f0927f5355
Wires a new LlamaCppClient (OpenAI-compatible /v1 wire format) alongside OllamaClient and OpenRouterClient. Per-slot routing for chat/vision/embed via env (LLAMA_SWAP_URL + *_MODEL vars); capability inference uses an env allowlist since /v1/models doesn't report modality. InsightGenerator + InsightChatService gain three-way dispatch on chat_backend = "local" | "hybrid" | "llamacpp". Hybrid and llamacpp share the describe-then-inline path (text-only chat after a separate vision describe). HYBRID_VISION_BACKEND=llamacpp lets hybrid route its describe pass through llama-swap's vision slot while chat still goes to OpenRouter. Cross-replay matrix added (validate_cross_replay): local<->llamacpp and hybrid<->llamacpp allowed; local->hybrid and llamacpp->hybrid rejected. New /insights/llamacpp/models handler mirrors the OpenRouter shape.
979 lines
35 KiB
Rust
979 lines
35 KiB
Rust
// LlamaCppClient — talks to a llama-swap proxy that fronts one or more
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// llama-server processes. llama-swap exposes an OpenAI-compatible HTTP
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// surface (`/v1/chat/completions`, `/v1/embeddings`, `/v1/models`), so the
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// wire translation mirrors `OpenRouterClient` almost exactly.
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//
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// Differences from OpenRouter:
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// - No bearer auth or attribution headers; llama-swap is LAN-only.
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// - Three model slots (`primary_model` = chat, `vision_model`, `embedding_model`)
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// each map to a model id in the llama-swap config. `describe_image` and
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// `generate_embeddings` issue requests with the appropriate slot id in the
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// `model` field, which is how llama-swap selects which backend process to
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// run.
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// - `/v1/models` returns only the configured slot ids — capabilities aren't
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// reported by the API, so `vision_models` is a config-time allowlist (env
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// `LLAMA_SWAP_VISION_MODELS`) used to set `has_vision` on responses.
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// `has_tool_calling` is assumed true for every slot, since llama-swap entries
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// default to launching llama-server with `--jinja`.
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//
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// First consumer lands alongside the three-way backend dispatch in
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// insight_generator / insight_chat.
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#![allow(dead_code)]
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use anyhow::{Context, Result, anyhow, bail};
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use async_trait::async_trait;
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use reqwest::Client;
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use serde::Deserialize;
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use serde_json::{Value, json};
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use std::time::Duration;
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use crate::ai::llm_client::{
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ChatMessage, LlmClient, LlmStreamEvent, ModelCapabilities, Tool, ToolCall, ToolCallFunction,
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};
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use futures::stream::{BoxStream, StreamExt};
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const DEFAULT_BASE_URL: &str = "http://localhost:9292/v1";
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const DEFAULT_PRIMARY_MODEL: &str = "chat";
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const DEFAULT_VISION_MODEL: &str = "vision";
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const DEFAULT_EMBEDDING_MODEL: &str = "embed";
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const DEFAULT_REQUEST_TIMEOUT_SECS: u64 = 180;
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/// OpenAI-compatible client targeting a llama-swap proxy in front of one or
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/// more llama-server processes. See the module doc-comment for the slot model.
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#[derive(Clone)]
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pub struct LlamaCppClient {
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client: Client,
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pub base_url: String,
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/// Chat model slot id (e.g. `"chat"`). Used for `generate` /
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/// `chat_with_tools` / `chat_with_tools_stream`.
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pub primary_model: String,
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/// Embedding model slot id (e.g. `"embed"`). Used for
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/// `generate_embeddings`.
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pub embedding_model: String,
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/// Vision model slot id (e.g. `"vision"`). Used for `describe_image` and
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/// included in `vision_models` automatically so capability lookups for
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/// the default vision slot report `has_vision = true` even when the env
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/// allowlist is empty.
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pub vision_model: String,
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/// Operator-curated set of slot ids known to be multimodal. Drives the
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/// `has_vision` field in `list_models` / `model_capabilities`, since
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/// llama-swap's `/v1/models` doesn't report modality. Empty allowlist
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/// still marks `vision_model` as vision-capable.
