使用远程模型的 VLM 流水线

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import logging
import os
from pathlib import Path

import logging import os from pathlib import Path

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import requests
from dotenv import load_dotenv

import requests from dotenv import load_dotenv

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from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import (
    ApiVlmOptions,
    ResponseFormat,
    VlmPipelineOptions,
)
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.pipeline.vlm_pipeline import VlmPipeline

from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import ( ApiVlmOptions, ResponseFormat, VlmPipelineOptions, ) from docling.document_converter import DocumentConverter, PdfFormatOption from docling.pipeline.vlm_pipeline import VlmPipeline

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def ollama_vlm_options(model: str, prompt: str):
    options = ApiVlmOptions(
        url="http://localhost:11434/v1/chat/completions",  # the default Ollama endpoint
        params=dict(
            model=model,
        ),
        prompt=prompt,
        timeout=90,
        scale=1.0,
        response_format=ResponseFormat.MARKDOWN,
    )
    return options

def ollama_vlm_options(model: str, prompt: str): options = ApiVlmOptions( url="http://localhost:11434/v1/chat/completions", # 默认的 Ollama 端点 params=dict( model=model, ), prompt=prompt, timeout=90, scale=1.0, response_format=ResponseFormat.MARKDOWN, ) return options

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def watsonx_vlm_options(model: str, prompt: str):
    load_dotenv()
    api_key = os.environ.get("WX_API_KEY")
    project_id = os.environ.get("WX_PROJECT_ID")

    def _get_iam_access_token(api_key: str) -> str:
        res = requests.post(
            url="https://iam.cloud.ibm.com/identity/token",
            headers={
                "Content-Type": "application/x-www-form-urlencoded",
            },
            data=f"grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey={api_key}",
        )
        res.raise_for_status()
        api_out = res.json()
        print(f"{api_out=}")
        return api_out["access_token"]

    options = ApiVlmOptions(
        url="https://us-south.ml.cloud.ibm.com/ml/v1/text/chat?version=2023-05-29",
        params=dict(
            model_id=model,
            project_id=project_id,
            parameters=dict(
                max_new_tokens=400,
            ),
        ),
        headers={
            "Authorization": "Bearer " + _get_iam_access_token(api_key=api_key),
        },
        prompt=prompt,
        timeout=60,
        response_format=ResponseFormat.MARKDOWN,
    )
    return options

def watsonx_vlm_options(model: str, prompt: str): load_dotenv() api_key = os.environ.get("WX_API_KEY") project_id = os.environ.get("WX_PROJECT_ID") def _get_iam_access_token(api_key: str) -> str: res = requests.post( url="https://iam.cloud.ibm.com/identity/token", headers={ "Content-Type": "application/x-www-form-urlencoded", }, data=f"grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey={api_key}", ) res.raise_for_status() api_out = res.json() print(f"{api_out=}") return api_out["access_token"] options = ApiVlmOptions( url="https://us-south.ml.cloud.ibm.com/ml/v1/text/chat?version=2023-05-29", params=dict( model_id=model, project_id=project_id, parameters=dict( max_new_tokens=400, ), ), headers={ "Authorization": "Bearer " + _get_iam_access_token(api_key=api_key), }, prompt=prompt, timeout=60, response_format=ResponseFormat.MARKDOWN, ) return options

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def main():
    logging.basicConfig(level=logging.INFO)

    # input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
    input_doc_path = Path("./tests/data/pdf/2305.03393v1-pg9.pdf")

    pipeline_options = VlmPipelineOptions(
        enable_remote_services=True  # <-- this is required!
    )

    # The ApiVlmOptions() allows to interface with APIs supporting
    # the multi-modal chat interface. Here follow a few example on how to configure those.

    # One possibility is self-hosting model, e.g. via Ollama.
    # Example using the Granite Vision  model: (uncomment the following lines)
    pipeline_options.vlm_options = ollama_vlm_options(
        model="granite3.2-vision:2b",
        prompt="OCR the full page to markdown.",
    )

    # Another possibility is using online services, e.g. watsonx.ai.
    # Using requires setting the env variables WX_API_KEY and WX_PROJECT_ID.
    # Uncomment the following line for this option:
    # pipeline_options.vlm_options = watsonx_vlm_options(
    #     model="ibm/granite-vision-3-2-2b", prompt="OCR the full page to markdown."
    # )

    # Create the DocumentConverter and launch the conversion.
    doc_converter = DocumentConverter(
        format_options={
            InputFormat.PDF: PdfFormatOption(
                pipeline_options=pipeline_options,
                pipeline_cls=VlmPipeline,
            )
        }
    )
    result = doc_converter.convert(input_doc_path)
    print(result.document.export_to_markdown())

def main(): logging.basicConfig(level=logging.INFO) # input_doc_path = Path("./tests/data/pdf/2206.01062.pdf") input_doc_path = Path("./tests/data/pdf/2305.03393v1-pg9.pdf") pipeline_options = VlmPipelineOptions( enable_remote_services=True # <--- 这是必需的! ) # ApiVlmOptions() 允许与支持以下功能的 API 进行交互 # 多模态聊天界面。这里提供了几个配置示例。 # 一种可能性是自托管模型，例如通过 Ollama。 # 使用 Granite Vision 模型的示例：（取消注释以下行） pipeline_options.vlm_options = ollama_vlm_options( model="granite3.2-vision:2b", prompt="OCR the full page to markdown.", ) # 另一种可能性是使用在线服务，例如 watsonx.ai。 # 使用需要设置环境变量 WX_API_KEY 和 WX_PROJECT_ID。 # 要使用此选项，请取消注释以下行： # pipeline_options.vlm_options = watsonx_vlm_options( # model="ibm/granite-vision-3-2-2b", prompt="OCR the full page to markdown." # ) # 创建 DocumentConverter 并启动转换。 doc_converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_options=pipeline_options, pipeline_cls=VlmPipeline, ) } ) result = doc_converter.convert(input_doc_path) print(result.document.export_to_markdown())

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if __name__ == "__main__":
    main()

if __name__ == "__main__": main()