Upload 12 files

.gitignore

@@ -1,5 +1,28 @@
-config.json
-password.txt
-venv/
-__pycache__/
-*.pyc 
+# 二进制文件
+*.exe
+*.exe~
+*.dll
+*.so
+*.dylib
+main
+
+# 测试文件
+*.test
+*.out
+
+# IDE配置
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# 依赖目录
+vendor/
+
+# 环境变量
+.env
+*.env
+
+# 系统文件
+.DS_Store
+Thumbs.db 

Dockerfile

@@ -1,24 +1,71 @@
-FROM python:3.11-slim
-
-# 设置用户为root
-USER root
-
-WORKDIR /app
-
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-# 设置环境变量
-ENV HOST=0.0.0.0
-ENV PORT=7860
-
-# 删除敏感文件
-RUN rm -f config.json password.txt
-
-# 暴露端口（Hugging Face默认使用7860端口）
-EXPOSE 7860
-
-# 启动命令
-CMD ["python", "app.py"] 
+# 这是一个临时的Dockerfile，用于在Hugging Face上部署占位符服务
+# TODO: 解决完整版本的依赖问题后替换此文件
+
+# 第一阶段：构建Go服务
+FROM golang:1.20-alpine AS go-builder
+
+# 安装基本依赖
+RUN apk add --no-cache git build-base ca-certificates
+
+# 设置工作目录
+WORKDIR /app
+
+# 初始化go.mod
+RUN go mod init tokenizer
+
+# 预先下载依赖
+RUN go get github.com/google/generative-ai-go/[email protected]
+RUN go get google.golang.org/[email protected]
+RUN go get github.com/gin-gonic/[email protected]
+RUN go get github.com/go-playground/validator/[email protected]
+RUN go get github.com/gabriel-vasile/[email protected]
+
+# 复制源代码
+COPY main.go .
+
+# 确保依赖关系
+RUN go mod tidy
+RUN go mod download
+
+# 构建
+RUN CGO_ENABLED=0 GOOS=linux go build -o tokenizer -a -installsuffix cgo -ldflags="-w -s" .
+
+# 第二阶段：构建Python环境
+FROM python:3.9-slim
+
+# 安装基本依赖
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+# 设置工作目录
+WORKDIR /app
+
+# 复制Go二进制文件
+COPY --from=go-builder /app/tokenizer .
+
+# 复制Python服务文件和tokenizer文件
+COPY deepseek_v3_tokenizer /app/deepseek_v3_tokenizer
+COPY openai_service.py /app/
+
+# 安装Python依赖
+RUN pip install --no-cache-dir flask transformers tiktoken
+
+# 设置环境变量
+ENV GIN_MODE=release
+ENV PORT=7860
+ENV DEEPSEEK_URL=http://127.0.0.1:7861
+ENV OPENAI_URL=http://127.0.0.1:7862
+
+# 创建启动脚本
+RUN echo '#!/bin/sh\n\
+python /app/deepseek_v3_tokenizer/deepseek_service.py & \n\
+python /app/openai_service.py & \n\
+sleep 5\n\
+./tokenizer' > /app/start.sh && chmod +x /app/start.sh
+
+# 暴露端口
+EXPOSE 7860 7861 7862
+
+# 启动服务
+CMD ["/app/start.sh"] 

