Set prompt for LLM model

fire_seq_search_server/Cargo.toml

@@ -47,7 +47,7 @@ stop-words = "0.7.2"
 
 regex = "1"
 lingua = { version = "1.4.0", default-features = false, features = ["chinese", "english"] }
-
+shellexpand = "3.1"
 
 #Highlight (Output)
 html-escape = "0.2.13"
@@ -63,6 +63,7 @@ pdf-extract-temporary-mitigation-panic = "0.7.1"
 
 
 # llm related
+# TODO I should make them optional
 sha256 = "1.5.0"
 reqwest = { version = "0.12", features = ["json"] }
 futures = "0.3"

fire_seq_search_server/src/http_client/endpoints.rs

@@ -33,8 +33,6 @@ pub async fn summarize(
 pub async fn get_llm_done_list(
     State(engine_arc): State<Arc<QueryEngine>>
     ) -> Html<String>{
-
-    info!("get list endpoint called");
     let r = engine_arc.get_llm_done_list();
     Html(r.await)
 }

fire_seq_search_server/src/local_llm/mod.rs

@@ -10,6 +10,18 @@ use serde_derive::Deserialize;
 use serde_derive::Serialize;
 use serde;
 
+// TODO Allow user to set prompt, instead of hard-coded in code
+const prompt_string: &'static str = r##"
+You are a seasoned summary expert, capable of condensing and summarizing given articles, papers, or posts, accurately conveying the main idea to make the content easier to understand.
+
+You place great emphasis on user experience, never adding irrelevant content like "Summary," "The summary is as follows," "Original text," "You can check the original text if interested," or "Original link." Your summaries always convey the core information directly.
+
+You are adept at handling various large, small, and even chaotic text content, always accurately extracting key information and summarizing the core content globally to make it easier to understand.
+
+=== Below is the article ===
+
+"##;
+
 #[derive(Debug, Serialize, Deserialize)]
 pub struct OpenAiData {
     pub model: String,
@@ -90,6 +102,8 @@ pub struct LlmEngine {
 
 
 
+use std::borrow::Cow;
+use std::borrow::Cow::Borrowed;
 
 use tokio::task::yield_now;
 use tokio::task;
@@ -154,15 +168,20 @@ impl LlmEngine {
         }
     }
 
-    fn build_data(full_text: &str) -> OpenAiData {
-        fn build_message(full_text:&str) -> Message {
+    fn build_data(full_text: Cow<'_, str>) -> OpenAiData {
+
+        fn build_message(chat:String) -> Message {
             Message{
                 role: "user".to_owned(),
-                content: full_text.to_owned(),
+                content: chat,
             }
         }
         let mut msgs = Vec::new();
-        msgs.push( build_message(full_text) );
+
+        let mut chat_text = prompt_string.to_owned();
+        chat_text += &full_text;
+        msgs.push( build_message(chat_text) );
+
         OpenAiData {
             model: "model".to_owned(),
             messages: msgs,
@@ -174,7 +193,7 @@ impl LlmEngine{
     pub async fn summarize(&self, full_text: &str) -> String {
         //http://localhost:8080/completion
         let ep = self.endpoint.to_owned() + "/v1/chat/completions";
-        let data = Self::build_data(full_text);
+        let data = Self::build_data( Borrowed(full_text) );
         let res = self.client.post(&ep)
             .header("Content-Type", "application/json")
             .json(&data)
@@ -242,7 +261,6 @@ impl LlmEngine{
         let mut r = Vec::new();
         let jcache = self.job_cache.lock().await;
         for (title, _text) in &jcache.done_job {
-            info!("already done : {}", &title);
             r.push(title.to_owned());
         }
         return r;
@@ -268,19 +286,17 @@ struct LlamaFileDef {
 }
 
 
+use shellexpand::tilde;
 async fn locate_llamafile() -> Option<String> {
-    // TODO
     let mut lf = LlamaFileDef {
         filename: "mistral-7b-instruct-v0.2.Q4_0.llamafile".to_owned(),
         filepath: None,
         sha256: "1903778f7defd921347b25327ebe5dd902f29417ba524144a8e4f7c32d83dee8".to_owned(),
         download_link: "mistral-7b-instruct-v0.2.Q4_0.llamafile".to_owned(),
     };
 
-    // TODO hack in dev
-    //let lf_path = "/var/home/lizhenbo/Downloads/mistral-7b-instruct-v0.2.Q4_0.llamafile";
-    let lf_base = "/Users/zhenboli/.llamafile/";
-    let lf_path = lf_base.to_owned() + &lf.filename;
+    let lf_base = tilde("~/.llamafile/");
+    let lf_path = lf_base.to_string() + &lf.filename;
     lf.filepath = Some(  lf_path.to_owned() );
     info!("lf {:?}", &lf);