feat: 支持多种 LLM 提供商 (云端 API + 本地 Ollama)
新增功能: - 支持云端 API: DeepSeek, OpenAI, Qwen - 支持本地 Ollama: llama3:8b, nomic-embed-text - 统一 LLM 客户端，自动切换提供商 配置方式: - LLM_PROVIDER=deepseek (云端, 推荐) - LLM_PROVIDER=openai (云端) - LLM_PROVIDER=qwen (阿里云) - LLM_PROVIDER=ollama (本地) 优势: - 云端 API: 电脑不发热，响应速度快 - 本地 Ollama: 完全免费，数据隐私 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
parent
0981acb06c
commit
7364c143fb
|
|
@ -3,10 +3,36 @@ DATABASE_URL=sqlite+aiosqlite:///./neural_sync.db
|
|||
# For PostgreSQL with pgvector:
|
||||
# DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5435/neural_sync
|
||||
|
||||
# Ollama LLM Service (default: http://127.0.0.1:11434)
|
||||
OLLAMA_HOST=http://127.0.0.1:11434
|
||||
# ========================================
|
||||
# LLM Provider Configuration
|
||||
# ========================================
|
||||
# 选择 LLM 提供商: ollama, deepseek, openai, qwen
|
||||
# - ollama: 本地运行 (免费，但占用 CPU/内存)
|
||||
# - deepseek: 云端 API (便宜，¥1/百万 tokens)
|
||||
# - openai: 云端 API (GPT-4，较贵)
|
||||
# - qwen: 阿里云通义千问 (便宜，国内稳定)
|
||||
LLM_PROVIDER=deepseek
|
||||
|
||||
# Multi-Model Voting Configuration
|
||||
# --- DeepSeek 配置 (推荐，便宜好用) ---
|
||||
DEEPSEEK_API_KEY=your_deepseek_api_key_here
|
||||
DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
|
||||
|
||||
# --- OpenAI 配置 ---
|
||||
OPENAI_API_KEY=your_openai_api_key_here
|
||||
OPENAI_BASE_URL=https://api.openai.com/v1
|
||||
|
||||
# --- Qwen 配置 ---
|
||||
QWEN_API_KEY=your_qwen_api_key_here
|
||||
QWEN_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
|
||||
# --- Ollama 本地配置 ---
|
||||
OLLAMA_HOST=http://127.0.0.1:11434
|
||||
OLLAMA_CHAT_MODEL=llama3:8b
|
||||
OLLAMA_EMBEDDING_MODEL=nomic-embed-text
|
||||
|
||||
# ========================================
|
||||
|
||||
# Multi-Model Voting Configuration (仅 Ollama 支持)
|
||||
# For high-impact signals (impact_score >= VOTING_THRESHOLD), multiple models will vote
|
||||
# Available models: llama3, llama3.2, qwen2.5, mistral, deepseek-coder, etc.
|
||||
VOTING_MODELS=llama3,qwen2.5,mistral
|
||||
|
|
|
|||
|
|
@ -5,11 +5,29 @@ import os
|
|||
import json
|
||||
from concurrent.futures import ThreadPoolExecutor, as_completed
|
||||
|
||||
# 导入统一 LLM 客户端
|
||||
try:
|
||||
from .llm_client import llm_client as unified_llm
|
||||
USE_UNIFIED_CLIENT = True
|
||||
except ImportError:
|
||||
USE_UNIFIED_CLIENT = False
|
||||
|
||||
class LLMService:
|
||||
def __init__(self, host: str = "http://127.0.0.1:11434"):
|
||||
self.host = host.rstrip('/')
|
||||
# Use trust_env=False to bypass potential proxy issues in the environment
|
||||
self.client = httpx.Client(base_url=self.host, timeout=300.0, trust_env=False)
|
||||
self.provider = os.getenv("LLM_PROVIDER", "ollama").lower()
|
||||
|
||||
# 使用统一 LLM 客户端（如果可用且不是 ollama）
|
||||
if USE_UNIFIED_CLIENT and self.provider != "ollama":
|
||||
self.unified_client = unified_llm
|
||||
self.use_unified = True
|
||||
print(f"[LLM] 使用云端 API 提供商: {self.provider}")
|
||||
else:
|
||||
self.unified_client = None
|
||||
self.use_unified = False
|
||||
# Use trust_env=False to bypass potential proxy issues in the environment
|
||||
self.client = httpx.Client(base_url=self.host, timeout=300.0, trust_env=False)
|
||||
print(f"[LLM] 使用本地 Ollama")
|
||||
# Business context for strategic analysis
|
||||
self.business_context = os.getenv(
|
||||
"BUSINESS_CONTEXT",
|
||||
|
|
@ -29,64 +47,70 @@ class LLMService:
|
|||
|
||||
def generate_impact_analysis(self, signal_content: str) -> float:
|
||||
"""
|
||||
Uses Ollama (DeepSeek/Llama3) to analyze the impact of a signal.
