flemming-it
027793bb02
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Signed-off-by: flemming-it <sf@flemming.it>
747 lines
25 KiB
Rust
747 lines
25 KiB
Rust
//! Multi-API chat client: Ollama (default), OpenAI, Anthropic.
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//!
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//! The wire format is selected via the optional `api` input:
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//!
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//! - `ollama` (default) — Ollama `/api/chat`; unchanged v0.1.0
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//! behavior including the best-effort model-digest probe.
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//! - `openai` — OpenAI Chat Completions wire format
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//! (`/v1/chat/completions`), the de-facto standard implemented
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//! by vLLM and most self-hosted inference servers. Bearer auth.
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//! - `anthropic` — Anthropic Messages API (`/v1/messages`),
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//! `x-api-key` + `anthropic-version` headers, top-level
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//! `system` field, mandatory `max_tokens`.
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//!
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//! No streaming, no tool calls — one prompt in, one completion out.
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//!
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//! All HTTP I/O lives behind a `LlmClient` trait so unit tests can
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//! exercise request building, response parsing, and the digest
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//! probe on the host without making real network calls.
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use serde::Serialize;
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/// Anthropic's Messages API requires `max_tokens`. This default is
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/// large enough for document-processing completions while staying
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/// well below every current model's output cap.
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const ANTHROPIC_DEFAULT_MAX_TOKENS: u32 = 4096;
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/// Pinned Messages API version header. Anthropic keeps old
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/// versions working; bump deliberately, never implicitly.
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const ANTHROPIC_VERSION: &str = "2023-06-01";
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#[allow(dead_code)]
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#[derive(Debug, thiserror::Error)]
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pub enum LlmError {
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#[error("missing required input '{0}'")]
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MissingInput(&'static str),
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#[error("unsupported api '{0}' (expected: ollama, openai, vllm, anthropic)")]
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UnsupportedApi(String),
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#[error("http error: {0}")]
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Http(String),
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#[error("non-success status: {0}")]
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Status(u16),
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#[error("response body could not be parsed as the expected schema: {0}")]
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Decode(String),
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}
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/// Which wire format to speak. Selected by the `api` input.
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#[derive(Debug, Clone, Copy, PartialEq, Eq)]
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pub enum Api {
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Ollama,
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Openai,
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/// vLLM speaks the OpenAI wire format verbatim; it is a
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/// separate variant ONLY so operators can write what they
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/// mean (`api: vllm`) and get vLLM-specific error hints —
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/// "openai" would read as the cloud company, not a
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/// self-hosted server. Same rationale as the hub's
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/// SystemLlmProvider vocabulary.
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Vllm,
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Anthropic,
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}
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impl Api {
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/// Parse the `api` input. Empty string means the input was not
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/// provided and falls back to Ollama (v0.1.0 behavior).
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pub fn parse(raw: &str) -> Result<Self, LlmError> {
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match raw.trim().to_ascii_lowercase().as_str() {
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"" | "ollama" => Ok(Api::Ollama),
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"openai" => Ok(Api::Openai),
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"vllm" => Ok(Api::Vllm),
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"anthropic" => Ok(Api::Anthropic),
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other => Err(LlmError::UnsupportedApi(other.to_string())),
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}
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}
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}
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pub trait LlmClient {
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/// POST `body` as JSON to `url` with the given extra headers.
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/// `Content-Type: application/json` is implied. Header VALUES
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/// may carry credentials — implementations must never log them.
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fn post_json(
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&self,
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url: &str,
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body: &str,
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headers: &[(&'static str, String)],
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) -> Result<String, LlmError>;
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}
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#[derive(Debug, Clone)]
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pub struct ChatParams<'a> {
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pub api: Api,
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pub endpoint: &'a str,
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pub model: &'a str,
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pub api_key: &'a str,
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pub system_prompt: &'a str,
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pub prompt: &'a str,
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}
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/// Auth/protocol headers for the selected API. The api_key is only
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/// ever placed into a header value here; it must not appear in any
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/// error, output, or log line.