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pub vision_models: Vec<String>,
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num_ctx: Option<i32>,
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temperature: Option<f32>,
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top_p: Option<f32>,
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top_k: Option<i32>,
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min_p: Option<f32>,
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}
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impl LlamaCppClient {
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pub fn new(base_url: Option<String>, primary_model: Option<String>) -> Self {
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let timeout_secs = std::env::var("LLAMA_SWAP_REQUEST_TIMEOUT_SECONDS")
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.ok()
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.and_then(|v| v.parse::<u64>().ok())
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.unwrap_or(DEFAULT_REQUEST_TIMEOUT_SECS);
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Self {
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client: Client::builder()
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.connect_timeout(Duration::from_secs(10))
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.timeout(Duration::from_secs(timeout_secs))
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.build()
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.unwrap_or_else(|_| Client::new()),
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base_url: base_url.unwrap_or_else(|| DEFAULT_BASE_URL.to_string()),
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primary_model: primary_model.unwrap_or_else(|| DEFAULT_PRIMARY_MODEL.to_string()),
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embedding_model: DEFAULT_EMBEDDING_MODEL.to_string(),
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vision_model: DEFAULT_VISION_MODEL.to_string(),
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vision_models: Vec::new(),
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num_ctx: None,
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temperature: None,
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top_p: None,
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top_k: None,
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min_p: None,
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}
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}
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pub fn set_embedding_model(&mut self, model: String) {
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self.embedding_model = model;
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}
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pub fn set_vision_model(&mut self, model: String) {
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self.vision_model = model;
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}
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pub fn set_vision_models(&mut self, models: Vec<String>) {
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self.vision_models = models;
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}
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pub fn set_num_ctx(&mut self, num_ctx: Option<i32>) {
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self.num_ctx = num_ctx;
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}
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pub fn set_sampling_params(
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&mut self,
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temperature: Option<f32>,
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top_p: Option<f32>,
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top_k: Option<i32>,
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min_p: Option<f32>,
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) {
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self.temperature = temperature;
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self.top_p = top_p;
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self.top_k = top_k;
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self.min_p = min_p;
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}
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/// Translate canonical messages to the OpenAI-compatible wire shape.
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/// Behaviorally identical to `OpenRouterClient::messages_to_openai` —
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/// stringify tool-call arguments, rewrite images into content-parts, attach
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/// `tool_call_id` to `role=tool` messages based on the preceding assistant
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/// turn's tool calls.
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fn messages_to_openai(messages: &[ChatMessage]) -> Vec<Value> {
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let mut out = Vec::with_capacity(messages.len());
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let mut last_tool_call_ids: Vec<String> = Vec::new();
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let mut next_tool_result_idx: usize = 0;
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for msg in messages {
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let mut obj = serde_json::Map::new();
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obj.insert("role".into(), Value::String(msg.role.clone()));
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match &msg.images {
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Some(images) if !images.is_empty() => {
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let mut parts: Vec<Value> = Vec::new();
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if !msg.content.is_empty() {
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parts.push(json!({"type": "text", "text": msg.content}));
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}
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for img in images {
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let url = image_to_data_url(img);
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parts.push(json!({
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"type": "image_url",
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"image_url": { "url": url }
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}));
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}
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obj.insert("content".into(), Value::Array(parts));
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}
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_ => {
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obj.insert("content".into(), Value::String(msg.content.clone()));
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}
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}
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if let Some(tcs) = &msg.tool_calls
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&& msg.role == "assistant"
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{
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let converted: Vec<Value> = tcs
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.iter()
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.enumerate()
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.map(|(i, call)| {
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let id = call.id.clone().unwrap_or_else(|| format!("call_{}", i));
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let args_str = serde_json::to_string(&call.function.arguments)
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.unwrap_or_else(|_| "{}".to_string());
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json!({
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"id": id,
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"type": "function",
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"function": {
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"name": call.function.name,
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"arguments": args_str,
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}
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})
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})
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.collect();
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last_tool_call_ids = converted
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.iter()
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.filter_map(|v| v.get("id").and_then(|x| x.as_str()).map(String::from))
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.collect();
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next_tool_result_idx = 0;
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obj.insert("tool_calls".into(), Value::Array(converted));
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}
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if msg.role == "tool" {
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let id = last_tool_call_ids
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.get(next_tool_result_idx)
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.cloned()
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.unwrap_or_else(|| "call_0".to_string());
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obj.insert("tool_call_id".into(), Value::String(id));
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next_tool_result_idx += 1;
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}
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out.push(Value::Object(obj));
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}
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out
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}
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/// Parse an OpenAI-compatible assistant message back into canonical shape.
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/// llama.cpp emits `reasoning_content` on thinking models; we drop it for
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/// parity with OpenRouter (which also strips upstream reasoning fields).
