crtx-llm 0.1.1

//! HTTP adapter that posts to any OpenAI-compatible `/v1/chat/completions`
//! endpoint.
//!
//! [`OpenAiCompatAdapter`] implements [`LlmAdapter`] and works with any server
//! that speaks the OpenAI chat-completions wire format, including LM Studio
//! (default `http://localhost:1234`), LocalAI, Ollama in OpenAI-proxy mode
//! (`http://localhost:11434`), vLLM, and the hosted OpenAI API itself.
//!
//! ## Key differences from [`crate::ollama_http::OllamaHttpAdapter`]
//!
//! - No loopback enforcement — `base_url` may be any valid HTTP(S) URL.
//! - No digest-pin requirement — local model names are arbitrary strings;
//!   any non-empty model identifier is accepted.
//! - `api_key` is optional; when absent or empty, the `Authorization` header
//!   is omitted entirely (appropriate for local servers).
//!
//! ## Runtime ceiling
//!
//! Determined at construction from `base_url`:
//!
//! - Loopback host (`localhost`, `127.x.x.x`, `::1`) → [`RuntimeCeiling::LocalUnsigned`].
//! - Any other host → [`RuntimeCeiling::RemoteUnsigned`].
//!
//! ## SSE streaming
//!
//! [`OpenAiCompatAdapter::stream_boxed`] overrides the trait default and reads
//! the response body as Server-Sent Events. Each `data:` line is parsed as an
//! OpenAI streaming chunk; `data: [DONE]` terminates the stream.

use std::net::IpAddr;
use std::time::Duration;

use async_trait::async_trait;
use serde::{Deserialize, Serialize};

use crate::adapter::{
    blake3_hex, BoxStream, LlmAdapter, LlmError, LlmRequest, LlmResponse, LlmRole, StreamChunk,
    TokenUsage,
};
use crate::sensitivity::{check_remote_prompt_sensitivity, MaxSensitivity};

// ---------------------------------------------------------------------------
// Runtime ceiling
// ---------------------------------------------------------------------------

/// Runtime ceiling derived from the `base_url` at adapter construction time.
///
/// Used by `cortex run` to gate persistence policy decisions (ADR 0037
/// weakest-link): a loopback endpoint carries `LocalUnsigned`; any other
/// endpoint carries `RemoteUnsigned`.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum RuntimeCeiling {
    /// Endpoint host is a loopback address (`localhost`, `127.x.x.x`, `::1`).
    LocalUnsigned,
    /// Endpoint host is non-loopback.
    RemoteUnsigned,
}

// ---------------------------------------------------------------------------
// Adapter struct
// ---------------------------------------------------------------------------

/// HTTP adapter for any OpenAI-compatible `/v1/chat/completions` server.
///
/// Construct via [`OpenAiCompatAdapter::new`]. The adapter is `Send + Sync`
/// and may be held behind an `Arc<dyn LlmAdapter>`.
#[derive(Debug, Clone)]
pub struct OpenAiCompatAdapter {
    /// Base URL, e.g. `http://localhost:1234`. No trailing slash.
    base_url: String,
    /// Model identifier passed verbatim in the JSON body.
    model: String,
    /// Optional API key; `None` means no `Authorization` header is sent.
    api_key: Option<String>,
    /// Per-call HTTP timeout.
    timeout_ms: u64,
    /// ADR 0037 runtime ceiling computed from `base_url` at construction.
    ceiling: RuntimeCeiling,
    /// Maximum data-classification level permitted in remote prompts.
    /// Defaults to [`MaxSensitivity::Medium`] when constructed via [`Self::new`].
    max_sensitivity: MaxSensitivity,
}

impl OpenAiCompatAdapter {
    /// Construct an adapter.
    ///
    /// `api_key` accepts `None` or an empty-string `Some("")`; both result in
    /// no `Authorization` header being sent.
    ///
    /// `max_sensitivity` controls the data-classification gate before remote
    /// dispatch. Pass `None` to use the default of [`MaxSensitivity::Medium`],
    /// which blocks high-sensitivity memories from being sent to the endpoint.
    ///
    /// A warning is printed to stderr when `base_url` resolves to a non-loopback
    /// host, because all prompt content will be sent to that remote server.
    ///
    /// Returns [`LlmError::InvalidRequest`] when:
    /// - `base_url` does not start with `http://` or `https://`.
    /// - `base_url` contains no host.
    /// - `model` is empty.
    pub fn new(
        base_url: impl Into<String>,
        model: impl Into<String>,
        api_key: Option<String>,
        timeout_ms: u64,
        max_sensitivity: Option<MaxSensitivity>,
    ) -> Result<Self, LlmError> {
        let base_url = base_url.into();
        let model = model.into();

        if model.is_empty() {
            return Err(LlmError::InvalidRequest(
                "openai-compat: model must not be empty".to_string(),
            ));
        }

        let ceiling = ceiling_for_url(&base_url)?;

