RAG & Search Applications

from openai import OpenAI

client = OpenAI(
    base_url="https://kymaapi.com/v1",
    api_key="ky-your-api-key"
)

def retrieve_context(query: str) -> list[str]:
    # Your retrieval logic: vector DB, keyword search, etc.
    return [
        "Kyma API exposes active models through /v1/models and one OpenAI-compatible endpoint.",
        "All models use OpenAI-compatible /v1/chat/completions endpoint.",
        "Gemini 2.5 Flash provides 1M context for large-document RAG.",
    ]

def rag_answer(question: str) -> str:
    chunks = retrieve_context(question)
    context = "\n\n".join(f"[{i+1}] {c}" for i, c in enumerate(chunks))

    response = client.chat.completions.create(
        model="gemini-2.5-flash",
        messages=[
            {
                "role": "system",
                "content": "Answer using only the provided context. "
                           "Cite sources with [1], [2] etc. "
                           "If the answer isn't in the context, say so."
            },
            {
                "role": "user",
                "content": f"Context:\n{context}\n\nQuestion: {question}"
            }
        ]
    )
    return response.choices[0].message.content

print(rag_answer("What models does Kyma API support?"))