README.md

@@ -1,121 +1,159 @@
----
-title: Abacus Chat Proxy
-emoji: 🤖
-colorFrom: blue
-colorTo: purple
-sdk: docker
-sdk_version: "3.9"
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
-
-# Abacus Chat Proxy
-
-> 📢 本项目基于 [orbitoo/abacus_chat_proxy](https://github.com/orbitoo/abacus_chat_proxy) 改进
-> 
-> 特别感谢 orbitoo 大佬提供的原始项目！
-> 
-> 本项目增加了：Docker部署支持、Hugging Face一键部署、自动保活功能等
-
-一个用于中转API请求的代理服务器。
-
-#### 注意创建space的时候把private改为public，因为不改为public导致的错误别来找我
-
-[![Deploy to Hugging Face Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/deploy-to-spaces-lg.svg)](https://huggingface.co/spaces/malt666/abacus_chat_proxy?duplicate=true)
-
-## ⚠️ 警告
-
-**本地部署方式已失效！**为了适配hugging face，本项目的本地部署方式已不再可用。目前只能通过Hugging Face Spaces部署来使用本代理服务。请使用下方的Hugging Face一键部署方法。
-
-## 🚀 快速开始
-
-### Hugging Face一键部署
-
-#### 注意创建space的时候把private改为public，因为不改为public导致的错误别来找我
-
-1. 点击上方的"Deploy to Hugging Face Spaces"按钮
-2. 登录你的Hugging Face账号（如果还没有，需要注册一个）
-3. 在弹出的页面中设置你的Space名称，注意创建space的时候把private改为public，因为不改为为public导致的错误别来找我
-4. 创建完Space后，在Space的Settings -> Repository Secrets中添加以下配置：
-   - `cookie_1`: 第一个cookie
-   - `cookie_2`: 第二个cookie（如果有）
-   - 以此类推...
-   - `password`: （必填，最好超过8位数，不然被盗用api导致点数用光，损失自负）api调用密码，也是dashboard登录密码
-5. 等待自动部署完成即可
-6. 登录dashboard查看使用情况，space里面是没办法登录的，点击弹出的登录页面的小窗口提示“请点击 https://xyz-abacus-chat-proxy.hf.space 来登录并查看使用情况”的链接来登录，登录密码是你设置的password
-7. **获取API链接**：部署成功后，网络的登录页面会显示api接口链接；或者你点击右上角的三个点按钮，在弹出的选项卡里面点击"Embed this Space"，然后在弹出的"Embed this Space"界面里的"Direct URL"就是你的访问链接，你可以用这个链接调用API和查看使用情况
-8. api调用：api调用的时候注意密钥填写你的password，不然没办法调用
-### 本地运行（已失效）
-
-> ⚠️ 以下本地运行方法已失效，仅作参考。请使用Hugging Face部署方式。
-
-#### Windows用户
-
-1. 双击运行 `start.bat`
-2. 首次运行选择 `0` 进行配置
-3. 配置完成后选择 `Y` 直接启动，或 `N` 返回菜单
-4. 之后可直接选择 `1` 启动代理
-5. 代理服务器默认运行在 `http://127.0.0.1:9876/`
-
-#### Linux/macOS用户
-
-```bash
-# 赋予脚本执行权限
-chmod +x start.sh
-
-# 运行脚本
-./start.sh
-```
-
-选项说明同Windows。
-
-### 🌐 Hugging Face部署
-
-1. Fork本仓库到你的GitHub账号
-2. 在Hugging Face上创建新的Space（选择Docker类型）
-3. 在Space的设置中连接你的GitHub仓库
-4. 在Space的设置中添加以下Secrets：
-   - 第1组配置：
-     - `cookie_1`: 第1个cookies字符串
-   - 第2组配置（如果需要）：
-     - `cookie_2`: 第2个cookies字符串
-   - 更多配置以此类推（`cookie_3`...）
-   - `password`: （可选）访问密码
-5. Space会自动部署，服务将在 `https://你的空间名-你的用户名.hf.space` 上运行
-
-## ⚙️ 环境要求
-
-- Python 3.8+
-- pip
-
-## 📦 依赖
-
-```bash
-Flask==3.1.0
-requests==2.32.3
-PyJWT==2.8.0
-```
-
-## 📝 配置说明
-
-### 本地配置
-
-首次运行时，请选择 `0` 进行配置，按照提示填写相关信息。配置文件将保存在 `config.json` 中。
-
-### 环境变量配置
-
-在Docker或云平台部署时，需要配置以下环境变量：
-
-- 必需的配置（至少需要一组）：
-  - `cookie_1`: 第1组配置
-  - `cookie_2`: 第2组配置（可选）
-  - 以此类推...
-- 可选配置：
-  - `password`: 访问密码
-
-## 🔒 安全说明
-
-- 建议在部署到Hugging Face时设置访问密码
-- 在Hugging Face上配置时，请使用Secrets来存储敏感信息 
+---
+title: tokenizer
+emoji: 🔢
+colorFrom: gray
+colorTo: blue
+sdk: docker
+sdk_version: "3.0.0"
+app_file: main.go
+pinned: false
+---
+
+# Tokenizer Service
+
+一个高效的API服务，用于计算大型语言模型的输入token数量。
+
+## 支持的模型
+
+### Claude系列模型
+- claude-3-7-sonnet-latest
+- claude-3-5-sonnet-latest
+- claude-3-5-haiku-latest
+- claude-3-opus-latest
+- claude-3-haiku-20240307
+
+### Gemini系列模型
+- gemini-1.5-flash
+- gemini-2.0-flash
+- 其他Gemini系列模型
+
+## 自动模型匹配
+服务支持智能模型名称匹配，不区分大小写：
+
+### Claude模型匹配规则
+- 包含"claude"、"3"和"7"的名称会匹配到`claude-3-7-sonnet-latest`
+- 包含"claude"、"3"、"5"和"sonnet"的名称会匹配到`claude-3-5-sonnet-latest`
+- 包含"claude"、"3"、"5"和"haiku"的名称会匹配到`claude-3-5-haiku-latest`
+- 包含"claude"、"3"和"opus"的名称会匹配到`claude-3-opus-latest`
+- 包含"claude"、"3"和"haiku"的名称会匹配到`claude-3-haiku-20240307`
+
+### Gemini模型匹配规则
+- 包含"gemini"和"2.0"或"2.5"的名称会匹配到`gemini-2.0-flash`
+- 包含"gemini"和"1.5"的名称会匹配到`gemini-1.5-flash`
+
+## 环境变量
+
+运行服务需要以下环境变量：
+
+- `ANTHROPIC_API_KEY`: Anthropic API密钥，用于计算Claude模型的token
+- `GOOGLE_API_KEY`: Google API密钥，用于计算Gemini模型的token
+- `SERVICE_URL`(可选): 服务的URL，用于防止Hugging Face空间休眠
+- `PORT`(可选): 服务监听的端口号，默认为7860
+
+## API接口
+
+### Token计算
+
+**端点**: `/count_tokens`
+
+**方法**: POST
+
+**请求格式**:
+```json
+{
+  "model": "模型名称",
+  "messages": [
+    {
+      "role": "user或assistant",
+      "content": "消息内容"
+    }
+  ],
+  "system": "系统提示（可选）"
+}
+```
+
+**响应格式**:
+```json
+{
+  "input_tokens": 123
+}
+```
+
+### 健康检查
+
+**端点**: `/health`
+
+**方法**: GET
+
+**响应格式**:
+```json
+{
+  "status": "healthy",
+  "time": "2023-04-01T12:34:56Z"
+}
+```
+
+## 部署指南
+
+### 本地运行
+
+1. 设置必要的环境变量
+```bash
+export ANTHROPIC_API_KEY=your_anthropic_key
+export GOOGLE_API_KEY=your_google_key
+```
+
+2. 启动服务
+```bash
+go run main.go
+```
+
+### Docker部署
+
+1. 构建Docker镜像
+```bash
+docker build -t tokenizer .
+```
+
+2. 运行容器
+```bash
+docker run -p 7860:7860 -e ANTHROPIC_API_KEY=your_anthropic_key -e GOOGLE_API_KEY=your_google_key tokenizer
+```
+
+### Hugging Face部署
+
+1. 创建一个新的Space，选择Docker作为运行环境
+2. 添加以下Secret:
+   - `ANTHROPIC_API_KEY`: 你的Anthropic API密钥
+   - `GOOGLE_API_KEY`: 你的Google API密钥
+   - `SERVICE_URL`: 服务的完整URL，格式为`https://你的空间名-用户名.hf.space`
+
+3. 将代码推送到Space关联的仓库
+4. Hugging Face会自动构建并部署服务
+
+## 示例代码
+
+### Python调用示例
+
+```python
+import requests
+import json
+
+url = "https://your-service-url/count_tokens"
+data = {
+    "model": "claude-3-5-sonnet-latest",
+    "messages": [
+        {
+            "role": "user",
+            "content": "Hello, world!"
+        }
+    ]
+}
+
+response = requests.post(url, json=data)
+print(response.json())
+```
+
+## License
+
+MIT 

