b8e8fbd8e6
There is a know bug when calling tool discussion: https://discord.com/channels/1128867683291627614/1402385744038858853 I have raised the issue with ollama team and they are currently fixing it. Release Notes: - ollama: Add support for gpt-oss
588 lines
18 KiB
Rust
588 lines
18 KiB
Rust
use anyhow::{Context as _, Result};
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use futures::{AsyncBufReadExt, AsyncReadExt, StreamExt, io::BufReader, stream::BoxStream};
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use http_client::{AsyncBody, HttpClient, Method, Request as HttpRequest, http};
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use serde::{Deserialize, Serialize};
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use serde_json::Value;
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use std::time::Duration;
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pub const OLLAMA_API_URL: &str = "http://localhost:11434";
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#[cfg_attr(feature = "schemars", derive(schemars::JsonSchema))]
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#[derive(Clone, Serialize, Deserialize, Debug, Eq, PartialEq)]
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#[serde(untagged)]
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pub enum KeepAlive {
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/// Keep model alive for N seconds
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Seconds(isize),
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/// Keep model alive for a fixed duration. Accepts durations like "5m", "10m", "1h", "1d", etc.
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Duration(String),
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}
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impl KeepAlive {
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/// Keep model alive until a new model is loaded or until Ollama shuts down
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fn indefinite() -> Self {
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Self::Seconds(-1)
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}
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}
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impl Default for KeepAlive {
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fn default() -> Self {
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Self::indefinite()
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}
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}
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#[cfg_attr(feature = "schemars", derive(schemars::JsonSchema))]
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#[derive(Clone, Debug, Default, Serialize, Deserialize, PartialEq)]
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pub struct Model {
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pub name: String,
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pub display_name: Option<String>,
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pub max_tokens: u64,
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pub keep_alive: Option<KeepAlive>,
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pub supports_tools: Option<bool>,
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pub supports_vision: Option<bool>,
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pub supports_thinking: Option<bool>,
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}
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fn get_max_tokens(name: &str) -> u64 {
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/// Default context length for unknown models.
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const DEFAULT_TOKENS: u64 = 4096;
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/// Magic number. Lets many Ollama models work with ~16GB of ram.
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const MAXIMUM_TOKENS: u64 = 16384;
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match name.split(':').next().unwrap() {
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"phi" | "tinyllama" | "granite-code" => 2048,
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"llama2" | "yi" | "vicuna" | "stablelm2" => 4096,
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"llama3" | "gemma2" | "gemma" | "codegemma" | "starcoder" | "aya" => 8192,
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"codellama" | "starcoder2" => 16384,
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"mistral" | "codestral" | "mixstral" | "llava" | "qwen2" | "qwen2.5-coder"
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| "dolphin-mixtral" => 32768,
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"magistral" => 40000,
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"llama3.1" | "llama3.2" | "llama3.3" | "phi3" | "phi3.5" | "phi4" | "command-r"
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| "qwen3" | "gemma3" | "deepseek-coder-v2" | "deepseek-v3" | "deepseek-r1" | "yi-coder"
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| "devstral" | "gpt-oss" => 128000,
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_ => DEFAULT_TOKENS,
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}
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.clamp(1, MAXIMUM_TOKENS)
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}
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impl Model {
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pub fn new(
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name: &str,
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display_name: Option<&str>,
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max_tokens: Option<u64>,
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supports_tools: Option<bool>,
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supports_vision: Option<bool>,
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supports_thinking: Option<bool>,
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) -> Self {
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Self {
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name: name.to_owned(),
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display_name: display_name
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.map(ToString::to_string)
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.or_else(|| name.strip_suffix(":latest").map(ToString::to_string)),
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max_tokens: max_tokens.unwrap_or_else(|| get_max_tokens(name)),
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keep_alive: Some(KeepAlive::indefinite()),
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supports_tools,
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supports_vision,
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supports_thinking,
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}
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}
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pub fn id(&self) -> &str {
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&self.name
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}
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pub fn display_name(&self) -> &str {
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self.display_name.as_ref().unwrap_or(&self.name)
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}
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pub fn max_token_count(&self) -> u64 {
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self.max_tokens
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}
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}
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#[derive(Serialize, Deserialize, Debug)]
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#[serde(tag = "role", rename_all = "lowercase")]
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pub enum ChatMessage {
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Assistant {
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content: String,
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tool_calls: Option<Vec<OllamaToolCall>>,
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#[serde(skip_serializing_if = "Option::is_none")]
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images: Option<Vec<String>>,
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thinking: Option<String>,
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},
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User {
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content: String,
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#[serde(skip_serializing_if = "Option::is_none")]
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images: Option<Vec<String>>,
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},
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System {
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content: String,
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},
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}
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#[derive(Serialize, Deserialize, Debug)]
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#[serde(rename_all = "lowercase")]
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pub enum OllamaToolCall {
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Function(OllamaFunctionCall),
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}
