From ab800acf2e0551c2c3b44f6bf802d66c982c0a31 Mon Sep 17 00:00:00 2001
From: Florian Maas <fpgmaas@gmail.com>
Date: Sun, 23 Jun 2024 13:27:35 +0200
Subject: [PATCH] Use simple in-memory vector database instead of Pinecone (#4)

* Add simple in-memory vector database
* switch to blue color style
* better UI

---------

Co-authored-by: florian <info@fpgmaas.com>
---
 .DS_Store                                     | Bin 6148 -> 0 bytes
 .env.template                                 |   5 +-
 .github/workflows/main.yml                    |   2 +-
 .github/workflows/publish.yml                 |   1 +
 README.md                                     |  47 +-
 SETUP.md                                      |   4 +-
 frontend/Dockerfile                           |   6 +-
 frontend/app/components/GitHubButton.tsx      |   2 +-
 frontend/app/components/GoogleAnalytics.tsx   |  29 +
 frontend/app/components/Header.tsx            |  37 ++
 frontend/app/components/InfoBox.tsx           |  20 +-
 .../app/components/SearchResultsTable.tsx     |  10 +-
 frontend/app/components/SupportButton.tsx     |   2 +-
 frontend/app/globals.css                      |   4 +-
 frontend/app/layout.tsx                       |   6 +-
 frontend/app/page.tsx                         | 137 ++--
 frontend/tailwind.config.ts                   |  26 +
 poetry.lock                                   | 594 ++++++++----------
 pypi_bigquery.sql                             |   4 +-
 pypi_scout/api/data_loader.py                 |  65 ++
 pypi_scout/api/main.py                        |  22 +-
 pypi_scout/api/utils.py                       |  38 --
 pypi_scout/config.py                          |  33 +-
 pypi_scout/data/description_cleaner.py        |   2 +-
 pypi_scout/data/raw_data_reader.py            |   8 +-
 pypi_scout/embeddings/embeddings_creator.py   |  60 ++
 .../embeddings/simple_vector_database.py      |  55 ++
 .../scripts/create_vector_embeddings.py       |  45 ++
 pypi_scout/scripts/setup.py                   |  22 +-
 pypi_scout/scripts/setup_pinecone.py          |  43 --
 .../scripts/upload_processed_datasets.py      |  56 +-
 pypi_scout/scripts/upsert_data.py             |  43 --
 pypi_scout/utils/blob_io.py                   |  36 +-
 pypi_scout/vector_database/__init__.py        |   5 -
 pypi_scout/vector_database/interface.py       |  80 ---
 pyproject.toml                                |   5 +-
 requirements-cpu.txt                          |   2 +-
 static/architecture.png                       | Bin 58699 -> 0 bytes
 tests/test_vdb.py                             |  64 +-
 39 files changed, 795 insertions(+), 825 deletions(-)
 delete mode 100644 .DS_Store
 create mode 100644 frontend/app/components/GoogleAnalytics.tsx
 create mode 100644 frontend/app/components/Header.tsx
 create mode 100644 pypi_scout/api/data_loader.py
 delete mode 100644 pypi_scout/api/utils.py
 create mode 100644 pypi_scout/embeddings/embeddings_creator.py
 create mode 100644 pypi_scout/embeddings/simple_vector_database.py
 create mode 100644 pypi_scout/scripts/create_vector_embeddings.py
 delete mode 100644 pypi_scout/scripts/setup_pinecone.py
 delete mode 100644 pypi_scout/scripts/upsert_data.py
 delete mode 100644 pypi_scout/vector_database/__init__.py
 delete mode 100644 pypi_scout/vector_database/interface.py
 delete mode 100644 static/architecture.png

