add vector search
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JMARyA 2024-12-30 21:25:40 +01:00
parent 609d520986
commit 7526280223
Signed by: jmarya
GPG key ID: 901B2ADDF27C2263
7 changed files with 116 additions and 29 deletions

2
Cargo.lock generated
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@ -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",

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@ -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"

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@ -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()

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@ -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,
}
}

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

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@ -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()),

View file

@ -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;
}
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;
// API Route
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?,
))
.await;
return Some(respond_json(&json!(&results)));
return Some(respond_json(&json!(&results.page(page as u64))));
}
let results = EmbedStore::search_vector(&pgvector::Vector::from(
generate_embedding(query.to_string()).await?,
))
.await;
let content = search.build_response(&ctx, results, page, real_query, gen_search_element);
// TODO : Implement Search UI with HTMX
None
return Some(render_page(content, ctx).await);
}
// Return new search site
let content = search.build("", html! {});
Some(render_page(content, ctx).await)
}