From 7364c143fbe142840b1ea754bc4e6c18d0e1f31f Mon Sep 17 00:00:00 2001 From: Yuuuu <1569752928@qq.com> Date: Fri, 20 Feb 2026 22:37:31 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=94=AF=E6=8C=81=E5=A4=9A=E7=A7=8D=20?= =?UTF-8?q?LLM=20=E6=8F=90=E4=BE=9B=E5=95=86=20(=E4=BA=91=E7=AB=AF=20API?= =?UTF-8?q?=20+=20=E6=9C=AC=E5=9C=B0=20Ollama)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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 --- backend/.env.example | 32 +++++- backend/app/services/llm.py | 114 +++++++++++++------ backend/app/services/llm_client.py | 174 +++++++++++++++++++++++++++++ 用户使用手册.md | 55 ++++++++- 4 files changed, 334 insertions(+), 41 deletions(-) create mode 100644 backend/app/services/llm_client.py diff --git a/backend/.env.example b/backend/.env.example index 8379453..1c5f723 100644 --- a/backend/.env.example +++ b/backend/.env.example @@ -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 diff --git a/backend/app/services/llm.py b/backend/app/services/llm.py index 95b96b7..4995e14 100644 --- a/backend/app/services/llm.py +++ b/backend/app/services/llm.py @@ -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) diff --git a/backend/app/services/llm_client.py b/backend/app/services/llm_client.py new file mode 100644 index 0000000..6fbf18a --- /dev/null +++ b/backend/app/services/llm_client.py @@ -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() diff --git a/用户使用手册.md b/用户使用手册.md index fca6a1c..8af70fe 100644 --- a/用户使用手册.md +++ b/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：