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mighty article
This commit is contained in:
Andre Bogus
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---
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title: "Semantic Search with Mighty and Qdrant"
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short_description: "Mighty offers a speedy scalable embedding, a perfect fit for the speedy scalable Qdrant search. Let's combine them!"
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description: "We combine Mighty and Qdrant to create a semantic search service in Rust with just a few lines of code."
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social_preview_image: /articles_data/mighty-integration/social_preview.png
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small_preview_image: /articles_data/mighty-integration/preview/preview.jpg
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preview_dir: /articles_data/mighty-integration/preview
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weight: 6
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author: Andre Bogus
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author_link: https://llogiq.github.io
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date: 2023-06-01T11:24:20+01:00
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draft: false
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keywords:
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- vector search
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- embeddings
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- mighty
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- rust
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- semantic search
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---
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We're always on the lookout for interesting services to combine with Qdrant to create something bigger than the sum of its parts. So when we found [mighty](https://max.io/) inference server, much like Qdrant written in Rust and promising to offer low latency and high scalability, we saw an opportunity to combine the two for a small demo. For me, it was also a chance to check out AWS lambda, because our simple demo should run within that service to make it a) very cheap, b) I wanted to test the Rust API for some time and c) see how the combined latency fares.
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## Setting up Mighty & Qdrant
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For mighty, we start up a [docker container](https://hub.docker.com/layers/maxdotio/mighty-sentence-transformers/0.9.9/images/sha256-0d92a89fbdc2c211d927f193c2d0d34470ecd963e8179798d8d391a4053f6caf?context=explore) with an open port 5050. We can check that it works by calling `curl https://<address>:5050/sentence-transformer?q=hello+mighty`. This will give us a result like (formatted via `jq`):
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```json
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{
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"outputs": [
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[
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-0.05019686743617058,
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0.051746174693107605,
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0.048117730766534805,
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...
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]
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],
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"shape": [
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1,
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384
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],
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"texts": [
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"Hello mighty"
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],
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"took": 77
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}
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```
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For qdrant, we simply spin up a [free tier](https://cloud.qdrant.io/) (click on the "start free" button, log in with google or github and follow the instructions). Note the qdrant address and API key.
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## The Pen is Mightier than the Sword
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Mighty offers a variety of model APIs which will download and cache the model on first use. Here we'll use the `sentence-transformer` API. Basically the
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Rust code to make the call is:
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```rust
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use anyhow::anyhow;
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use reqwest::Client;
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use serde::Deserialize;
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#[derive(Deserialize)]
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struct EmbeddingsResponse {
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pub outputs: Vec<Vec<f32>>,
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}
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pub async fn get_mighty_embedding(
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client: &Client,
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url: &str,
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text: &str
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) -> anyhow::Result<Vec<f32>> {
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let response = client.get(url).query(&[("text", text)]).send().await?;
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if !response.status().is_success() {
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return Err(anyhow!(
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"Mighty API returned status code {}",
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response.status()
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));
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}
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let embeddings: Result<EmbeddingsResponse, _> = response.json().await?;
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Ok(embeddings[0])
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}
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```
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We can use this code to create embeddings both for insertion and search. On the Qdrant side, we can take the embedding and run a query:
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```rust
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use anyhow::anyhow;
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use qdrant_client::prelude::*;
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use qdrant_client::qdrant::value::Kind;
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use serde::{Deserialize, Serialize};
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use serde_json::Value;
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pub const SEARCH_LIMIT: usize = 5;
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pub const TEXT_LIMIT: usize = 80;
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const COLLECTION_NAME: &str = "site-mighty";
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#[derive(Deserialize, Serialize, Debug)]
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pub struct SearchResponseHit {
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pub payload: HashMap<String, Value>,
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pub score: Option<f32>,
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pub highlight: String,
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}
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pub async fn qdrant_search_embeddings(
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qdrant_client: &QdrantClient,
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vector: Vec<f32>,
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section: Option<&str>,
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tags: &[&str],
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) -> anyhow::Result<Vec<SearchResponseHit>> {
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let search_request = SearchPoints {
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collection_name: COLLECTION_NAME.to_string(),
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vector,
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filter: Some(get_qdrant_filters(section, tags)),
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limit: SEARCH_LIMIT as u64,
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with_payload: Some(true.into()),
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..Default::default()
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};
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let res = qdrant_client
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.search_points(&search_request)
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.await
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.map_err(|err| anyhow!("Failed to search Qdrant: {}", err))?;
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let hits = res
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.result
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.into_iter()
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.map(|point| {
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let highlight = match point.payload.get("text") {
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None => "".to_string(),
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Some(val) => match &val.kind {
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Some(Kind::StringValue(s)) => s.to_string(),
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_ => "".to_string(),
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},
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};
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SearchResponseHit {
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payload: point
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.payload
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.into_iter()
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.map(|(key, val)| (key, qdrant_value_to_json(val)))
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.collect(),
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score: Some(point.score),
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highlight: highlight_with_bold(
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text,
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&limit_text(highlight, TEXT_LIMIT)
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),
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}
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})
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.collect();
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Ok(hits)
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}
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```
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I have omitted some functions for brevity (e.g. `qdrant_value_to_json`, which I
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am in the process of rolling into the client (via a `From<Value>` implementation
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for `serde_json::Value`)) besides some other simplifications.
