feat: selectable wire format — ollama (default), openai (vLLM etc.), anthropic

Port the multi-API client from llm.chat (llm.rs kept in lockstep):
the optional api input selects the dialect, default stays the
unchanged v0.1.x Ollama behavior. openai covers vLLM, LM Studio,
LiteLLM and cloud OpenAI; api-specific error hints; api_key sent
as Bearer (ollama/openai) or x-api-key (anthropic). model_digest
stays an Ollama-only best-effort probe and is documented as such.

Proven end-to-end against a hermetic OpenAI-wire fake (request
shape validated, response parsed) via a hub flow run.

Signed-off-by: flemming-it <sf@flemming.it>

src/llm.rs

@@ -1,35 +1,84 @@
-//! Ollama-shaped chat client.
+//! Multi-API chat client: Ollama (default), OpenAI, Anthropic.
 //!
-//! v0.1.0 targets the Ollama `/api/chat` endpoint. OpenAI- and
-//! Anthropic-compatible adapters are deliberately deferred — each
-//! has its own request/response shape that warrants its own crate
-//! (or at least its own module here) once a flow needs it.
+//! The wire format is selected via the optional `api` input:
+//!
+//! - `ollama` (default) — Ollama `/api/chat`; unchanged v0.1.0
+//!   behavior including the best-effort model-digest probe.
+//! - `openai` — OpenAI Chat Completions wire format
+//!   (`/v1/chat/completions`), the de-facto standard implemented
+//!   by vLLM and most self-hosted inference servers. Bearer auth.
+//! - `anthropic` — Anthropic Messages API (`/v1/messages`),
+//!   `x-api-key` + `anthropic-version` headers, top-level
+//!   `system` field, mandatory `max_tokens`.
+//!
+//! No streaming, no tool calls — one prompt in, one completion out.
 //!
 //! All HTTP I/O lives behind a `LlmClient` trait so unit tests can
-//! exercise prompt building, response parsing, and the digest
+//! exercise request building, response parsing, and the digest
 //! probe on the host without making real network calls.
 
 use serde::Serialize;
 
+/// Anthropic's Messages API requires `max_tokens`. This default is
+/// large enough for document-processing completions while staying
+/// well below every current model's output cap.
+const ANTHROPIC_DEFAULT_MAX_TOKENS: u32 = 4096;
+
+/// Pinned Messages API version header. Anthropic keeps old
+/// versions working; bump deliberately, never implicitly.
+const ANTHROPIC_VERSION: &str = "2023-06-01";
+
 #[allow(dead_code)]
 #[derive(Debug, thiserror::Error)]
 pub enum LlmError {
     #[error("missing required input '{0}'")]
     MissingInput(&'static str),
+    #[error("unsupported api '{0}' (expected: ollama, openai, anthropic)")]
+    UnsupportedApi(String),
     #[error("http error: {0}")]
     Http(String),
     #[error("non-success status: {0}")]
     Status(u16),
-    #[error("response body could not be parsed as Ollama schema: {0}")]
+    #[error("response body could not be parsed as the expected schema: {0}")]
     Decode(String),
 }
 
+/// Which wire format to speak. Selected by the `api` input.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum Api {
+    Ollama,
+    Openai,
+    Anthropic,
+}
+
+impl Api {
+    /// Parse the `api` input. Empty string means the input was not
+    /// provided and falls back to Ollama (v0.1.0 behavior).
+    pub fn parse(raw: &str) -> Result<Self, LlmError> {
+        match raw.trim().to_ascii_lowercase().as_str() {
+            "" | "ollama" => Ok(Api::Ollama),
+            "openai" => Ok(Api::Openai),
+            "anthropic" => Ok(Api::Anthropic),
+            other => Err(LlmError::UnsupportedApi(other.to_string())),
+        }
+    }
+}
+
 pub trait LlmClient {
-    fn post_json(&self, url: &str, body: &str, api_key: &str) -> Result<String, LlmError>;
+    /// POST `body` as JSON to `url` with the given extra headers.
+    /// `Content-Type: application/json` is implied. Header VALUES
+    /// may carry credentials — implementations must never log them.
+    fn post_json(
+        &self,
+        url: &str,
+        body: &str,
+        headers: &[(&'static str, String)],
+    ) -> Result<String, LlmError>;
 }
 
 #[derive(Debug, Clone)]
 pub struct ChatParams<'a> {
+    pub api: Api,
     pub endpoint: &'a str,
     pub model: &'a str,
     pub api_key: &'a str,
@@ -37,8 +86,29 @@ pub struct ChatParams<'a> {
     pub prompt: &'a str,
 }
 
