ollama-api-rs 0.1.0

// Copyright 2026 Cloudflavor GmbH

// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at

// http://www.apache.org/licenses/LICENSE-2.0

// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

//! OpenAI-compatible API endpoints for Ollama
//!
//! This module provides OpenAI-compatible endpoints that work with
//! standard OpenAI client libraries.

use crate::client::ModelClient;
use crate::error::{OllamaError, Result};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

// Chat Completions Types

/// A chat message
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChatMessage {
    pub role: String,
    pub content: serde_json::Value,
}

/// Request for chat completions
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct ChatCompletionsRequest {
    pub model: String,
    pub messages: Vec<ChatMessage>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub frequency_penalty: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub presence_penalty: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub response_format: Option<serde_json::Value>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub seed: Option<i32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub stop: Option<Vec<String>>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub stream: Option<bool>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub stream_options: Option<StreamOptions>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub temperature: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub top_p: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub max_tokens: Option<u32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tools: Option<Vec<serde_json::Value>>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub reasoning_effort: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub reasoning: Option<serde_json::Value>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tool_choice: Option<serde_json::Value>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub logit_bias: Option<HashMap<String, f32>>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub user: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub n: Option<u32>,
}

/// Stream options for chat completions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StreamOptions {
    #[serde(skip_serializing_if = "Option::is_none")]
    pub include_usage: Option<bool>,
}

/// Choice in chat completion response
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Choice {
    pub index: u32,
    pub message: ChatMessage,
    pub finish_reason: String,
}

/// Usage information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Usage {
    pub prompt_tokens: u32,
    pub completion_tokens: u32,
    pub total_tokens: u32,
}

/// Response for chat completions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChatCompletionsResponse {
    pub id: String,
    pub choices: Vec<Choice>,
    pub created: u64,
    pub model: String,
    pub usage: Usage,
}

// Embeddings Types

/// Input for embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(untagged)]
pub enum OpenAIEmbeddingsInput {
    Single(String),
    Multiple(Vec<String>),
    Tokens(Vec<u32>),
    TokenArrays(Vec<Vec<u32>>),
}

impl Default for OpenAIEmbeddingsInput {
    fn default() -> Self {
        Self::Single(String::new())
    }
}

/// Request for embeddings
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct OpenAIEmbeddingsRequest {
    pub model: String,
    pub input: OpenAIEmbeddingsInput,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub encoding_format: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub dimensions: Option<u32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub user: Option<String>,
}

/// Embedding vector
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OpenAIEmbedding {
    pub embedding: Vec<f32>,
    pub index: u32,
    pub object: String,
}

/// Response for embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct OpenAIEmbeddingsResponse {
    pub data: Vec<OpenAIEmbedding>,
    pub model: String,
    pub usage: Usage,
}

// Responses Types

/// Request for responses endpoint
#[derive(Debug, Clone, Serialize, Deserialize, Default)]
pub struct ResponsesRequest {
    pub model: String,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub input: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub instructions: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tools: Option<Vec<serde_json::Value>>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub stream: Option<bool>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub temperature: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub top_p: Option<f32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub max_output_tokens: Option<u32>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub previous_response_id: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub conversation: Option<Vec<serde_json::Value>>,
    #[serde(skip_serializing_if = "Option::is_none")]
    pub truncation: Option<String>,
}

/// Response for responses endpoint
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ResponsesResponse {
    pub output: String,
    pub done: bool,
    pub model: String,
    pub done_reason: String,
    pub tool_calls: Vec<serde_json::Value>,
    pub prompt_evals: u32,
    pub eval_count: u32,
    pub total_duration: u64,
    pub load_duration: u64,
    pub prompt_eval_duration: u64,
    pub eval_duration: u64,
    pub output_eval_count: u32,
    pub output_eval_duration: u64,
}