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pub fn build_headers(p: &ChatParams) -> Vec<(&'static str, String)> {
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let mut headers = Vec::with_capacity(2);
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match p.api {
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Api::Ollama | Api::Openai | Api::Vllm => {
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if !p.api_key.is_empty() {
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headers.push(("Authorization", format!("Bearer {}", p.api_key)));
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}
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}
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Api::Anthropic => {
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if !p.api_key.is_empty() {
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headers.push(("x-api-key", p.api_key.to_string()));
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}
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headers.push(("anthropic-version", ANTHROPIC_VERSION.to_string()));
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}
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}
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headers
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}
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#[derive(Serialize)]
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struct ChatMessage<'a> {
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role: &'a str,
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content: &'a str,
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}
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#[derive(Serialize)]
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struct OllamaRequest<'a> {
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model: &'a str,
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messages: Vec<ChatMessage<'a>>,
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stream: bool,
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}
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#[derive(Serialize)]
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struct OpenAiRequest<'a> {
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model: &'a str,
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messages: Vec<ChatMessage<'a>>,
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stream: bool,
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}
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#[derive(Serialize)]
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struct AnthropicRequest<'a> {
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model: &'a str,
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max_tokens: u32,
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#[serde(skip_serializing_if = "Option::is_none")]
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system: Option<&'a str>,
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messages: Vec<ChatMessage<'a>>,
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}
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/// System-then-user message list shared by the Ollama and OpenAI
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/// wire formats. The system message is omitted when empty so a
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/// deployment can use the model's built-in system prompt.
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fn build_messages<'a>(p: &ChatParams<'a>) -> Vec<ChatMessage<'a>> {
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let mut messages = Vec::with_capacity(2);
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if !p.system_prompt.is_empty() {
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messages.push(ChatMessage {
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role: "system",
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content: p.system_prompt,
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});
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}
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messages.push(ChatMessage {
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role: "user",
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content: p.prompt,
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});
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messages
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}
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/// Build the request body for an Ollama `/api/chat` invocation.
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pub fn build_ollama_body(p: &ChatParams) -> String {
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let req = OllamaRequest {
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model: p.model,
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messages: build_messages(p),
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stream: false,
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};
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serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
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}
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/// Build the request body for an OpenAI-compatible
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/// `/v1/chat/completions` invocation (OpenAI, vLLM, most
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/// self-hosted inference servers).
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pub fn build_openai_body(p: &ChatParams) -> String {
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let req = OpenAiRequest {
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model: p.model,
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messages: build_messages(p),
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stream: false,
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};
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serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
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}
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/// Build the request body for an Anthropic `/v1/messages`
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/// invocation. `system` is a top-level field (not a message);
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/// `max_tokens` is mandatory in the Messages API.
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pub fn build_anthropic_body(p: &ChatParams) -> String {
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let req = AnthropicRequest {
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model: p.model,
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max_tokens: ANTHROPIC_DEFAULT_MAX_TOKENS,
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system: if p.system_prompt.is_empty() {
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None
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} else {
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Some(p.system_prompt)
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},
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messages: vec![ChatMessage {
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role: "user",
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content: p.prompt,
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}],
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};
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serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
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}
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/// Extract the assistant's message text from an Ollama
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/// /api/chat response.
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pub fn extract_ollama_content(body: &str) -> Result<String, LlmError> {
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let v: serde_json::Value =
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serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
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v.get("message")
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.and_then(|m| m.get("content"))
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.and_then(|c| c.as_str())
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.map(|s| s.to_string())
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.ok_or_else(|| LlmError::Decode("missing message.content".into()))
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}
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/// Extract the assistant text from an OpenAI Chat Completions
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/// response: `choices[0].message.content`.
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pub fn extract_openai_content(body: &str) -> Result<String, LlmError> {
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let v: serde_json::Value =
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serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
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v.get("choices")
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.and_then(|c| c.get(0))
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.and_then(|c| c.get("message"))
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.and_then(|m| m.get("content"))
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.and_then(|c| c.as_str())
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.map(|s| s.to_string())
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.ok_or_else(|| LlmError::Decode("missing choices[0].message.content".into()))
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}
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/// Extract the assistant text from an Anthropic Messages
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/// response: `content[0].text`.
