适配 google gemini 优化为从用户input中提取文件 (#1419)

适配 google gemini 优化为从用户input中提取文件

config.py

@@ -89,12 +89,14 @@
 LLM_MODEL = "gpt-3.5-turbo" # 可选 ↓↓↓
 AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-preview",
                     "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
-                    "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
                     "gpt-4", "gpt-4-32k", "azure-gpt-4", "api2d-gpt-4",
-                    "chatglm3", "moss", "claude-2"]
-# P.S. 其他可用的模型还包括 ["zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random"
+                    "gemini-pro", "chatglm3", "moss", "claude-2"]
+# P.S. 其他可用的模型还包括 [
+# "qwen-turbo", "qwen-plus", "qwen-max"
+# "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", 
+# "gpt-3.5-turbo-16k-0613",  "gpt-3.5-random", "api2d-gpt-3.5-turbo", 'api2d-gpt-3.5-turbo-16k',
 # "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"
-# “qwen-turbo", "qwen-plus", "qwen-max"]
+# ]
 
 
 # 定义界面上“询问多个GPT模型”插件应该使用哪些模型，请从AVAIL_LLM_MODELS中选择，并在不同模型之间用`&`间隔，例如"gpt-3.5-turbo&chatglm3&azure-gpt-4"
@@ -204,6 +206,10 @@
 CUSTOM_API_KEY_PATTERN = ""
 
 
+# Google Gemini API-Key
+GEMINI_API_KEY = ''
+
+
 # HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
 HUGGINGFACE_ACCESS_TOKEN = "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"
 
@@ -292,6 +298,9 @@
 ├── "qwen-turbo" 等通义千问大模型
 │   └──  DASHSCOPE_API_KEY
 │
+├── "Gemini"
+│   └──  GEMINI_API_KEY
+│
 └── "newbing" Newbing接口不再稳定，不推荐使用
     ├── NEWBING_STYLE
     └── NEWBING_COOKIES

request_llms/bridge_all.py

@@ -28,6 +28,9 @@
 from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
 from .bridge_qianfan import predict as qianfan_ui
 
+from .bridge_google_gemini import predict as genai_ui
+from .bridge_google_gemini import predict_no_ui_long_connection  as genai_noui
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
 
 class LazyloadTiktoken(object):
@@ -246,6 +249,22 @@ def decode(self, *args, **kwargs):
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "gemini-pro": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": None,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "gemini-pro-vision": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": None,
+        "max_token": 1024 * 32,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 }
 
 # -=-=-=-=-=-=- api2d 对齐支持 -=-=-=-=-=-=-

request_llms/bridge_google_gemini.py

@@ -0,0 +1,101 @@
+# encoding: utf-8
+# @Time   : 2023/12/21
+# @Author : Spike
+# @Descr   :
+import json
+import re
+import time
+from request_llms.com_google import GoogleChatInit
+from toolbox import get_conf, update_ui, update_ui_lastest_msg
+
+proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
+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 predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
+                                  console_slience=False):
+    # 检查API_KEY
+    if get_conf("GEMINI_API_KEY") == "":
+        raise ValueError(f"请配置 GEMINI_API_KEY。")
+
+    genai = GoogleChatInit()
+    watch_dog_patience = 5  # 看门狗的耐心, 设置5秒即可
+    gpt_replying_buffer = ''
+    stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
+    for response in stream_response:
+        results = response.decode()
+        match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
+        error_match = re.search(r'\"message\":\s*\"(.*?)\"', results, flags=re.DOTALL)
+        if match:
+            try:
+                paraphrase = json.loads('{"text": "%s"}' % match.group(1))
+            except:
+                raise ValueError(f"解析GEMINI消息出错。")
+            buffer = paraphrase['text']
+            gpt_replying_buffer += buffer
+            if len(observe_window) >= 1:
+                observe_window[0] = gpt_replying_buffer
+            if len(observe_window) >= 2:
+                if (time.time() - observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+        if error_match:
+            raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
+    return gpt_replying_buffer
+
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
+    # 检查API_KEY
+    if get_conf("GEMINI_API_KEY") == "":
+        yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
+        return
+
+    chatbot.append((inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history)
+    genai = GoogleChatInit()
+    retry = 0
+    while True:
+        try:
+            stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
+            break
+        except Exception as e:
+            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 = ""
+    gpt_security_policy = ""
+    history.extend([inputs, ''])
+    for response in stream_response:
+        results = response.decode("utf-8")    # 被这个解码给耍了。。
+        gpt_security_policy += results
+        match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
+        error_match = re.search(r'\"message\":\s*\"(.*)\"', results, flags=re.DOTALL)
+        if match:
+            try:
+                paraphrase = json.loads('{"text": "%s"}' % match.group(1))
+            except:
+                raise ValueError(f"解析GEMINI消息出错。")
+            gpt_replying_buffer += paraphrase['text']    # 使用 json 解析库进行处理
+            chatbot[-1] = (inputs, gpt_replying_buffer)
+            history[-1] = gpt_replying_buffer
+            yield from update_ui(chatbot=chatbot, history=history)
+        if error_match:
+            history = history[-2]  # 错误的不纳入对话
+            chatbot[-1] = (inputs, gpt_replying_buffer + f"对话错误，请查看message\n\n```\n{error_match.group(1)}\n```")
+            yield from update_ui(chatbot=chatbot, history=history)
+            raise RuntimeError('对话错误')
+    if not gpt_replying_buffer:
+        history = history[-2]  # 错误的不纳入对话
+        chatbot[-1] = (inputs, gpt_replying_buffer + f"触发了Google的安全访问策略，没有回答\n\n```\n{gpt_security_policy}\n```")
+        yield from update_ui(chatbot=chatbot, history=history)
+
+
+
+if __name__ == '__main__':
+    import sys
+
+    llm_kwargs = {'llm_model': 'gemini-pro'}
+    result = predict('Write long a story about a magic backpack.', llm_kwargs, llm_kwargs, [])
+    for i in result:
+        print(i)