        // Warn when the endpoint is non-loopback: all prompt content is sent remotely.
        if ceiling == RuntimeCeiling::RemoteUnsigned {
            eprintln!(
                "cortex: openai-compat: WARNING: endpoint {} is not loopback-only. \
                 All prompt content will be sent to this remote server.",
                base_url
            );
        }

        // Normalise: treat empty string as absent key.
        let api_key = api_key.filter(|k| !k.is_empty());

        Ok(Self {
            base_url,
            model,
            api_key,
            timeout_ms,
            ceiling,
            max_sensitivity: max_sensitivity.unwrap_or(MaxSensitivity::Medium),
        })
    }

    /// Return the runtime ceiling determined at construction from `base_url`.
    #[must_use]
    pub fn runtime_ceiling(&self) -> RuntimeCeiling {
        self.ceiling
    }
}

// ---------------------------------------------------------------------------
// URL / ceiling helper
// ---------------------------------------------------------------------------

fn ceiling_for_url(base_url: &str) -> Result<RuntimeCeiling, LlmError> {
    let rest = if let Some(r) = base_url.strip_prefix("http://") {
        r
    } else if let Some(r) = base_url.strip_prefix("https://") {
        r
    } else {
        return Err(LlmError::InvalidRequest(format!(
            "openai-compat: base_url must start with http:// or https://: {base_url}"
        )));
    };

    let host = extract_host(rest).ok_or_else(|| {
        LlmError::InvalidRequest(format!(
            "openai-compat: base_url must contain a host: {base_url}"
        ))
    })?;

    if is_loopback_host(host) {
        Ok(RuntimeCeiling::LocalUnsigned)
    } else {
        Ok(RuntimeCeiling::RemoteUnsigned)
    }
}

fn extract_host(rest: &str) -> Option<&str> {
    // Strip userinfo if present (e.g. `user:pass@host:port/path`).
    let authority = rest.split(['/', '?', '#']).next().unwrap_or_default();
    if authority.is_empty() {
        return None;
    }

    // IPv6 literal `[::1]:port`
    if let Some(after_open) = authority.strip_prefix('[') {
        let (host, suffix) = after_open.split_once(']')?;
        if suffix.is_empty() || suffix.starts_with(':') {
            return Some(host);
        }
        return None;
    }

    // Strip optional port.
    let host = authority.split(':').next().unwrap_or_default();
    if host.is_empty() {
        None
    } else {
        Some(host)
    }
}

fn is_loopback_host(host: &str) -> bool {
    if host.eq_ignore_ascii_case("localhost") {
        return true;
    }
    host.parse::<IpAddr>().is_ok_and(|ip| ip.is_loopback())
}

// ---------------------------------------------------------------------------
// Wire types — non-streaming
// ---------------------------------------------------------------------------

/// Outgoing body for `POST /v1/chat/completions` (non-streaming).
#[derive(Debug, Serialize)]
struct ChatCompletionRequest<'a> {
    model: &'a str,
    messages: Vec<OpenAiMessage<'a>>,
    stream: bool,
    max_tokens: u32,
}

/// One message in the OpenAI chat format.
#[derive(Debug, Serialize)]
struct OpenAiMessage<'a> {
    role: &'a str,
    content: &'a str,
}

/// Top-level `/v1/chat/completions` response envelope.
#[derive(Debug, Deserialize)]
struct ChatCompletionResponse {
    #[serde(default)]
    choices: Vec<Choice>,
    #[serde(default)]
    usage: Option<OpenAiUsage>,
}

/// One element of the `choices` array.
#[derive(Debug, Deserialize)]
struct Choice {
    #[serde(default)]
    message: ChoiceMessage,
}

/// The `message` field inside a non-streaming choice.
#[derive(Debug, Default, Deserialize)]
struct ChoiceMessage {
    #[serde(default)]
    content: String,
}

/// Token usage reported by the provider (optional).
#[derive(Debug, Deserialize)]
struct OpenAiUsage {
    #[serde(default)]
    prompt_tokens: u32,
    #[serde(default)]
    completion_tokens: u32,
}

// ---------------------------------------------------------------------------
// Wire types — SSE streaming
// ---------------------------------------------------------------------------

/// One `data:` line from an OpenAI SSE stream.
#[derive(Debug, Deserialize)]
struct StreamChunkEnvelope {
    #[serde(default)]
    choices: Vec<StreamChoice>,
}

/// One element of `choices` in a streaming delta.
#[derive(Debug, Default, Deserialize)]
struct StreamChoice {
    #[serde(default)]
    delta: StreamDelta,
    finish_reason: Option<String>,
}

