// SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
// SPDX-License-Identifier: Apache-2.0
//
// Based on https://github.com/64bit/async-openai/ by Himanshu Neema
// Original Copyright (c) 2022 Himanshu Neema
// Licensed under MIT License (see ATTRIBUTIONS-Rust.md)
//
// Modifications Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES.
// Licensed under Apache 2.0

use base64::engine::{Engine, general_purpose};
use derive_builder::Builder;
use serde::{Deserialize, Serialize};
use utoipa::ToSchema;

use crate::error::OpenAIError;

#[derive(ToSchema, Debug, Serialize, Clone, PartialEq, Deserialize)]
#[serde(untagged)]
pub enum EmbeddingInput {
    String(String),
    StringArray(Vec<String>),
    // Minimum value is 0, maximum value is 100257 (inclusive).
    IntegerArray(Vec<u32>),
    ArrayOfIntegerArray(Vec<Vec<u32>>),
}

#[derive(ToSchema, Debug, Serialize, Default, Clone, PartialEq, Deserialize)]
#[serde(rename_all = "lowercase")]
pub enum EncodingFormat {
    #[default]
    Float,
    Base64,
}

#[derive(ToSchema, Debug, Serialize, Default, Clone, Builder, PartialEq, Deserialize)]
#[builder(name = "CreateEmbeddingRequestArgs")]
#[builder(pattern = "mutable")]
#[builder(setter(into, strip_option), default)]
#[builder(derive(Debug))]
#[builder(build_fn(error = "OpenAIError"))]
pub struct CreateEmbeddingRequest {
    /// ID of the model to use. You can use the
    /// [List models](https://platform.openai.com/docs/api-reference/models/list)
    /// API to see all of your available models, or see our
    /// [Model overview](https://platform.openai.com/docs/models/overview)
    /// for descriptions of them.
    pub model: String,

    ///  Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
    pub input: EmbeddingInput,

    /// The format to return the embeddings in. Can be either `float` or [`base64`](https://pypi.org/project/pybase64/). Defaults to float
    #[serde(skip_serializing_if = "Option::is_none")]
    pub encoding_format: Option<EncodingFormat>,

    /// A unique identifier representing your end-user, which will help OpenAI
    ///  to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/usage-policies/end-user-ids).
    #[serde(skip_serializing_if = "Option::is_none")]
    pub user: Option<String>,

    /// The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub dimensions: Option<u32>,
}

/// Represents an embedding vector returned by embedding endpoint.
#[derive(ToSchema, Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct Embedding {
    /// The index of the embedding in the list of embeddings.
    pub index: u32,
    /// The object type, which is always "embedding".
    pub object: String,
    /// The embedding vector, which is a list of floats. The length of vector
    /// depends on the model as listed in the [embedding guide](https://platform.openai.com/docs/guides/embeddings).
    pub embedding: Vec<f32>,
}

#[derive(ToSchema, Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct Base64EmbeddingVector(pub String);

impl From<Base64EmbeddingVector> for Vec<f32> {
    fn from(value: Base64EmbeddingVector) -> Self {
        let bytes = general_purpose::STANDARD
            .decode(value.0)
            .expect("openai base64 encoding to be valid");
        let chunks = bytes.chunks_exact(4);
        chunks
            .map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
            .collect()
    }
}

/// Represents an base64-encoded embedding vector returned by embedding endpoint.
#[derive(ToSchema, Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct Base64Embedding {
    /// The index of the embedding in the list of embeddings.
    pub index: u32,
    /// The object type, which is always "embedding".
    pub object: String,
    /// The embedding vector, encoded in base64.
    pub embedding: Base64EmbeddingVector,
}

#[derive(ToSchema, Debug, Deserialize, Serialize, Clone, PartialEq)]
pub struct EmbeddingUsage {
    /// The number of tokens used by the prompt.
    pub prompt_tokens: u32,
    /// The total number of tokens used by the request.
    pub total_tokens: u32,
}

#[derive(ToSchema, Debug, Deserialize, Clone, PartialEq, Serialize)]
pub struct CreateEmbeddingResponse {
    pub object: String,
    /// The name of the model used to generate the embedding.
    pub model: String,
    /// The list of embeddings generated by the model.
    pub data: Vec<Embedding>,
    /// The usage information for the request.
    pub usage: EmbeddingUsage,
}

#[derive(ToSchema, Debug, Deserialize, Clone, PartialEq, Serialize)]
pub struct CreateBase64EmbeddingResponse {
    pub object: String,
    /// The name of the model used to generate the embedding.
    pub model: String,
    /// The list of embeddings generated by the model.
    pub data: Vec<Base64Embedding>,
    /// The usage information for the request.
    pub usage: EmbeddingUsage,
}
