update

olmocr/bench/runners/run_chatgpt.py

@@ -4,7 +4,7 @@ import json
 from openai import OpenAI
 
 from olmocr.prompts.anchor import get_anchor_text
-from olmocr.prompts.prompts import build_openai_silver_data_prompt, openai_response_format_schema, PageResponse
+from olmocr.prompts.prompts import build_silver_data_prompt, response_format_schema, PageResponse
 from olmocr.data.renderpdf import render_pdf_to_base64png
 
 
@@ -33,14 +33,14 @@ def run_chatgpt(pdf_path: str, page_num: int = 1, model: str = "gpt-4o-2024-08-0
             {
                 "role": "user",
                 "content": [
-                    {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
+                    {"type": "text", "text": build_silver_data_prompt(anchor_text)},
                     {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
                 ],
             }
         ],
         temperature=temperature,
         max_tokens=3000,
-        response_format=openai_response_format_schema(),
+        response_format=response_format_schema(),
     )
 
     raw_response = response.choices[0].message.content

olmocr/bench/runners/run_claude.py

@@ -0,0 +1,89 @@
+import os
+import json
+import base64
+from anthropic import Anthropic
+from olmocr.prompts.anchor import get_anchor_text
+from olmocr.prompts.prompts import build_silver_data_prompt, response_format_schema, PageResponse
+from olmocr.data.renderpdf import render_pdf_to_base64png
+
+def run_claude(pdf_path: str, page_num: int = 1, model: str = "claude-3-7-sonnet-20250219", temperature: float=0.1) -> str:
+    """
+    Convert page of a PDF file to markdown using Claude OCR.
+    This function renders the specified page of the PDF to an image, runs OCR on that image,
+    and returns the OCR result as a markdown-formatted string.
+    
+    Args:
+        pdf_path (str): The local path to the PDF file.
+        page_num (int): The page number to process (starting from 1).
+        model (str): The Claude model to use.
+        temperature (float): The temperature parameter for generation.
+        
+    Returns:
+        str: The OCR result in markdown format.
+    """
+    # Convert the specified page of the PDF to a base64-encoded PNG image
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=2048)
+    
+    # Get anchor text for the page
+    anchor_text = get_anchor_text(pdf_path, page_num, pdf_engine="pdfreport")
+    
+    # Initialize the Claude client
+    client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
+    
+    # Create the message with the prompt and image
+    response = client.messages.create(
+        model=model,
+        max_tokens=3000,
+        temperature=temperature,
+        system=build_silver_data_prompt(anchor_text),
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "image/png",
+                            "data": image_base64
+                        }
+                    },
+                    {"type": "json_object", "schema": response_format_schema()}
+                ]
+            }
+        ],
+        
+    )
+    
+    # Extract and validate the response
+    raw_response = response.content#[0].text
+    print(raw_response)
+    # Parse the JSON response
+    data = json.loads(raw_response)
+    data = PageResponse(**data)
+    
+    return data.natural_text
+
+if __name__ == "__main__":
+    import argparse
+    
+    # Set up command-line argument parsing
+    parser = argparse.ArgumentParser(description="Extract text from a PDF using Claude OCR")
+    parser.add_argument("pdf_path", help="Path to the PDF file")
+    parser.add_argument("--page", type=int, default=1, help="Page number to process (default: 1)")
+    parser.add_argument("--model", default="claude-3-7-sonnet-20250219", help="Claude model to use")
+    parser.add_argument("--temperature", type=float, default=0.1, help="Temperature for generation")
+    
+    # Parse the arguments
+    args = parser.parse_args()
+    
+    # Run the OCR function
+    result = run_claude(
+        pdf_path=args.pdf_path,
+        page_num=args.page,
+        model=args.model,
+        temperature=args.temperature
+    )
+    
+    # Print the result
+    print(result)

olmocr/data/buildsilver.py

@@ -14,8 +14,8 @@ from tqdm import tqdm
 from olmocr.data.renderpdf import render_pdf_to_base64png
 from olmocr.filter import PdfFilter
 from olmocr.prompts import (
-    build_openai_silver_data_prompt,
-    openai_response_format_schema,
+    build_silver_data_prompt,
+    response_format_schema,
 )
 from olmocr.prompts.anchor import get_anchor_text
 