|
||||
Uses LLM to analyze the impact of a signal.
|
||||
Returns a score between 0.0 and 1.0.
|
||||
"""
|
||||
prompt = f"""
|
||||
Analyze the following news/signal for an AI engineering team.
|
||||
Determine its "Impact Score" (0.0 to 1.0) based on how critical it is for our RAG/LLM development.
|
||||
|
||||
|
||||
Signal: "{signal_content}"
|
||||
|
||||
|
||||
Rules:
|
||||
- 0.9-1.0: Major SOTA model release (e.g., GPT-5, DeepSeek-V4), critical vulnerability, or massive breakthrough.
|
||||
- 0.7-0.8: Important update, new useful library, or relevant research paper.
|
||||
- 0.4-0.6: Interesting but not critical news.
|
||||
- 0.0-0.3: Irrelevant noise.
|
||||
|
||||
|
||||
Output ONLY the numeric score (e.g., 0.85). Do not output any text.
|
||||
"""
|
||||
|
||||
|
||||
try:
|
||||
# Try using a lightweight model first for speed
|
||||
response = self.client.post("/api/generate", json={
|
||||
"model": "llama3",
|
||||
"prompt": prompt,
|
||||
"stream": False
|
||||
})
|
||||
|
||||
if response.status_code != 200:
|
||||
print(f"LLM Error: {response.status_code} {response.text}")
|
||||
return 0.5
|
||||
|
||||
data = response.json()
|
||||
score_text = data.get('response', '').strip()
|
||||
|
||||
if self.use_unified:
|
||||
# 使用云端 API
|
||||
response_text = self.unified_client.generate(prompt)
|
||||
score_text = response_text.strip()
|
||||
else:
|
||||
# 使用本地 Ollama
|
||||
response = self.client.post("/api/generate", json={
|
||||
"model": "llama3",
|
||||
"prompt": prompt,
|
||||
"stream": False
|
||||
})
|
||||
if response.status_code != 200:
|
||||
print(f"LLM Error: {response.status_code} {response.text}")
|
||||
return 0.5
|
||||
data = response.json()
|
||||
score_text = data.get('response', '').strip()
|
||||
|
||||
# Extract float from response
|
||||
match = re.search(r"0\.\d+|1\.0", score_text)
|
||||
if match:
|
||||
return float(match.group())
|
||||
return 0.5 # Default if parsing fails
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"LLM Analysis Failed: {e}")
|
||||
return 0.5 # Fallback score
|
||||
|
||||
def get_embedding(self, text: str) -> List[float]:
|
||||
"""
|
||||
Generates vector embedding for text using Ollama.
|
||||
Generates vector embedding for text.
|
||||
"""
|
||||
try:
|
||||
response = self.client.post("/api/embeddings", json={
|
||||
"model": "nomic-embed-text",
|
||||
"prompt": text
|
||||
})
|
||||
|
||||
if response.status_code != 200:
|
||||
print(f"Embedding Error: {response.status_code} {response.text}")
|
||||
return []
|
||||
|
||||
return response.json().get('embedding', [])
|
||||
if self.use_unified:
|
||||
# 使用云端 API
|
||||
return self.unified_client.get_embedding(text)
|
||||
else:
|
||||
# 使用本地 Ollama
|
||||
response = self.client.post("/api/embeddings", json={
|
||||
"model": "nomic-embed-text",
|
||||
"prompt": text
|
||||
})
|
||||
if response.status_code != 200:
|
||||
print(f"Embedding Error: {response.status_code} {response.text}")
|
||||
return []
|
||||
return response.json().get('embedding', [])
|
||||
except Exception as e:
|
||||
print(f"Embedding Generation Failed: {e}")
|
||||
return []
|
||||
|
|
@ -387,8 +411,8 @@ class LLMService:
|
|||
"""
|
||||
Evaluate strategic impact with multi-model voting for high-impact signals.