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 692e3682b622e099b02943a17317dec49f01965a..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/.env.template b/.env.template
index c7b1fc6..9b41733 100644
--- a/.env.template
+++ b/.env.template
@@ -1 +1,4 @@
-PINECONE_TOKEN=your-api-token
+STORAGE_BACKEND=BLOB
+STORAGE_BACKEND_BLOB_ACCOUNT_NAME=
+STORAGE_BACKEND_BLOB_CONTAINER_NAME=
+STORAGE_BACKEND_BLOB_KEY=
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index fde63c2..1c880c1 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -29,7 +29,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
       fail-fast: false
     steps:
       - name: Check out
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 737c04c..cddcca7 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -55,3 +55,4 @@ jobs:
           tags: pypiscoutacr.azurecr.io/pypi-scout-frontend:latest
           build-args: |
             NEXT_PUBLIC_API_URL=https://pypiscout.com/api
+            NEXT_PUBLIC_GA_TRACKING_ID=${{ secrets.NEXT_PUBLIC_GA_TRACKING_ID }}
diff --git a/README.md b/README.md
index 3f088bc..a09814f 100644
--- a/README.md
+++ b/README.md
@@ -12,48 +12,38 @@ Inspired by [this blog post](https://koaning.io/posts/search-boxes/) about findi
 
 ## How does this work?
 
-The project works by collecting project summaries and descriptions for all packages on PyPI with more than 50 weekly downloads. These are then converted into vector representations using [Sentence Transformers](https://www.sbert.net/). When the user enters a query, it is converted into a vector representation, and the most similar package descriptions are fetched from the vector database. Additional weight is given to the amount of weekly downloads before presenting the results to the user in a dashboard.
+The project works by collecting project summaries and descriptions for all packages on PyPI with more than 100 weekly downloads. These are then converted into vector representations using [Sentence Transformers](https://www.sbert.net/). When the user enters a query, it is converted into a vector representation, and the most similar package descriptions are fetched from the vector database. Additional weight is given to the amount of weekly downloads before presenting the results to the user in a dashboard.
 
-## Architecture
+## Stack
 
 The project uses the following technologies:
 
-1. **[Pinecone](https://www.pinecone.io/)** as vector database
-2. **[FastAPI](https://fastapi.tiangolo.com/)** for the API backend
-3. **[NextJS](https://nextjs.org/) and [TailwindCSS](https://tailwindcss.com/)** for the frontend
-4. **[Sentence Transformers](https://www.sbert.net/)** for vector embeddings
-
-<br/>
-
-![Architecture](./static/architecture.png)
+1. **[FastAPI](https://fastapi.tiangolo.com/)** for the API backend
+2. **[NextJS](https://nextjs.org/) and [TailwindCSS](https://tailwindcss.com/)** for the frontend
+3. **[Sentence Transformers](https://www.sbert.net/)** for vector embeddings
 
 ## Getting Started
 
-### Prerequisites
-
-1. **Set Up Pinecone**
-
-   Since PyPI Scout uses [Pinecone](https://www.pinecone.io/) as the vector database, register for a free account on their website. Obtain your API key using the instructions [here](https://docs.pinecone.io/guides/get-started/quickstart).
-
-2. **Create a `.env` File**
+### Build and Setup
 
-   Copy the `.env.template` to create a new `.env` file:
+#### 1. (Optional) **Create a `.env` file**
 
-   ```sh
-   cp .env.template .env
-   ```
+By default, all data will be stored on your local machine. It is also possible to store the data for the API on Azure Blob storage, and
+have the API read from there. To do so, create a `.env` file:
 
-   Then add your Pinecone API key from step 1 to this file.
+```sh
+cp .env.template .env
+```
 
-### Build and Setup
+and fill in the required fields.
 
-#### 1. **Run the Setup Script**
+#### 2. **Run the Setup Script**
 
 The setup script will:
 
 - Download and process the PyPI dataset and store the results in the `data` directory.
-- Set up your Pinecone index.
-- Create vector embeddings for the PyPI dataset and upsert them to the Pinecone index.
+- Create vector embeddings for the PyPI dataset.
+- If the `STORAGE_BACKEND` environment variable is set to `BLOB`: Upload the datasets to blob storage.
 