+/// Auth/protocol headers for the selected API. The api_key is only
+/// ever placed into a header value here; it must not appear in any
+/// error, output, or log line.
+pub fn build_headers(p: &ChatParams) -> Vec<(&'static str, String)> {
+    let mut headers = Vec::with_capacity(2);
+    match p.api {
+        Api::Ollama | Api::Openai => {
+            if !p.api_key.is_empty() {
+                headers.push(("Authorization", format!("Bearer {}", p.api_key)));
+            }
+        }
+        Api::Anthropic => {
+            if !p.api_key.is_empty() {
+                headers.push(("x-api-key", p.api_key.to_string()));
+            }
+            headers.push(("anthropic-version", ANTHROPIC_VERSION.to_string()));
+        }
+    }
+    headers
+}
+
 #[derive(Serialize)]
-struct OllamaMessage<'a> {
+struct ChatMessage<'a> {
     role: &'a str,
     content: &'a str,
 }
@@ -46,33 +116,86 @@ struct OllamaMessage<'a> {
 #[derive(Serialize)]
 struct OllamaRequest<'a> {
     model: &'a str,
-    messages: Vec<OllamaMessage<'a>>,
+    messages: Vec<ChatMessage<'a>>,
     stream: bool,
 }
 
-/// Build the request body for an Ollama `/api/chat` invocation.
-/// The system prompt is omitted from the messages list when empty
-/// so a deployment can use the model's built-in system prompt.
-pub fn build_ollama_body(p: &ChatParams) -> String {
+#[derive(Serialize)]
+struct OpenAiRequest<'a> {
+    model: &'a str,
+    messages: Vec<ChatMessage<'a>>,
+    stream: bool,
+}
+
+#[derive(Serialize)]
+struct AnthropicRequest<'a> {
+    model: &'a str,
+    max_tokens: u32,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    system: Option<&'a str>,
+    messages: Vec<ChatMessage<'a>>,
+}
+
+/// System-then-user message list shared by the Ollama and OpenAI
+/// wire formats. The system message is omitted when empty so a
+/// deployment can use the model's built-in system prompt.
+fn build_messages<'a>(p: &ChatParams<'a>) -> Vec<ChatMessage<'a>> {
     let mut messages = Vec::with_capacity(2);
     if !p.system_prompt.is_empty() {
-        messages.push(OllamaMessage {
+        messages.push(ChatMessage {
             role: "system",
             content: p.system_prompt,
         });
     }
-    messages.push(OllamaMessage {
+    messages.push(ChatMessage {
         role: "user",
         content: p.prompt,
     });
+    messages
+}
+
+/// Build the request body for an Ollama `/api/chat` invocation.
+pub fn build_ollama_body(p: &ChatParams) -> String {
     let req = OllamaRequest {
         model: p.model,
-        messages,
+        messages: build_messages(p),
         stream: false,
     };
     serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
 }
 
+/// Build the request body for an OpenAI-compatible
+/// `/v1/chat/completions` invocation (OpenAI, vLLM, most
+/// self-hosted inference servers).
+pub fn build_openai_body(p: &ChatParams) -> String {
+    let req = OpenAiRequest {
+        model: p.model,
+        messages: build_messages(p),
+        stream: false,
+    };
+    serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
+}
+
+/// Build the request body for an Anthropic `/v1/messages`
+/// invocation. `system` is a top-level field (not a message);
+/// `max_tokens` is mandatory in the Messages API.
+pub fn build_anthropic_body(p: &ChatParams) -> String {
+    let req = AnthropicRequest {
+        model: p.model,
+        max_tokens: ANTHROPIC_DEFAULT_MAX_TOKENS,
+        system: if p.system_prompt.is_empty() {
+            None
+        } else {
+            Some(p.system_prompt)
+        },
+        messages: vec![ChatMessage {
+            role: "user",
+            content: p.prompt,
+        }],
+    };
+    serde_json::to_string(&req).unwrap_or_else(|_| String::from("{}"))
+}
+
 /// Extract the assistant's message text from an Ollama
 /// /api/chat response.
 pub fn extract_ollama_content(body: &str) -> Result<String, LlmError> {
@@ -85,16 +208,44 @@ pub fn extract_ollama_content(body: &str) -> Result<String, LlmError> {
         .ok_or_else(|| LlmError::Decode("missing message.content".into()))
 }
 