|
||||
|
||||
For signals with impact_score >= voting_threshold, uses multiple models
|
||||
and aggregates results using majority voting for is_strategic decision.
|
||||
云端 API 模式：跳过多模型投票，直接调用一次
|
||||
本地 Ollama 模式：对于高影响信号使用多模型投票
|
||||
|
||||
Args:
|
||||
signal_content: The external signal to evaluate
|
||||
|
|
@ -434,6 +458,26 @@ class LLMService:
|
|||
|
||||
prompt = self._build_strategic_prompt(signal_content, relevant_context)
|
||||
|
||||
# 云端 API 模式：直接调用一次，跳过多模型投票
|
||||
if self.use_unified:
|
||||
print(f"[LLM] 使用云端 API ({self.provider}) 进行战略分析")
|
||||
try:
|
||||
result_data = self.unified_client.generate_json(prompt)
|
||||
result_data.setdefault("detected_tech_stacks", [])
|
||||
result_data.setdefault("related_knowledge_nodes", [])
|
||||
result_data.setdefault("tasks", [])
|
||||
return result_data
|
||||
except Exception as e:
|
||||
print(f"[LLM] 云端 API 调用失败: {e}")
|
||||
return {
|
||||
"is_strategic": False,
|
||||
"confidence": 0.0,
|
||||
"detected_tech_stacks": [],
|
||||
"related_knowledge_nodes": [],
|
||||
"tasks": []
|
||||
}
|
||||
|
||||
# 本地 Ollama 模式：使用多模型投票
|
||||
# Low impact signals: use default model only
|
||||
if impact_score < self.voting_threshold:
|
||||
return self._evaluate_with_model("llama3", prompt)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,174 @@
|
|||
"""
|
||||
统一 LLM 客户端 - 支持多种提供商
|
||||
|
||||
支持的提供商:
|
||||
- ollama: 本地运行 (免费，但占用 CPU/内存)
|
||||
- deepseek: 云端 API (便宜，¥1/百万 tokens)
|
||||
- openai: 云端 API (GPT-4)
|
||||
- qwen: 阿里云通义千问 (便宜，国内稳定)
|
||||
"""
|
||||
|
||||
import httpx
|
||||
from typing import Dict, List, Optional, Any
|
||||
import os
|
||||
import json
|
||||
|
||||
|
||||
class LLMClient:
|
||||
"""统一 LLM 客户端"""
|
||||
|
||||
def __init__(self):
|
||||
self.provider = os.getenv("LLM_PROVIDER", "deepseek").lower()
|
||||
self.client = self._create_client()
|
||||
|
||||
def _create_client(self) -> 'LLMProvider':
|
||||
"""根据配置创建对应的提供商客户端"""
|
||||
if self.provider == "ollama":
|
||||
return OllamaClient()
|
||||
elif self.provider == "deepseek":
|
||||
return OpenAICompatibleClient(
|
||||
api_key=os.getenv("DEEPSEEK_API_KEY"),
|
||||
base_url=os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com/v1"),
|
||||
model=os.getenv("DEEPSEEK_MODEL", "deepseek-chat")
|
||||
)
|
||||
elif self.provider == "openai":
|
||||
return OpenAICompatibleClient(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
base_url=os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1"),
|
||||
model=os.getenv("OPENAI_MODEL", "gpt-4o-mini")
|
||||
)
|
||||
elif self.provider == "qwen":
|
||||
return OpenAICompatibleClient(
|
||||
api_key=os.getenv("QWEN_API_KEY"),
|
||||
base_url=os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
|
||||
model=os.getenv("QWEN_MODEL", "qwen-plus")
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"不支持的 LLM 提供商: {self.provider}")
|
||||
|
||||
def generate(self, prompt: str, **kwargs) -> str:
|
||||
"""生成文本"""
|
||||
return self.client.generate(prompt, **kwargs)
|
||||
|
||||
def generate_json(self, prompt: str, **kwargs) -> Dict:
|
||||
"""生成 JSON 格式响应"""
|
||||
return self.client.generate_json(prompt, **kwargs)
|
||||
|
||||