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### A Small Diversion
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So instead of that `match`ing on `highlight`, we soon can simply write:
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```rust
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let highlight = point.get("text").as_string().unwrap_or_default();
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```
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and as our `Value` now displays as compact JSON, we can simply avoid `serde_json::Value`
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completely. Should we need a `serde_json::Value` anyway, we can still use `into()`.
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I think we should go further and provide streamlined builders for common
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requests, but that's beside the point of this article. Stay tuned!
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## Putting It All Together
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Anyway, putting those together into a lambda service just requires running both
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functions in sequence:
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```rust
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async fn function_handler(
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event: Request,
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client: &Client,
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qdrant_client: &QdrantClient,
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) -> Result<Response<Body>, Error> {
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let query = match event.query_string_parameters().first("q") {
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None => return Ok(error_response(400, "Missing query string parameter `q`")),
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Some(q) => q.to_string(),
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};
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let params = event.query_string_parameters();
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let section = params.first("section");
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let prefix_search_future = query_qdrant_text(
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qdrant_client,
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&query,
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section,
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&["h1", "h2", "h3", "h4", "h5", "h6"],
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);
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let full_search_future = if query.chars().nth(3).is_some() {
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qdrant_search_embeddings(
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client,
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qdrant_client,
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&query,
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section,
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&[]
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)
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} else {
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query_qdrant_text(
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qdrant_client,
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&query,
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section,
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&[]
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)
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};
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let (prefix_results, full_results) = try_join!(
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prefix_search_future,
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full_search_future
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);
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let search_results: Vec<_> = prefix_results
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.into_iter()
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.chain(full_results)
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.take(SEARCH_LIMIT)
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.collect();
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let response_body = serde_json::to_string(&search_results).unwrap();
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Ok(Response::builder()
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.status(200)
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.header("content-type", "application/json")
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.body(response_body.into())
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.map_err(Box::new)?)
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}
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```
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## Setting It Up
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We can build the whole project with
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[`cargo-lambda`](https://www.cargo-lambda.info/), as in:
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```sh
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cargo install cargo-lambda
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cargo lambda build --release --arm64 --output-format zip
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```
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Now we can either use the web interface to upload our function or call the AWS
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cli to do it:
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```sh
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# Deploy to AWS Lambda
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aws lambda create-function --function-name $LAMBDA_FUNCTION_NAME \
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--handler bootstrap \
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--architectures arm64 \
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--zip-file fileb://./target/lambda/page-search/bootstrap.zip \
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--runtime provided.al2 \
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--region $LAMBDA_REGION \
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--role $LAMBDA_ROLE \
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--environment "Variables={QDRANT_URI=$QDRANT_URI,QDRANT_API_KEY=$QDRANT_API_KEY,MIGHTY_URI=$MIGHTY_URI}" \
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--tracing-config Mode=Active
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# Grant public access to the function
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# https://docs.amazonaws.cn/en_us/lambda/latest/dg/urls-tutorial.html
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aws lambda add-permission \
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--function-name $LAMBDA_FUNCTION_NAME \
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--action lambda:InvokeFunctionUrl \
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--principal "*" \
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--function-url-auth-type "NONE" \
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--region $LAMBDA_REGION \
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--statement-id url
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# Assign URL to the function
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# https://docs.aws.amazon.com/de_de/cli/latest/reference/lambda/create-function-url-config.html
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aws lambda create-function-url-config \
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--function-name $LAMBDA_FUNCTION_NAME \
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--region $LAMBDA_REGION \
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--cors "AllowOrigins=*,AllowMethods=*,AllowHeaders=*" \
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--auth-type NONE
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```
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## Networking Troubles
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Ok, here is the point where I'm going to admit that I messed up the setup. When
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I deployed the mighty docker container, I chose the Europe zone, thinking that
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I'd get the lowest latency, and only later I understood that the demo should be
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otherwise free-tier only, which in Qdrant's case means US-East-1. So to let you
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have a laugh at my expense, here's a small diagram to show the network traffic:
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## Results
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In my tests, the first request always has some 2+ seconds latency due to lambda
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startup. Subsequent requests run in a matter of 200-300ms factoring in two
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roundtrips from Europe to US-East and back. Shaving off another
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transcontinental round trip will probably easily win some 50-100ms.
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So given that I'm on the free tier for both Lambda and Qdrant *and* screwed up
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the config, that isn't half bad. If you find that your traffic is high enough
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to matter, you'll be able to get Qdrant and mighty hosted for a fair price; if
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you're interested, feel free to
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[ask us](https://discord.com/channels/907569970500743200/1047555268499755152)!
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After Width: | Height: | Size: 4.7 KiB |
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|
After Width: | Height: | Size: 33 KiB |
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|
After Width: | Height: | Size: 25 KiB |
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After Width: | Height: | Size: 184 KiB |
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After Width: | Height: | Size: 81 KiB |
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After Width: | Height: | Size: 66 KiB |
Reference in New Issue
Block a user