 There are three methods to run the setup script, dependent on if you have a NVIDIA GPU and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) installed. Please run the setup script using the method that is applicable for you:
 
@@ -62,9 +52,10 @@ There are three methods to run the setup script, dependent on if you have a NVID
 - [Option 3: Using Docker without NVIDIA GPU and NVIDIA Container Toolkit](SETUP.md#option-3-using-docker-without-nvidia-gpu-and-nvidia-container-toolkit)
 
 > [!NOTE]
-> Although the dataset contains all packages on PyPI with more than 50 weekly downloads, by default only the top 25% of packages with the highest weekly downloads (those with more than approximately 650 downloads per week) are added to the vector database. To include packages with less weekly downloads in the database, you can increase the value of `FRAC_DATA_TO_INCLUDE` in `pypi_scout/config.py`.
+> The dataset contains approximately 100.000 packages on PyPI with more than 100 weekly downloads. To speed up local development,
+> you can lower the amount of packages that is processed locally by lowering the value of `FRAC_DATA_TO_INCLUDE` in `pypi_scout/config.py`.
 
-#### 2. **Run the Application**
+#### 3. **Run the Application**
 
 Start the application using Docker Compose:
 
diff --git a/SETUP.md b/SETUP.md
index 56a1ba6..1a0d3f9 100644
--- a/SETUP.md
+++ b/SETUP.md
@@ -3,8 +3,8 @@
 The setup script will:
 
 - Download and process the PyPI dataset and store the results in the `data` directory.
-- Set up your Pinecone index.
-- Create vector embeddings for the PyPI dataset and upsert them to the Pinecone index.
+- Create vector embeddings for the PyPI dataset.
+- If the `STORAGE_BACKEND` environment variable is set to `BLOB`: Upload the datasets to blob storage.
 
 There are three ways to run the setup script:
 
diff --git a/frontend/Dockerfile b/frontend/Dockerfile
index ac7d507..0f346a5 100644
--- a/frontend/Dockerfile
+++ b/frontend/Dockerfile
@@ -13,11 +13,11 @@ RUN npm install
 # Copy the rest of the application code to the container
 COPY . .
 
-# Build argument to accept the API URL during build time
+# Add build arguments to environment
 ARG NEXT_PUBLIC_API_URL
-
-# Set environment variable within the container
+ARG NEXT_PUBLIC_GA_TRACKING_ID
 ENV NEXT_PUBLIC_API_URL=${NEXT_PUBLIC_API_URL}
+ENV NEXT_PUBLIC_GA_TRACKING_ID=${NEXT_PUBLIC_GA_TRACKING_ID}
 
