split_brain_harness/backends/
ollama.rs

1use super::InferenceEngine;
2use async_trait::async_trait;
3use reqwest::Client;
4use serde_json::json;
5
6pub struct OllamaNativeEngine {
7    pub endpoint: String,
8    pub model: String,
9    pub client: Client,
10}
11
12#[async_trait]
13impl InferenceEngine for OllamaNativeEngine {
14    async fn generate(&self, system_prompt: &str, prompt_payload: &str) -> Result<String, String> {
15        let body = json!({
16            "model": self.model,
17            "messages": [
18                { "role": "system", "content": system_prompt },
19                { "role": "user",   "content": prompt_payload }
20            ],
21            "think":  false,
22            "stream": false,
23            "options": { "temperature": 0.1, "num_predict": 600 }
24        });
25
26        let resp = self
27            .client
28            .post(format!("{}/api/chat", self.endpoint.trim_end_matches('/')))
29            .json(&body)
30            .send()
31            .await
32            .map_err(|e| e.to_string())?;
33
34        let json: serde_json::Value = resp.json().await.map_err(|e| e.to_string())?;
35
36        json["message"]["content"]
37            .as_str()
38            .map(|s| s.to_string())
39            .ok_or_else(|| "missing content field in Ollama response".into())
40    }
41}
split_brain_harness/backends/ollama.rs

split_brain_harness/backends/
ollama.rs
