lakehouse/crates/gateway/src/v1/ollama.rs

//! Phase 38 — Ollama shape adapter.
//!
//! Translates `/v1/chat` (OpenAI-compatible) requests into the
//! existing aibridge `GenerateRequest` shape, and the `GenerateResponse`
//! back into an OpenAI-compatible `ChatResponse`. This is a bridge,
//! not a new client — aibridge + the Python sidecar stay as-is.
//!
//! Phase 39 replaces this direct call with a `ProviderAdapter` trait
//! dispatch so the same `/v1/chat` handler routes to any provider.

use aibridge::client::{AiClient, GenerateRequest};
use super::{ChatRequest, ChatResponse, Choice, Message, UsageBlock};

pub async fn chat(client: &AiClient, req: &ChatRequest) -> Result<ChatResponse, String> {
    let (system, prompt) = flatten_messages(&req.messages);

    let gen_req = GenerateRequest {
        prompt,
        model: Some(req.model.clone()),
        system: if system.is_empty() { None } else { Some(system) },
        temperature: req.temperature,
        max_tokens: req.max_tokens,
        // Phase 38 default: think=false. Hot-path discipline for
        // thinking models (qwen3.5, qwen3, gpt-oss) which otherwise
        // burn the max_tokens budget on hidden reasoning before any
        // visible output, producing empty responses. Callers that
        // actually want reasoning (overseers, T3+ tiers) opt in via
        // the `think: true` extension field. Phase 40 routing engine
        // flips this per task class.
        think: Some(req.think.unwrap_or(false)),
    };

    let t0 = std::time::Instant::now();
    let resp = client.generate(gen_req).await.map_err(|e| e.to_string())?;
    let latency_ms = t0.elapsed().as_millis();

    // Prefer sidecar-reported token counts when present. Fall back to
    // chars/4 estimate (biased safe ~15%, matches Phase 21 convention
    // in crates/aibridge/src/context.rs::estimate_tokens).
    let prompt_tokens = resp.tokens_evaluated
        .map(|n| n as u32)
        .unwrap_or_else(|| estimate_prompt_tokens(&req.messages));
    let completion_tokens = resp.tokens_generated
        .map(|n| n as u32)
        .unwrap_or_else(|| ((resp.text.chars().count() + 3) / 4) as u32);

    tracing::info!(
        target: "v1.chat",
        model = %req.model,
        prompt_tokens,
        completion_tokens,
        latency_ms = latency_ms as u64,
        "ollama chat completed",
    );

    Ok(ChatResponse {
        id: format!("chatcmpl-{}", chrono::Utc::now().timestamp_nanos_opt().unwrap_or(0)),
        object: "chat.completion",
        created: chrono::Utc::now().timestamp(),
        model: resp.model,
        choices: vec![Choice {
            index: 0,
            message: Message::new_text("assistant", resp.text),
            finish_reason: "stop".into(),
        }],
        usage: UsageBlock {
            prompt_tokens,
            completion_tokens,
            total_tokens: prompt_tokens + completion_tokens,
        },
    })
}

/// Public re-export of the flattener so sibling adapters (Ollama Cloud,
/// future OpenRouter) can reuse the same shape collapse without
/// duplicating the logic. Keeps `(system, prompt)` format consistent
/// across providers.
pub fn flatten_messages_public(messages: &[Message]) -> (String, String) {
    flatten_messages(messages)
}

/// Collapse a message array into (system, prompt). Multiple system
/// messages concatenate with a newline — matches OpenAI's documented
/// behavior. Non-system messages become role-labeled turns.
fn flatten_messages(messages: &[Message]) -> (String, String) {
    let mut system = String::new();
    let mut prompt = String::new();
    for m in messages {
        let body = m.text();
        if m.role == "system" {
            if !system.is_empty() { system.push('\n'); }
            system.push_str(&body);
        } else {
            prompt.push_str(&m.role);
            prompt.push_str(": ");
            prompt.push_str(&body);
            prompt.push_str("\n\n");
        }
    }
    prompt.push_str("assistant:");
    (system, prompt)
}

fn estimate_prompt_tokens(messages: &[Message]) -> u32 {
    let chars: usize = messages.iter().map(|m| m.text().chars().count()).sum();
    ((chars + 3) / 4) as u32
}

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

    #[test]
    fn flatten_separates_system_from_turns() {
        let msgs = vec![
            Message { role: "system".into(), content: "Rules here.".into() },
            Message { role: "user".into(), content: "Q1".into() },
            Message { role: "assistant".into(), content: "A1".into() },
            Message { role: "user".into(), content: "Q2".into() },
        ];
        let (system, prompt) = flatten_messages(&msgs);
        assert_eq!(system, "Rules here.");
        assert!(prompt.contains("user: Q1"));
        assert!(prompt.contains("assistant: A1"));
        assert!(prompt.contains("user: Q2"));
        assert!(prompt.trim_end().ends_with("assistant:"));
    }

    #[test]
    fn flatten_concatenates_multiple_system_messages() {
        let msgs = vec![
            Message { role: "system".into(), content: "First.".into() },
            Message { role: "system".into(), content: "Second.".into() },
            Message { role: "user".into(), content: "Hi".into() },
        ];
        let (system, _) = flatten_messages(&msgs);
        assert_eq!(system, "First.\nSecond.");
    }

    #[test]
    fn flatten_with_no_system_returns_empty_system() {
        let msgs = vec![Message { role: "user".into(), content: "hi".into() }];
        let (system, prompt) = flatten_messages(&msgs);
        assert!(system.is_empty());
        assert!(prompt.contains("user: hi"));
    }

    #[test]
    fn estimate_tokens_chars_div_4_ceiling() {
        let msgs = vec![Message { role: "user".into(), content: "abcdefgh".into() }];
        // 8 chars / 4 = 2, with ceiling → 2
        assert_eq!(estimate_prompt_tokens(&msgs), 2);

        let msgs2 = vec![Message { role: "user".into(), content: "abcdefghi".into() }];
        // 9 chars → (9+3)/4 = 3 (ceiling)
        assert_eq!(estimate_prompt_tokens(&msgs2), 3);
    }
}