# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

import random

import numpy as np
import pytest

from llama_stack.apis.vector_io import Chunk

EMBEDDING_DIMENSION = 384


@pytest.fixture
def vector_db_id() -> str:
    return f"test-vector-db-{random.randint(1, 100)}"


@pytest.fixture(scope="session")
def embedding_dimension() -> int:
    return EMBEDDING_DIMENSION


@pytest.fixture(scope="session")
def sample_chunks():
    """Generates chunks that force multiple batches for a single document to expose ID conflicts."""
    n, k = 10, 3
    sample = [
        Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
        for j in range(k)
        for i in range(n)
    ]
    return sample


@pytest.fixture(scope="session")
def sample_embeddings(sample_chunks):
    np.random.seed(42)
    return np.array([np.random.rand(EMBEDDING_DIMENSION).astype(np.float32) for _ in sample_chunks])