dhenara-ai 1.1.4

Dhenara Package for Multi Provider AI-Model API calls

Dhenara

Dhenara is a genuinely open source Python package for interacting with various AI models in a unified way. It is a lightweight, straightforward framework for integrating multiple AI models into Python applications. It's similar in spirit to LangChain but with a focus on simplicity and minimal dependencies along with type safety using Pydantic Models.

For full documentation, visit docs.dhenara.com.

Why Dhenara?

• Genuinely Open Source: Built from the ground up as a community resource, not an afterthought or internal tool
• Unified API: Interact with different AI providers through a consistent interface
• Type Safety: Built with Pydantic for robust type checking and validation
• Easy Regeneration across Providers: With a unified Pydantic output and built-in prompt formatting, send output from a model to any other model easily
• Streaming: First-class support for streaming responses along with accumulated responses similar to non-streaming responses
• Async Support: Both synchronous and asynchronous interfaces for maximum flexibility
• Resource Management: Automatic handling of connections, retries, and timeouts
• Foundation Models: Pre-configured models with sensible defaults
• Test Mode: Bring up your app with dummy responses for streaming and non-streaming generation
• Cost/Usage Data: Derived cost and usage data along with responses, with optional charge for each model endpoint for commercial deployment
• Community-Oriented Design: An architecture separating API credentials, models, and configurations for flexible deployment and scaling

Example Usage

Here's a simple example of using Dhenara to interact with an AI model. You can find more examples in docs.dhenara.com.

from dhenara.ai import AIModelClient
from dhenara.ai.types import AIModelCallConfig, AIModelEndpoint
from dhenara.ai.types.external_api import AIModelAPIProviderEnum
from dhenara.ai.types.genai import AIModelAPI
from dhenara.ai.types.genai.foundation_models.anthropic.chat import Claude37Sonnet

# Create an API
api = AIModelAPI(
    provider=AIModelAPIProviderEnum.ANTHROPIC,
    api_key="your_api_key",
)

# Create an endpoint using a pre-configured model
model_endpoint = AIModelEndpoint(
    api=api,
    ai_model=Claude37Sonnet,
)

# Configure the api call
config = AIModelCallConfig(
    max_output_tokens=16000,
    reasoning=True,  # Thinking/reasoning mode
    max_reasoning_tokens=8000,
    streaming=False,
)

# Create the client
client = AIModelClient(
    model_endpoint=model_endpoint,
    config=config,
    is_async=False,
)

# Create a prompt
prompt = {
    "role": "user",
    "content": "Explain quantum computing in simple terms",
}

# Generate a response
response = client.generate(prompt=prompt)

# If not streaming
if response.chat_response:
    print(response.chat_response.choices[0].contents[0].get_text())

# If streaming
elif response.stream_generator:
    for chunk, _ in response.stream_generator:
        if chunk:
            print(
                chunk.data.choice_deltas[0].content_deltas[0].get_text_delta(),
                end="",
                flush=True,
            )

Documentation

For full documentation, visit docs.dhenara.com.