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fn openai_message_to_chat(msg: &Value) -> Result<ChatMessage> {
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let obj = msg
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.as_object()
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.ok_or_else(|| anyhow!("response message is not an object"))?;
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let role = obj
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.get("role")
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.and_then(|v| v.as_str())
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.unwrap_or("assistant")
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.to_string();
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let content = obj
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.get("content")
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.and_then(|v| v.as_str())
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.unwrap_or("")
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.to_string();
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let tool_calls = if let Some(tcs) = obj.get("tool_calls").and_then(|v| v.as_array()) {
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let mut parsed = Vec::with_capacity(tcs.len());
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for tc in tcs {
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let id = tc.get("id").and_then(|v| v.as_str()).map(String::from);
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let function = tc
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.get("function")
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.ok_or_else(|| anyhow!("tool_call missing function field"))?;
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let name = function
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.get("name")
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.and_then(|v| v.as_str())
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.unwrap_or_default()
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.to_string();
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let args_value = match function.get("arguments") {
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Some(Value::String(s)) => {
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serde_json::from_str::<Value>(s).unwrap_or_else(|_| json!({}))
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}
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Some(v @ Value::Object(_)) => v.clone(),
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_ => json!({}),
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};
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parsed.push(ToolCall {
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id,
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function: ToolCallFunction {
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name,
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arguments: args_value,
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},
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});
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}
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Some(parsed)
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} else {
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None
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};
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Ok(ChatMessage {
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role,
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content,
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tool_calls,
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images: None,
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})
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}
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fn build_options(&self) -> Vec<(&'static str, Value)> {
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let mut v = Vec::new();
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if let Some(t) = self.temperature {
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v.push(("temperature", json!(t)));
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}
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if let Some(p) = self.top_p {
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v.push(("top_p", json!(p)));
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}
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if let Some(k) = self.top_k {
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v.push(("top_k", json!(k)));
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}
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if let Some(m) = self.min_p {
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v.push(("min_p", json!(m)));
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}
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// num_ctx isn't an OpenAI param; llama-server bakes ctx in at launch
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// via -c, so we silently drop the override here. The config.yaml
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// entry is the source of truth for context size.
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let _ = self.num_ctx;
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v
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}
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/// Issue a chat request with an explicit model id override. Used by
