refactor: introduce LlmClient trait (no-op)

Preparation for a second LLM backend (OpenRouter) and hybrid vision-local /
chat-remote mode. Shared wire types (ChatMessage, Tool, ToolCall, etc.) move
into a new src/ai/llm_client.rs and are re-exported from ollama.rs so
existing imports keep working. OllamaClient now implements LlmClient.

No behavior change; callers still hold the concrete OllamaClient. Caller
migration to Arc<dyn LlmClient> is deferred to the PR that wires hybrid
backend routing.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/ai/llm_client.rs

@@ -0,0 +1,140 @@
+use anyhow::Result;
+use async_trait::async_trait;
+use serde::{Deserialize, Serialize};
+
+/// Provider-agnostic surface for LLM backends (Ollama, OpenRouter, …).
+///
+/// Impls translate these canonical shapes at the wire boundary: tool-call
+/// arguments stay as `serde_json::Value` in memory and are stringified only
+/// when a provider requires it (OpenAI-compatible APIs do), and `images`
+/// stays as base64 strings here and is rewritten into content-parts where
+/// needed.
+// First consumer lands in a later PR (OpenRouter impl + hybrid mode routing).
+#[allow(dead_code)]
+#[async_trait]
+pub trait LlmClient: Send + Sync {
+    /// Single-shot text generation. Optional system prompt and optional
+    /// base64 images (ignored by providers without vision support).
+    async fn generate(
+        &self,
+        prompt: &str,
+        system: Option<&str>,
+        images: Option<Vec<String>>,
+    ) -> Result<String>;
+
+    /// Multi-turn chat with tool definitions. Returns the assistant message
+    /// (which may contain tool_calls) plus optional prompt/eval token counts.
+    async fn chat_with_tools(
+        &self,
+        messages: Vec<ChatMessage>,
+        tools: Vec<Tool>,
+    ) -> Result<(ChatMessage, Option<i32>, Option<i32>)>;
+
+    /// Batch embedding generation. Dimensionality is provider/model specific.
+    async fn generate_embeddings(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>>;
+
+    /// One-shot vision description of an image. Used to convert images into
+    /// plain text for the hybrid-mode conversation flow.
+    async fn describe_image(&self, image_base64: &str) -> Result<String>;
+
+    /// Enumerate available models with their capabilities.
+    async fn list_models(&self) -> Result<Vec<ModelCapabilities>>;
+
+    /// Look up capabilities for a single model.
+    async fn model_capabilities(&self, model: &str) -> Result<ModelCapabilities>;
+
+    /// Primary model identifier this client was constructed with.
+    fn primary_model(&self) -> &str;
+}
+
+/// Tool definition sent to the model (OpenAI-compatible function schema).
+#[derive(Serialize, Clone, Debug)]
+pub struct Tool {
+    #[serde(rename = "type")]
+    pub tool_type: String, // always "function"
+    pub function: ToolFunction,
+}
+
+#[derive(Serialize, Clone, Debug)]
+pub struct ToolFunction {
+    pub name: String,
+    pub description: String,
+    pub parameters: serde_json::Value,
+}
+
+impl Tool {
+    pub fn function(name: &str, description: &str, parameters: serde_json::Value) -> Self {
+        Self {
+            tool_type: "function".to_string(),
+            function: ToolFunction {
+                name: name.to_string(),
+                description: description.to_string(),
+                parameters,
+            },
+        }
+    }
+}
+
+/// A message in the chat conversation history.
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ChatMessage {
+    pub role: String, // "system" | "user" | "assistant" | "tool"
+    /// Empty string (not null) when tool_calls is present — Ollama quirk.
+    #[serde(default)]
+    pub content: String,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub tool_calls: Option<Vec<ToolCall>>,
+    /// Base64 images — only on user messages to vision-capable models.
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub images: Option<Vec<String>>,
+}
+
+impl ChatMessage {
+    pub fn system(content: impl Into<String>) -> Self {
+        Self {
+            role: "system".to_string(),
+            content: content.into(),
+            tool_calls: None,
+            images: None,
+        }
+    }
+    pub fn user(content: impl Into<String>) -> Self {
+        Self {
+            role: "user".to_string(),
+            content: content.into(),
+            tool_calls: None,
+            images: None,
+        }
+    }
+    pub fn tool_result(content: impl Into<String>) -> Self {
+        Self {
+            role: "tool".to_string(),
+            content: content.into(),
+            tool_calls: None,
+            images: None,
+        }
+    }
+}
+
+/// Tool call returned by the model in an assistant message.
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ToolCall {
+    pub function: ToolCallFunction,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    pub id: Option<String>,
+}
+
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ToolCallFunction {
+    pub name: String,
+    /// Canonical shape: native JSON. Providers that use JSON-encoded-string
+    /// arguments (OpenAI-compatible) translate at their wire boundary.
+    pub arguments: serde_json::Value,
+}
+
+#[derive(Serialize, Deserialize, Clone, Debug)]
+pub struct ModelCapabilities {
+    pub name: String,
+    pub has_vision: bool,
+    pub has_tool_calling: bool,
+}