datapizza-ai-clients-openai-like 0.0.2

OpenAI client for the datapizza-ai framework

DataPizza AI - OpenAI-Like Client

A versatile client for DataPizza AI that supports OpenAI-compatible APIs, including local models through Ollama, Together AI, and other OpenAI-compatible services.

Installation

pip install datapizza-ai-clients-openai-like

Quick Start

With Ollama (Local Models)

from datapizza.clients.openai_like import OpenAILikeClient

# Create client for Ollama
client = OpenAILikeClient(
    api_key="",  # Ollama doesn't require an API key
    model="gemma2:2b",
    system_prompt="You are a helpful assistant.",
    base_url="http://localhost:11434/v1",
)

response = client.invoke("What is the capital of France?")
print(response.content)

With Together AI

import os
from datapizza.clients.openai_like import OpenAILikeClient

client = OpenAILikeClient(
    api_key=os.getenv("TOGETHER_API_KEY"),
    model="meta-llama/Llama-2-7b-chat-hf",
    system_prompt="You are a helpful assistant.",
    base_url="https://api.together.xyz/v1",
)

response = client.invoke("Explain quantum computing")
print(response.content)

With Other OpenAI-Compatible Services

import os
from datapizza.clients.openai_like import OpenAILikeClient

client = OpenAILikeClient(
    api_key=os.getenv("YOUR_API_KEY"),
    model="your-model-name",
    system_prompt="You are a helpful assistant.",
    base_url="https://your-service-url/v1",
)

response = client.invoke("Your question here")
print(response.content)

Features

• OpenAI-Compatible: Works with any service that implements the OpenAI API standard
• Local Models: Perfect for running with Ollama for privacy and cost control
• Memory Support: Built-in memory adapter for conversation history
• Streaming: Support for real-time streaming responses
• Structured Outputs: Generate structured data with Pydantic models
• Tool Calling: Function calling capabilities where supported

Supported Services

• Ollama - Local model inference
• Together AI - Cloud-based model hosting
• Perplexity AI - Search-augmented models
• Groq - Fast inference API
• Any OpenAI-compatible API

Advanced Usage

With Memory

from datapizza.clients.openai_like import OpenAILikeClient
from datapizza.memory import Memory

client = OpenAILikeClient(
    api_key="",
    model="llama3.1:8b",
    base_url="http://localhost:11434/v1",
)

memory = Memory(client=client)
memory.add("I'm working on a Python project about machine learning.")
response = memory.query("What libraries should I use?")

Streaming Responses

client = OpenAILikeClient(
    api_key="",
    model="gemma2:7b",
    base_url="http://localhost:11434/v1",
)

for chunk in client.stream("Tell me a story about AI"):
    print(chunk.content, end="", flush=True)

Structured Outputs

from pydantic import BaseModel
from datapizza.clients.openai_like import OpenAILikeClient

class Person(BaseModel):
    name: str
    age: int
    occupation: str

client = OpenAILikeClient(
    api_key="",
    model="llama3.1:8b",
    base_url="http://localhost:11434/v1",
)

response = client.invoke(
    "Generate a person profile",
    response_format=Person
)
print(response.parsed)  # Person object

Configuration Options

Parameter	Description	Default
api_key	API key for the service	Required (empty string for Ollama)
model	Model name to use	Required
base_url	Base URL for the API	Required
system_prompt	System message for the model	None
temperature	Sampling temperature (0-2)	0.7
max_tokens	Maximum tokens in response	None
timeout	Request timeout in seconds	30

Ollama Setup

1. Install Ollama from ollama.ai
2. Pull a model: ollama pull gemma2:2b
3. Start Ollama: ollama serve
4. Use with DataPizza AI as shown in the examples above

Popular Ollama Models

• gemma2:2b - Lightweight, fast responses
• gemma2:7b - Balanced performance
• llama3.1:8b - High quality, more resource intensive
• codellama:7b - Specialized for coding tasks
• mistral:7b - Good general purpose model

Error Handling

from datapizza.clients.openai_like import OpenAILikeClient
from datapizza.core.clients.exceptions import ClientError

try:
    client = OpenAILikeClient(
        api_key="",
        model="nonexistent-model",
        base_url="http://localhost:11434/v1",
    )
    response = client.invoke("Hello")
except ClientError as e:
    print(f"Client error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.