add vector search

Cargo.lock

@@ -164,7 +164,7 @@ checksum = "8c3c1a368f70d6cf7302d78f8f7093da241fb8e8807c05cc9e51a125895a6d5b"
 [[package]]
 name = "based"
 version = "0.1.0"
-source = "git+https://git.hydrar.de/jmarya/based#d6555edc29de66ff5190b716a1f8ebac8dbb2110"
+source = "git+https://git.hydrar.de/jmarya/based#00bb6f152d758252d62a511705ef35c8aa118168"
 dependencies = [
  "bcrypt",
  "chrono",

Cargo.toml

@@ -16,7 +16,7 @@ tokio = { version = "1.35.1", features = ["full"] }
 uuid = { version = "1.8.0", features = ["v4", "serde"] }
 sqlx = { version = "0.8", features = ["postgres", "runtime-tokio-native-tls", "derive", "uuid", "chrono", "json"] }
 maud = "0.26.0"
-based = { git = "https://git.hydrar.de/jmarya/based", features = [] }
+based = { git = "https://git.hydrar.de/jmarya/based", features = ["htmx"] }
 url = "2.5.4"
 reqwest = "0.12.11"
 ollama-rs = "0.2.2"

src/ai.rs

@@ -1,8 +1,9 @@
 use std::collections::VecDeque;
 
-use based::get_pg;
+use based::{get_pg, request::api::ToAPI};
 use ollama_rs::generation::embeddings::request::{EmbeddingsInput, GenerateEmbeddingsRequest};
 use serde::Serialize;
+use serde_json::json;
 use sqlx::FromRow;
 
 use crate::archive::{Document, Domain, WebsiteArchive};
@@ -20,6 +21,17 @@ pub struct DocEmbedding {
     pub similarity: f64,
 }
 
+impl ToAPI for DocEmbedding {
+    async fn api(&self) -> serde_json::Value {
+        json!({
+            "domain": self.domain,
+            "path": self.path,
+            "ver": self.ver,
+            "similarity": self.similarity
+        })
+    }
+}
+
 pub trait Embedding {
     fn embedding(&self, ver: Option<String>)
         -> impl std::future::Future<Output = Option<Vec<f32>>>;
@@ -41,7 +53,7 @@ impl Embedding for Document {
     }
 }
 
-pub async fn generate_embedding(input: String) -> Option<Vec<f32>> {
+pub async fn generate_embedding(mut input: String) -> Option<Vec<f32>> {
     if let Ok(ollama_url) = std::env::var("OLLAMA_URL") {
         let (host, port) = ollama_url.split_once(':')?;
         let ollama = ollama_rs::Ollama::new(format!("http://{host}"), port.parse().ok()?);
@@ -59,6 +71,10 @@ pub async fn generate_embedding(input: String) -> Option<Vec<f32>> {
                 .ok()?;
         }
 
+        if input.is_empty() {
+            input = " ".to_string();
+        }
+
         let res = ollama
             .generate_embeddings(GenerateEmbeddingsRequest::new(
                 "mxbai-embed-large".to_string(),
@@ -129,11 +145,13 @@ impl EmbedStore {
         }
     }
 
-    pub async fn search_vector(v: &pgvector::Vector) -> Vec<DocEmbedding> {
+    pub async fn search_vector(v: &pgvector::Vector, limit: i64, offset: i64) -> Vec<DocEmbedding> {
         sqlx::query_as(
-            "SELECT *, 1 / (1 + (embed_mxbai_embed_large <-> $1)) AS similarity FROM doc_embedding ORDER BY embed_mxbai_embed_large <-> $1 LIMIT 5",
+            "SELECT *, 1 / (1 + (embed_mxbai_embed_large <-> $1)) AS similarity FROM doc_embedding ORDER BY embed_mxbai_embed_large <-> $1 LIMIT $2 OFFSET $3",
         )
         .bind(v)
+        .bind(limit)
+        .bind(offset)
         .fetch_all(get_pg!())
         .await
         .unwrap()