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pub fn extract_anthropic_content(body: &str) -> Result<String, LlmError> {
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let v: serde_json::Value =
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serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
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v.get("content")
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.and_then(|c| c.get(0))
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.and_then(|c| c.get("text"))
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.and_then(|t| t.as_str())
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.map(|s| s.to_string())
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.ok_or_else(|| LlmError::Decode("missing content[0].text".into()))
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}
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#[derive(Debug, Clone)]
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pub struct ChatWithIdentity {
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pub response: String,
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pub model_digest: Option<String>,
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}
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/// Run one chat call in the selected wire format AND, for Ollama,
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/// probe the model digest. The probe is best-effort — non-Ollama
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/// APIs expose no digest endpoint, so `model_digest = None` there
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/// (documented empty, never invented); transient probe failures on
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/// Ollama also yield `None` rather than failing the whole call.
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pub fn chat_with_identity<C: LlmClient>(
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client: &C,
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p: &ChatParams,
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) -> Result<ChatWithIdentity, LlmError> {
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if p.endpoint.is_empty() {
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return Err(LlmError::MissingInput("endpoint"));
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}
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if p.model.is_empty() {
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return Err(LlmError::MissingInput("model"));
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}
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if p.prompt.is_empty() {
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return Err(LlmError::MissingInput("prompt"));
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}
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let body = match p.api {
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Api::Ollama => build_ollama_body(p),
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Api::Openai | Api::Vllm => build_openai_body(p),
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Api::Anthropic => build_anthropic_body(p),
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};
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let headers = build_headers(p);
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let response_body = client.post_json(p.endpoint, &body, &headers)?;
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let response = match p.api {
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Api::Ollama => extract_ollama_content(&response_body)?,
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Api::Openai | Api::Vllm => extract_openai_content(&response_body)?,
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Api::Anthropic => extract_anthropic_content(&response_body)?,
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};
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let model_digest = match p.api {
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Api::Ollama => probe_model_digest(client, p),
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Api::Openai | Api::Vllm | Api::Anthropic => None,
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};
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Ok(ChatWithIdentity {
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response,
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model_digest,
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})
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}
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fn probe_model_digest<C: LlmClient>(client: &C, p: &ChatParams) -> Option<String> {
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let show_url = derive_show_url(p.endpoint)?;
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let body = serde_json::to_string(&serde_json::json!({ "name": p.model })).ok()?;
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let headers = build_headers(p);
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let response = client.post_json(&show_url, &body, &headers).ok()?;
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extract_show_digest(&response)
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}
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pub fn derive_show_url(chat_endpoint: &str) -> Option<String> {
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if chat_endpoint.ends_with("/api/chat") {
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let head_len = chat_endpoint.len() - "/api/chat".len();
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let mut url = String::with_capacity(head_len + "/api/show".len());
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url.push_str(&chat_endpoint[..head_len]);
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url.push_str("/api/show");
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Some(url)
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} else {
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None
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}
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}
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pub fn extract_show_digest(body: &str) -> Option<String> {
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let v: serde_json::Value = serde_json::from_str(body).ok()?;
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let candidates = [
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v.get("digest"),
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v.get("details").and_then(|d| d.get("digest")),
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v.get("model_info").and_then(|d| d.get("digest")),
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];
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for cand in candidates {
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if let Some(s) = cand.and_then(|v| v.as_str()) {
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if !s.is_empty() {
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return Some(s.to_string());
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}
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}
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}
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None
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}
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#[cfg(test)]
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mod tests {
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#![allow(clippy::unwrap_used, clippy::expect_used, clippy::panic)]
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use super::*;
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use std::cell::RefCell;
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/// (url, body, headers) of one recorded call.
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type RecordedCall = (String, String, Vec<(String, String)>);
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struct MockClient {
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responses: RefCell<Vec<Result<String, LlmError>>>,
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/// Every call, in call order.