impl ModelClient {
    /// Create chat completions using OpenAI-compatible API
    ///
    /// This endpoint is compatible with OpenAI client libraries.
    /// Use base URL `http://localhost:11434/v1/` with any API key.
    ///
    /// # Example
    ///
    /// ```no_run
    /// use oai_sdk::{ModelClient, ChatCompletionsRequest, ChatMessage};
    ///
    /// #[tokio::main]
    /// async fn main() -> Result<(), Box<dyn std::error::Error>> {
    ///     let client = ModelClient::builder()
    ///         .base_url("http://localhost:11434".to_string())
    ///         .build()?;
    ///
    ///     let request = ChatCompletionsRequest {
    ///         model: "llama3".to_string(),
    ///         messages: vec![
    ///             ChatMessage {
    ///                 role: "user".to_string(),
    ///                 content: serde_json::json!("Why is the sky blue?"),
    ///             }
    ///         ],
    ///         stream: Some(false),
    ///         ..Default::default()
    ///     };
    ///
    ///     let response = client.chat_completions(request).await?;
    ///     println!("{}", response.choices[0].message.content);
    ///
    ///     Ok(())
    /// }
    /// ```
    pub async fn chat_completions(
        &self,
        request: ChatCompletionsRequest,
    ) -> Result<ChatCompletionsResponse> {
        let url = self
            .base_url
            .join("v1/chat/completions")
            .map_err(OllamaError::UrlError)?;
        let response = self
            .client
            .post(url)
            .json(&request)
            .send()
            .await
            .map_err(OllamaError::RequestError)?;

        self.handle_response(response, Some(&request.model)).await
    }

    /// Generate embeddings using OpenAI-compatible API
    ///
    /// This endpoint is compatible with OpenAI client libraries.
    /// Use base URL `http://localhost:11434/v1/` with any API key.
    ///
    /// # Example
    ///
    /// ```no_run
    /// use oai_sdk::{ModelClient, OpenAIEmbeddingsRequest, OpenAIEmbeddingsInput};
    ///
    /// #[tokio::main]
    /// async fn main() -> Result<(), Box<dyn std::error::Error>> {
    ///     let client = ModelClient::builder()
    ///         .base_url("http://localhost:11434".to_string())
    ///         .build()?;
    ///
    ///     let request = OpenAIEmbeddingsRequest {
    ///         model: "llama3".to_string(),
    ///         input: OpenAIEmbeddingsInput::Single("Why is the sky blue?".to_string()),
    ///         encoding_format: Some("float".to_string()),
    ///         ..Default::default()
    ///     };
    ///
    ///     let response = client.openai_embeddings(request).await?;
    ///     println!("Embeddings: {:?}", response.data[0].embedding);
    ///
    ///     Ok(())
    /// }
    /// ```
    pub async fn openai_embeddings(
        &self,
        request: OpenAIEmbeddingsRequest,
    ) -> Result<OpenAIEmbeddingsResponse> {
        let url = self
            .base_url
            .join("v1/embeddings")
            .map_err(OllamaError::UrlError)?;
        let response = self
            .client
            .post(url)
            .json(&request)
            .send()
            .await
            .map_err(OllamaError::RequestError)?;

        self.handle_response(response, Some(&request.model)).await
    }

    /// Generate responses using OpenAI-compatible API
    ///
    /// This endpoint is compatible with OpenAI client libraries.
    /// Use base URL `http://localhost:11434/v1/` with any API key.
    ///
    /// # Example
    ///
    /// ```no_run
    /// use oai_sdk::{ModelClient, ResponsesRequest};
    ///
    /// #[tokio::main]
    /// async fn main() -> Result<(), Box<dyn std::error::Error>> {
    ///     let client = ModelClient::builder()
    ///         .base_url("http://localhost:11434".to_string())
    ///         .build()?;
    ///
    ///     let request = ResponsesRequest {
    ///         model: "llama3".to_string(),
    ///         input: Some("Why is the sky blue?".to_string()),
    ///         stream: Some(false),
    ///         ..Default::default()
    ///     };
    ///
    ///     let response = client.responses(request).await?;
    ///     println!("{}", response.output);
    ///
    ///     Ok(())
    /// }
    /// ```
    pub async fn responses(&self, request: ResponsesRequest) -> Result<ResponsesResponse> {
        let url = self
            .base_url
            .join("v1/responses")
            .map_err(OllamaError::UrlError)?;
        let response = self
            .client
            .post(url)
            .json(&request)
            .send()
            .await
            .map_err(OllamaError::RequestError)?;

        self.handle_response(response, Some(&request.model)).await
    }
}