src/archive.rs

@@ -151,7 +151,11 @@ impl Document {
     pub fn new(domain: &str, path: &str, base_dir: PathBuf) -> Self {
         Self {
             domain: domain.to_string(),
-            path: path.to_string(),
+            path: path
+                .split('/')
+                .filter(|x| !x.is_empty())
+                .collect::<Vec<&str>>()
+                .join("/"),
             base_dir,
         }
     }

src/main.rs

@@ -34,6 +34,7 @@ async fn launch() -> _ {
         .mount(
             "/",
             routes![
+                based::htmx::htmx_script_route,
                 pages::index,
                 pages::render_website,
                 pages::domain_info_route,

src/pages/component.rs

@@ -59,10 +59,18 @@ pub fn gen_path_link(
 ///
 /// # Returns
 /// A `PreEscaped<String>` containing the HTML markup for the path header.
-pub fn gen_path_header(path_seperations: Vec<&str>, domain: &str) -> PreEscaped<String> {
+pub fn gen_path_header(
+    path_seperations: Vec<&str>,
+    domain: &str,
+    link: bool,
+) -> PreEscaped<String> {
     html! {
         @for (index, path) in path_seperations.iter().enumerate() {
+            @if link {
             (gen_path_link(path, index, &path_seperations, domain))
+            } @else {
+                p { (path) }
+            }
             @if index < path_seperations.len()-1 {
                 (slash_seperator())
             };
@@ -79,6 +87,7 @@ pub async fn render_page(content: PreEscaped<String>, ctx: RequestContext) -> St
             html! {
                 script src="https://cdn.tailwindcss.com" {};
                 meta name="viewport" content="width=device-width, initial-scale=1.0" {};
+                script src="/assets/htmx.min.js" {};
             },
             html! {},
             Some("bg-zinc-950 text-white min-h-screen flex pt-8 justify-center".to_string()),

src/pages/mod.rs

@@ -1,7 +1,12 @@
 use std::{io::Read, path::PathBuf};
 
-use based::request::{assets::DataResponse, respond_json, RequestContext, StringResponse};
-use maud::html;
+use based::{
+    page::search::Search,
+    request::{
+        api::GeneratedPager, assets::DataResponse, respond_json, RequestContext, StringResponse,
+    },
+};
+use maud::{html, PreEscaped};
 use rocket::{get, State};
 
 pub mod component;
@@ -9,10 +14,12 @@ use component::*;
 use serde_json::json;
 
 use crate::{
-    ai::{generate_embedding, EmbedStore},
+    ai::{generate_embedding, DocEmbedding, EmbedStore},
     archive::WebsiteArchive,
 };
 
+const SEARCH_BAR_STYLE: &'static str = "w-full px-4 mb-4 py-2 text-white bg-black border-2 border-neon-blue placeholder-neon-blue focus:ring-2 focus:ring-neon-pink focus:outline-none font-mono text-lg";
+
 /// Get the favicon of a domain
 #[get("/favicon/<domain>")]
 pub async fn favicon_route(domain: &str) -> Option<DataResponse> {
@@ -36,9 +43,17 @@ pub async fn index(ctx: RequestContext, arc: &State<WebsiteArchive>) -> StringRe
 
     let content = html! {
         div class="container mx-auto p-4" {
+
+            div class="mb-4" {
+                input type="search" name="query" placeholder="Search..." class=(SEARCH_BAR_STYLE)
+                hx-get=("/vector_search")
+                hx-target="#website_grid" hx-push-url="true" hx-swap="outerHTML" {};
+            };
+
+
+        div id="website_grid" {
             h1 class="text-5xl font-bold text-center mb-10" { "Websites" };
             div class="grid grid-cols-2 sm:grid-cols-3 lg:grid-cols-5 xl:grid-cols-6 2xl:grid-cols-8 gap-6" {
-
                 @for site in websites {
                     a href=(format!("/d/{site}")) class="bg-neutral-900 shadow-md rounded-lg hover:bg-neutral-800 bg-gray-1 hover:cursor-pointer transition-all duration-300 flex flex-col items-center justify-center aspect-square max-w-60" {
                         div class="bg-blue-500 text-white rounded-full p-4" {
@@ -48,6 +63,7 @@ pub async fn index(ctx: RequestContext, arc: &State<WebsiteArchive>) -> StringRe
                     };
                 };
             };
+        };
         }
     };
 
