Generate Answer

client.answer.generate(
    *,
    namespace: str,
    query: str,
    top_k: int | None = None,
    threshold: float | None = None,
    kiosk_mode: bool = False,
    temperature: float | None = None,
    ai_model: str | None = None,
    header_prompt: str | None = None,
    footer_prompt: str | None = None,
    chat_history: list[dict[str, str]] | None = None,
    structured_response: dict | None = None,
) -> dict[str, Any]

from moorcheh import MoorchehClient

with MoorchehClient("http://localhost:8080") as client:
    response = client.answer.generate(
        namespace="my-documents",
        query="What are the main benefits?",
        top_k=5,
    )
    print(response["answer"])
    print(response["context_count"])

with MoorchehClient("http://localhost:8080") as client:
    response = client.answer.generate(
        namespace="",
        query="Explain vector search in one paragraph",
        temperature=0.5,
        header_prompt="You are a concise technical writer.",
    )

with MoorchehClient("http://localhost:8080") as client:
    response = client.answer.generate(
        namespace="",
        query="Can you give an example?",
        chat_history=[
            {"role": "user", "content": "What is RAG?"},
            {"role": "assistant", "content": "RAG combines retrieval with generation..."},
        ],
    )

with MoorchehClient("http://localhost:8080") as client:
    response = client.answer.generate(
        namespace="docs",
        query="Summarize the return policy",
        structured_response={"enabled": True},
    )
    print(response.get("structured_data"))