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#[derive(Serialize, Deserialize, Debug)]
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pub struct OllamaFunctionCall {
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pub name: String,
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pub arguments: Value,
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}
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#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
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pub struct OllamaFunctionTool {
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pub name: String,
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pub description: Option<String>,
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pub parameters: Option<Value>,
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}
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#[derive(Serialize, Deserialize, Debug, Eq, PartialEq)]
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#[serde(tag = "type", rename_all = "lowercase")]
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pub enum OllamaTool {
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Function { function: OllamaFunctionTool },
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}
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#[derive(Serialize, Debug)]
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pub struct ChatRequest {
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pub model: String,
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pub messages: Vec<ChatMessage>,
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pub stream: bool,
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pub keep_alive: KeepAlive,
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pub options: Option<ChatOptions>,
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pub tools: Vec<OllamaTool>,
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pub think: Option<bool>,
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}
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impl ChatRequest {
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pub fn with_tools(mut self, tools: Vec<OllamaTool>) -> Self {
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self.stream = false;
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self.tools = tools;
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self
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}
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}
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// https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
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#[derive(Serialize, Default, Debug)]
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pub struct ChatOptions {
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pub num_ctx: Option<u64>,
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pub num_predict: Option<isize>,
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pub stop: Option<Vec<String>>,
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pub temperature: Option<f32>,
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pub top_p: Option<f32>,
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}
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#[derive(Deserialize, Debug)]
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pub struct ChatResponseDelta {
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#[allow(unused)]
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pub model: String,
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#[allow(unused)]
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pub created_at: String,
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pub message: ChatMessage,
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#[allow(unused)]
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pub done_reason: Option<String>,
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#[allow(unused)]
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pub done: bool,
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pub prompt_eval_count: Option<u64>,
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pub eval_count: Option<u64>,
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}
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#[derive(Serialize, Deserialize)]
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pub struct LocalModelsResponse {
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pub models: Vec<LocalModelListing>,
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}
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#[derive(Serialize, Deserialize)]
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pub struct LocalModelListing {
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pub name: String,
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pub modified_at: String,
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pub size: u64,
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pub digest: String,
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pub details: ModelDetails,
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}
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#[derive(Serialize, Deserialize)]
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pub struct LocalModel {
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pub modelfile: String,
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pub parameters: String,
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pub template: String,
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pub details: ModelDetails,
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}
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#[derive(Serialize, Deserialize)]
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pub struct ModelDetails {
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pub format: String,
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pub family: String,
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pub families: Option<Vec<String>>,
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pub parameter_size: String,
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pub quantization_level: String,
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}
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#[derive(Deserialize, Debug)]
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pub struct ModelShow {
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#[serde(default)]
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pub capabilities: Vec<String>,
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}
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impl ModelShow {
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pub fn supports_tools(&self) -> bool {
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// .contains expects &String, which would require an additional allocation
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self.capabilities.iter().any(|v| v == "tools")
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}
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pub fn supports_vision(&self) -> bool {
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self.capabilities.iter().any(|v| v == "vision")
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}
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pub fn supports_thinking(&self) -> bool {
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self.capabilities.iter().any(|v| v == "thinking")
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}
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}
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pub async fn complete(
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client: &dyn HttpClient,
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api_url: &str,
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request: ChatRequest,
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) -> Result<ChatResponseDelta> {
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let uri = format!("{api_url}/api/chat");
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let request_builder = HttpRequest::builder()
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.method(Method::POST)
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.uri(uri)
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.header("Content-Type", "application/json");
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let serialized_request = serde_json::to_string(&request)?;
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let request = request_builder.body(AsyncBody::from(serialized_request))?;
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let mut response = client.send(request).await?;
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let mut body = Vec::new();
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response.body_mut().read_to_end(&mut body).await?;
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if response.status().is_success() {
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let response_message: ChatResponseDelta = serde_json::from_slice(&body)?;
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Ok(response_message)
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} else {
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let body_str = std::str::from_utf8(&body)?;
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anyhow::bail!(
