qdrant-landing/content/documentation/examples/mighty.md

---
title: "Inference with Mighty"
short_description: "Mighty offers a speedy scalable embedding, a perfect fit for the speedy scalable Qdrant search. Let's combine them!"
description: "We combine Mighty and Qdrant to create a semantic search service in Rust with just a few lines of code."
weight: 17
author: Andre Bogus
author_link: https://llogiq.github.io
date: 2023-06-01T11:24:20+01:00
aliases:
  - /documentation/tutorials/mighty.md/
keywords:
  - vector search
  - embeddings
  - mighty
  - rust
  - semantic search
---

# Semantic Search with Mighty and Qdrant

Much like Qdrant, the [Mighty](https://max.io/) inference server is written in Rust and promises to offer low latency and high scalability. This brief demo combines Mighty and Qdrant into a simple semantic search service that is efficient, affordable and easy to setup. We will use [Rust](https://rust-lang.org) and our [qdrant\_client crate](https://docs.rs/qdrant_client) for this integration.

## Initial setup

For Mighty, 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. Just loading the port in a window shows the following:

```json
{
  "name": "sentence-transformers/all-MiniLM-L6-v2",
  "architectures": [
    "BertModel"
  ],
  "model_type": "bert",
  "max_position_embeddings": 512,
  "labels": null,
  "named_entities": null,
  "image_size": null,
  "source": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"
}
```

Note that this uses the `MiniLM-L6-v2` model from Hugging Face. As per their website, the model "maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search". The distance measure to use is cosine similarity.

Verify that mighty works by calling `curl https://<address>:5050/sentence-transformer?q=hello+mighty`. This will give you a result like (formatted via `jq`):

```json
{
    "outputs": [
        [
            -0.05019686743617058,
            0.051746174693107605,
            0.048117730766534805,
            ... (381 values skipped)
        ]
    ],
    "shape": [
        1,
        384
    ],
    "texts": [
        "Hello mighty"
    ],
    "took": 77
}
```

For Qdrant, follow our [cloud documentation](../../cloud/cloud-quick-start/) to spin up a [free tier](https://cloud.qdrant.io/). Make sure to retrieve an API key.

## Implement model API

For mighty, you will need a way to emit HTTP(S) requests. This version uses the [reqwest](https://docs.rs/reqwest) crate, so add the following to your `Cargo.toml`'s dependencies section:

```toml
[dependencies]
reqwest =  { version = "0.11.18", default-features = false, features = ["json", "rustls-tls"] }
```

Mighty offers a variety of model APIs which will download and cache the model on first use. For semantic search, use the `sentence-transformer` API (as in the above `curl` command). The Rust code to make the call is:

```rust
use anyhow::anyhow;
use reqwest::Client;
use serde::Deserialize;
use serde_json::Value as JsonValue;

#[derive(Deserialize)]
struct EmbeddingsResponse {
    pub outputs: Vec<Vec<f32>>,
}

pub async fn get_mighty_embedding(
    client: &Client,
    url: &str,
    text: &str
) -> anyhow::Result<Vec<f32>> {
    let response = client.get(url).query(&[("text", text)]).send().await?;

    if !response.status().is_success() {
        return Err(anyhow!(
            "Mighty API returned status code {}",
            response.status()
        ));
    }

    let embeddings: EmbeddingsResponse = response.json().await?;
    // ignore multiple embeddings at the moment
    embeddings.get(0).ok_or_else(|| anyhow!("mighty returned empty embedding"))
}
```

Note that mighty can return multiple embeddings (if the input is too long to fit the model, it is automatically split).

## Create embeddings and run a query

Use this code to create embeddings both for insertion and search. On the Qdrant side, take the embedding and run a query:

```rust
use anyhow::anyhow;
use qdrant_client::prelude::*;

pub const SEARCH_LIMIT: u64 = 5;
const COLLECTION_NAME: &str = "mighty";

pub async fn qdrant_search_embeddings(
    qdrant_client: &QdrantClient,
    vector: Vec<f32>,
) -> anyhow::Result<Vec<ScoredPoint>> {
    qdrant_client
        .search_points(&SearchPoints {
            collection_name: COLLECTION_NAME.to_string(),
            vector,
            limit: SEARCH_LIMIT,
            with_payload: Some(true.into()),
            ..Default::default()
        })
        .await
        .map_err(|err| anyhow!("Failed to search Qdrant: {}", err))
}
```

You can convert the [`ScoredPoint`](https://docs.rs/qdrant-client/latest/qdrant_client/qdrant/struct.ScoredPoint.html)s to fit your desired output format.