+/// Extract the assistant text from an OpenAI Chat Completions
+/// response: `choices[0].message.content`.
+pub fn extract_openai_content(body: &str) -> Result<String, LlmError> {
+    let v: serde_json::Value =
+        serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
+    v.get("choices")
+        .and_then(|c| c.get(0))
+        .and_then(|c| c.get("message"))
+        .and_then(|m| m.get("content"))
+        .and_then(|c| c.as_str())
+        .map(|s| s.to_string())
+        .ok_or_else(|| LlmError::Decode("missing choices[0].message.content".into()))
+}
+
+/// Extract the assistant text from an Anthropic Messages
+/// response: `content[0].text`.
+pub fn extract_anthropic_content(body: &str) -> Result<String, LlmError> {
+    let v: serde_json::Value =
+        serde_json::from_str(body).map_err(|e| LlmError::Decode(e.to_string()))?;
+    v.get("content")
+        .and_then(|c| c.get(0))
+        .and_then(|c| c.get("text"))
+        .and_then(|t| t.as_str())
+        .map(|s| s.to_string())
+        .ok_or_else(|| LlmError::Decode("missing content[0].text".into()))
+}
+
 #[derive(Debug, Clone)]
 pub struct ChatWithIdentity {
     pub response: String,
     pub model_digest: Option<String>,
 }
 
-/// Run an Ollama chat call AND probe the model digest. Probe is
-/// best-effort — non-Ollama endpoints (no `/api/chat` suffix) and
-/// transient failures yield `model_digest = None` rather than
-/// failing the whole call.
+/// Run one chat call in the selected wire format AND, for Ollama,
+/// probe the model digest. The probe is best-effort — non-Ollama
+/// APIs expose no digest endpoint, so `model_digest = None` there
+/// (documented empty, never invented); transient probe failures on
+/// Ollama also yield `None` rather than failing the whole call.
 pub fn chat_with_identity<C: LlmClient>(
     client: &C,
     p: &ChatParams,
@@ -108,10 +259,22 @@ pub fn chat_with_identity<C: LlmClient>(
     if p.prompt.is_empty() {
         return Err(LlmError::MissingInput("prompt"));
     }
-    let body = build_ollama_body(p);
-    let response_body = client.post_json(p.endpoint, &body, p.api_key)?;
-    let response = extract_ollama_content(&response_body)?;
-    let model_digest = probe_model_digest(client, p);
+    let body = match p.api {
+        Api::Ollama => build_ollama_body(p),
+        Api::Openai => build_openai_body(p),
+        Api::Anthropic => build_anthropic_body(p),
+    };
+    let headers = build_headers(p);
+    let response_body = client.post_json(p.endpoint, &body, &headers)?;
+    let response = match p.api {
+        Api::Ollama => extract_ollama_content(&response_body)?,
+        Api::Openai => extract_openai_content(&response_body)?,
+        Api::Anthropic => extract_anthropic_content(&response_body)?,
+    };
+    let model_digest = match p.api {
+        Api::Ollama => probe_model_digest(client, p),
+        Api::Openai | Api::Anthropic => None,
+    };
     Ok(ChatWithIdentity {
         response,
         model_digest,
@@ -121,7 +284,8 @@ pub fn chat_with_identity<C: LlmClient>(
 fn probe_model_digest<C: LlmClient>(client: &C, p: &ChatParams) -> Option<String> {
     let show_url = derive_show_url(p.endpoint)?;
     let body = serde_json::to_string(&serde_json::json!({ "name": p.model })).ok()?;
-    let response = client.post_json(&show_url, &body, p.api_key).ok()?;
+    let headers = build_headers(p);
+    let response = client.post_json(&show_url, &body, &headers).ok()?;
     extract_show_digest(&response)
 }
 
@@ -160,20 +324,39 @@ mod tests {
     use super::*;
     use std::cell::RefCell;
 
+    /// (url, body, headers) of one recorded call.
+    type RecordedCall = (String, String, Vec<(String, String)>);
+
     struct MockClient {
         responses: RefCell<Vec<Result<String, LlmError>>>,
+        /// Every call, in call order.
+        calls: RefCell<Vec<RecordedCall>>,
     }
 
     impl MockClient {
         fn new(responses: Vec<Result<String, LlmError>>) -> Self {
             Self {
                 responses: RefCell::new(responses),
+                calls: RefCell::new(Vec::new()),
             }
         }
     }
 
     impl LlmClient for MockClient {
-        fn post_json(&self, _url: &str, _body: &str, _api_key: &str) -> Result<String, LlmError> {
+        fn post_json(
+            &self,
+            url: &str,
+            body: &str,
+            headers: &[(&'static str, String)],
+        ) -> Result<String, LlmError> {
+            self.calls.borrow_mut().push((
+                url.to_string(),
+                body.to_string(),
+                headers
+                    .iter()
+                    .map(|(k, v)| (k.to_string(), v.clone()))
+                    .collect(),
+            ));
             self.responses
                 .borrow_mut()
                 .pop()
@@ -181,14 +364,42 @@ mod tests {
         }
     }
 
