spec-ai 0.6.12

//! Model Provider Abstraction Layer
//!
//! This module defines the core traits and types for integrating with various LLM providers.
//! It provides a unified interface that abstracts away provider-specific details.

use anyhow::Result;
use async_trait::async_trait;
use futures::Stream;
use serde::{Deserialize, Serialize};
use std::pin::Pin;

/// Configuration for model generation requests
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GenerationConfig {
    /// Sampling temperature (0.0 - 2.0)
    pub temperature: Option<f32>,
    /// Maximum tokens to generate
    pub max_tokens: Option<u32>,
    /// Stop sequences
    pub stop_sequences: Option<Vec<String>>,
    /// Top-p sampling
    pub top_p: Option<f32>,
    /// Frequency penalty
    pub frequency_penalty: Option<f32>,
    /// Presence penalty
    pub presence_penalty: Option<f32>,
}

impl Default for GenerationConfig {
    fn default() -> Self {
        Self {
            temperature: Some(0.7),
            max_tokens: Some(2048),
            stop_sequences: None,
            top_p: Some(1.0),
            frequency_penalty: None,
            presence_penalty: None,
        }
    }
}

/// Tool call from a model response
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ToolCall {
    /// Unique identifier for this tool call
    pub id: String,
    /// Name of the function/tool to call
    pub function_name: String,
    /// Arguments as JSON
    pub arguments: serde_json::Value,
}

/// Parse thinking/reasoning tokens from model response
///
/// Extracts content between `<think>` and `</think>` tags as reasoning,
/// and returns the content after `</think>` as the main response.
///
/// # Arguments
/// * `response` - Raw model response that may contain thinking tokens
///
/// # Returns
/// A tuple of (reasoning, content) where:
/// - `reasoning` is Some(String) if thinking tags were found, None otherwise
/// - `content` is the text after `</think>`, or the full response if no tags present
///
/// # Example
/// ```
/// use spec_ai_core::agent::model::parse_thinking_tokens;
///
/// let response = "<think>Let me consider this...</think>Here's my answer.";
/// let (reasoning, content) = parse_thinking_tokens(response);
/// assert_eq!(reasoning, Some("Let me consider this...".to_string()));
/// assert_eq!(content, "Here's my answer.");
/// ```
pub fn parse_thinking_tokens(response: &str) -> (Option<String>, String) {
    // Pattern to match content between <think> and </think>
    let think_pattern = regex::Regex::new(r"<think>([\s\S]*?)</think>").unwrap();

    // Try to find thinking content
    let reasoning = if let Some(captures) = think_pattern.captures(response) {
        captures.get(1).map(|m| m.as_str().trim().to_string())
    } else {
        None
    };

    // Extract content after </think> tag, or return full response if no tags
    let content = if let Some(end_idx) = response.find("</think>") {
        // Get everything after </think>
        let after_think = &response[end_idx + "</think>".len()..];
        after_think.trim().to_string()
    } else {
        // No thinking tags found, return original response
        response.to_string()
    };

    (reasoning, content)
}

/// Response from a model generation request
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelResponse {
    /// Generated content (with thinking tokens removed if present)
    pub content: String,
    /// Model used for generation
    pub model: String,
    /// Token usage statistics
    pub usage: Option<TokenUsage>,
    /// Finish reason
    pub finish_reason: Option<String>,
    /// Tool calls from the model (if any)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub tool_calls: Option<Vec<ToolCall>>,
    /// Reasoning/thinking content extracted from <think> tags (if present)
    #[serde(skip_serializing_if = "Option::is_none")]
    pub reasoning: Option<String>,
}

/// Token usage statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TokenUsage {
    pub prompt_tokens: u32,
    pub completion_tokens: u32,
    pub total_tokens: u32,
}

/// Binary image attachment for multimodal prompts.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ImageAttachment {
    pub mime: String,
    pub data: Vec<u8>,
}

/// Provider metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProviderMetadata {
    /// Provider name
    pub name: String,
    /// Supported models
    pub supported_models: Vec<String>,
    /// Supports streaming
    pub supports_streaming: bool,
}

/// Types of model providers
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
#[serde(rename_all = "lowercase")]
pub enum ProviderKind {
    Mock,
    #[cfg(feature = "openai")]
    OpenAI,
    #[cfg(feature = "anthropic")]
    Anthropic,
    #[cfg(feature = "ollama")]
    Ollama,
    #[cfg(feature = "mlx")]
    MLX,
    #[cfg(feature = "lmstudio")]
    LMStudio,
}

impl ProviderKind {
    #[allow(clippy::should_implement_trait)]
    pub fn from_str(s: &str) -> Option<Self> {
        match s.to_lowercase().as_str() {
            "mock" => Some(ProviderKind::Mock),
            #[cfg(feature = "openai")]
            "openai" => Some(ProviderKind::OpenAI),
            #[cfg(feature = "anthropic")]
            "anthropic" => Some(ProviderKind::Anthropic),
            #[cfg(feature = "ollama")]
            "ollama" => Some(ProviderKind::Ollama),
            #[cfg(feature = "mlx")]
            "mlx" => Some(ProviderKind::MLX),
            #[cfg(feature = "lmstudio")]
            "lmstudio" => Some(ProviderKind::LMStudio),
            _ => None,
        }
    }

    pub fn as_str(&self) -> &'static str {
        match self {
            ProviderKind::Mock => "mock",
            #[cfg(feature = "openai")]
            ProviderKind::OpenAI => "openai",
            #[cfg(feature = "anthropic")]
            ProviderKind::Anthropic => "anthropic",
            #[cfg(feature = "ollama")]
            ProviderKind::Ollama => "ollama",
            #[cfg(feature = "mlx")]
            ProviderKind::MLX => "mlx",
            #[cfg(feature = "lmstudio")]
            ProviderKind::LMStudio => "lmstudio",
        }
    }
}