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calls: RefCell<Vec<RecordedCall>>,
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}
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impl MockClient {
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fn new(responses: Vec<Result<String, LlmError>>) -> Self {
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Self {
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responses: RefCell::new(responses),
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calls: RefCell::new(Vec::new()),
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}
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}
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}
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impl LlmClient for MockClient {
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fn post_json(
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&self,
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url: &str,
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body: &str,
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headers: &[(&'static str, String)],
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) -> Result<String, LlmError> {
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self.calls.borrow_mut().push((
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url.to_string(),
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body.to_string(),
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headers
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.iter()
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.map(|(k, v)| (k.to_string(), v.clone()))
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.collect(),
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));
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self.responses
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.borrow_mut()
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.pop()
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.unwrap_or(Err(LlmError::Http("no more mock responses".into())))
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}
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}
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fn params<'a>(api: Api) -> ChatParams<'a> {
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ChatParams {
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api,
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endpoint: "http://x/api/chat",
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model: "qwen",
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api_key: "",
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system_prompt: "",
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prompt: "hello",
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}
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}
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// ---------------- api input parsing ----------------
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#[test]
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fn api_parse_defaults_to_ollama() {
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assert_eq!(Api::parse("").unwrap(), Api::Ollama);
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assert_eq!(Api::parse("ollama").unwrap(), Api::Ollama);
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assert_eq!(Api::parse(" OpenAI ").unwrap(), Api::Openai);
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assert_eq!(Api::parse("anthropic").unwrap(), Api::Anthropic);
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}
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|
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#[test]
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fn api_parse_rejects_unknown() {
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assert!(matches!(
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Api::parse("gemini"),
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Err(LlmError::UnsupportedApi(_))
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));
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}
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|
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// ---------------- Ollama wire format (regression) ----------------
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#[test]
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fn ollama_body_includes_system_when_provided() {
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let p = ChatParams {
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system_prompt: "be helpful",
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..params(Api::Ollama)
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};
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let body = build_ollama_body(&p);
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let v: serde_json::Value = serde_json::from_str(&body).unwrap();
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let messages = v["messages"].as_array().unwrap();
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assert_eq!(messages.len(), 2);
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assert_eq!(messages[0]["role"], "system");
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assert_eq!(messages[0]["content"], "be helpful");
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assert_eq!(messages[1]["role"], "user");
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assert_eq!(messages[1]["content"], "hello");
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assert_eq!(v["stream"], false);
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}
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|
|
|
#[test]
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fn ollama_body_omits_system_when_empty() {
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let body = build_ollama_body(¶ms(Api::Ollama));
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let v: serde_json::Value = serde_json::from_str(&body).unwrap();
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let messages = v["messages"].as_array().unwrap();
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assert_eq!(messages.len(), 1);
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assert_eq!(messages[0]["role"], "user");
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}
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|
|
|
#[test]
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fn extract_content_pulls_message_content() {
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let body = r#"{"message":{"role":"assistant","content":"hi there"},"done":true}"#;
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assert_eq!(extract_ollama_content(body).unwrap(), "hi there");
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}
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|
|
|
#[test]
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fn extract_content_errors_on_missing_field() {
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let body = r#"{"done":true}"#;
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assert!(matches!(
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extract_ollama_content(body),
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Err(LlmError::Decode(_))
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));
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}
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|
|
#[test]
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|
fn ollama_bearer_header_only_with_key() {
|
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assert!(build_headers(¶ms(Api::Ollama)).is_empty());
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let p = ChatParams {
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api_key: "sk-test",
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..params(Api::Ollama)
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};
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assert_eq!(
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build_headers(&p),
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vec![("Authorization", "Bearer sk-test".to_string())]
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);
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}
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|
|
|
// ---------------- OpenAI wire format ----------------
|
|
|
|
#[test]
|
|
fn openai_body_matches_chat_completions_format() {
|
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let p = ChatParams {
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api: Api::Openai,
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endpoint: "http://vllm:8000/v1/chat/completions",
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model: "meta-llama/Llama-3.1-8B-Instruct",
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api_key: "sk-x",
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system_prompt: "be terse",
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prompt: "hello",
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};
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let body = build_openai_body(&p);
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let v: serde_json::Value = serde_json::from_str(&body).unwrap();
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assert_eq!(v["model"], "meta-llama/Llama-3.1-8B-Instruct");
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assert_eq!(v["stream"], false);
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let messages = v["messages"].as_array().unwrap();
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assert_eq!(messages.len(), 2);
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assert_eq!(messages[0]["role"], "system");
|
|
assert_eq!(messages[0]["content"], "be terse");
|
|
assert_eq!(messages[1]["role"], "user");
|
|
assert_eq!(messages[1]["content"], "hello");
|
|
// No Anthropic-only fields leak into the OpenAI body.