-    #[test]
-    fn ollama_body_includes_system_when_provided() {
-        let p = ChatParams {
+    fn params<'a>(api: Api) -> ChatParams<'a> {
+        ChatParams {
+            api,
             endpoint: "http://x/api/chat",
             model: "qwen",
             api_key: "",
-            system_prompt: "be helpful",
+            system_prompt: "",
             prompt: "hello",
+        }
+    }
+
+    // ---------------- api input parsing ----------------
+
+    #[test]
+    fn api_parse_defaults_to_ollama() {
+        assert_eq!(Api::parse("").unwrap(), Api::Ollama);
+        assert_eq!(Api::parse("ollama").unwrap(), Api::Ollama);
+        assert_eq!(Api::parse("  OpenAI ").unwrap(), Api::Openai);
+        assert_eq!(Api::parse("anthropic").unwrap(), Api::Anthropic);
+    }
+
+    #[test]
+    fn api_parse_rejects_unknown() {
+        assert!(matches!(
+            Api::parse("gemini"),
+            Err(LlmError::UnsupportedApi(_))
+        ));
+    }
+
+    // ---------------- Ollama wire format (regression) ----------------
+
+    #[test]
+    fn ollama_body_includes_system_when_provided() {
+        let p = ChatParams {
+            system_prompt: "be helpful",
+            ..params(Api::Ollama)
         };
         let body = build_ollama_body(&p);
         let v: serde_json::Value = serde_json::from_str(&body).unwrap();
@@ -198,18 +409,12 @@ mod tests {
         assert_eq!(messages[0]["content"], "be helpful");
         assert_eq!(messages[1]["role"], "user");
         assert_eq!(messages[1]["content"], "hello");
+        assert_eq!(v["stream"], false);
     }
 
     #[test]
     fn ollama_body_omits_system_when_empty() {
-        let p = ChatParams {
-            endpoint: "http://x/api/chat",
-            model: "qwen",
-            api_key: "",
-            system_prompt: "",
-            prompt: "hello",
-        };
-        let body = build_ollama_body(&p);
+        let body = build_ollama_body(&params(Api::Ollama));
         let v: serde_json::Value = serde_json::from_str(&body).unwrap();
         let messages = v["messages"].as_array().unwrap();
         assert_eq!(messages.len(), 1);
@@ -231,6 +436,208 @@ mod tests {
         ));
     }
 
+    #[test]
+    fn ollama_bearer_header_only_with_key() {
+        assert!(build_headers(&params(Api::Ollama)).is_empty());
+        let p = ChatParams {
+            api_key: "sk-test",
+            ..params(Api::Ollama)
+        };
+        assert_eq!(
+            build_headers(&p),
+            vec![("Authorization", "Bearer sk-test".to_string())]
+        );
+    }
+
+    // ---------------- OpenAI wire format ----------------
+
+    #[test]
+    fn openai_body_matches_chat_completions_format() {
+        let p = ChatParams {
+            api: Api::Openai,
+            endpoint: "http://vllm:8000/v1/chat/completions",
+            model: "meta-llama/Llama-3.1-8B-Instruct",
+            api_key: "sk-x",
+            system_prompt: "be terse",
+            prompt: "hello",
+        };
+        let body = build_openai_body(&p);
+        let v: serde_json::Value = serde_json::from_str(&body).unwrap();
+        assert_eq!(v["model"], "meta-llama/Llama-3.1-8B-Instruct");
+        assert_eq!(v["stream"], false);
+        let messages = v["messages"].as_array().unwrap();
+        assert_eq!(messages.len(), 2);
+        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(&params(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!(
@@ -263,10 +670,7 @@ mod tests {
         let client = MockClient::new(vec![Ok(canned_show), Ok(canned_chat)]);
         let p = ChatParams {
             endpoint: "http://localhost:11434/api/chat",
-            model: "qwen",
-            api_key: "",
-            system_prompt: "",
-            prompt: "hi",
+            ..params(Api::Ollama)
         };
         let result = chat_with_identity(&client, &p).unwrap();
         assert_eq!(result.response, "the response");
@@ -279,10 +683,7 @@ mod tests {
         let client = MockClient::new(vec![Err(LlmError::Status(500)), Ok(canned_chat)]);
         let p = ChatParams {
             endpoint: "http://localhost:11434/api/chat",
-            model: "qwen",
-            api_key: "",
-            system_prompt: "",
-            prompt: "hi",
+            ..params(Api::Ollama)
         };
         let result = chat_with_identity(&client, &p).unwrap();
         assert_eq!(result.response, "ok");
@@ -290,30 +691,26 @@ mod tests {
     }
 
     #[test]
-    fn chat_with_identity_skips_probe_for_non_ollama() {
+    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://api.openai.com/v1/chat/completions",
-            model: "gpt",
-            api_key: "k",
-            system_prompt: "",
-            prompt: "hi",
+            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 {
-            endpoint: "http://x/api/chat",
-            model: "x",
-            api_key: "",
-            system_prompt: "",
             prompt: "",
+            ..params(Api::Ollama)
         };
         assert!(matches!(
             chat_with_identity(&client, &p),