darca-llm 0.2.0

Features

• ✅ Unified AIClient interface for all LLMs

• 🔌 OpenAI integration out of the box (GPT-4, GPT-3.5)

• 🧱 Extensible abstract interface (BaseLLMClient) for new providers

• 🧪 Full pytest support with 100% coverage

• 📦 Rich exception handling with structured DarcaException

• 📋 Markdown-aware content formatting using _strip_markdown_prefix

• 🧠 Logging support via darca-log-facility

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Quickstart

1. Install dependencies:

make install

2. Run all quality checks (format, test, docs):

make check

3. Run tests only:

make test

4. Use the client:

from darca_llm import AIClient

ai = AIClient()
response = ai.get_raw_response(
    system="You are a helpful assistant.",
    user="What is a Python decorator?"
)
print(response)

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Using get_file_content_response

The get_file_content_response() method allows for structured file content prompting with LLMs.

Example:

from darca_llm import AIClient

client = AIClient()

user_prompt = "Provide the content of a simple Python file."

result = client.get_file_content_response(
    system="Explain the code.",
    user=user_prompt
)
print(result)

This method ensures that only a single code block is returned and properly stripped of formatting using _strip_markdown_prefix().

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Error Handling

All exceptions are subclasses of DarcaException and include:

• LLMException: Base for all LLM-specific errors

• LLMAPIKeyMissing: Raised when the API key is missing for the selected backend

• LLMContentFormatError: Raised when: - Multiple blocks are detected within the response - The response cannot be properly stripped of markdown/code block formatting

• LLMResponseError: Raised when the LLM provider returns an error or response parsing fails

All exceptions include:

• error_code

• message

• Optional metadata

• Optional cause

• Full stack trace logging

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Documentation

Build and view the docs:

make docs

Open the HTML documentation at:

docs/build/html/index.html

For detailed usage, refer to the usage.rst documentation.

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