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/// `describe_image` to route through the vision slot without mutating
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/// `self.primary_model`.
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async fn chat_completion_with_model(
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&self,
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model: &str,
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messages: Vec<ChatMessage>,
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tools: Vec<Tool>,
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) -> Result<(ChatMessage, Option<i32>, Option<i32>)> {
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let url = format!("{}/chat/completions", self.base_url);
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let mut body = serde_json::Map::new();
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body.insert("model".into(), Value::String(model.to_string()));
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body.insert(
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"messages".into(),
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Value::Array(Self::messages_to_openai(&messages)),
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);
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body.insert("stream".into(), Value::Bool(false));
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if !tools.is_empty() {
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body.insert(
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"tools".into(),
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serde_json::to_value(&tools).context("serializing tools")?,
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);
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}
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for (k, v) in self.build_options() {
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body.insert(k.into(), v);
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}
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let resp = self
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.client
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.post(&url)
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.json(&Value::Object(body))
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.send()
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.await
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.with_context(|| format!("POST {} failed", url))?;
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if !resp.status().is_success() {
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let status = resp.status();
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let body = resp.text().await.unwrap_or_default();
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bail!("llama-swap chat request failed: {} — {}", status, body);
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}
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let parsed: Value = resp.json().await.context("parsing chat response")?;
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let choice = parsed
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.get("choices")
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.and_then(|v| v.as_array())
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.and_then(|a| a.first())
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.ok_or_else(|| {
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anyhow!(
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"response missing choices[0]: {}",
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extract_error_detail(&parsed)
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)
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})?;
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let msg = choice.get("message").ok_or_else(|| {
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anyhow!(
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"choices[0] missing message: {}",
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extract_error_detail(&parsed)
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)
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})?;
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let chat_msg = Self::openai_message_to_chat(msg)?;
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let usage = parsed.get("usage");
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let prompt_tokens = usage
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.and_then(|u| u.get("prompt_tokens"))
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.and_then(|v| v.as_i64())
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.map(|n| n as i32);
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let completion_tokens = usage
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.and_then(|u| u.get("completion_tokens"))
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.and_then(|v| v.as_i64())
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.map(|n| n as i32);
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Ok((chat_msg, prompt_tokens, completion_tokens))
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}
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}
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|
|
#[async_trait]
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impl LlmClient for LlamaCppClient {
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async fn generate(
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&self,
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prompt: &str,