deepseek_v3_tokenizer/deepseek_service.py

@@ -0,0 +1,52 @@
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer
+import os
+
+app = Flask(__name__)
+
+# 加载tokenizer
+tokenizer = AutoTokenizer.from_pretrained(os.path.dirname(__file__))
+
+@app.route('/count_tokens', methods=['POST'])
+def count_tokens():
+    try:
+        data = request.json
+        messages = data.get('messages', [])
+        system = data.get('system')
+        
+        # 构建完整文本
+        text = ""
+        if system:
+            text += f"System: {system}\n\n"
+            
+        for msg in messages:
+            role = msg.get('role', '')
+            content = msg.get('content', '')
+            if role == 'user':
+                text += f"User: {content}\n"
+            elif role == 'assistant':
+                text += f"Assistant: {content}\n"
+            else:
+                text += f"{role}: {content}\n"
+        
+        # 计算token数量
+        tokens = tokenizer.encode(text)
+        token_count = len(tokens)
+        
+        return jsonify({
+            'input_tokens': token_count
+        })
+    except Exception as e:
+        return jsonify({
+            'error': str(e)
+        }), 400
+
+@app.route('/health', methods=['GET'])
+def health():
+    return jsonify({
+        'status': 'healthy',
+        'tokenizer': 'deepseek-v3'
+    })
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=7861) 

deepseek_v3_tokenizer/deepseek_tokenizer.py

@@ -0,0 +1,12 @@
+# pip3 install transformers
+# python3 deepseek_tokenizer.py
+import transformers
+
+chat_tokenizer_dir = "./"
+
+tokenizer = transformers.AutoTokenizer.from_pretrained( 
+        chat_tokenizer_dir, trust_remote_code=True
+        )
+
+result = tokenizer.encode("Hello!")
+print(result)