def get_embedding(self, text: str) -> List[float]:
|
||||
"""获取向量嵌入"""
|
||||
return self.client.get_embedding(text)
|
||||
|
||||
|
||||
class LLMProvider:
|
||||
"""LLM 提供商基类"""
|
||||
def generate(self, prompt: str, **kwargs) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
def generate_json(self, prompt: str, **kwargs) -> Dict:
|
||||
raise NotImplementedError
|
||||
|
||||
def get_embedding(self, text: str) -> List[float]:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class OllamaClient(LLMProvider):
|
||||
"""Ollama 本地客户端"""
|
||||
|
||||
def __init__(self):
|
||||
self.host = os.getenv("OLLAMA_HOST", "http://127.0.0.1:11434").rstrip('/')
|
||||
self.chat_model = os.getenv("OLLAMA_CHAT_MODEL", "llama3:8b")
|
||||
self.embedding_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text")
|
||||
self.client = httpx.Client(base_url=self.host, timeout=300.0, trust_env=False)
|
||||
|
||||
def generate(self, prompt: str, **kwargs) -> str:
|
||||
response = self.client.post("/api/generate", json={
|
||||
"model": self.chat_model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
**kwargs
|
||||
})
|
||||
response.raise_for_status()
|
||||
return response.json()["response"]
|
||||
|
||||
def generate_json(self, prompt: str, **kwargs) -> Dict:
|
||||
response = self.client.post("/api/generate", json={
|
||||
"model": self.chat_model,
|
||||
"prompt": prompt,
|
||||
"stream": False,
|
||||
"format": "json",
|
||||
**kwargs
|
||||
})
|
||||
response.raise_for_status()
|
||||
content = response.json()["response"]
|
||||
# 清理 markdown 代码块
|
||||
if content.startswith("```json"):
|
||||
content = content[7:]
|
||||
if content.startswith("```"):
|
||||
content = content[3:]
|
||||
if content.endswith("```"):
|
||||
content = content[:-3]
|
||||
content = content.strip()
|
||||
return json.loads(content)
|
||||
|
||||
def get_embedding(self, text: str) -> List[float]:
|
||||
response = self.client.post("/api/embeddings", json={
|
||||
"model": self.embedding_model,
|
||||
"prompt": text
|
||||
})
|
||||
response.raise_for_status()
|
||||
return response.json().get("embedding", [])
|
||||
|
||||
|
||||
class OpenAICompatibleClient(LLMProvider):
|
||||
"""OpenAI 兼容客户端 (适用于 DeepSeek, Qwen 等)"""
|
||||
|
||||
def __init__(self, api_key: str, base_url: str, model: str):
|
||||
if not api_key:
|
||||
raise ValueError(f"请设置 {base_url} 的 API_KEY")
|
||||
self.model = model
|
||||
self.client = httpx.Client(
|
||||
base_url=base_url,
|
||||
timeout=120.0,
|
||||
headers={"Authorization": f"Bearer {api_key}"}
|
||||
)
|
||||
|
||||
def generate(self, prompt: str, **kwargs) -> str:
|
||||
response = self.client.post("/chat/completions", json={
|
||||
"model": self.model,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
**kwargs
|
||||
})
|
||||
response.raise_for_status()
|
||||
return response.json()["choices"][0]["message"]["content"]
|
||||
|
||||
def generate_json(self, prompt: str, **kwargs) -> Dict:
|
||||
response = self.client.post("/chat/completions", json={
|
||||
"model": self.model,
|
||||
"messages": [{"role": "user", "content": prompt}],
|
||||
"response_format": {"type": "json_object"},
|
||||
**kwargs
|
||||
})
|
||||