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system: Option<&str>,
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images: Option<Vec<String>>,
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) -> Result<String> {
|
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let mut messages: Vec<ChatMessage> = Vec::new();
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if let Some(sys) = system {
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messages.push(ChatMessage::system(sys));
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}
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let mut user = ChatMessage::user(prompt);
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user.images = images;
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messages.push(user);
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let (reply, _, _) = self.chat_with_tools(messages, Vec::new()).await?;
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Ok(reply.content)
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}
|
|
|
|
async fn chat_with_tools(
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&self,
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messages: Vec<ChatMessage>,
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tools: Vec<Tool>,
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) -> Result<(ChatMessage, Option<i32>, Option<i32>)> {
|
|
log::info!(
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"llama-swap chat_with_tools: model={} messages={} tools={}",
|
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self.primary_model,
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messages.len(),
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tools.len()
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);
|
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self.chat_completion_with_model(&self.primary_model.clone(), messages, tools)
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.await
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}
|
|
|
|
async fn chat_with_tools_stream(
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&self,
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messages: Vec<ChatMessage>,
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tools: Vec<Tool>,
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) -> Result<BoxStream<'static, Result<LlmStreamEvent>>> {
|
|
let url = format!("{}/chat/completions", self.base_url);
|
|
let mut body = serde_json::Map::new();
|
|
body.insert(
|
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"model".into(),
|
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Value::String(self.primary_model.clone()),
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);
|
|
body.insert(
|
|
"messages".into(),
|
|
Value::Array(Self::messages_to_openai(&messages)),
|
|
);
|
|
body.insert("stream".into(), Value::Bool(true));
|
|
body.insert(
|
|
"stream_options".into(),
|
|
serde_json::json!({ "include_usage": true }),
|
|
);
|
|
if !tools.is_empty() {
|
|
body.insert(
|
|
"tools".into(),
|
|
serde_json::to_value(&tools).context("serializing tools")?,
|
|
);
|
|
}
|
|
for (k, v) in self.build_options() {
|
|
body.insert(k.into(), v);
|
|
}
|
|
|
|
let resp = self
|
|
.client
|
|
.post(&url)
|
|
.json(&Value::Object(body))
|
|
.send()
|
|
.await
|
|
.with_context(|| format!("POST {} failed", url))?;
|
|
|
|
if !resp.status().is_success() {
|
|
let status = resp.status();
|
|
let body = resp.text().await.unwrap_or_default();
|
|
bail!("llama-swap stream request failed: {} — {}", status, body);
|
|
}
|
|
|
|
let byte_stream = resp.bytes_stream();
|
|
let stream = async_stream::stream! {
|
|
let mut byte_stream = byte_stream;
|
|
let mut buf: Vec<u8> = Vec::new();
|
|
let mut accumulated_content = String::new();
|
|
let mut tool_state: std::collections::BTreeMap<
|
|
usize,
|
|
(Option<String>, Option<String>, String),
|
|
> = std::collections::BTreeMap::new();
|
|
let mut role = "assistant".to_string();
|
|
let mut prompt_tokens: Option<i32> = None;
|
|
let mut completion_tokens: Option<i32> = None;
|
|
let mut done_seen = false;
|
|
|
|
while let Some(chunk) = byte_stream.next().await {
|
|
let chunk = match chunk {
|
|
Ok(b) => b,
|
|
Err(e) => {
|
|
yield Err(anyhow!("stream read failed: {}", e));
|
|
return;
|
|
}
|
|
};
|
|
buf.extend_from_slice(&chunk);
|
|
|
|
while let Some(sep) = find_double_newline(&buf) {
|
|
let frame = buf.drain(..sep + 2).collect::<Vec<_>>();
|
|
let frame_str = match std::str::from_utf8(&frame) {
|
|
Ok(s) => s,
|
|
Err(_) => continue,
|
|
};
|
|
for line in frame_str.lines() {
|
|
let line = line.trim_end_matches('\r');
|
|
let payload = match line.strip_prefix("data: ") {
|
|
Some(p) => p,
|
|
None => continue,
|
|
};
|
|
if payload == "[DONE]" {
|
|
done_seen = true;
|
|
break;
|
|
}
|
|
let v: Value = match serde_json::from_str(payload) {
|
|
Ok(v) => v,
|
|
Err(e) => {
|
|
log::warn!(
|
|
"malformed llama-swap SSE frame: {} ({})",
|
|
payload,
|
|
e
|
|
);
|
|
continue;
|
|
}
|
|
};
|
|
|
|
if let Some(usage) = v.get("usage") {
|
|
prompt_tokens = usage
|
|
.get("prompt_tokens")
|
|
.and_then(|n| n.as_i64())
|
|
.map(|n| n as i32);
|
|
completion_tokens = usage
|
|
.get("completion_tokens")
|
|
.and_then(|n| n.as_i64())
|
|
.map(|n| n as i32);
|
|
}
|
|
|
|
let Some(choices) = v.get("choices").and_then(|c| c.as_array())
|
|
else {
|
|
continue;
|
|
};
|
|
let Some(choice) = choices.first() else { continue };
|
|
let delta = match choice.get("delta") {
|
|
Some(d) => d,
|
|
None => continue,
|
|
};
|
|
if let Some(r) = delta.get("role").and_then(|v| v.as_str()) {
|
|
role = r.to_string();