deepseek_v3_tokenizer/tokenizer_config.json

@@ -0,0 +1,35 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": {
+    "__type": "AddedToken",
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sp_model_kwargs": {},
+  "unk_token": null,
+  "tokenizer_class": "LlamaTokenizerFast",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='', is_first_sp=true) %}{%- for message in messages %}{%- if message['role'] == 'system' %}{%- if ns.is_first_sp %}{% set ns.system_prompt = ns.system_prompt + message['content'] %}{% set ns.is_first_sp = false %}{%- else %}{% set ns.system_prompt = ns.system_prompt + '\\n\\n' + message['content'] %}{%- endif %}{%- endif %}{%- endfor %}{{ bos_token }}{{ ns.system_prompt }}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and 'tool_calls' in message %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls'] %}{%- if not ns.is_first %}{%- if message['content'] is none %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- else %}{{'<｜Assistant｜>' + message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- endif %}{%- endfor %}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- if message['role'] == 'assistant' and 'tool_calls' not in message %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}"
+}

go.mod

@@ -0,0 +1,57 @@
+module tokenizer
+
+go 1.21
+
+toolchain go1.24.1
+
+require github.com/gin-gonic/gin v1.9.1
+
+require (
+	cloud.google.com/go/ai v0.3.0 // indirect
+	cloud.google.com/go/compute v1.23.3 // indirect
+	cloud.google.com/go/compute/metadata v0.2.3 // indirect
+	cloud.google.com/go/longrunning v0.5.4 // indirect
+	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
+	github.com/golang/protobuf v1.5.3 // indirect
+	github.com/google/s2a-go v0.1.7 // indirect
+	github.com/googleapis/enterprise-certificate-proxy v0.3.2 // indirect
+	github.com/googleapis/gax-go/v2 v2.12.0 // indirect
+	go.opencensus.io v0.24.0 // indirect
+	golang.org/x/oauth2 v0.14.0 // indirect
+	golang.org/x/sync v0.5.0 // indirect
+	golang.org/x/time v0.5.0 // indirect
+	google.golang.org/appengine v1.6.7 // indirect
+	google.golang.org/genproto v0.0.0-20231106174013-bbf56f31fb17 // indirect
+	google.golang.org/genproto/googleapis/api v0.0.0-20231106174013-bbf56f31fb17 // indirect
+	google.golang.org/genproto/googleapis/rpc v0.0.0-20231120223509-83a465c0220f // indirect
+	google.golang.org/grpc v1.59.0 // indirect
+)
+
+require (
+	github.com/bytedance/sonic v1.9.1 // indirect
+	github.com/chenzhuoyu/base64x v0.0.0-20221115062448-fe3a3abad311 // indirect
+	github.com/gabriel-vasile/mimetype v1.4.2 // indirect
+	github.com/gin-contrib/sse v0.1.0 // indirect
+	github.com/go-playground/locales v0.14.1 // indirect
+	github.com/go-playground/universal-translator v0.18.1 // indirect
+	github.com/go-playground/validator/v10 v10.14.0 // indirect
+	github.com/goccy/go-json v0.10.2 // indirect
+	github.com/google/generative-ai-go v0.8.0
+	github.com/json-iterator/go v1.1.12 // indirect
+	github.com/klauspost/cpuid/v2 v2.2.4 // indirect
+	github.com/leodido/go-urn v1.2.4 // indirect
+	github.com/mattn/go-isatty v0.0.19 // indirect
+	github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd // indirect
+	github.com/modern-go/reflect2 v1.0.2 // indirect
+	github.com/pelletier/go-toml/v2 v2.0.8 // indirect
+	github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
+	github.com/ugorji/go/codec v1.2.11 // indirect
+	golang.org/x/arch v0.3.0 // indirect
+	golang.org/x/crypto v0.15.0 // indirect
+	golang.org/x/net v0.18.0 // indirect
+	golang.org/x/sys v0.14.0 // indirect
+	golang.org/x/text v0.14.0 // indirect
+	google.golang.org/api v0.152.0
+	google.golang.org/protobuf v1.31.0 // indirect
+	gopkg.in/yaml.v3 v3.0.1 // indirect
+)