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"Failed to connect to API: {} {}",
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response.status(),
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body_str
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);
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}
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}
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pub async fn stream_chat_completion(
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client: &dyn HttpClient,
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api_url: &str,
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request: ChatRequest,
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) -> Result<BoxStream<'static, Result<ChatResponseDelta>>> {
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let uri = format!("{api_url}/api/chat");
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let request_builder = http::Request::builder()
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.method(Method::POST)
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.uri(uri)
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.header("Content-Type", "application/json");
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let request = request_builder.body(AsyncBody::from(serde_json::to_string(&request)?))?;
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let mut response = client.send(request).await?;
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if response.status().is_success() {
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let reader = BufReader::new(response.into_body());
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Ok(reader
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.lines()
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.map(|line| match line {
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Ok(line) => serde_json::from_str(&line).context("Unable to parse chat response"),
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Err(e) => Err(e.into()),
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})
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.boxed())
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} else {
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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anyhow::bail!(
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"Failed to connect to Ollama API: {} {}",
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response.status(),
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body,
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);
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}
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}
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pub async fn get_models(
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client: &dyn HttpClient,
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api_url: &str,
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_: Option<Duration>,
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) -> Result<Vec<LocalModelListing>> {
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let uri = format!("{api_url}/api/tags");
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let request_builder = HttpRequest::builder()
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.method(Method::GET)
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.uri(uri)
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.header("Accept", "application/json");
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let request = request_builder.body(AsyncBody::default())?;
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let mut response = client.send(request).await?;
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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anyhow::ensure!(
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response.status().is_success(),
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"Failed to connect to Ollama API: {} {}",
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response.status(),
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body,
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);
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let response: LocalModelsResponse =
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serde_json::from_str(&body).context("Unable to parse Ollama tag listing")?;
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Ok(response.models)
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}
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/// Fetch details of a model, used to determine model capabilities
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pub async fn show_model(client: &dyn HttpClient, api_url: &str, model: &str) -> Result<ModelShow> {
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let uri = format!("{api_url}/api/show");
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let request = HttpRequest::builder()
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.method(Method::POST)
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.uri(uri)
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.header("Content-Type", "application/json")
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.body(AsyncBody::from(
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serde_json::json!({ "model": model }).to_string(),
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))?;
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let mut response = client.send(request).await?;
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let mut body = String::new();
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response.body_mut().read_to_string(&mut body).await?;
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anyhow::ensure!(
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response.status().is_success(),
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"Failed to connect to Ollama API: {} {}",
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response.status(),
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body,
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);
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let details: ModelShow = serde_json::from_str(body.as_str())?;
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Ok(details)
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn parse_completion() {
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let response = serde_json::json!({
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"model": "llama3.2",
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"created_at": "2023-12-12T14:13:43.416799Z",
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"message": {
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"role": "assistant",
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"content": "Hello! How are you today?"
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},
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"done": true,
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"total_duration": 5191566416u64,
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"load_duration": 2154458,
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"prompt_eval_count": 26,
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"prompt_eval_duration": 383809000,
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"eval_count": 298,
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"eval_duration": 4799921000u64
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});
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let _: ChatResponseDelta = serde_json::from_value(response).unwrap();
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}
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#[test]
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fn parse_streaming_completion() {
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let partial = serde_json::json!({
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"model": "llama3.2",
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"created_at": "2023-08-04T08:52:19.385406455-07:00",
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"message": {
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"role": "assistant",
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"content": "The",
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"images": null
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},
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"done": false
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});
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let _: ChatResponseDelta = serde_json::from_value(partial).unwrap();
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let last = serde_json::json!({
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"model": "llama3.2",
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"created_at": "2023-08-04T19:22:45.499127Z",
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"message": {
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"role": "assistant",
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"content": ""
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},
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"done": true,
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"total_duration": 4883583458u64,
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"load_duration": 1334875,