|
|
assert!(v.get("max_tokens").is_none());
|
|
assert!(v.get("system").is_none());
|
|
}
|
|
|
|
#[test]
|
|
fn openai_response_parses_fixture() {
|
|
// Shape as returned by OpenAI / vLLM /v1/chat/completions.
|
|
let body = r#"{
|
|
"id": "chatcmpl-123",
|
|
"object": "chat.completion",
|
|
"created": 1719000000,
|
|
"model": "meta-llama/Llama-3.1-8B-Instruct",
|
|
"choices": [{
|
|
"index": 0,
|
|
"message": {"role": "assistant", "content": "Hi from vLLM"},
|
|
"finish_reason": "stop"
|
|
}],
|
|
"usage": {"prompt_tokens": 9, "completion_tokens": 4, "total_tokens": 13}
|
|
}"#;
|
|
assert_eq!(extract_openai_content(body).unwrap(), "Hi from vLLM");
|
|
}
|
|
|
|
#[test]
|
|
fn openai_response_errors_on_missing_choices() {
|
|
assert!(matches!(
|
|
extract_openai_content(r#"{"object":"chat.completion","choices":[]}"#),
|
|
Err(LlmError::Decode(_))
|
|
));
|
|
assert!(matches!(
|
|
extract_openai_content(r#"{"error":{"message":"invalid key"}}"#),
|
|
Err(LlmError::Decode(_))
|
|
));
|
|
}
|
|
|
|
#[test]
|
|
fn openai_uses_bearer_auth() {
|
|
let p = ChatParams {
|
|
api_key: "sk-test",
|
|
..params(Api::Openai)
|
|
};
|
|
assert_eq!(
|
|
build_headers(&p),
|
|
vec![("Authorization", "Bearer sk-test".to_string())]
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn openai_chat_returns_content_and_no_digest() {
|
|
let canned = r#"{"choices":[{"message":{"role":"assistant","content":"ok"}}]}"#;
|
|
let client = MockClient::new(vec![Ok(canned.to_string())]);
|
|
let p = ChatParams {
|
|
api: Api::Openai,
|
|
endpoint: "http://vllm:8000/v1/chat/completions",
|
|
model: "m",
|
|
api_key: "sk",
|
|
system_prompt: "",
|
|
prompt: "hi",
|
|
};
|
|
let result = chat_with_identity(&client, &p).unwrap();
|
|
assert_eq!(result.response, "ok");
|
|
// No digest API on OpenAI-compatible endpoints — documented
|
|
// empty, and exactly one HTTP call (no probe).
|
|
assert_eq!(result.model_digest, None);
|
|
assert_eq!(client.calls.borrow().len(), 1);
|
|
}
|
|
|
|
// ---------------- Anthropic wire format ----------------
|
|
|
|
#[test]
|
|
fn anthropic_body_matches_messages_format() {
|
|
let p = ChatParams {
|
|
api: Api::Anthropic,
|
|
endpoint: "https://api.anthropic.com/v1/messages",
|
|
model: "claude-fable-5",
|
|
api_key: "sk-ant",
|
|
system_prompt: "be terse",
|
|
prompt: "hello",
|
|
};
|
|
let body = build_anthropic_body(&p);
|
|
let v: serde_json::Value = serde_json::from_str(&body).unwrap();
|
|
assert_eq!(v["model"], "claude-fable-5");
|
|
// max_tokens is mandatory in the Messages API.
|
|
assert_eq!(v["max_tokens"], 4096);
|
|
// system is a top-level field, never a message.
