ragas-ainative

Configure ragas to use AINative's free LLMs + embeddings for RAG evaluation. No OpenAI key needed.

Zero setup. Evaluate faithfulness, answer relevancy, and more with Llama 3.3 70B — completely free.

Install

pip install ragas-ainative

Quick Start

from ragas_ainative import configure_ragas
from ragas.metrics import faithfulness, answer_relevancy
from ragas import evaluate

configure_ragas()  # Auto-provisions free API key

results = evaluate(dataset, metrics=[faithfulness, answer_relevancy])
print(results)

How It Works

ragas-ainative is a companion package (NOT a fork) that configures ragas to use AINative's free, OpenAI-compatible API. It sets OPENAI_API_KEY and OPENAI_API_BASE so ragas routes all LLM judge calls and embeddings through AINative.

On first use, the package auto-provisions a free API key (72-hour TTL). Claim your account at ainative.studio/signup for permanent access.

Available Models

from ragas_ainative import configure_ragas

configure_ragas(model="llama")     # meta-llama/Llama-3.3-70B-Instruct (default)
configure_ragas(model="qwen")      # qwen3-coder-flash
configure_ragas(model="deepseek")  # deepseek-4-flash
configure_ragas(model="kimi")      # kimi-k2

Store Results to ZeroDB

Track evaluation scores over time:

from ragas_ainative import configure_ragas, store_results

configure_ragas()

# After evaluation
results = {"faithfulness": 0.85, "answer_relevancy": 0.92}
store_results(results, dataset_name="my-rag-pipeline")
# Stored to ZeroDB — searchable and trackable

Explicit API Key

configure_ragas(api_key="your-key-here")

Or set the environment variable:

export AINATIVE_API_KEY=your-key-here

API Key Resolution Order

1. Explicit api_key parameter
2. AINATIVE_API_KEY environment variable
3. ZERODB_API_KEY environment variable
4. ~/.zerodb/credentials.json (shared with zerodb ecosystem)
5. Auto-provision via instant-db

Requirements

• Python >= 3.9
• ragas >= 0.1.0

License

MIT