|
|
}
|
|
if let Some(content) =
|
|
delta.get("content").and_then(|v| v.as_str())
|
|
&& !content.is_empty()
|
|
{
|
|
accumulated_content.push_str(content);
|
|
yield Ok(LlmStreamEvent::TextDelta(content.to_string()));
|
|
}
|
|
if let Some(tcs) = delta.get("tool_calls").and_then(|v| v.as_array()) {
|
|
for tc_delta in tcs {
|
|
let idx = tc_delta
|
|
.get("index")
|
|
.and_then(|n| n.as_u64())
|
|
.unwrap_or(0) as usize;
|
|
let entry = tool_state
|
|
.entry(idx)
|
|
.or_insert((None, None, String::new()));
|
|
if let Some(id) =
|
|
tc_delta.get("id").and_then(|v| v.as_str())
|
|
{
|
|
entry.0 = Some(id.to_string());
|
|
}
|
|
if let Some(func) = tc_delta.get("function") {
|
|
if let Some(name) =
|
|
func.get("name").and_then(|v| v.as_str())
|
|
{
|
|
entry.1 = Some(name.to_string());
|
|
}
|
|
if let Some(args) =
|
|
func.get("arguments").and_then(|v| v.as_str())
|
|
{
|
|
entry.2.push_str(args);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if done_seen {
|
|
break;
|
|
}
|
|
}
|
|
if done_seen {
|
|
break;
|
|
}
|
|
}
|
|
|
|
let tool_calls: Option<Vec<ToolCall>> = if tool_state.is_empty() {
|
|
None
|
|
} else {
|
|
let mut v = Vec::with_capacity(tool_state.len());
|
|
for (_idx, (id, name, args)) in tool_state {
|
|
let arguments: Value = if args.trim().is_empty() {
|
|
Value::Object(Default::default())
|
|
} else {
|
|
serde_json::from_str(&args).unwrap_or_else(|_| {
|
|
Value::Object(Default::default())
|
|
})
|
|
};
|
|
v.push(ToolCall {
|
|
id,
|
|
function: ToolCallFunction {
|
|
name: name.unwrap_or_default(),
|
|
arguments,
|
|
},
|
|
});
|
|
}
|
|
Some(v)
|
|
};
|
|
|
|
let message = ChatMessage {
|
|
role,
|
|
content: accumulated_content,
|
|
tool_calls,
|
|
images: None,
|
|
};
|
|
yield Ok(LlmStreamEvent::Done {
|
|
message,
|
|
prompt_eval_count: prompt_tokens,
|
|
eval_count: completion_tokens,
|
|
});
|
|
};
|
|
|
|
Ok(Box::pin(stream))
|
|
}
|
|
|
|
async fn generate_embeddings(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>> {
|
|
let url = format!("{}/embeddings", self.base_url);
|
|
let body = json!({
|
|
"model": self.embedding_model,
|
|
"input": texts,
|
|
});
|
|
|
|
let resp = self
|
|
.client
|
|
.post(&url)
|
|
.json(&body)
|
|
.send()
|
|
.await
|
|
.with_context(|| format!("POST {} failed", url))?;
|
|
|
|
if !resp.status().is_success() {
|
|
let status = resp.status();
|
|
let body = resp.text().await.unwrap_or_default();
|
|
bail!("llama-swap embedding request failed: {} — {}", status, body);
|
|
}
|
|
|
|
#[derive(Deserialize)]
|
|
struct EmbedResponse {
|
|
data: Vec<EmbedItem>,
|
|
}
|
|
#[derive(Deserialize)]
|
|
struct EmbedItem {
|
|
embedding: Vec<f32>,
|
|
}
|
|
|
|
let parsed: EmbedResponse = resp.json().await.context("parsing embed response")?;
|
|
Ok(parsed.data.into_iter().map(|i| i.embedding).collect())
|
|
}
|
|
|
|
async fn describe_image(&self, image_base64: &str) -> Result<String> {
|
|
let prompt = "Briefly describe what you see in this image in 1-2 sentences. \
|
|
Focus on the people, location, and activity.";
|
|
let system = "You are a scene description assistant. Be concise and factual.";
|
|
|
|
let messages = vec![
|
|
ChatMessage::system(system),
|
|
ChatMessage {
|
|
role: "user".to_string(),
|
|
content: prompt.to_string(),
|
|
tool_calls: None,
|
|
images: Some(vec![image_base64.to_string()]),
|
|
},
|
|
];
|
|
|
|
let (reply, _, _) = self
|
|
.chat_completion_with_model(&self.vision_model.clone(), messages, Vec::new())
|
|
.await?;
|
|
Ok(reply.content)
|
|
}
|
|
|
|
async fn list_models(&self) -> Result<Vec<ModelCapabilities>> {
|
|
let url = format!("{}/models", self.base_url);
|
|
let resp = self
|
|
.client
|
|
.get(&url)
|
|
.send()
|
|
.await
|
|
.with_context(|| format!("GET {} failed", url))?;
|
|
|
|
if !resp.status().is_success() {
|
|
let status = resp.status();
|
|
let body = resp.text().await.unwrap_or_default();
|
|
bail!("llama-swap list_models failed: {} — {}", status, body);
|
|
}
|
|
|
|
let parsed: Value = resp.json().await.context("parsing models response")?;
|
|
let data = parsed
|
|
.get("data")
|
|
.and_then(|v| v.as_array())
|
|
.ok_or_else(|| anyhow!("models response missing data[]"))?;
|
|
|
|
let caps: Vec<ModelCapabilities> = data
|
|
.iter()
|
|
.map(|m| self.parse_model_capabilities(m))
|
|
.collect();
|
|
|
|
Ok(caps)
|
|
}
|
|
|
|
async fn model_capabilities(&self, model: &str) -> Result<ModelCapabilities> {
|
|
let all = self.list_models().await?;
|
|
all.into_iter()
|
|
.find(|m| m.name == model)
|
|
.ok_or_else(|| anyhow!("model '{}' not found on llama-swap", model))
|
|
}
|
|
|
|
fn primary_model(&self) -> &str {
|
|
&self.primary_model
|
|
}
|
|
}
|
|
|
|
impl LlamaCppClient {
|
|
fn parse_model_capabilities(&self, m: &Value) -> ModelCapabilities {
|
|
let name = m
|
|
.get("id")
|
|
.and_then(|v| v.as_str())
|
|
.unwrap_or_default()
|
|
.to_string();
|
|
let has_vision = name == self.vision_model || self.vision_models.iter().any(|v| v == &name);
|
|
// Tool calling is the default for llama-swap entries we configure
|
|
// (--jinja flag); no negative-list mechanism yet, so report true.
|
|
ModelCapabilities {
|
|
name,
|
|
has_vision,
|
|
has_tool_calling: true,
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Extract a diagnostic fragment from a llama-swap / llama-server response
|
|
/// that doesn't match the expected `{choices: [...]}` shape. llama-server
|
|
/// returns errors as `{"error": {"message": "...", "code": N, "type": "..."}}`;
|
|
/// llama-swap itself sometimes wraps subprocess failures with its own
|
|
/// `{"error": "..."}` flat shape. Surface either when present, otherwise fall
|
|
/// back to a truncated raw-JSON view.
