koboldapi 0.5.1

Python library for interacting with KoboldCPP API

KoboldAPI

A Python library for interacting with KoboldCPP API, providing robust text and image processing capabilities with built-in chunking and templating support.

Features

• Text and document processing with Apache Tika integration
• Smart chunking with regex-based natural language boundaries
• Image processing with support for various formats including RAW
• Chat templating system supporting multiple LLM formats
• Async and streaming generation capabilities
• Built-in error handling and retry mechanisms

Installation

pip install koboldapi

Quick Start

Basic API Usage

from koboldapi import KoboldAPICore

# Initialize the core API client
api = KoboldAPICore("http://localhost:5001")

# Simple generation
response = api.api_client.generate("What is the capital of France?")
print(response)

# Get model info
model_info = api.get_model_info()
print(f"Model: {model_info['name']}")
print(f"Context Length: {model_info['context_length']}")

Processing Text Documents

import asyncio
from pathlib import Path
from koboldapi import KoboldAPICore, ChunkingProcessor

async def process_document(file_path: str):
    # Initialize API
    core = KoboldAPICore("http://localhost:5001")
    
    # Create processor with 2048 token chunks
    processor = ChunkingProcessor(core.api_client, max_chunk_length=2048)
    
    # Process document - works with any format supported by Apache Tika
    chunks, metadata = processor.chunk_file(Path(file_path))
    
    print(f"Document metadata: {metadata}")
    print(f"Number of chunks: {len(chunks)}")
    
    # Process each chunk
    for i, (chunk, token_count) in enumerate(chunks):
        response = core.api_client.generate(
            prompt=chunk,
            max_length=1024
        )
        print(f"\nChunk {i+1} ({token_count} tokens):")
        print(response)

# Run with asyncio
asyncio.run(process_document("document.pdf"))

Image Processing

from koboldapi import ImageProcessor, InstructTemplate, KoboldAPI

def analyze_image(image_path: str, instruction: str = "Describe this image"):
    # Initialize components
    api = KoboldAPI("http://localhost:5001")
    processor = ImageProcessor(max_dimension=1024)
    template = InstructTemplate("http://localhost:5001")
    
    # Process image
    encoded_image, _ = processor.process_image(image_path)
    if not encoded_image:
        raise ValueError("Failed to process image")
    
    # Create prompt with template
    prompt = template.wrap_prompt(instruction)
    
    # Generate description
    return api.generate(
        prompt=prompt,
        images=[encoded_image],
        temperature=0.1
    )

# Use the function
result = analyze_image("photo.jpg", "What objects do you see in this image?")
print(result)

Advanced Features

Streaming Generation

import asyncio
from koboldapi import KoboldAPI

async def stream_response(prompt: str):
    api = KoboldAPI("http://localhost:5001")
    
    async for token in api.stream_generate(prompt):
        print(token, end='', flush=True)
    print()

# Use with asyncio
asyncio.run(stream_response("Write a short story about a robot"))

Custom Chunking Configuration

from koboldapi import ChunkingProcessor, KoboldAPI

# Initialize with custom chunking parameters
processor = ChunkingProcessor(
    api_client=KoboldAPI("http://localhost:5001"),
    max_chunk_length=4096,
    max_total_chunks=1000
)

# Process with metadata
chunks, metadata = processor.chunk_file("large_document.txt")

Error Handling

The library provides custom exceptions for proper error handling:

from koboldapi import KoboldAPIError

try:
    api = KoboldAPI("http://localhost:5001")
    response = api.generate("Test prompt")
except KoboldAPIError as e:
    print(f"API Error: {e}")

Best Practices

1. Chunking: Always use the ChunkingProcessor for large documents to ensure proper token management.

2. Templates: Use the InstructTemplate system to ensure proper formatting for different models.

3. Error Handling: Implement proper error handling using try/except blocks with KoboldAPIError.

4. Resource Management: For large files or batch processing, consider using async methods and proper cleanup.

5. Image Processing: Set appropriate max_dimension and quality parameters based on your model's requirements.