@@ -39,7 +39,7 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di
     #             {
     #                 "role": "user",
     #                 "content": [
-    #                     {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
+    #                     {"type": "text", "text": build_silver_data_prompt(anchor_text)},
     #                     {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}
     #                 ],
     #             }
@@ -48,7 +48,7 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di
     #     max_tokens=3000,
     #     logprobs=True,
     #     top_logprobs=5,
-    #     response_format=openai_response_format_schema()
+    #     response_format=response_format_schema()
     # )
     # print(response)
 
@@ -70,7 +70,7 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di
                 {
                     "role": "user",
                     "content": [
-                        {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
+                        {"type": "text", "text": build_silver_data_prompt(anchor_text)},
                         {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
                     ],
                 }
@@ -79,7 +79,7 @@ def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> di
             "max_tokens": 6000,
             "logprobs": True,
             "top_logprobs": 5,
-            "response_format": openai_response_format_schema(),
+            "response_format": response_format_schema(),
         },
     }
 

olmocr/data/convertsilver_openai.py

@@ -65,8 +65,8 @@ def process_file(input_file: str, output_file: str, rewrite_prompt_str: bool):
                         obj = build_page_query(local_pdf_path, s3_path, page)
                         # raw_page_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport")
 
-                        # from olmocr.prompts import build_openai_silver_data_prompt
-                        # obj["body"]["messages"][0]["content"][0]["text"] = build_openai_silver_data_prompt(raw_page_text)
+                        # from olmocr.prompts import build_silver_data_prompt
+                        # obj["body"]["messages"][0]["content"][0]["text"] = build_silver_data_prompt(raw_page_text)
 
                 if obj is not None:
                     outfile.write(json.dumps(obj) + "\n")

olmocr/prompts/__init__.py

@@ -1,7 +1,7 @@
 from .prompts import (
     PageResponse,
     build_finetuning_prompt,
-    build_openai_silver_data_prompt,
+    build_silver_data_prompt,
     extract_raw_text,
-    openai_response_format_schema,
+    response_format_schema,
 )

olmocr/prompts/prompts.py

@@ -4,7 +4,7 @@ from typing import Optional
 
 
 # This is the prompt we use for getting chat gpt 4o to convert documents into our silver training data
-def build_openai_silver_data_prompt(base_text: str) -> str:
+def build_silver_data_prompt(base_text: str) -> str:
     return (
         f"Below is the image of one page of a PDF document, as well as some raw textual content that was previously extracted for it that includes position information for each image and block of text (The origin [0x0] of the coordinates is in the lower left corner of the image). "
         f"Just return the plain text representation of this document as if you were reading it naturally.\n"
@@ -46,7 +46,7 @@ class PageResponse:
             raise TypeError("natural_text must be of type Optional[str].")
 
 
-def openai_response_format_schema() -> dict:
+def response_format_schema() -> dict:
     return {
         "type": "json_schema",
         "json_schema": {

olmocr/train/inference.py

@@ -8,7 +8,7 @@ from transformers import AutoConfig, AutoProcessor, Qwen2_5_VLForConditionalGene
 
 from olmocr.data.renderpdf import render_pdf_to_base64png
 from olmocr.prompts.anchor import get_anchor_text
-from olmocr.prompts.prompts import build_openai_silver_data_prompt
+from olmocr.prompts.prompts import build_silver_data_prompt
 
 
 @torch.no_grad()
@@ -33,7 +33,7 @@ def run_inference(model_name: str):
         {
             "role": "user",
             "content": [
-                {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
+                {"type": "text", "text": build_silver_data_prompt(anchor_text)},
                 {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
             ],
         }