feat: 增强 AI-Core 文档解析器
- 添加 VLM 客户端支持 - 优化解析器配置 - 添加配置示例文件 - 生成新的 gRPC protobuf 文件 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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ai-core/parser/vlm_client.py
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209
ai-core/parser/vlm_client.py
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"""
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VLM 客户端 - 用于调用 VLM 模型进行文档理解
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"""
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import logging
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import base64
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import requests
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from typing import Optional, Dict, Any
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logger = logging.getLogger(__name__)
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class VLMClient:
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"""VLM 客户端，支持多种提供商"""
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def __init__(self, config: Dict[str, Any]):
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"""
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初始化 VLM 客户端
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Args:
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config: VLM 配置，包含 provider, model, api_key, base_url, prompt 等
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"""
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self.config = config
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self.provider = config.get("provider", "openai")
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self.model = config.get("model", "gpt-4o")
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self.api_key = config.get("api_key", "")
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self.base_url = config.get("base_url", "")
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self.prompt = config.get("prompt", "") or self._default_prompt()
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logger.info(f"VLMClient initialized: provider={self.provider}, model={self.model}")
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def _default_prompt(self) -> str:
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"""默认提示词"""
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return """请分析这张图片中的文档内容，并将其转换为 Markdown 格式。
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要求：
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1. 保持原文的格式和结构
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2. 表格用 Markdown 表格格式
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3. 标题用 # ## ### 标记
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4. 代码块用 ``` 标记
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5. 尽量保留原文的所有信息"""
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def analyze_image(self, image_data: bytes, mime_type: str = "image/png") -> Dict[str, Any]:
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"""
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使用 VLM 分析图片
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Args:
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image_data: 图片二进制数据
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mime_type: 图片 MIME 类型
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Returns:
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包含分析结果的字典
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"""
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if self.provider == "openai":
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return self._call_openai(image_data, mime_type)
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elif self.provider == "anthropic":
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return self._call_anthropic(image_data, mime_type)
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elif self.provider == "qwen":
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return self._call_qwen(image_data, mime_type)
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else:
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return {
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"success": False,
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"content": "",
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"error": f"Unsupported provider: {self.provider}"
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}
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def _call_openai(self, image_data: bytes, mime_type: str) -> Dict[str, Any]:
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"""调用 OpenAI GPT-4o API"""
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try:
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url = (self.base_url or "https://api.openai.com/v1") + "/chat/completions"
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# Base64 编码图片
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image_base64 = base64.b64encode(image_data).decode("utf-8")
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data_url = f"data:{mime_type};base64,{image_base64}"
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": self.prompt},
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{"type": "image_url", "image_url": {"url": data_url}}
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]
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}
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],
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"max_tokens": 4096
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}
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response = requests.post(url, headers=headers, json=payload, timeout=120)
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response.raise_for_status()
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result = response.json()
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content = result["choices"][0]["message"]["content"]
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return {
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"success": True,
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"content": content,
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"usage": result.get("usage", {})
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}
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except Exception as e:
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logger.error(f"OpenAI API error: {e}")
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return {
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"success": False,
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"content": "",
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"error": str(e)
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}
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def _call_anthropic(self, image_data: bytes, mime_type: str) -> Dict[str, Any]:
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"""调用 Anthropic Claude API"""
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try:
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url = (self.base_url or "https://api.anthropic.com/v1") + "/messages"
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image_base64 = base64.b64encode(image_data).decode("utf-8")
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headers = {
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"x-api-key": self.api_key,
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"anthropic-version": "2023-06-01",
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"Content-Type": "application/json"
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}
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# Anthropic 支持 image 类型
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payload = {
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"model": self.model,
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"max_tokens": 4096,
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": self.prompt},
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{
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"type": "image",
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"source": {
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"type": "base64",
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"media_type": mime_type,
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"data": image_base64
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}
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}
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]
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}
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]
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}
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response = requests.post(url, headers=headers, json=payload, timeout=120)
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response.raise_for_status()
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result = response.json()
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content = result["content"][0]["text"]
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return {
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"success": True,
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"content": content,
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"usage": result.get("usage", {})
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}
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except Exception as e:
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logger.error(f"Anthropic API error: {e}")
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return {
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"success": False,
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"content": "",
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"error": str(e)
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}
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def _call_qwen(self, image_data: bytes, mime_type: str) -> Dict[str, Any]:
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"""调用阿里 Qwen VL API"""
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try:
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url = (self.base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1") + "/chat/completions"
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image_base64 = base64.b64encode(image_data).decode("utf-8")
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# Qwen 格式
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payload = {
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"model": self.model,
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": self.prompt},
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{"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{image_base64}"}}
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]
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}
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]
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}
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response = requests.post(url, headers=headers, json=payload, timeout=120)
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response.raise_for_status()
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result = response.json()
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content = result["choices"][0]["message"]["content"]
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return {
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"success": True,
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"content": content,
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"usage": {}
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}
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except Exception as e:
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logger.error(f"Qwen API error: {e}")
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return {
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"success": False,
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"content": "",
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"error": str(e)
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}
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