rezunate-llm-sdk 0.1.0

Unified Python SDK for chat completions across multiple AI providers

LLM-Router

Unified Python SDK for chat completions across OpenAI, Anthropic, and Google Gemini. All requests/responses use OpenAI format.

Clone the Project

git clone https://github.com/Deeptechs-ai/LLM-Router

Installation

uv sync

Terminal Usage

from gateway import chat_complete, ChatCompletionRequest, Message

request = ChatCompletionRequest(
    model="claude-sonnet-4-20250514",
    messages=[Message(role="user", content="Hello!")],
    max_tokens=100,
)

response = chat_complete(
    provider="anthropic",  # or "openai", "google"
    api_key="your-api-key",
    request=request,
)

print(response.choices[0].message.content)

Guardrails

Regex Guardrails

Regex guardrails let you block or flag sensitive content (SSNs, emails, credit cards, etc.) in both input and output messages using pattern matching.

Setup

1. Create a YAML config file (see guardrails.example.yaml):

Each rule requires:

• name: Identifier for the rule
• pattern: Regex pattern to match
• description: Human-readable description
• action: block (raises GuardrailsError) or flag (logs a warning but allows the request)

2. Set the environment variable to enable automatic loading:

GUARDRAILS_FILE_PATH=path-to-guardrails-config-yaml-file

Usage

Guardrails are applied automatically when GUARDRAILS_FILE_PATH is set. Both user input and LLM output are checked against all rules.