/// The `delta` field inside a streaming choice.
#[derive(Debug, Default, Deserialize)]
struct StreamDelta {
    #[serde(default)]
    content: String,
}

// ---------------------------------------------------------------------------
// LlmAdapter implementation
// ---------------------------------------------------------------------------

#[async_trait]
impl LlmAdapter for OpenAiCompatAdapter {
    fn adapter_id(&self) -> &'static str {
        "openai-compat"
    }

    async fn complete(&self, req: LlmRequest) -> Result<LlmResponse, LlmError> {
        // Sensitivity gate: reject prompts containing high-sensitivity markers
        // before any bytes leave the machine.
        let prompt_text: String = std::iter::once(req.system.as_str())
            .chain(req.messages.iter().map(|m| m.content.as_str()))
            .collect::<Vec<_>>()
            .join("\n");
        check_remote_prompt_sensitivity(&prompt_text, self.max_sensitivity)?;

        let base_url = self.base_url.clone();
        let model = self.model.clone();
        let api_key = self.api_key.clone();
        let timeout_ms = self.timeout_ms;

        let result = tokio::task::spawn_blocking(move || {
            call_openai_compat(&base_url, &model, api_key.as_deref(), &req, timeout_ms)
        })
        .await
        .map_err(|e| LlmError::Transport(format!("spawn_blocking join error: {e}")))?;

        result
    }

    /// Override with true OpenAI SSE streaming via `POST /v1/chat/completions`
    /// with `"stream": true`.
    ///
    /// Parses `data:` lines, extracts `choices[0].delta.content`, and skips
    /// the terminal `data: [DONE]` sentinel.
    fn stream_boxed(&self, req: LlmRequest) -> BoxStream<'_> {
        stream_openai_compat_sse(
            self.base_url.clone(),
            self.model.clone(),
            self.api_key.clone(),
            req,
        )
    }
}

// ---------------------------------------------------------------------------
// Synchronous HTTP call (non-streaming)
// ---------------------------------------------------------------------------

fn call_openai_compat(
    base_url: &str,
    model: &str,
    api_key: Option<&str>,
    req: &LlmRequest,
    timeout_ms: u64,
) -> Result<LlmResponse, LlmError> {
    let url = format!("{base_url}/v1/chat/completions");

    let messages: Vec<OpenAiMessage<'_>> = req
        .messages
        .iter()
        .map(|m| OpenAiMessage {
            role: role_to_str(m.role),
            content: &m.content,
        })
        .collect();

    let body = ChatCompletionRequest {
        model,
        messages,
        stream: false,
        max_tokens: req.max_tokens,
    };

    let body_value = serde_json::to_value(&body)
        .map_err(|e| LlmError::Transport(format!("request serialization failed: {e}")))?;

    let timeout = Duration::from_millis(timeout_ms);
    let agent = ureq::AgentBuilder::new().timeout(timeout).build();

    let mut request = agent.post(&url).set("content-type", "application/json");
    if let Some(key) = api_key {
        request = request.set("authorization", &format!("Bearer {key}"));
    }

    let raw_response = request
        .send_json(body_value)
        .map_err(|err| map_ureq_error(err, timeout_ms))?;

    let status = raw_response.status();
    if status != 200 {
        return Err(LlmError::Upstream(format!("HTTP {status}")));
    }

    let response_text = raw_response
        .into_string()
        .map_err(|e| LlmError::Transport(format!("reading response body: {e}")))?;

    let parsed: ChatCompletionResponse = serde_json::from_str(&response_text)
        .map_err(|e| LlmError::Parse(format!("openai-compat response parse: {e}")))?;

    let text = parsed
        .choices
        .into_iter()
        .next()
        .map(|c| c.message.content)
        .ok_or_else(|| {
            LlmError::Parse("openai-compat response contained no choices".to_string())
        })?;

    let raw_hash = blake3_hex(response_text.as_bytes());
    let usage = parsed.usage.map(|u| TokenUsage {
        prompt_tokens: u.prompt_tokens,
        completion_tokens: u.completion_tokens,
    });

    Ok(LlmResponse {
        text,
        parsed_json: None,
        model: model.to_string(),
        usage,
        raw_hash,
    })
}

// ---------------------------------------------------------------------------
// SSE streaming implementation
// ---------------------------------------------------------------------------

/// Build a [`BoxStream`] that drives OpenAI-compatible SSE streaming.
///
/// Extracted as a free function so the `async_stream::stream!` macro is not
/// nested inside an `impl` block, which can confuse lifetime inference.
fn stream_openai_compat_sse(
    base_url: String,
    model: String,
    api_key: Option<String>,
    req: LlmRequest,
) -> BoxStream<'static> {
    Box::pin(async_stream::stream! {
        let timeout_ms = req.timeout_ms;
        let result = tokio::task::spawn_blocking(move || {
            call_openai_compat_streaming(&base_url, &model, api_key.as_deref(), &req, timeout_ms)
        })
        .await;

        match result {
            Ok(chunks) => {
                for chunk in chunks {
                    yield chunk;
                }
            }
            Err(e) => yield Err(LlmError::Transport(format!("spawn_blocking join error: {e}"))),
        }
    })
}

