refactor: 统一使用 OpenAI 兼容 API，支持自定义 base_url/key/model

- 移除 Gemini 和 Ollama 独立适配，统一使用 ChatOpenAI + base_url
- config.ini 简化为 BASE_URL / API_KEY / MODEL / TEMPERATURE / MAX_RETRIES
- 新增 config.example.ini 示例配置
- 移除 langchain-google-genai / langchain-ollama / pymupdf 依赖
- main.py 新增断点续跑：跳过已有 index.md / index_refined.md
- LLM 请求支持 max_retries 自动重试（默认 3 次）
- 优化 README

pdf_convertor.py

@@ -1,6 +1,5 @@
 import re
 import base64
-import os
 from docling.datamodel.accelerator_options import AcceleratorDevice, AcceleratorOptions
 from docling.datamodel.base_models import InputFormat
 from docling.datamodel.pipeline_options import (
@@ -11,14 +10,11 @@ from docling.datamodel.settings import settings
 from docling.document_converter import DocumentConverter, PdfFormatOption
 from docling_core.types.doc.base import ImageRefMode
 from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_ollama import ChatOllama
 from langchain_openai import ChatOpenAI
-from llm import set_api_key, get_model_name, get_temperature
+from llm import set_api_key, get_model_name, get_temperature, get_base_url, get_max_retries
 from io import BytesIO
 from pathlib import Path
-import configparser
-import fitz
+import base64
 
 
 def save_md_images(
@@ -117,35 +113,21 @@ def convert_pdf_to_markdown(pdf: bytes) -> tuple[str, dict[str, bytes]]:
 def refine_content(md: str, images: dict[str, bytes], pdf: bytes) -> str:
     """Refines the Markdown content using an LLM."""
 
-    config = configparser.ConfigParser()
-    config.read("config.ini")
-    provider = config.get("llm", "PROVIDER", fallback="gemini")
-
     set_api_key()
 
     try:
-        if provider == "gemini":
-            llm = ChatGoogleGenerativeAI(
-                model=get_model_name(), temperature=get_temperature()
-            )
-        elif provider == "ollama":
-            llm = ChatOllama(
-                model=get_model_name(),
-                temperature=get_temperature(),
-                base_url=os.environ["OLLAMA_BASE_URL"],
-                num_ctx=256000,
-                num_predict=-1,
-            )
-        elif provider == "openai":
-            llm = ChatOpenAI(
-                model=get_model_name(),
-                temperature=get_temperature(),
-            )
-        else:
-            raise ValueError(f"Unsupported LLM provider: {provider}")
+        kwargs = {
+            "model": get_model_name(),
+            "temperature": get_temperature(),
+        }
+        base_url = get_base_url()
+        if base_url:
+            kwargs["base_url"] = base_url
+        kwargs["max_retries"] = get_max_retries()
+        llm = ChatOpenAI(**kwargs)
     except Exception as e:
         raise BaseException(
-            f"Error initializing LLM. Make sure your LLM configuration is correct. Error: {e}"
+            f"Error initializing LLM. Make sure your configuration is correct. Error: {e}"
         )
 
     with open("pdf_convertor_prompt.md", "r") as f:
@@ -204,36 +186,16 @@ def refine_content(md: str, images: dict[str, bytes], pdf: bytes) -> str:
         }
     )
 
-    if provider == "gemini" or provider == "openai":
-        human_message_parts.extend(
-            [
-                {
-                    "type": "file",
-                    "base64": base64.b64encode(pdf).decode("utf-8"),
-                    "mime_type": "application/pdf",
-                    "filename": "origin.pdf",
-                },
-            ]
-        )
-    if provider == "ollama":
-        doc = fitz.open(stream=pdf, filetype="pdf")
-        for page_num in range(doc.page_count):
-            page = doc.load_page(page_num)
-            pix = page.get_pixmap()
-            img_bytes = pix.tobytes("png")
-            human_message_parts.append(
-                {
-                    "type": "text",
-                    "text": f"This is page {page_num + 1} of the original PDF file:\n",
-                }
-            )
-            human_message_parts.append(
-                {
-                    "type": "image_url",
-                    "image_url": f"data:image/png;base64,{base64.b64encode(img_bytes).decode('utf-8')}",
-                }
-            )
-        doc.close()
+    human_message_parts.extend(
+        [
+            {
+                "type": "file",
+                "base64": base64.b64encode(pdf).decode("utf-8"),
+                "mime_type": "application/pdf",
+                "filename": "origin.pdf",
+            },
+        ]
+    )
 
     message_content = [
         SystemMessage(content=prompt),
@@ -241,7 +203,7 @@ def refine_content(md: str, images: dict[str, bytes], pdf: bytes) -> str:
     ]
 
     print(
-        f"Sending request to {provider} with the PDF, Markdown and referenced images... This may take a moment."
+        "Sending request to LLM with the PDF, Markdown and referenced images... This may take a moment."
     )
     try:
         response = llm.invoke(message_content)
@@ -250,7 +212,7 @@ def refine_content(md: str, images: dict[str, bytes], pdf: bytes) -> str:
         raise BaseException(f"An error occurred while invoking the LLM: {e}")
 
     if str(refined_content) == "":
-        raise BaseException(f"Response of {provider} is empty")
+        raise BaseException("Response of LLM is empty")
 
     return fix_output(str(refined_content))