fix(provider): enable native tool calling for OllamaProvider

src/providers/ollama.rs

@@ -1,4 +1,4 @@
-use crate::providers::traits::Provider;
+use crate::providers::traits::{ChatMessage, ChatResponse, Provider, ToolCall};
 use async_trait::async_trait;
 use reqwest::Client;
 use serde::{Deserialize, Serialize};
@@ -16,6 +16,8 @@ struct ChatRequest {
     messages: Vec<Message>,
     stream: bool,
     options: Options,
+    #[serde(skip_serializing_if = "Option::is_none")]
+    tools: Option<Vec<serde_json::Value>>,
 }
 
 #[derive(Debug, Serialize)]
@@ -112,29 +114,33 @@ impl OllamaProvider {
         Ok((normalized_model, should_auth))
     }
 
-    /// Send a request to Ollama and get the parsed response
+    /// Send a request to Ollama and get the parsed response.
+    /// Pass `tools` to enable native function-calling for models that support it.
     async fn send_request(
         &self,
         messages: Vec<Message>,
         model: &str,
         temperature: f64,
         should_auth: bool,
+        tools: Option<&[serde_json::Value]>,
     ) -> anyhow::Result<ApiChatResponse> {
         let request = ChatRequest {
             model: model.to_string(),
             messages,
             stream: false,
             options: Options { temperature },
+            tools: tools.map(|t| t.to_vec()),
         };
 
         let url = format!("{}/api/chat", self.base_url);
 
         tracing::debug!(
-            "Ollama request: url={} model={} message_count={} temperature={}",
+            "Ollama request: url={} model={} message_count={} temperature={} tool_count={}",
             url,
             model,
             request.messages.len(),
-            temperature
+            temperature,
+            request.tools.as_ref().map_or(0, |t| t.len()),
         );
 
         let mut request_builder = self.http_client().post(&url).json(&request);
@@ -281,7 +287,7 @@ impl Provider for OllamaProvider {
         });
 
         let response = self
-            .send_request(messages, &normalized_model, temperature, should_auth)
+            .send_request(messages, &normalized_model, temperature, should_auth, None)
             .await?;
 
         // If model returned tool calls, format them for loop_.rs's parse_tool_calls
@@ -331,7 +337,7 @@ impl Provider for OllamaProvider {
             .collect();
 
         let response = self
-            .send_request(api_messages, &normalized_model, temperature, should_auth)
+            .send_request(api_messages, &normalized_model, temperature, should_auth, None)
             .await?;
 
         // If model returned tool calls, format them for loop_.rs's parse_tool_calls
@@ -343,6 +349,7 @@ impl Provider for OllamaProvider {
             return Ok(self.format_tool_calls_for_loop(&response.message.tool_calls));
         }
 
+
         // Plain text response
         let content = response.message.content;
 
@@ -366,11 +373,88 @@ impl Provider for OllamaProvider {
         Ok(content)
     }
 
+    async fn chat_with_tools(
+        &self,
+        messages: &[ChatMessage],
+        tools: &[serde_json::Value],
+        model: &str,
+        temperature: f64,
+    ) -> anyhow::Result<ChatResponse> {
+        let (normalized_model, should_auth) = self.resolve_request_details(model)?;
+
+        let api_messages: Vec<Message> = messages
+            .iter()
+            .map(|m| Message {
+                role: m.role.clone(),
+                content: m.content.clone(),
+            })
+            .collect();
+
+        // Tools arrive pre-formatted in OpenAI/Ollama-compatible JSON from
+        // tools_to_openai_format() in loop_.rs — pass them through directly.
+        let tools_opt = if tools.is_empty() { None } else { Some(tools) };
+
+        let response = self
+            .send_request(api_messages, &normalized_model, temperature, should_auth, tools_opt)
+            .await?;
+
+        // Native tool calls returned by the model.
+        if !response.message.tool_calls.is_empty() {
+            let tool_calls: Vec<ToolCall> = response
+                .message
+                .tool_calls
+                .iter()
+                .map(|tc| {
+                    let (name, args) = self.extract_tool_name_and_args(tc);
+                    ToolCall {
+                        id: tc
+                            .id
+                            .clone()
+                            .unwrap_or_else(|| uuid::Uuid::new_v4().to_string()),
+                        name,
+                        arguments: serde_json::to_string(&args)
+                            .unwrap_or_else(|_| "{}".to_string()),
+                    }
+                })
+                .collect();
+            let text = if response.message.content.is_empty() {
+                None
+            } else {
+                Some(response.message.content)
+            };
+            return Ok(ChatResponse { text, tool_calls });
+        }
+
+
+        // Plain text response.
+        let content = response.message.content;
+        if content.is_empty() {
+            if let Some(thinking) = &response.message.thinking {
+                tracing::warn!(
+                    "Ollama returned empty content with only thinking: '{}'. Model may have stopped prematurely.",
+                    if thinking.len() > 100 { &thinking[..100] } else { thinking }
+                );
+                return Ok(ChatResponse {
+                    text: Some(format!(
+                        "I was thinking about this: {}... but I didn't complete my response. Could you try asking again?",
+                        if thinking.len() > 200 { &thinking[..200] } else { thinking }
+                    )),
+                    tool_calls: vec![],
+                });
+            }
+            tracing::warn!("Ollama returned empty content with no tool calls");
+        }
+        Ok(ChatResponse {
+            text: Some(content),
+            tool_calls: vec![],
+        })
+    }
+
     fn supports_native_tools(&self) -> bool {
-        // Return false since loop_.rs uses XML-style tool parsing via system prompt
-        // The model may return native tool_calls but we convert them to JSON format
-        // that parse_tool_calls() understands
-        false
+        // Ollama's /api/chat supports native function-calling for capable models
+        // (qwen2.5, llama3.1, mistral-nemo, etc.). chat_with_tools() sends tool
+        // definitions in the request and returns structured ToolCall objects.
+        true
     }
 }
 
@@ -557,4 +641,4 @@ mod tests {
         // arguments should be a string (JSON-encoded)
         assert!(func.get("arguments").unwrap().is_string());
     }
-}
+}