131 lines
2.9 KiB
Markdown
131 lines
2.9 KiB
Markdown
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# LLM 工厂模块说明
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## 模块概述
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LLM 工厂模块提供统一的 LLM(大语言模型)客户端构建接口,支持多种 LLM 提供商的初始化和管理。
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## 支持的 LLM 提供商
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| 提供商 | 模型 | 说明 |
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|-------|------|------|
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| DeepSeek | deepseek-chat | 深度求索 |
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| OpenAI | gpt-4o-mini, gpt-4 | OpenAI GPT 系列 |
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| 通义千问 | qwen-max, qwen-turbo | 阿里云 |
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| Groq | llama3-70b-8192 | Groq 推理加速 |
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| Moonshot | moonshot-v1-128k | Kimi |
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| 豆包 | 自定义 | 字节跳动 |
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| 文心一言 | ernie-bot-turbo | 百度 |
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## 使用方式
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### 基本用法
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```python
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from modules.llm_factory import build_llm
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# 构建 LLM 实例
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llm = build_llm(
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provider="DeepSeek",
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api_key="sk-xxx",
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model="deepseek-chat",
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temperature=0.7
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)
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# 使用 LLM
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response = llm.invoke("你好")
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```
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### 获取默认模型
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```python
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from modules.llm_factory import get_default_model
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model = get_default_model("DeepSeek")
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# 返回: "deepseek-chat"
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```
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### 获取支持的提供商列表
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```python
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from modules.llm_factory import get_supported_providers
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providers = get_supported_providers()
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# 返回: ["DeepSeek", "OpenAI (GPT)", "Tongyi (通义千问)", ...]
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```
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## 技术实现
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### 核心文件
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| 文件 | 说明 |
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|------|------|
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| `modules/llm_factory.py` | LLM 工厂模块 |
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| `modules/chat_doubao.py` | 豆包聊天模型封装 |
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### 设计模式
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采用工厂模式,统一管理多种 LLM 提供商:
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```
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build_llm(provider, api_key, model, temperature)
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├── DeepSeek → ChatDeepSeek
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├── OpenAI → ChatOpenAI
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├── Tongyi → ChatTongyi
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├── Groq → ChatGroq
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├── Moonshot → ChatMoonshot
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├── 豆包 → ChatDoubao (自定义)
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└── 文心一言 → QianfanChatEndpoint
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```
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### 缓存机制
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在 `geo_tool.py` 中使用 Streamlit 的 `@st.cache_resource` 装饰器缓存 LLM 实例:
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```python
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@st.cache_resource(show_spinner=False)
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def build_llm(provider, api_key, model, temperature):
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from modules.llm_factory import build_llm as _build_llm
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return _build_llm(provider, api_key, model, temperature)
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```
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## 特殊配置
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### 豆包 API Key 格式
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豆包使用特殊的 API Key 格式:
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```
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access_key:secret_key:endpoint_id
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```
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示例:
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```
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AKLTxxx:SKxxx:ep-xxx
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```
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### 文心一言 API Key 格式
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文心一言使用百度千帆平台的 API Key:
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```
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app_key:app_secret
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```
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## 错误处理
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模块提供清晰的错误提示:
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```python
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try:
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llm = build_llm("Unknown", api_key, model, temperature)
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except ValueError as e:
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print(e) # "不支持的 LLM 提供商: Unknown"
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```
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## 后续优化方向
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1. **负载均衡**:支持多个 API Key 轮询使用
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2. **自动降级**:主提供商失败时自动切换到备用
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3. **成本优化**:根据任务复杂度自动选择合适的模型
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4. **性能监控**:记录每个提供商的响应时间和成功率
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