pdf-invoke 0.1.1

A LLM utility for working with pdfs

📄 pdf-invoke

pdf-invoke is a lightweight utility for invoking multimodal LLMs using PDF files or image inputs.

It converts PDFs to images, prepares base64-encoded payloads, and sends them to a LangChain-compatible chat model with optional structured output support.

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Features

• Convert PDFs to images automatically
• Accept raw image bytes or file paths
• Prepare multimodal LLM payloads
• Support structured output via Pydantic models
• Sync and async invocation
• MIME type validation for image safety

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Installation

pip install pdf-invoke

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Quick Example

from langchain_openai import ChatOpenAI
from pdf_invoke import MultiModalLLM

llm = ChatOpenAI(model="gpt-4o")

mm = MultiModalLLM(
    prompt="Extract the key information from this document.",
    model=llm,
)

response = mm.invoke(pdf="example.pdf")

print(response)

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Structured Output Example

from pydantic import BaseModel

class ExtractedData(BaseModel):
    title: str
    summary: str

response = mm.invoke(
    pdf="example.pdf",
    output_model=ExtractedData,
)

print(response.title)

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Async Usage

response = await mm.ainvoke(pdf="example.pdf")

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Accepted Inputs

You may provide:

• pdf → file path, Path, or raw PDF bytes
• images → list of image paths or image bytes

Only one of pdf or images may be provided per invocation.

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How It Works

1. PDF inputs are converted to images.
2. Images are validated and base64 encoded.
3. A multimodal payload is created.
4. The payload is sent to a LangChain chat model.
5. Optional structured output is enforced via Pydantic.

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Requirements

• Python 3.10+
• LangChain-compatible chat model
• Pydantic

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License

MIT License

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