|
|
assert_eq!(v["system"], "be terse");
|
|
let messages = v["messages"].as_array().unwrap();
|
|
assert_eq!(messages.len(), 1);
|
|
assert_eq!(messages[0]["role"], "user");
|
|
assert_eq!(messages[0]["content"], "hello");
|
|
// No OpenAI/Ollama-only fields leak in.
|
|
assert!(v.get("stream").is_none());
|
|
}
|
|
|
|
#[test]
|
|
fn anthropic_body_omits_system_when_empty() {
|
|
let body = build_anthropic_body(¶ms(Api::Anthropic));
|
|
let v: serde_json::Value = serde_json::from_str(&body).unwrap();
|
|
assert!(v.get("system").is_none());
|
|
assert_eq!(v["max_tokens"], 4096);
|
|
}
|
|
|
|
#[test]
|
|
fn anthropic_response_parses_fixture() {
|
|
// Shape as returned by the Anthropic Messages API.
|
|
let body = r#"{
|
|
"id": "msg_01XFDUDYJgAACzvnptvVoYEL",
|
|
"type": "message",
|
|
"role": "assistant",
|
|
"model": "claude-fable-5",
|
|
"content": [{"type": "text", "text": "Hi from Claude"}],
|
|
"stop_reason": "end_turn",
|
|
"usage": {"input_tokens": 10, "output_tokens": 5}
|
|
}"#;
|
|
assert_eq!(extract_anthropic_content(body).unwrap(), "Hi from Claude");
|
|
}
|
|
|
|
#[test]
|
|
fn anthropic_response_errors_on_missing_content() {
|
|
assert!(matches!(
|
|
extract_anthropic_content(r#"{"type":"message","content":[]}"#),
|
|
Err(LlmError::Decode(_))
|
|
));
|
|
assert!(matches!(
|
|
extract_anthropic_content(r#"{"type":"error","error":{"message":"x"}}"#),
|
|
Err(LlmError::Decode(_))
|
|
));
|
|
}
|
|
|
|
#[test]
|
|
fn anthropic_uses_x_api_key_and_version_headers() {
|
|
let p = ChatParams {
|
|
api_key: "sk-ant-test",
|
|
..params(Api::Anthropic)
|
|
};
|
|
let headers = build_headers(&p);
|
|
assert_eq!(
|
|
headers,
|
|
vec![
|
|
("x-api-key", "sk-ant-test".to_string()),
|
|
("anthropic-version", "2023-06-01".to_string()),
|
|
]
|
|
);
|
|
// Never Bearer auth on the Anthropic path.
|
|
assert!(headers.iter().all(|(k, _)| *k != "Authorization"));
|
|
}
|
|
|
|
#[test]
|
|
fn anthropic_chat_returns_content_and_no_digest() {
|
|
let canned = r#"{"content":[{"type":"text","text":"ok"}]}"#;
|
|
let client = MockClient::new(vec![Ok(canned.to_string())]);
|
|
let p = ChatParams {
|
|
api: Api::Anthropic,
|
|
endpoint: "https://api.anthropic.com/v1/messages",
|
|
model: "claude-fable-5",
|
|
api_key: "sk-ant",
|
|
system_prompt: "",
|
|
prompt: "hi",
|
|
};
|
|
let result = chat_with_identity(&client, &p).unwrap();
|
|
assert_eq!(result.response, "ok");
|
|
assert_eq!(result.model_digest, None);
|
|
assert_eq!(client.calls.borrow().len(), 1);
|
|
}
|
|
|
|
// ---------------- digest probe (Ollama only) ----------------
|
|
|
|
#[test]
|
|
fn derive_show_url_swaps_chat_suffix() {
|
|
assert_eq!(
|
|
derive_show_url("http://localhost:11434/api/chat").as_deref(),
|
|
Some("http://localhost:11434/api/show")
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn derive_show_url_returns_none_for_non_ollama() {
|
|
assert!(derive_show_url("https://api.openai.com/v1/chat/completions").is_none());
|
|
}
|
|
|
|
#[test]
|
|
fn extract_show_digest_finds_top_level() {
|
|