go.sum

@@ -0,0 +1,207 @@
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main.go

@@ -0,0 +1,512 @@
+package main
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"log"
+	"net/http"
+	"os"
+	"strings"
+	"sync"
+	"time"
+
+	"github.com/gin-gonic/gin"
+	"github.com/google/generative-ai-go/genai"
+	"google.golang.org/api/option"
+)
+
+// 配置结构
+type Config struct {
+	AnthropicKey string
+	GoogleKey    string
+	ServiceURL   string
+	DeepseekURL  string
+	OpenAIURL    string
+}
+
+var (
+	config     Config
+	configOnce sync.Once
+)
+
+// 请求结构
+type TokenCountRequest struct {
+	Model    string    `json:"model" binding:"required"`
+	Messages []Message `json:"messages" binding:"required"`
+	System   *string   `json:"system,omitempty"`
+}
+
+type Message struct {
+	Role    string `json:"role" binding:"required"`
+	Content string `json:"content" binding:"required"`
+}
+
+// 响应结构
+type TokenCountResponse struct {
+	InputTokens int `json:"input_tokens"`
+}
+
+// 错误响应结构
+type ErrorResponse struct {
+	Error string `json:"error"`
+}
+
+// 模型映射规则
+type ModelRule struct {
+	Keywords []string
+	Target   string
+}
+
+var modelRules = []ModelRule{
+	{
+		Keywords: []string{"gpt"},
+		Target:   "gpt-3.5-turbo",
+	},
+	{
+		Keywords: []string{"openai"},
+		Target:   "gpt-3.5-turbo",
+	},
+	{
+		Keywords: []string{"deepseek"},
+		Target:   "deepseek-v3",
+	},
+	{
+		Keywords: []string{"claude", "3", "sonnet"},
+		Target:   "claude-3-sonnet-20240229",
+	},
+	{
+		Keywords: []string{"claude", "3", "7"},
+		Target:   "claude-3-7-sonnet-latest",
+	},
+	{
+		Keywords: []string{"claude", "3", "5", "sonnet"},
+		Target:   "claude-3-5-sonnet-latest",
+	},
+	{
+		Keywords: []string{"claude", "3", "5", "haiku"},
+		Target:   "claude-3-5-haiku-latest",
+	},
+	{
+		Keywords: []string{"claude", "3", "opus"},
+		Target:   "claude-3-opus-latest",
+	},
+	{
+		Keywords: []string{"claude", "3", "haiku"},
+		Target:   "claude-3-haiku-20240307",
+	},
+	{
+		Keywords: []string{"gemini", "2.0"},
+		Target:   "gemini-2.0-flash",
+	},
+	{
+		Keywords: []string{"gemini", "2.5"},
+		Target:   "gemini-2.0-flash", // 目前使用2.0-flash作为2.5的替代
+	},
+	{
+		Keywords: []string{"gemini", "1.5"},
+		Target:   "gemini-1.5-flash",
+	},
+}
+
+// 智能匹配模型名称
+func matchModelName(input string) string {
+	// 转换为小写进行匹配
+	input = strings.ToLower(input)
+
+	// 特殊规则：OpenAI GPT-4o
+	if (strings.Contains(input, "gpt") && strings.Contains(input, "4o")) ||
+		strings.Contains(input, "o1") ||
+		strings.Contains(input, "o3") {
+		return "gpt-4o"
+	}
+
+	// 特殊规则：OpenAI GPT-4
+	if (strings.Contains(input, "gpt") && strings.Contains(input, "3") && strings.Contains(input, "5")) ||
+		(strings.Contains(input, "gpt") && strings.Contains(input, "4") && !strings.Contains(input, "4o")) {
+		return "gpt-4"
+	}
+
+	// 遍历所有规则
+	for _, rule := range modelRules {
+		matches := true
+		for _, keyword := range rule.Keywords {
+			if !strings.Contains(input, strings.ToLower(keyword)) {
+				matches = false
+				break
+			}
+		}
+		if matches {
+			return rule.Target
+		}
+	}
+
+	// 如果没有匹配到，返回原始输入
+	return input
+}
+
+// 加载配置
+func loadConfig() Config {
+	configOnce.Do(func() {
+		config.AnthropicKey = os.Getenv("ANTHROPIC_API_KEY")
+		if config.AnthropicKey == "" {
+			log.Println("警告: ANTHROPIC_API_KEY 环境变量未设置，Claude模型将无法使用")
+		}
+
+		config.GoogleKey = os.Getenv("GOOGLE_API_KEY")
+		if config.GoogleKey == "" {
+			log.Println("警告: GOOGLE_API_KEY 环境变量未设置，Gemini模型将无法使用")
+		}
+
+		// 获取Deepseek服务URL
+		config.DeepseekURL = os.Getenv("DEEPSEEK_URL")
+		if config.DeepseekURL == "" {
+			config.DeepseekURL = "http://127.0.0.1:7861" // 默认本地地址
+			log.Println("使用默认Deepseek服务地址:", config.DeepseekURL)
+		}
+
+		// 获取OpenAI服务URL
+		config.OpenAIURL = os.Getenv("OPENAI_URL")
+		if config.OpenAIURL == "" {
+			config.OpenAIURL = "http://127.0.0.1:7862" // 默认本地地址
+			log.Println("使用默认OpenAI服务地址:", config.OpenAIURL)
+		}
+
+		// 获取服务URL，用于防休眠
+		config.ServiceURL = os.Getenv("SERVICE_URL")
+		if config.ServiceURL == "" {
+			log.Println("SERVICE_URL 未设置，防休眠功能将被禁用")
+		}
+	})
+	return config
+}
+
+// 使用Claude API计算token
+func countTokensWithClaude(req TokenCountRequest) (TokenCountResponse, error) {
+	// 准备请求Anthropic API
+	client := &http.Client{}