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"prompt_eval_count": 26,
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"prompt_eval_duration": 342546000,
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"eval_count": 282,
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"eval_duration": 4535599000u64
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});
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let _: ChatResponseDelta = serde_json::from_value(last).unwrap();
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}
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#[test]
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fn parse_tool_call() {
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let response = serde_json::json!({
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"model": "llama3.2:3b",
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"created_at": "2025-04-28T20:02:02.140489Z",
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"message": {
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"function": {
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"name": "weather",
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"arguments": {
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"city": "london",
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}
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}
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}
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]
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},
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"done_reason": "stop",
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"done": true,
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"total_duration": 2758629166u64,
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"load_duration": 1770059875,
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"prompt_eval_count": 147,
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"prompt_eval_duration": 684637583,
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"eval_count": 16,
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"eval_duration": 302561917,
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});
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let result: ChatResponseDelta = serde_json::from_value(response).unwrap();
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match result.message {
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ChatMessage::Assistant {
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content,
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tool_calls,
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images: _,
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thinking,
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} => {
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assert!(content.is_empty());
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assert!(tool_calls.is_some_and(|v| !v.is_empty()));
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assert!(thinking.is_none());
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}
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_ => panic!("Deserialized wrong role"),
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}
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}
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#[test]
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fn parse_show_model() {
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let response = serde_json::json!({
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"license": "LLAMA 3.2 COMMUNITY LICENSE AGREEMENT...",
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"details": {
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"parent_model": "",
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"format": "gguf",
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"family": "llama",
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"families": ["llama"],
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"parameter_size": "3.2B",
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"quantization_level": "Q4_K_M"
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},
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"model_info": {
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"general.architecture": "llama",
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"general.basename": "Llama-3.2",
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"general.file_type": 15,
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"general.finetune": "Instruct",
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"general.languages": ["en", "de", "fr", "it", "pt", "hi", "es", "th"],
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"general.parameter_count": 3212749888u64,
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"general.quantization_version": 2,
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"general.size_label": "3B",
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"general.tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "text-generation"],
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"general.type": "model",
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"llama.attention.head_count": 24,
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"llama.attention.head_count_kv": 8,
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"llama.attention.key_length": 128,
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"llama.attention.layer_norm_rms_epsilon": 0.00001,
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"llama.attention.value_length": 128,
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"llama.block_count": 28,
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"llama.context_length": 131072,
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"llama.embedding_length": 3072,
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"llama.feed_forward_length": 8192,
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"llama.rope.dimension_count": 128,
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"llama.rope.freq_base": 500000,
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"llama.vocab_size": 128256,
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"tokenizer.ggml.bos_token_id": 128000,
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"tokenizer.ggml.eos_token_id": 128009,
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"tokenizer.ggml.merges": null,
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"tokenizer.ggml.model": "gpt2",
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"tokenizer.ggml.pre": "llama-bpe",
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"tokenizer.ggml.token_type": null,
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"tokenizer.ggml.tokens": null
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},
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"tensors": [
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{ "name": "rope_freqs.weight", "type": "F32", "shape": [64] },
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{ "name": "token_embd.weight", "type": "Q4_K_S", "shape": [3072, 128256] }
|
|
],
|
|
"capabilities": ["completion", "tools"],
|
|
"modified_at": "2025-04-29T21:24:41.445877632+03:00"
|
|
});
|
|
|
|
let result: ModelShow = serde_json::from_value(response).unwrap();
|
|
assert!(result.supports_tools());
|
|
assert!(result.capabilities.contains(&"tools".to_string()));
|
|
assert!(result.capabilities.contains(&"completion".to_string()));
|
|
}
|
|
|
|
#[test]
|
|
fn serialize_chat_request_with_images() {
|
|
let base64_image = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==";
|
|
|
|
let request = ChatRequest {
|
|
model: "llava".to_string(),
|
|
messages: vec![ChatMessage::User {
|
|
content: "What do you see in this image?".to_string(),
|
|
images: Some(vec![base64_image.to_string()]),
|
|
}],
|
|
stream: false,
|
|
keep_alive: KeepAlive::default(),
|
|
options: None,
|
|
think: None,
|
|
tools: vec![],
|
|
};
|
|
|
|
let serialized = serde_json::to_string(&request).unwrap();
|
|
assert!(serialized.contains("images"));
|
|
assert!(serialized.contains(base64_image));
|
|
}
|
|
|
|
#[test]
|
|
fn serialize_chat_request_without_images() {
|
|
let request = ChatRequest {
|
|
model: "llama3.2".to_string(),
|
|
messages: vec![ChatMessage::User {
|
|
content: "Hello, world!".to_string(),
|
|
images: None,
|
|
}],
|
|
stream: false,
|
|
keep_alive: KeepAlive::default(),
|
|
options: None,
|
|
think: None,
|
|
tools: vec![],
|
|
};
|
|
|
|
let serialized = serde_json::to_string(&request).unwrap();
|
|
assert!(!serialized.contains("images"));
|
|
}
|
|
|
|
#[test]
|
|
fn test_json_format_with_images() {
|
|
let base64_image = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==";
|
|
|
|
let request = ChatRequest {
|
|
model: "llava".to_string(),
|
|
messages: vec![ChatMessage::User {
|
|
content: "What do you see?".to_string(),
|
|
images: Some(vec![base64_image.to_string()]),
|
|
}],
|
|
stream: false,
|
|
keep_alive: KeepAlive::default(),
|
|
options: None,
|
|
think: None,
|
|
tools: vec![],
|
|
};
|
|
|
|
let serialized = serde_json::to_string(&request).unwrap();
|
|
|
|
let parsed: serde_json::Value = serde_json::from_str(&serialized).unwrap();
|
|
let message_images = parsed["messages"][0]["images"].as_array().unwrap();
|
|
assert_eq!(message_images.len(), 1);
|
|
assert_eq!(message_images[0].as_str().unwrap(), base64_image);
|
|
}
|
|
}
|