request_llms/com_google.py

@@ -0,0 +1,198 @@
+# encoding: utf-8
+# @Time   : 2023/12/25
+# @Author : Spike
+# @Descr   :
+import json
+import os
+import re
+import requests
+from typing import List, Dict, Tuple
+from toolbox import get_conf, encode_image
+
+proxies, TIMEOUT_SECONDS = get_conf('proxies', 'TIMEOUT_SECONDS')
+
+"""
+========================================================================
+第五部分 一些文件处理方法
+files_filter_handler 根据type过滤文件
+input_encode_handler 提取input中的文件，并解析
+file_manifest_filter_html 根据type过滤文件, 并解析为html or md 文本
+link_mtime_to_md 文件增加本地时间参数，避免下载到缓存文件
+html_view_blank 超链接
+html_local_file 本地文件取相对路径
+to_markdown_tabs 文件list 转换为 md tab
+"""
+
+
+def files_filter_handler(file_list):
+    new_list = []
+    filter_ = ['png', 'jpg', 'jpeg', 'bmp', 'svg', 'webp', 'ico', 'tif', 'tiff', 'raw', 'eps']
+    for file in file_list:
+        file = str(file).replace('file=', '')
+        if os.path.exists(file):
+            if str(os.path.basename(file)).split('.')[-1] in filter_:
+                new_list.append(file)
+    return new_list
+
+
+def input_encode_handler(inputs):
+    md_encode = []
+    pattern_md_file = r"(!?\[[^\]]+\]\([^\)]+\))"
+    matches_path = re.findall(pattern_md_file, inputs)
+    for md_path in matches_path:
+        pattern_file = r"\((file=.*)\)"
+        matches_path = re.findall(pattern_file, md_path)
+        encode_file = files_filter_handler(file_list=matches_path)
+        if encode_file:
+            md_encode.extend([{
+                "data": encode_image(i),
+                "type": os.path.splitext(i)[1].replace('.', '')
+            } for i in encode_file])
+            inputs = inputs.replace(md_path, '')
+    return inputs, md_encode
+
+
+def file_manifest_filter_html(file_list, filter_: list = None, md_type=False):
+    new_list = []
+    if not filter_:
+        filter_ = ['png', 'jpg', 'jpeg', 'bmp', 'svg', 'webp', 'ico', 'tif', 'tiff', 'raw', 'eps']
+    for file in file_list:
+        if str(os.path.basename(file)).split('.')[-1] in filter_:
+            new_list.append(html_local_img(file, md=md_type))
+        elif os.path.exists(file):
+            new_list.append(link_mtime_to_md(file))
+        else:
+            new_list.append(file)
+    return new_list
+
+
+def link_mtime_to_md(file):
+    link_local = html_local_file(file)
+    link_name = os.path.basename(file)
+    a = f"[{link_name}]({link_local}?{os.path.getmtime(file)})"
+    return a
+
+
+def html_local_file(file):
+    base_path = os.path.dirname(__file__)  # 项目目录
+    if os.path.exists(str(file)):
+        file = f'file={file.replace(base_path, ".")}'
+    return file
+
+
+def html_local_img(__file, layout='left', max_width=None, max_height=None, md=True):
+    style = ''
+    if max_width is not None:
+        style += f"max-width: {max_width};"
+    if max_height is not None:
+        style += f"max-height: {max_height};"
+    __file = html_local_file(__file)
+    a = f'<div align="{layout}"><img src="{__file}" style="{style}"></div>'
+    if md:
+        a = f'![{__file}]({__file})'
+    return a
+
+
+def to_markdown_tabs(head: list, tabs: list, alignment=':---:', column=False):
+    """
+    Args:
+        head: 表头：[]
+        tabs: 表值：[[列1], [列2], [列3], [列4]]
+        alignment: :--- 左对齐， :---: 居中对齐， ---: 右对齐
+        column: True to keep data in columns, False to keep data in rows (default).
+    Returns:
+        A string representation of the markdown table.
+    """
+    if column:
+        transposed_tabs = list(map(list, zip(*tabs)))
+    else:
+        transposed_tabs = tabs
+    # Find the maximum length among the columns
+    max_len = max(len(column) for column in transposed_tabs)
+
+    tab_format = "| %s "