 # Build the Next.js application
 RUN npm run build
diff --git a/frontend/app/components/GitHubButton.tsx b/frontend/app/components/GitHubButton.tsx
index 8104cc5..1047f4b 100644
--- a/frontend/app/components/GitHubButton.tsx
+++ b/frontend/app/components/GitHubButton.tsx
@@ -6,7 +6,7 @@ const GitHubButton: React.FC = () => {
       href="https://github.com/fpgmaas/pypi-scout"
       target="_blank"
       rel="noopener noreferrer"
-      className="flex items-center p-2 border border-gray-700 rounded bg-gray-900 text-white hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-gray-700"
+      className="flex items-center p-2 border border-sky-700 rounded bg-sky-900 text-white hover:bg-sky-700 focus:outline-none focus:ring-2 focus:ring-sky-700"
     >
       <svg
         height="24"
diff --git a/frontend/app/components/GoogleAnalytics.tsx b/frontend/app/components/GoogleAnalytics.tsx
new file mode 100644
index 0000000..617fe3c
--- /dev/null
+++ b/frontend/app/components/GoogleAnalytics.tsx
@@ -0,0 +1,29 @@
+// app/components/GoogleAnalytics.tsx
+"use client";
+
+import { useEffect } from "react";
+
+const GoogleAnalytics = () => {
+  useEffect(() => {
+    const trackingId = process.env.NEXT_PUBLIC_GA_TRACKING_ID;
+    if (trackingId) {
+      const script1 = document.createElement("script");
+      script1.async = true;
+      script1.src = `https://www.googletagmanager.com/gtag/js?id=${trackingId}`;
+      document.head.appendChild(script1);
+
+      const script2 = document.createElement("script");
+      script2.innerHTML = `
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){dataLayer.push(arguments);}
+        gtag('js', new Date());
+        gtag('config', '${trackingId}');
+      `;
+      document.head.appendChild(script2);
+    }
+  }, []);
+
+  return null;
+};
+
+export default GoogleAnalytics;
diff --git a/frontend/app/components/Header.tsx b/frontend/app/components/Header.tsx
new file mode 100644
index 0000000..99f29da
--- /dev/null
+++ b/frontend/app/components/Header.tsx
@@ -0,0 +1,37 @@
+import { useState } from "react";
+import GitHubButton from "./GitHubButton";
+import SupportButton from "./SupportButton";
+import { FaBars, FaTimes } from "react-icons/fa";
+
+const Header: React.FC = () => {
+  const [isMenuOpen, setIsMenuOpen] = useState(false);
+
+  const toggleMenu = () => {
+    setIsMenuOpen(!isMenuOpen);
+  };
+
+  return (
+    <header className="w-full flex justify-end items-center p-4 bg-sky-950">
+      <div className="hidden md:flex space-x-4 ">
+        <GitHubButton />
+        <SupportButton />
+      </div>
+      <div className="md:hidden flex-grow flex justify-end">
+        <button
+          onClick={toggleMenu}
+          className="text-white focus:outline-none focus:ring-2 focus:ring-sky-700"
+        >
+          {isMenuOpen ? <FaTimes size={24} /> : <FaBars size={24} />}
+        </button>
+      </div>
+      {isMenuOpen && (
+        <div className="absolute top-16 right-4 bg-sky-900 p-4 rounded shadow-lg flex flex-col space-y-4 md:hidden">
+          <GitHubButton />
+          <SupportButton />
+        </div>
+      )}
+    </header>
+  );
+};
+
+export default Header;
diff --git a/frontend/app/components/InfoBox.tsx b/frontend/app/components/InfoBox.tsx
index c13857a..88c5cd4 100644
--- a/frontend/app/components/InfoBox.tsx
+++ b/frontend/app/components/InfoBox.tsx
@@ -8,21 +8,23 @@ const InfoBox: React.FC<InfoBoxProps> = ({ infoBoxVisible }) => {
   if (!infoBoxVisible) return null;
 
   return (
-    <div className="w-3/5 bg-gray-800 p-6 rounded-lg shadow-lg mt-4 text-white">
-      <h2 className="text-2xl font-bold mb-2">How does this work?</h2>
-      <p className="text-gray-300">
+    <div className="w-3/5 bg-sky-900 p-6 rounded-lg shadow-lg mt-4 text-white">
+      <h2 className="text-2xl text-bold mb-2 text-gray-100">
+        How does this work?
+      </h2>
+      <p className="text-gray-100">
         This application allows you to search for Python packages on PyPI using
         natural language queries. For example, a query could be &quot;a package
         that creates plots and beautiful visualizations&quot;.
       </p>
       <br />
-      <p className="text-gray-300">
+      <p className="text-gray-100">
         Once you click search, your query will be matched against the summary
-        and the first part of the description of the ~30.000 most popular
-        packages on PyPI, which are all packages with at least ~600 downloads
-        per week. The results are then scored based on their similarity to the
-        query and their number of weekly downloads, and the best results are
-        displayed in the table below.
+        and the first part of the description of the ~100.000 most popular
+        packages on PyPI, which includes all packages with at least ~100
+        downloads per week. The results are then scored based on their
+        similarity to the query and their number of weekly downloads, and the
+        best results are displayed in the table below.
       </p>
     </div>
   );
diff --git a/frontend/app/components/SearchResultsTable.tsx b/frontend/app/components/SearchResultsTable.tsx
index befefa5..4f64d90 100644
--- a/frontend/app/components/SearchResultsTable.tsx
+++ b/frontend/app/components/SearchResultsTable.tsx
@@ -33,8 +33,8 @@ const SearchResultsTable: React.FC<SearchResultsTableProps> = ({
 
