k-llms 0.0.50

Retab official python library

k-LLMS

Built with 🩷 at retab

k-llms is a wrapper around the OpenAI client that adds consensus functionality through the n_consensus parameter.

Features

• Drop-in replacement for OpenAI client
• Adds n_consensus parameter to make multiple parallel requests
• Automatic result consolidation using majority voting
• Likelihood computations
• Support for both sync and async operations
• Compatible with all OpenAI chat completion parameters
• Support for structured outputs with parse()

Installation

# The wrapper uses the official OpenAI client
pip install openai
pip install k-llms

Usage

Basic Usage

from k_llms import KLLMs
from openai import OpenAI

# Initialize the client (uses OPENAI_API_KEY env var by default)
kllms_client = KLLMs()

openai_client = OpenAI()

# Make a single request (normal OpenAI behavior)
response = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Make multiple requests with consensus
consensus_response = kllms_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    n_consensus=3  # Makes 3 parallel requests and consolidates
)

Structured Outputs with Parse

from pydantic import BaseModel

class UserInfo(BaseModel):
    name: str
    age: int

# Single parse request
result = openai_client.chat.completions.parse(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "John is 30 years old"}],
    response_format=UserInfo
)

# Multiple parse requests with consensus
result = kllms_client.chat.completions.parse(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "John is 30 years old"}],
    response_format=UserInfo,
    n_consensus=3
)

# Access consolidated result
consensus_user = result.choices[0].message.parsed  # Consolidated UserInfo object
original_users = [choice.message.parsed for choice in result.choices[1:]]  # Original results

Async Usage

from k_llms import AsyncKLLMs
from openai import AsyncOpenAI
import asyncio

async def main():
    kllms_client = AsyncKLLMs()
    openai_client = AsyncOpenAI()
    
    response = await openai_client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": "Hello!"}],
        n_consensus=3
    )
    print(response.choices[0].message.content)

asyncio.run(main())

How Consensus Works

When n_consensus > 1:

1. The wrapper makes n_consensus parallel requests to the API
2. For both completions.create() and parse(): Results are consolidated using majority voting
   • For simple values: Most common value wins
   • For JSON/dict responses: Field-by-field majority voting
   • For lists: Element-by-element consolidation
3. All responses return a choices array where:
   • choices[0]: Consolidated/consensus result
   • choices[1...n]: Individual original results from each API call

API Compatibility

The wrapper maintains full compatibility with the OpenAI client API. All parameters supported by the official client work seamlessly, including:

• temperature, top_p, max_tokens
• response_format, tools, tool_choice
• stream (automatically disabled - all responses are non-streaming)
• All other OpenAI parameters

Limitations

• Streaming is not supported (all requests return KLLMsChatCompletion objects)
• The n parameter is ignored when using n_consensus to avoid conflicts