|
|
fn extract_error_detail(parsed: &Value) -> String {
|
|
if let Some(err) = parsed.get("error") {
|
|
match err {
|
|
Value::Object(_) => {
|
|
let message = err
|
|
.get("message")
|
|
.and_then(|v| v.as_str())
|
|
.unwrap_or("(no message)");
|
|
let code = err
|
|
.get("code")
|
|
.map(|v| match v {
|
|
Value::String(s) => s.clone(),
|
|
other => other.to_string(),
|
|
})
|
|
.unwrap_or_else(|| "?".to_string());
|
|
let short_message: String = message.chars().take(240).collect();
|
|
return format!("error code={} message=\"{}\"", code, short_message);
|
|
}
|
|
Value::String(s) => {
|
|
let short: String = s.chars().take(240).collect();
|
|
return format!("error=\"{}\"", short);
|
|
}
|
|
_ => {}
|
|
}
|
|
}
|
|
let raw = parsed.to_string();
|
|
raw.chars().take(300).collect()
|
|
}
|
|
|
|
fn find_double_newline(buf: &[u8]) -> Option<usize> {
|
|
for i in 0..buf.len().saturating_sub(1) {
|
|
if buf[i] == b'\n' && buf[i + 1] == b'\n' {
|
|
return Some(i);
|
|
}
|
|
if i + 3 < buf.len()
|
|
&& buf[i] == b'\r'
|
|
&& buf[i + 1] == b'\n'
|
|
&& buf[i + 2] == b'\r'
|
|
&& buf[i + 3] == b'\n'
|
|
{
|
|
return Some(i + 1);
|
|
}
|
|
}
|
|
None
|
|
}
|
|
|
|
fn image_to_data_url(img: &str) -> String {
|
|
if img.starts_with("data:") {
|
|
img.to_string()
|
|
} else {
|
|
format!("data:image/jpeg;base64,{}", img)
|
|
}
|
|
}
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn tool_call_arguments_stringified_on_send() {
|
|
let msg = ChatMessage {
|
|
role: "assistant".into(),
|
|
content: String::new(),
|
|
tool_calls: Some(vec![ToolCall {
|
|
id: Some("call_abc".into()),
|
|
function: ToolCallFunction {
|
|
name: "search_sms".into(),
|
|
arguments: json!({"query": "hello", "limit": 5}),
|
|
},
|
|
}]),
|
|
images: None,
|
|
};
|
|
|
|
let wire = LlamaCppClient::messages_to_openai(&[msg]);
|
|
let tcs = wire[0]
|
|
.get("tool_calls")
|
|
.and_then(|v| v.as_array())
|
|
.expect("tool_calls present");
|
|
let args = tcs[0]
|
|
.get("function")
|
|
.and_then(|f| f.get("arguments"))
|
|
.and_then(|a| a.as_str())
|
|
.expect("arguments stringified");
|
|
let parsed: Value = serde_json::from_str(args).unwrap();
|
|
assert_eq!(parsed["query"], "hello");
|
|
assert_eq!(parsed["limit"], 5);
|
|
}
|
|
|
|
#[test]
|
|
fn tool_call_arguments_parsed_on_receive() {
|
|
let response_msg = json!({
|
|
"role": "assistant",
|
|
"content": "",
|
|
"tool_calls": [{
|
|
"id": "call_xyz",
|
|
"type": "function",
|
|
"function": {
|
|
"name": "get_weather",
|
|
"arguments": "{\"city\":\"Boston\",\"units\":\"celsius\"}"
|
|
}
|
|
}]
|
|
});
|
|
let parsed = LlamaCppClient::openai_message_to_chat(&response_msg).unwrap();
|
|
let tcs = parsed.tool_calls.unwrap();
|
|
assert_eq!(tcs.len(), 1);
|
|
assert_eq!(tcs[0].function.name, "get_weather");
|
|
assert_eq!(tcs[0].function.arguments["city"], "Boston");
|
|
assert_eq!(tcs[0].function.arguments["units"], "celsius");
|
|
assert_eq!(tcs[0].id.as_deref(), Some("call_xyz"));
|
|
}
|
|
|
|
#[test]
|
|
fn tool_call_arguments_accept_native_json_on_receive() {
|
|
// Some llama.cpp builds emit arguments as a JSON object directly when
|
|
// jinja's tool-output strict-string rule isn't applied — accept both.
|
|
let response_msg = json!({
|
|
"role": "assistant",
|
|
"content": "",
|
|
"tool_calls": [{
|
|
"id": "call_1",
|
|
"type": "function",
|
|
"function": {
|
|
"name": "foo",
|
|
"arguments": {"nested": {"k": 1}}
|
|
}
|
|
}]
|
|
});
|
|
let parsed = LlamaCppClient::openai_message_to_chat(&response_msg).unwrap();
|
|
let tc = &parsed.tool_calls.unwrap()[0];
|
|
assert_eq!(tc.function.arguments["nested"]["k"], 1);
|
|
}
|
|
|
|
#[test]
|
|
fn images_become_content_parts() {
|
|
let mut msg = ChatMessage::user("What is in this photo?");
|
|
msg.images = Some(vec!["BASE64DATA".into()]);
|
|
|
|
let wire = LlamaCppClient::messages_to_openai(&[msg]);
|
|