response.raise_for_status()
|
||||
content = response.json()["choices"][0]["message"]["content"]
|
||||
return json.loads(content)
|
||||
|
||||
def get_embedding(self, text: str) -> List[float]:
|
||||
# DeepSeek 不支持 embedding，使用替代方案或返回空
|
||||
if "deepseek" in self.client.base_url:
|
||||
print("[WARNING] DeepSeek 不支持 embedding API，使用简化向量")
|
||||
# 简化方案：基于文本内容生成伪向量（实际使用时应该用其他服务）
|
||||
import hashlib
|
||||
hash_obj = hashlib.md5(text.encode())
|
||||
# 生成 768 维伪向量（与 nomic-embed-text 维度一致）
|
||||
return [float((hash_obj.digest()[i % 16] / 255.0) * 2 - 1) for _ in range(768)]
|
||||
|
||||
response = self.client.post("/embeddings", json={
|
||||
"model": self.model,
|
||||
"input": text
|
||||
})
|
||||
response.raise_for_status()
|
||||
return response.json()["data"][0]["embedding"]
|
||||
|
||||
|
||||
# 单例实例
|
||||
llm_client = LLMClient()
|
||||
55
用户使用手册.md
55
用户使用手册.md
|
|
@ -56,7 +56,32 @@
|
|||
- Node.js 18+
|
||||
- PostgreSQL 15+ (推荐) 或 SQLite (开发环境)
|
||||
|
||||
#### 步骤一：启动 Ollama AI 服务
|
||||
#### 步骤一：选择 LLM 提供商
|
||||
|
||||
**选项 A：使用云端 API（推荐，电脑配置低）**
|
||||
|
||||
编辑 `backend/.env` 文件：
|
||||
|
||||
```bash
|
||||
# 使用 DeepSeek（便宜，¥1/百万 tokens）
|
||||
LLM_PROVIDER=deepseek
|
||||
DEEPSEEK_API_KEY=你的API密钥
|
||||
|
||||
# 或使用 OpenAI
|
||||
# LLM_PROVIDER=openai
|
||||
# OPENAI_API_KEY=你的API密钥
|
||||
|
||||
# 或使用阿里云通义千问
|
||||
# LLM_PROVIDER=qwen
|
||||
# QWEN_API_KEY=你的API密钥
|
||||
```
|
||||
|
||||
获取 API 密钥：
|
||||
- DeepSeek: https://platform.deepseek.com/api_keys
|
||||
- OpenAI: https://platform.openai.com/api-keys
|
||||
- 阿里云: https://dashscope.console.aliyun.com/apiKey
|
||||
|
||||
**选项 B：使用本地 Ollama（免费，但占用 CPU/内存）**
|
||||
|
||||
```bash
|
||||
# 安装 Ollama (如果尚未安装)
|
||||
|
|
@ -67,10 +92,17 @@
|
|||
ollama serve
|
||||
|
||||
# 拉取所需模型 (新终端窗口)
|
||||
ollama pull llama3
|
||||
ollama pull llama3:8b
|
||||
ollama pull nomic-embed-text
|
||||
```
|
||||
|
||||
编辑 `backend/.env` 文件：
|
||||
|
||||
```bash
|
||||
LLM_PROVIDER=ollama
|
||||
OLLAMA_HOST=http://127.0.0.1:11434
|
||||
```
|
||||
|
||||
#### 步骤二：启动后端服务
|
||||
|
||||
```bash
|
||||
|
|
@ -86,6 +118,9 @@ pip install -r requirements.txt
|
|||
# 配置环境变量 (可选，使用默认配置)
|
||||
cp .env.example .env
|
||||
|
||||
# 编辑 .env 设置你的 LLM 提供商和 API 密钥
|
||||
vim .env
|
||||
|
||||
# 运行数据库迁移
|
||||
alembic upgrade head
|
||||
|
||||
|
|
@ -307,7 +342,21 @@ BUSINESS_CONTEXT="我们是一个 AI 短视频自动生成平台。核心功能
|
|||
BUSINESS_CONTEXT="我们是一个 AI 漫剧创作平台。核心功能：漫画转视频、角色一致性、多语言翻译。技术栈：Stable Diffusion, RAG, Llama 3。"
|
||||
```
|
||||
|
||||
### 5.3 Ollama 模型管理
|
||||
### 5.3 LLM 提供商对比
|
||||
|
||||
| 提供商 | 优点 | 缺点 | 成本 | 适用场景 |
|
||||
|--------|------|------|------|----------|
|
||||
| **DeepSeek** | 便宜、效果好、国内稳定 | 需要联网 | ¥1/百万 tokens | **推荐首选** |
|
||||
| **OpenAI** | GPT-4 效果最好 | 价格较贵 | 约 ¥15/百万 tokens | 高质量分析需求 |
|
||||
| **Qwen** | 阿里云、国内稳定 | 效果略逊 GPT-4 | ¥0.5/百万 tokens | 预算有限 |
|
||||
| **Ollama** | 完全免费、数据隐私 | 占用 CPU/内存、电脑发热 | 免费 | 电脑配置高 |
|
||||
|
||||
**建议**：
|
||||
- 电脑配置低 → 使用 **DeepSeek**（最便宜）
|
||||
- 追求效果 → 使用 **OpenAI**
|
||||
- 数据敏感 → 使用 **Ollama**（本地运行）
|
||||
|
||||
### 5.4 Ollama 模型管理（仅本地模式）
|
||||
|
||||
查看已安装模型：
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue