llm-kit-pro 0.2.5

A unified toolkit for working with multiple LLM providers

llm-kit-pro

llm-kit-pro is a unified, async-first Python toolkit for interacting with multiple Large Language Model (LLM) providers through a consistent, provider-agnostic API.

It supports:

• Multiple LLM providers (OpenAI, Gemini, Anthropic, etc.)
• Text and structured JSON generation
• Multimodal inputs (PDFs, images, text files)
• Clean abstractions and strong typing

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✨ Core Ideas

1. Outputs are explicit

LLMs generate outputs, not OCR results.

llm-kit-pro exposes two primary operations:

• generate_text → free-form text
• generate_json → structured, schema-driven output

2. Inputs can be text and/or files

Files (PDFs, images, etc.) are first-class inputs and can be passed directly to generation methods.

OCR and file parsing are treated as provider implementation details.

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📦 Installation

pip install llm-kit-pro

Optional provider support (example):

pip install llm-kit-pro[openai]
pip install llm-kit-pro[gemini]

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🚀 Quick Start

Text-only generation

text = await llm.generate_text(
    prompt="Explain what power factor is in simple terms"
)

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Text generation with a file (PDF, image, etc.)

from llm_kit_pro.core import LLMFile

pdf = LLMFile(
    content=pdf_bytes,
    mime_type="application/pdf",
    filename="bill.pdf",
)

summary = await llm.generate_text(
    prompt="Summarize this electricity bill",
    files=[pdf],
)

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Structured JSON extraction using Pydantic models

from pydantic import BaseModel
from typing import Optional

class BillDetails(BaseModel):
    consumer_name: str
    bill_amount: float
    due_date: Optional[str] = None

data = await llm.generate_json(
    prompt="Extract billing details from this document",
    schema=BillDetails.model_json_schema(),
    files=[pdf],
)

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🧠 Design Philosophy

• Provider-agnostic: No OpenAI/Gemini specifics in the public API
• Async-first: Built for modern Python backends
• Composable: Easy to plug into pipelines (FastAPI, background jobs, ETL)
• Explicit contracts: Clear separation of inputs, outputs, and providers

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🧩 Core Abstractions

BaseLLMClient

All providers implement the same interface:

class BaseLLMClient:
    async def generate_text(...)
    async def generate_json(...)

LLMFile

A provider-agnostic representation of file inputs:

from llm_kit_pro.core import LLMFile

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🔌 Providers

Each provider is implemented as an adapter that conforms to BaseLLMClient.

Supported / planned:

• OpenAI
• Gemini
• Anthropic
• Bedrock
• Local / OSS models (future)

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📍 Status

🚧 Under active development

The public API is stabilizing. Expect rapid iteration until v1.0.

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📄 License

MIT License