markdown
 pygments