/// Synchronous OpenAI-compatible SSE streaming call, executed inside
/// `spawn_blocking`.
///
/// Posts to `/v1/chat/completions` with `stream: true`, then reads the
/// response body line by line. SSE protocol:
/// - Empty lines are event separators — skip them.
/// - Lines beginning with `event:` are event-type hints — skip them.
/// - `data: [DONE]` is the terminal sentinel — stop processing.
/// - Lines beginning with `data:` carry the JSON delta payload.
fn call_openai_compat_streaming(
    base_url: &str,
    model: &str,
    api_key: Option<&str>,
    req: &LlmRequest,
    timeout_ms: u64,
) -> Vec<Result<StreamChunk, LlmError>> {
    let url = format!("{base_url}/v1/chat/completions");

    let messages: Vec<OpenAiMessage<'_>> = req
        .messages
        .iter()
        .map(|m| OpenAiMessage {
            role: role_to_str(m.role),
            content: &m.content,
        })
        .collect();

    let body = ChatCompletionRequest {
        model,
        messages,
        stream: true,
        max_tokens: req.max_tokens,
    };

    let body_value = match serde_json::to_value(&body) {
        Ok(v) => v,
        Err(e) => {
            return vec![Err(LlmError::Transport(format!(
                "request serialization failed: {e}"
            )))]
        }
    };

    let timeout = Duration::from_millis(timeout_ms);
    let agent = ureq::AgentBuilder::new().timeout(timeout).build();

    let mut request = agent.post(&url).set("content-type", "application/json");
    if let Some(key) = api_key {
        request = request.set("authorization", &format!("Bearer {key}"));
    }

    let raw_response = match request.send_json(body_value) {
        Ok(r) => r,
        Err(err) => return vec![Err(map_ureq_error(err, timeout_ms))],
    };

    let status = raw_response.status();
    if status != 200 {
        return vec![Err(LlmError::Upstream(format!("HTTP {status}")))];
    }

    let body_text = match raw_response.into_string() {
        Ok(s) => s,
        Err(e) => {
            return vec![Err(LlmError::Transport(format!(
                "reading streaming response body: {e}"
            )))]
        }
    };

    let mut chunks = Vec::new();

    for line in body_text.lines() {
        if line.is_empty() || line.starts_with("event:") {
            continue;
        }

        let data = match line.strip_prefix("data:") {
            Some(rest) => rest.trim(),
            None => continue,
        };

        // Terminal sentinel — no further lines need processing.
        if data == "[DONE]" {
            chunks.push(Ok(StreamChunk {
                delta: String::new(),
                finish_reason: Some("stop".into()),
            }));
            return chunks;
        }

        let envelope: StreamChunkEnvelope = match serde_json::from_str(data) {
            Ok(v) => v,
            Err(e) => {
                chunks.push(Err(LlmError::Parse(format!(
                    "openai-compat SSE data parse: {e}: {data}"
                ))));
                continue;
            }
        };

        let choice = match envelope.choices.into_iter().next() {
            Some(c) => c,
            None => continue,
        };

        let finish_reason = choice.finish_reason;
        let delta_text = choice.delta.content;

        chunks.push(Ok(StreamChunk {
            delta: delta_text,
            finish_reason,
        }));
    }

    chunks
}

// ---------------------------------------------------------------------------
// ureq error mapping
// ---------------------------------------------------------------------------

fn map_ureq_error(err: ureq::Error, timeout_ms: u64) -> LlmError {
    match err {
        ureq::Error::Transport(t) => {
            let msg = t.to_string();
            if is_timeout_message(&msg) {
                LlmError::Timeout { timeout_ms }
            } else {
                LlmError::Transport(msg)
            }
        }
        ureq::Error::Status(code, _) => LlmError::Upstream(format!("HTTP {code}")),
    }
}

fn is_timeout_message(msg: &str) -> bool {
    let lower = msg.to_ascii_lowercase();
    lower.contains("timed out") || lower.contains("deadline exceeded") || lower.contains("timeout")
}

// ---------------------------------------------------------------------------
// Role serialization helper
// ---------------------------------------------------------------------------

/// Return the lowercase role string used by the OpenAI chat-completions API.
fn role_to_str(role: LlmRole) -> &'static str {
    match role {
        LlmRole::User => "user",
        LlmRole::Assistant => "assistant",
        LlmRole::Tool => "tool",
    }
}