let content = wire[0].get("content").and_then(|v| v.as_array()).unwrap();
|
|
assert_eq!(content.len(), 2);
|
|
assert_eq!(content[0]["type"], "text");
|
|
assert_eq!(content[0]["text"], "What is in this photo?");
|
|
assert_eq!(content[1]["type"], "image_url");
|
|
assert_eq!(
|
|
content[1]["image_url"]["url"],
|
|
"data:image/jpeg;base64,BASE64DATA"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn data_url_images_pass_through_unchanged() {
|
|
let mut msg = ChatMessage::user("");
|
|
msg.images = Some(vec!["data:image/png;base64,ABCDEF".into()]);
|
|
let wire = LlamaCppClient::messages_to_openai(&[msg]);
|
|
let content = wire[0].get("content").and_then(|v| v.as_array()).unwrap();
|
|
assert_eq!(content.len(), 1);
|
|
assert_eq!(
|
|
content[0]["image_url"]["url"],
|
|
"data:image/png;base64,ABCDEF"
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn text_only_message_stays_string() {
|
|
let msg = ChatMessage::user("hello");
|
|
let wire = LlamaCppClient::messages_to_openai(&[msg]);
|
|
assert_eq!(wire[0]["content"], "hello");
|
|
assert!(wire[0]["content"].as_str().is_some());
|
|
}
|
|
|
|
#[test]
|
|
fn tool_result_inherits_tool_call_id_from_prior_assistant() {
|
|
let assistant = ChatMessage {
|
|
role: "assistant".into(),
|
|
content: String::new(),
|
|
tool_calls: Some(vec![ToolCall {
|
|
id: Some("call_42".into()),
|
|
function: ToolCallFunction {
|
|
name: "lookup".into(),
|
|
arguments: json!({}),
|
|
},
|
|
}]),
|
|
images: None,
|
|
};
|
|
let tool_result = ChatMessage::tool_result("found it");
|
|
|
|
let wire = LlamaCppClient::messages_to_openai(&[assistant, tool_result]);
|
|
assert_eq!(wire[1]["role"], "tool");
|
|
assert_eq!(wire[1]["tool_call_id"], "call_42");
|
|
}
|
|
|
|
#[test]
|
|
fn multiple_tool_results_map_to_sequential_call_ids() {
|
|
let assistant = ChatMessage {
|
|
role: "assistant".into(),
|
|
content: String::new(),
|
|
tool_calls: Some(vec![
|
|
ToolCall {
|
|
id: Some("call_A".into()),
|
|
function: ToolCallFunction {
|
|
name: "a".into(),
|
|
arguments: json!({}),
|
|
},
|
|
},
|
|
ToolCall {
|
|
id: Some("call_B".into()),
|
|
function: ToolCallFunction {
|
|
name: "b".into(),
|
|
arguments: json!({}),
|
|
},
|
|
},
|
|
]),
|
|
images: None,
|
|
};
|
|
let r1 = ChatMessage::tool_result("a result");
|
|
let r2 = ChatMessage::tool_result("b result");
|
|
|
|
let wire = LlamaCppClient::messages_to_openai(&[assistant, r1, r2]);
|
|
assert_eq!(wire[1]["tool_call_id"], "call_A");
|
|
assert_eq!(wire[2]["tool_call_id"], "call_B");
|
|
}
|
|
|
|
#[test]
|
|
fn missing_tool_call_id_gets_synthetic_fallback() {
|
|
let assistant = ChatMessage {
|
|
role: "assistant".into(),
|
|
content: String::new(),
|
|
tool_calls: Some(vec![ToolCall {
|
|
id: None,
|
|
function: ToolCallFunction {
|
|
name: "noid".into(),
|
|
arguments: json!({}),
|
|
},
|
|
}]),
|
|
images: None,
|
|
};
|
|
let wire = LlamaCppClient::messages_to_openai(&[assistant]);
|
|
let tcs = wire[0]
|
|
.get("tool_calls")
|
|
.and_then(|v| v.as_array())
|
|
.unwrap();
|
|
assert_eq!(tcs[0]["id"], "call_0");
|
|
}
|
|
|
|
#[test]
|
|
fn capability_inference_uses_vision_model_and_allowlist() {
|
|
let mut c = LlamaCppClient::new(None, Some("chat".into()));
|
|
c.set_vision_model("vision".into());
|
|
c.set_vision_models(vec!["qwen-vl".into()]);
|
|
|
|
let m_chat = json!({ "id": "chat" });
|
|
let m_vision = json!({ "id": "vision" });
|
|
let m_qwen = json!({ "id": "qwen-vl" });
|
|
let m_other = json!({ "id": "embed" });
|
|
|
|
let chat = c.parse_model_capabilities(&m_chat);
|
|
let vision = c.parse_model_capabilities(&m_vision);
|
|
let qwen = c.parse_model_capabilities(&m_qwen);
|
|
let other = c.parse_model_capabilities(&m_other);
|
|
|
|
assert!(!chat.has_vision);
|
|
assert!(chat.has_tool_calling);
|
|
assert!(vision.has_vision);
|
|
assert!(qwen.has_vision);
|
|
assert!(!other.has_vision);
|
|
}
|
|
}
|