/// Core trait that all model providers must implement
#[async_trait]
pub trait ModelProvider: Send + Sync {
    /// Generate a response to the given prompt
    async fn generate(&self, prompt: &str, config: &GenerationConfig) -> Result<ModelResponse>;

    /// Generate a response with optional image attachments (default: ignore attachments).
    async fn generate_with_attachments(
        &self,
        prompt: &str,
        attachments: &[ImageAttachment],
        config: &GenerationConfig,
    ) -> Result<ModelResponse> {
        let _ = attachments;
        self.generate(prompt, config).await
    }

    /// Stream a response to the given prompt
    async fn stream(
        &self,
        prompt: &str,
        config: &GenerationConfig,
    ) -> Result<Pin<Box<dyn Stream<Item = Result<String>> + Send>>>;

    /// Stream a response with optional image attachments (default: ignore attachments).
    async fn stream_with_attachments(
        &self,
        prompt: &str,
        attachments: &[ImageAttachment],
        config: &GenerationConfig,
    ) -> Result<Pin<Box<dyn Stream<Item = Result<String>> + Send>>> {
        let _ = attachments;
        self.stream(prompt, config).await
    }

    /// Get provider metadata
    fn metadata(&self) -> ProviderMetadata;

    /// Get the provider kind
    fn kind(&self) -> ProviderKind;
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_provider_kind_from_str() {
        assert_eq!(ProviderKind::from_str("mock"), Some(ProviderKind::Mock));
        assert_eq!(ProviderKind::from_str("Mock"), Some(ProviderKind::Mock));
        assert_eq!(ProviderKind::from_str("MOCK"), Some(ProviderKind::Mock));
        assert_eq!(ProviderKind::from_str("invalid"), None);
    }

    #[test]
    fn test_provider_kind_as_str() {
        assert_eq!(ProviderKind::Mock.as_str(), "mock");
    }

    #[test]
    fn test_generation_config_default() {
        let config = GenerationConfig::default();
        assert_eq!(config.temperature, Some(0.7));
        assert_eq!(config.max_tokens, Some(2048));
        assert_eq!(config.top_p, Some(1.0));
    }

    #[test]
    fn test_generation_config_serialization() {
        let config = GenerationConfig {
            temperature: Some(0.9),
            max_tokens: Some(1024),
            stop_sequences: Some(vec!["STOP".to_string()]),
            top_p: Some(0.95),
            frequency_penalty: None,
            presence_penalty: None,
        };

        let json = serde_json::to_string(&config).unwrap();
        let deserialized: GenerationConfig = serde_json::from_str(&json).unwrap();

        assert_eq!(config.temperature, deserialized.temperature);
        assert_eq!(config.max_tokens, deserialized.max_tokens);
    }

    #[test]
    fn test_parse_thinking_tokens_with_tags() {
        let response = "<think>Let me consider this carefully...</think>Here's my final answer.";
        let (reasoning, content) = parse_thinking_tokens(response);

        assert_eq!(
            reasoning,
            Some("Let me consider this carefully...".to_string())
        );
        assert_eq!(content, "Here's my final answer.");
    }

    #[test]
    fn test_parse_thinking_tokens_without_tags() {
        let response = "This is a normal response without thinking tags.";
        let (reasoning, content) = parse_thinking_tokens(response);

        assert_eq!(reasoning, None);
        assert_eq!(content, "This is a normal response without thinking tags.");
    }

    #[test]
    fn test_parse_thinking_tokens_multiline() {
        let response = "<think>\nFirst, I need to analyze the problem.\nThen I'll formulate a solution.\n</think>\n\nHere's the answer: 42";
        let (reasoning, content) = parse_thinking_tokens(response);

        assert!(reasoning.is_some());
        let reasoning_text = reasoning.unwrap();
        assert!(reasoning_text.contains("analyze the problem"));
        assert!(reasoning_text.contains("formulate a solution"));
        assert_eq!(content, "Here's the answer: 42");
    }

    #[test]
    fn test_parse_thinking_tokens_empty_think() {
        let response = "<think></think>Content after empty think.";
        let (reasoning, content) = parse_thinking_tokens(response);

        assert_eq!(reasoning, Some("".to_string()));
        assert_eq!(content, "Content after empty think.");
    }

    #[test]
    fn test_parse_thinking_tokens_whitespace_handling() {
        let response = "<think>  \n  Some reasoning  \n  </think>  \n  Final answer";
        let (reasoning, content) = parse_thinking_tokens(response);

        assert_eq!(reasoning, Some("Some reasoning".to_string()));
        assert_eq!(content, "Final answer");
    }

    #[test]
    fn test_parse_thinking_tokens_incomplete_tag() {
        let response = "<think>Incomplete thinking...";
        let (reasoning, content) = parse_thinking_tokens(response);

        // No closing tag means no reasoning extracted
        assert_eq!(reasoning, None);
        assert_eq!(content, "<think>Incomplete thinking...");
    }
}