+	data, err := json.Marshal(req)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("序列化请求失败: %v", err)
+	}
+
+	// 创建请求
+	request, err := http.NewRequest("POST", "https://api.anthropic.com/v1/messages/count_tokens", bytes.NewBuffer(data))
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("创建请求失败: %v", err)
+	}
+
+	// 设置请求头
+	request.Header.Set("x-api-key", config.AnthropicKey)
+	request.Header.Set("anthropic-version", "2023-06-01")
+	request.Header.Set("content-type", "application/json")
+
+	// 发送请求
+	response, err := client.Do(request)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("发送请求到Anthropic API失败: %v", err)
+	}
+	defer response.Body.Close()
+
+	// 读取响应
+	var result TokenCountResponse
+	if err := json.NewDecoder(response.Body).Decode(&result); err != nil {
+		return TokenCountResponse{}, fmt.Errorf("解码响应失败: %v", err)
+	}
+
+	return result, nil
+}
+
+// 使用Gemini API计算token
+func countTokensWithGemini(req TokenCountRequest) (TokenCountResponse, error) {
+	// 检查API密钥
+	if config.GoogleKey == "" {
+		return TokenCountResponse{}, fmt.Errorf("GOOGLE_API_KEY 未设置")
+	}
+
+	// 创建Gemini客户端
+	ctx := context.Background()
+	client, err := genai.NewClient(ctx, option.WithAPIKey(config.GoogleKey))
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("创建Gemini客户端失败: %v", err)
+	}
+	defer client.Close()
+
+	// 使用已经匹配好的模型名称
+	modelName := req.Model
+
+	// 创建Gemini模型
+	model := client.GenerativeModel(modelName)
+
+	// 构建提示内容
+	var content string
+	if req.System != nil && *req.System != "" {
+		content += *req.System + "\n\n"
+	}
+
+	for _, msg := range req.Messages {
+		if msg.Role == "user" {
+			content += "用户: " + msg.Content + "\n"
+		} else if msg.Role == "assistant" {
+			content += "助手: " + msg.Content + "\n"
+		} else {
+			content += msg.Role + ": " + msg.Content + "\n"
+		}
+	}
+
+	// 计算token
+	tokResp, err := model.CountTokens(ctx, genai.Text(content))
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("计算Gemini token失败: %v", err)
+	}
+
+	return TokenCountResponse{InputTokens: int(tokResp.TotalTokens)}, nil
+}
+
+// 使用Deepseek API计算token
+func countTokensWithDeepseek(req TokenCountRequest) (TokenCountResponse, error) {
+	// 准备请求
+	client := &http.Client{}
+	data, err := json.Marshal(req)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("序列化请求失败: %v", err)
+	}
+
+	// 创建请求
+	request, err := http.NewRequest("POST", config.DeepseekURL+"/count_tokens", bytes.NewBuffer(data))
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("创建请求失败: %v", err)
+	}
+
+	// 设置请求头
+	request.Header.Set("Content-Type", "application/json")
+
+	// 发送请求
+	response, err := client.Do(request)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("发送请求到Deepseek服务失败: %v", err)
+	}
+	defer response.Body.Close()
+
+	// 读取响应
+	var result TokenCountResponse
+	if err := json.NewDecoder(response.Body).Decode(&result); err != nil {
+		return TokenCountResponse{}, fmt.Errorf("解码响应失败: %v", err)
+	}
+
+	return result, nil
+}
+
+// 使用OpenAI API计算token
+func countTokensWithOpenAI(req TokenCountRequest) (TokenCountResponse, error) {
+	// 准备请求
+	client := &http.Client{}
+	data, err := json.Marshal(req)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("序列化请求失败: %v", err)
+	}
+
+	// 创建请求
+	request, err := http.NewRequest("POST", config.OpenAIURL+"/count_tokens", bytes.NewBuffer(data))
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("创建请求失败: %v", err)
+	}
+
+	// 设置请求头
+	request.Header.Set("Content-Type", "application/json")
+
+	// 发送请求
+	response, err := client.Do(request)
+	if err != nil {
+		return TokenCountResponse{}, fmt.Errorf("发送请求到OpenAI服务失败: %v", err)
+	}
+	defer response.Body.Close()
+
+	// 读取响应
+	var result struct {
+		InputTokens int    `json:"input_tokens"`
+		Model       string `json:"model"`
+		Encoding    string `json:"encoding"`
+	}
+	if err := json.NewDecoder(response.Body).Decode(&result); err != nil {
+		return TokenCountResponse{}, fmt.Errorf("解码响应失败: %v", err)
+	}
+
+	return TokenCountResponse{InputTokens: result.InputTokens}, nil
+}
+
+// 计算token
+func countTokens(c *gin.Context) {
+	var req TokenCountRequest
+	if err := c.ShouldBindJSON(&req); err != nil {
+		c.JSON(http.StatusBadRequest, ErrorResponse{Error: err.Error()})
+		return
+	}
+
+	// 保存原始模型名称
+	originalModel := req.Model
+
+	// 检查是否为不支持的模型
+	isUnsupportedModel := true
+