   return (
     <div className="overflow-x-auto w-full">
-      <table className="min-w-full divide-y divide-gray-700">
-        <thead className="bg-gray-800">
+      <table className="min-w-full divide-y divide-sky-800">
+        <thead className="bg-sky-950">
           <tr>
             <th
               className="px-4 py-2 text-left text-xs font-medium text-gray-200 uppercase tracking-wider cursor-pointer whitespace-nowrap"
@@ -72,9 +72,9 @@ const SearchResultsTable: React.FC<SearchResultsTableProps> = ({
             </th>
           </tr>
         </thead>
-        <tbody className="bg-gray-800 divide-y divide-gray-700">
+        <tbody className="bg-sky-900 divide-y divide-sky-800">
           {results.map((result, index) => (
-            <tr key={index} className="hover:bg-gray-700">
+            <tr key={index} className="hover:bg-sky-800">
               <td className="px-4 py-2 whitespace-nowrap text-gray-200">
                 {truncateText(result.name, 20)}
               </td>
@@ -92,7 +92,7 @@ const SearchResultsTable: React.FC<SearchResultsTableProps> = ({
                   href={`https://pypi.org/project/${result.name}/`}
                   target="_blank"
                   rel="noopener noreferrer"
-                  className="text-blue-400 hover:underline flex items-center"
+                  className="text-sky-500 hover:underline flex items-center hover:text-orange-800"
                 >
                   <FaExternalLinkAlt className="mr-1" />
                   PyPI
diff --git a/frontend/app/components/SupportButton.tsx b/frontend/app/components/SupportButton.tsx
index a55a2fb..242ab1b 100644
--- a/frontend/app/components/SupportButton.tsx
+++ b/frontend/app/components/SupportButton.tsx
@@ -6,7 +6,7 @@ const SupportButton: React.FC = () => {
       href="https://ko-fi.com/fpgmaas"
       target="_blank"
       rel="noopener noreferrer"
-      className="flex items-center p-2 border border-gray-700 rounded bg-gray-900 text-white hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-gray-700"
+      className="flex items-center p-2 border border-sky-700 rounded bg-sky-900 text-white hover:bg-sky-700 focus:outline-none focus:ring-2 focus:ring-sky-700"
     >
       <img
         src="kofi.png"
diff --git a/frontend/app/globals.css b/frontend/app/globals.css
index 7c5c7b4..3bdb6ba 100644
--- a/frontend/app/globals.css
+++ b/frontend/app/globals.css
@@ -6,8 +6,8 @@
   --foreground-rgb: 0, 0, 0;
   --background-start-rgb: 214, 219, 220;
   --background-end-rgb: 255, 255, 255;
-  --dark-bg-start-rgb: 17, 24, 39; /* Dark gray (bg-gray-900) */
-  --dark-bg-end-rgb: 17, 24, 39; /* Dark gray (bg-gray-900) */
+  --dark-bg-start-rgb: 8, 47, 73; /* Dark sky (bg-sky-950) */
+  --dark-bg-end-rgb: 8, 47, 73; /* Dark sky (bg-sky-950) */
   --dark-foreground-rgb: 255, 255, 255;
 }
 
diff --git a/frontend/app/layout.tsx b/frontend/app/layout.tsx
index cb39f81..6a9c932 100644
--- a/frontend/app/layout.tsx
+++ b/frontend/app/layout.tsx
@@ -1,6 +1,7 @@
 import type { Metadata } from "next";
 import { Inter } from "next/font/google";
 import "./globals.css";
+import GoogleAnalytics from "./components/GoogleAnalytics";
 
 const inter = Inter({ subsets: ["latin"] });
 