@@ -75,7 +91,7 @@ pub async fn domain_info_route(
             img class="p-2" src=(format!("/favicon/{}", &domain.name)) {};
             a href=(format!("/d/{}", &domain.name)) { (domain.name) };
             (slash_seperator())
-            (gen_path_header(path_seperations, &domain.name))
+            (gen_path_header(path_seperations, &domain.name, true))
         };
 
         @if !versions.is_empty() {
@@ -160,27 +176,66 @@ pub async fn render_website(
     None
 }
 
-#[get("/vector_search?<query>")]
-pub async fn vector_search(query: &str) -> Option<StringResponse> {
+pub fn gen_search_element(x: &DocEmbedding) -> PreEscaped<String> {
+    html! {
+        div class="text-xl font-bold mt-4 p-4 flex items-center w-full max-w-4xl max-h-40 mx-auto bg-neutral-800 shadow-md rounded-lg overflow-hidden border border-neutral-900 hover:cursor-pointer"
+        hx-get=(format!("/d/{}/{}", x.domain, x.path))
+        hx-target="#main_content" hx-push-url="true" hx-swap="innerHTML"
+        {
+                img class="p-2" src=(format!("/favicon/{}", &x.domain));
+                a { (x.domain) };
+                (slash_seperator());
+                (gen_path_header(x.path.split('/').collect(), &x.domain, false));
+                p class="font-bold p-2 text-stone-400" { (format!("{:.2} %", x.similarity * 100.0)) };
+        };
+    }
+}
+
+#[get("/vector_search?<query>&<page>")]
+pub async fn vector_search(
+    query: Option<&str>,
+    page: Option<i64>,
+    ctx: RequestContext,
+) -> Option<StringResponse> {
     if std::env::var("OLLAMA_URL").is_err() {
         return None;
     }
 
-    if query.ends_with(".json") {
-        let query = query.trim_end_matches(".json");
-        let results = EmbedStore::search_vector(&pgvector::Vector::from(
-            generate_embedding(query.to_string()).await?,
-        ))
+    let page = page.unwrap_or(1);
+
+    // Search
+    let search =
+        Search::new("/vector_search".to_string()).search_class(SEARCH_BAR_STYLE.to_string());
+
+    if let Some(query) = query {
+        // If we have query
+        let real_query = query.trim_end_matches(".json");
+
+        // Search Results
+        let vector = pgvector::Vector::from(generate_embedding(real_query.to_string()).await?);
+
+        let results = GeneratedPager::new(
+            |input, offset, limit| {
+                Box::pin(async move {
+                    EmbedStore::search_vector(&input, limit as i64, offset as i64).await
+                })
+            },
+            5,
+        )
+        .pager(page as u64, vector)
         .await;
-        return Some(respond_json(&json!(&results)));
+
+        // API Route
+        if query.ends_with(".json") {
+            return Some(respond_json(&json!(&results.page(page as u64))));
+        }
+
+        let content = search.build_response(&ctx, results, page, real_query, gen_search_element);
+
+        return Some(render_page(content, ctx).await);
     }
 
-    let results = EmbedStore::search_vector(&pgvector::Vector::from(
-        generate_embedding(query.to_string()).await?,
-    ))
-    .await;
-
-    // TODO : Implement Search UI with HTMX
-
-    None
+    // Return new search site
+    let content = search.build("", html! {});
+    Some(render_page(content, ctx).await)
 }