+	// 检查是否为支持的模型类型
+	modelLower := strings.ToLower(req.Model)
+	if strings.Contains(modelLower, "gpt") || strings.Contains(modelLower, "openai") ||
+		strings.Contains(modelLower, "o1") || strings.Contains(modelLower, "o3") ||
+		strings.HasPrefix(modelLower, "claude") ||
+		strings.Contains(modelLower, "gemini") ||
+		strings.Contains(modelLower, "deepseek") {
+		isUnsupportedModel = false
+	}
+
+	// 智能匹配模型名称
+	req.Model = matchModelName(req.Model)
+
+	var result TokenCountResponse
+	var err error
+
+	// 优先检查是否为Deepseek模型
+	if strings.Contains(strings.ToLower(req.Model), "deepseek") {
+		// 使用Deepseek API
+		result, err = countTokensWithDeepseek(req)
+	} else if strings.Contains(strings.ToLower(req.Model), "gpt") || strings.Contains(strings.ToLower(req.Model), "openai") {
+		// 使用OpenAI API
+		result, err = countTokensWithOpenAI(req)
+	} else if strings.HasPrefix(strings.ToLower(req.Model), "claude") {
+		// 使用Claude API
+		if config.AnthropicKey == "" {
+			c.JSON(http.StatusBadRequest, ErrorResponse{Error: "ANTHROPIC_API_KEY 未设置，无法使用Claude模型"})
+			return
+		}
+		result, err = countTokensWithClaude(req)
+	} else if strings.Contains(strings.ToLower(req.Model), "gemini") {
+		// 使用Gemini API
+		if config.GoogleKey == "" {
+			c.JSON(http.StatusBadRequest, ErrorResponse{Error: "GOOGLE_API_KEY 未设置，无法使用Gemini模型"})
+			return
+		}
+		result, err = countTokensWithGemini(req)
+	} else if isUnsupportedModel {
+		// 不支持的模型，使用GPT-4o估算
+		// 创建新的请求，使用GPT-4o
+		gptReq := req
+		gptReq.Model = "gpt-4o"
+
+		// 使用OpenAI API
+		result, err = countTokensWithOpenAI(gptReq)
+
+		if err == nil {
+			// 返回估算值，但添加警告信息
+			c.JSON(http.StatusOK, gin.H{
+				"input_tokens":   result.InputTokens,
+				"warning":        fmt.Sprintf("The tokenizer for model '%s' is not supported yet. This is an estimation based on gpt-4o and may not be accurate.", originalModel),
+				"estimated_with": "gpt-4o",
+			})
+			return
+		}
+	} else {
+		// 完全不支持的情况，返回错误但仍提供估算值
+		// 使用GPT-4o进行估算
+		gptReq := req
+		gptReq.Model = "gpt-4o"
+
+		estimatedResult, estimateErr := countTokensWithOpenAI(gptReq)
+		if estimateErr == nil {
+			c.JSON(http.StatusOK, gin.H{
+				"input_tokens":   estimatedResult.InputTokens,
+				"warning":        fmt.Sprintf("The tokenizer for model '%s' is not supported yet. This is an estimation based on gpt-4o and may not be accurate.", originalModel),
+				"estimated_with": "gpt-4o",
+			})
+		} else {
+			c.JSON(http.StatusBadRequest, ErrorResponse{Error: fmt.Sprintf("The tokenizer for model '%s' is not supported yet.", originalModel)})
+		}
+		return
+	}
+
+	if err != nil {
+		c.JSON(http.StatusInternalServerError, ErrorResponse{Error: err.Error()})
+		return
+	}
+
+	// 返回结果
+	c.JSON(http.StatusOK, result)
+}
+
+// 健康检查
+func healthCheck(c *gin.Context) {
+	c.JSON(http.StatusOK, gin.H{
+		"status": "healthy",
+		"time":   time.Now().Format(time.RFC3339),
+	})
+}
+
+// 防休眠任务
+func startKeepAlive() {
+	if config.ServiceURL == "" {
+		return
+	}
+
+	healthURL := fmt.Sprintf("%s/health", config.ServiceURL)
+	ticker := time.NewTicker(10 * time.Hour)
+
+	// 立即执行一次检查
+	go func() {
+		log.Printf("Starting keep-alive checks to %s", healthURL)
+		for {
+			resp, err := http.Get(healthURL)
+			if err != nil {
+				log.Printf("Keep-alive check failed: %v", err)
+			} else {
+				resp.Body.Close()
+				log.Printf("Keep-alive check successful")
+			}
+
+			// 等待下一次触发
+			<-ticker.C
+		}
+	}()
+}
+
+func main() {
+	// 加载配置
+	loadConfig()
+
+	// 设置gin模式
+	gin.SetMode(gin.ReleaseMode)
+
+	// 创建路由
+	r := gin.Default()
+
+	// 添加中间件
+	r.Use(gin.Recovery())
+	r.Use(func(c *gin.Context) {
+		c.Writer.Header().Set("Access-Control-Allow-Origin", "*")
+		c.Writer.Header().Set("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
+		c.Writer.Header().Set("Access-Control-Allow-Headers", "Content-Type")
+		if c.Request.Method == "OPTIONS" {
+			c.AbortWithStatus(204)
+			return
+		}
+		c.Next()
+	})
+
+	// 路由
+	r.GET("/health", healthCheck)
+	r.POST("/count_tokens", countTokens)
+
+	// 获取端口
+	port := os.Getenv("PORT")
+	if port == "" {
+		port = "7860" // Hugging Face默认端口
+	}
+
+	// 启动防休眠任务
+	startKeepAlive()
+
+	// 启动服务器
+	log.Printf("Server starting on port %s", port)
+	if err := r.Run(":" + port); err != nil {
+		log.Fatal(err)
+	}
+}

