{% extends "main.html" %}
{% block tabs %}
{{ super() }}

<section class="es-container">
    <div class="md-grid md-typeset">
        <div class="es-product">
            <div class="es-product__image">
                <img src="images/embedding_studio_product.png" draggable="false">
            </div>
            <div class="es-product__content">
                <h1> Embedding Studio </h1>
                <p class="es-logo-slogan">Turning your embedding model into a <b>search engine</b>.</p>
                <a href="{{ page.next_page.url | url }}" title="{{ page.next_page.title | striptags }}"
                   class="md-button md-button--primary">
                    Get started
                </a>
                <a href="{{ config.repo_url }}" title="{{ lang.t('source.link.title') }}" class="md-button">
                    Support us on GitHub
                </a>
            </div>
        </div>
    </div>
</section>

<div class="top-hr">
    <div class="feature-item">
        <h2>
            <img src="images/loop.svg"/>
            Embeddings-in-the-loop
        </h2>
        <p>Embedding Studio adapts search results nearly online and runs automatic fine-tuning procedure.</p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/search.svg"/>
            Smart search query parser
        </h2>
        <p>Combine structured and unstructured search with our <a class="es-color"
                                                                  href="https://huggingface.co/EmbeddingStudio/query-parser-falcon-7b-instruct">LLM
            for natural query search parsing</a>.</p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/click.svg"/>
            Collect user search sessions
        </h2>
        <p>Use collected clicks for resource saving and accuracy effective fine-tuning procedure.</p>
    </div>

</div>

<div class="top-hr">
    <div class="feature-item">
        <h2>
            <img src="images/python_logo.svg"/>
            Python ecosystem
        </h2>
        <p>Use Embedding Studio together with your preferred Python libraries. </p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/plugin.svg"/>
            Plugin-based system
        </h2>
        <p>Connect your own Vector DB, Data Source and Embedding model by implementing a Python plugin.</p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/data_model.svg"/>
            Use your models
        </h2>
        <p>Import embeddings model for your data type.</p>
    </div>
</div>

<div class="top-hr">
    <div class="feature-item">
        <h2>
            <img src="images/github.svg"/>
            Open Source
        </h2>
        <p>All our code is open sourced under the Apache 2.0 license.</p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/docker.svg"/>
            Easy deployment
        </h2>
        <p>Our goal is to make it simple for customers. You can deploy it and calibrate it without much effort.</p>
    </div>
    <div class="feature-item">
        <h2>
            <img src="images/mlflow.png"/>
            MLflow Native
        </h2>
        <p>We use MLflow as a fine-tuning progress tracking system by default.</p>
    </div>
</div>

<div class="top-hr">
    <div class="hr">

    </div>
</div>

<div class="text-center">
    <h2>Tasks solved with Embedding Studio</h2>
    <center>
        <div class="disclaimer">
            <p>
                Embedding Studio is a framework which allows you transform your Vector Database into a
                feature-rich Search Engine.
            </p>
        </div>
    </center>
</div>

<div class="top-hr cards">
    <div class="card">
        <div class="card-image">
            <img src="./images/semantic-search.png" alt="Semantic Search">
        </div>
        <div class="card-content">
            <div class="card-header">
                <h2>
                    <img src="images/logo2.svg"/>
                    Semantic search engine
                </h2>
            </div>
            <div class="card-text">
                <p class="feature-item-text">Your users look into some images inspired by <i>True Detective</i> or <i>Breaking
                    Bad</i> series, or some blogposts related to the <i>vector search topic</i>.</p>
                <p class="feature-item-text">Using multimodal embeddings like <i>CLIP</i> or <i>BLIP</i>, a vector
                    database like <i>QDrant</i> or <i>Milivus</i>, and Embedding Studio you can easily allow your users
                    to do it, without using a hashtags, classifiers and classic text indexes.</p>
            </div>
        </div>
    </div>
</div>

<div class="top-hr cards">
    <div class="card">
        <div class="card-image">
            <img src="./images/similarity-search.png" alt="Similarity Search">
        </div>
        <div class="card-content">
            <div class="card-header">
                <h2>
                    <img src="images/logo4.svg"/>
                    Similarity search engine
                </h2>
            </div>
            <div class="card-text">
                <p>Your IT company is at the stage, when users pushing tons of support tickets, bug reports and customer
                    requests, and your support is looking into tons of logs and messages to find similar cases and a
                    solution.</p>
                <p>Using embedding like <a href="https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2"><i>sentence-transformers/all-MiniLM-L6-v2</i></a>,
                    a vector database and Embedding Studio, you can easily implement internal tickets search and
                    grouping, which will be fine-tuned with your specific domain.</p>
            </div>
        </div>
    </div>
</div>

<div class="top-hr cards">
    <div class="card">
        <div class="card-image">
            <img src="./images/structured-unstructured.png" alt="Structured and Unstructured">
        </div>
        <div class="card-content">
            <div class="card-header">
                <h2>
                    <img src="images/logo7.svg"/>
                    Structured and unstructured search
                </h2>
            </div>
            <div class="card-text">
                <p>It's a highly rare case when a company will use unstructured search as is. And by searching <i>brick
                    red houses san francisco area for april</i> user definitely wants to find some houses in San
                    Francisco for a month-long rent in April, and then maybe brick-red houses. So, companies need to mix
                    structured and unstructured search.</p>
                <p>Embedding Studio team decided to dive into <i>LLM instruct fine-tuning</i> for <i>Zero-Shot query
                    parsing task</i> to close the first gap while a company doesn't have any rules and data being
                    collected, or even eliminate exhausted rules implementation, but in the future.</p>
            </div>
        </div>
    </div>
</div>

<div class="top-hr cards">
    <div class="card">
        <div class="card-image">
            <img src="./images/structured-unstructured-queries.png" alt="Structured search with Unstructured queries">
        </div>
        <div class="card-content">
            <div class="card-header">
                <h2>
                    <img src="images/logo6.svg"/>
                    Structured search / unstructured queries
                </h2>
            </div>
            <div class="card-text">
                <p>You don't need any unstructured search underneath you search system, that's ok, especially if you're
                    a service like taxi or booking, because your customers usually want to arrive exactly to <i>221B
                        Baker Street, London</i>, and nothing <i>very similar</i>. You'll probably try to implement
                    search queries parser.</p>
                <p>So, Embedding Studio can help you too in two ways:</p>
                <p> 1. <a href="https://huggingface.co/EmbeddingStudio/query-parser-falcon-7b-instruct">Zero-Shot
                    queries parser</a>, which doesn't need anything except filters schema.<br/>
                    2. With capability of vector databases and Embedding Studio you can implement query to filters
                    mapper, which will improve session by session.
                </p>
            </div>
        </div>
    </div>
</div>

{% endblock %}
{% block content %}{% endblock %}
{% block footer %}{{ super() }}{% endblock %}