let body = r#"{"digest":"sha256:abc"}"#;
|
|
assert_eq!(extract_show_digest(body).as_deref(), Some("sha256:abc"));
|
|
}
|
|
|
|
#[test]
|
|
fn extract_show_digest_falls_back_to_nested() {
|
|
let body = r#"{"details":{"digest":"sha256:nested"}}"#;
|
|
assert_eq!(extract_show_digest(body).as_deref(), Some("sha256:nested"));
|
|
}
|
|
|
|
#[test]
|
|
fn chat_with_identity_records_digest_when_show_responds() {
|
|
let canned_chat = r#"{"message":{"content":"the response"},"done":true}"#.to_string();
|
|
let canned_show = r#"{"digest":"sha256:deadbeef"}"#.to_string();
|
|
let client = MockClient::new(vec![Ok(canned_show), Ok(canned_chat)]);
|
|
let p = ChatParams {
|
|
endpoint: "http://localhost:11434/api/chat",
|
|
..params(Api::Ollama)
|
|
};
|
|
let result = chat_with_identity(&client, &p).unwrap();
|
|
assert_eq!(result.response, "the response");
|
|
assert_eq!(result.model_digest.as_deref(), Some("sha256:deadbeef"));
|
|
}
|
|
|
|
#[test]
|
|
fn chat_with_identity_swallows_show_failure() {
|
|
let canned_chat = r#"{"message":{"content":"ok"}}"#.to_string();
|
|
let client = MockClient::new(vec![Err(LlmError::Status(500)), Ok(canned_chat)]);
|
|
let p = ChatParams {
|
|
endpoint: "http://localhost:11434/api/chat",
|
|
..params(Api::Ollama)
|
|
};
|
|
let result = chat_with_identity(&client, &p).unwrap();
|
|
assert_eq!(result.response, "ok");
|
|
assert_eq!(result.model_digest, None);
|
|
}
|
|
|
|
#[test]
|
|
fn chat_with_identity_skips_probe_for_non_ollama_url() {
|
|
let canned_chat = r#"{"message":{"content":"ok"}}"#.to_string();
|
|
let client = MockClient::new(vec![Ok(canned_chat)]);
|
|
let p = ChatParams {
|
|
endpoint: "https://example.com/proxy/chat",
|
|
..params(Api::Ollama)
|
|
};
|
|
let result = chat_with_identity(&client, &p).unwrap();
|
|
assert_eq!(result.response, "ok");
|
|
assert_eq!(result.model_digest, None);
|
|
}
|
|
|
|
// ---------------- input guards ----------------
|
|
|
|
#[test]
|
|
fn empty_prompt_is_rejected() {
|
|
let client = MockClient::new(vec![]);
|
|
let p = ChatParams {
|
|
prompt: "",
|
|
..params(Api::Ollama)
|
|
};
|
|
assert!(matches!(
|
|
chat_with_identity(&client, &p),
|
|
Err(LlmError::MissingInput("prompt"))
|
|
));
|
|
}
|
|
|
|
/// Guard: `api: vllm` exists ONLY for operator clarity — the
|
|
/// request it emits (URL, body, headers) must stay identical to
|
|
/// `api: openai`. If the two ever diverge, that is a new wire
|
|
/// dialect and needs its own deliberate design, not drift.
|
|
#[test]
|
|
fn vllm_is_the_openai_wire_verbatim() {
|
|
assert_eq!(Api::parse("vllm").unwrap(), Api::Vllm);
|
|
let openai_reply = r#"{"choices":[{"message":{"role":"assistant","content":"x"}}]}"#;
|
|
let recorded: Vec<RecordedCall> = [Api::Vllm, Api::Openai]
|
|
.into_iter()
|
|
.map(|api| {
|
|
let client = MockClient::new(vec![Ok(openai_reply.to_string())]);
|
|
chat_with_identity(&client, ¶ms(api)).unwrap();
|
|
client.calls.into_inner().pop().unwrap()
|
|
})
|
|
.collect();
|
|
assert_eq!(recorded[0], recorded[1], "vllm request drifted from openai");
|
|
}
|
|
}
|