openai_service.py

@@ -0,0 +1,118 @@
+from flask import Flask, request, jsonify
+import tiktoken
+import os
+
+app = Flask(__name__)
+
+# OpenAI模型映射
+MODEL_MAPPINGS = {
+    # GPT-4系列
+    "gpt-4o": "o200k_base",
+    "gpt-4-turbo": "cl100k_base",
+    "gpt-4": "cl100k_base",
+    
+    # GPT-3.5系列
+    "gpt-3.5-turbo": "cl100k_base",
+    "gpt-35-turbo": "cl100k_base",
+    
+    # 旧模型
+    "text-davinci-003": "p50k_base",
+    "text-davinci-002": "p50k_base",
+    "davinci": "r50k_base",
+    
+    # 嵌入模型
+    "text-embedding-ada-002": "cl100k_base",
+}
+
+@app.route('/count_tokens', methods=['POST'])
+def count_tokens():
+    try:
+        data = request.json
+        messages = data.get('messages', [])
+        system = data.get('system')
+        model = data.get('model', 'gpt-3.5-turbo')
+        
+        # 根据模型名称选择合适的编码器
+        model_key = model.lower()
+        encoding_name = None
+        
+        # 查找完全匹配
+        if model_key in MODEL_MAPPINGS:
+            encoding_name = MODEL_MAPPINGS[model_key]
+        else:
+            # 查找部分匹配
+            for key in MODEL_MAPPINGS:
+                if key in model_key:
+                    encoding_name = MODEL_MAPPINGS[key]
+                    break
+        
+        # 如果没有找到匹配，使用默认的cl100k_base编码器
+        if not encoding_name:
+            encoding_name = "cl100k_base"  # 最常用的编码器
+        
+        # 获取编码器
+        try:
+            encoding = tiktoken.get_encoding(encoding_name)
+        except KeyError:
+            # 如果找不到编码器，使用gpt-3.5-turbo的编码器
+            encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
+        
+        # 计算tokens
+        total_tokens = 0
+        
+        # 按照OpenAI的格式计算tokens
+        # 参考: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
+        
+        # 对于ChatGPT模型，每个请求都有3个隐藏tokens
+        if encoding_name in ["cl100k_base", "o200k_base"]:
+            # 每条消息开头有3个token，结尾有1个token
+            total_tokens += 3  # 每个请求的起始tokens
+            
+            # 计算每条消息的tokens
+            for message in messages:
+                total_tokens += 4  # 每条消息增加4个token (包括角色)
+                
+                for key, value in message.items():
+                    total_tokens += len(encoding.encode(value))
+                    
+                    # 名称字段比较少见，但也计入
+                    if key == "name":
+                        total_tokens -= 1  # 角色名称单独token计算减免
+            
+            # 计算system消息的token
+            if system:
+                total_tokens += 4  # system消息也视为一条消息
+                total_tokens += len(encoding.encode(system))
+        else:
+            # 对于旧模型，只计算文本的token数量
+            all_text = ""
+            if system:
+                all_text += system + "\n\n"
+                
+            for message in messages:
+                role = message.get('role', '')
+                content = message.get('content', '')
+                all_text += f"{role}: {content}\n"
+                
+            total_tokens = len(encoding.encode(all_text))
+        
+        return jsonify({
+            'input_tokens': total_tokens,
+            'model': model,
+            'encoding': encoding_name
+        })
+    except Exception as e:
+        return jsonify({
+            'error': str(e)
+        }), 400
+
+@app.route('/health', methods=['GET'])
+def health():
+    return jsonify({
+        'status': 'healthy',
+        'tokenizer': 'openai-tiktoken',
+        'supported_models': list(MODEL_MAPPINGS.keys())
+    })
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=7862)