+    tabs_list = "".join([tab_format % i for i in head]) + '|\n'
+    tabs_list += "".join([tab_format % alignment for i in head]) + '|\n'
+
+    for i in range(max_len):
+        row_data = [tab[i] if i < len(tab) else '' for tab in transposed_tabs]
+        row_data = file_manifest_filter_html(row_data, filter_=None)
+        tabs_list += "".join([tab_format % i for i in row_data]) + '|\n'
+
+    return tabs_list
+
+
+class GoogleChatInit:
+
+    def __init__(self):
+        self.url_gemini = 'https://generativelanguage.googleapis.com/v1beta/models/%m:streamGenerateContent?key=%k'
+
+    def __conversation_user(self, user_input):
+        what_i_have_asked = {"role": "user", "parts": []}
+        if 'vision' not in self.url_gemini:
+            input_ = user_input
+            encode_img = []
+        else:
+            input_, encode_img = input_encode_handler(user_input)
+        what_i_have_asked['parts'].append({'text': input_})
+        if encode_img:
+            for data in encode_img:
+                what_i_have_asked['parts'].append(
+                    {'inline_data': {
+                        "mime_type": f"image/{data['type']}",
+                        "data": data['data']
+                    }})
+        return what_i_have_asked
+
+    def __conversation_history(self, history):
+        messages = []
+        conversation_cnt = len(history) // 2
+        if conversation_cnt:
+            for index in range(0, 2 * conversation_cnt, 2):
+                what_i_have_asked = self.__conversation_user(history[index])
+                what_gpt_answer = {
+                    "role": "model",
+                    "parts": [{"text": history[index + 1]}]
+                }
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+        return messages
+
+    def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
+        headers, payload = self.generate_message_payload(inputs, llm_kwargs, history, system_prompt)
+        response = requests.post(url=self.url_gemini, headers=headers, data=json.dumps(payload),
+                                 stream=True, proxies=proxies, timeout=TIMEOUT_SECONDS)
+        return response.iter_lines()
+
+    def generate_message_payload(self, inputs, llm_kwargs, history, system_prompt) -> Tuple[Dict, Dict]:
+        messages = [
+            # {"role": "system", "parts": [{"text": system_prompt}]},  # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。
+            # {"role": "user", "parts": [{"text": ""}]},
+            # {"role": "model", "parts": [{"text": ""}]}
+        ]
+        self.url_gemini = self.url_gemini.replace(
+            '%m', llm_kwargs['llm_model']).replace(
+            '%k', get_conf('GEMINI_API_KEY')
+        )
+        header = {'Content-Type': 'application/json'}
+        if 'vision' not in self.url_gemini:  # 不是vision 才处理history
+            messages.extend(self.__conversation_history(history))  # 处理 history
+        messages.append(self.__conversation_user(inputs))  # 处理用户对话
+        payload = {
+            "contents": messages,
+            "generationConfig": {
+                "stopSequences": str(llm_kwargs.get('stop', '')).split(' '),
+                "temperature": llm_kwargs.get('temperature', 1),
+                # "maxOutputTokens": 800,
+                "topP": llm_kwargs.get('top_p', 0.8),
+                "topK": 10
+            }
+        }
+        return header, payload
+
+
+if __name__ == '__main__':
+    google = GoogleChatInit()
+    # print(gootle.generate_message_payload('你好呀', {},
+    #                                                 ['123123', '3123123'], ''))
+    # gootle.input_encode_handle('123123[123123](./123123), ![53425](./asfafa/fff.jpg)')