@@ -16,7 +17,10 @@ export default function RootLayout({
 }>) {
   return (
     <html lang="en">
-      <body className={inter.className}>{children}</body>
+      <body className={inter.className}>
+        <GoogleAnalytics />
+        {children}
+      </body>
     </html>
   );
 }
diff --git a/frontend/app/page.tsx b/frontend/app/page.tsx
index b539365..6a26d08 100644
--- a/frontend/app/page.tsx
+++ b/frontend/app/page.tsx
@@ -5,8 +5,7 @@ import { handleSearch, sortResults } from "./utils/search";
 import SearchResultsTable from "./components/SearchResultsTable";
 import InfoBox from "./components/InfoBox";
 import { ClipLoader } from "react-spinners";
-import GitHubButton from "./components/GitHubButton";
-import SupportButton from "./components/SupportButton";
+import Header from "./components/Header";
 
 interface Match {
   name: string;
@@ -33,80 +32,76 @@ export default function Home() {
   };
 
   return (
-    <main className="flex flex-col items-center justify-start min-h-screen p-4 space-y-4 max-w-[2200px] mx-auto relative">
-      <header className="w-full flex justify-end p-4">
-        <div className="flex space-x-4">
-          <GitHubButton />
-          <SupportButton />
+    <div className="min-h-screen w-full bg-sky-950 flex flex-col items-center">
+      <Header />
+      <main className="w-full max-w-[2200px] flex flex-col items-center p-4 space-y-4">
+        <div className="flex flex-col items-center text-center mb-4">
+          <picture>
+            <img
+              alt="pypi-scout logo"
+              width="420"
+              height="220"
+              src="./pypi.svg"
+              className="mx-auto"
+            ></img>
+          </picture>
+          <p className="text-gray-100 text-xl ">
+            Find packages on PyPI with natural language queries
+          </p>
         </div>
-      </header>
 
-      <div className="flex flex-col items-center text-center mb-4">
-        <picture>
-          <img
-            alt="pypi-scout logo"
-            width="420"
-            height="220"
-            src="./pypi.svg"
-            className="mx-auto"
-          ></img>
-        </picture>
-        <p className="text-lg text-gray-300 mt-2">
-          Enter your query to search for Python packages
-        </p>
-      </div>
-
-      <div className="flex flex-col items-center space-y-4 w-3/5 bg-gray-800 p-6 rounded-lg shadow-lg">
-        <textarea
-          className="w-full h-24 p-2 border border-gray-700 rounded resize-none overflow-auto focus:outline-none focus:ring-2 focus:ring-blue-700 bg-gray-700 text-white"
-          value={text}
-          onChange={(e) => setText(e.target.value)}
-          placeholder="Enter your query here..."
-        ></textarea>
-        <button
-          className="w-full max-w-[250px] p-2 border border-gray-700 rounded bg-blue-600 text-white hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-700"
-          onClick={() =>
-            handleSearch(
-              text,
-              sortField,
-              sortDirection,
-              setResults,
-              setLoading,
-              setError,
-            )
-          }
-        >
-          Search
-        </button>
-        {loading && (
-          <ClipLoader color={"#123abc"} loading={loading} size={50} />
-        )}
-        {error && <p className="text-red-500">{error}</p>}
-      </div>
+        <div className="flex flex-col items-center space-y-4 w-full max-w-5xl bg-sky-900 p-6 rounded-lg shadow-lg">
+          <textarea
+            className="placeholder-gray-400 w-full h-24 p-2 border border-sky-900 rounded resize-none overflow-auto focus:outline-none focus:ring-2 focus:ring-sky-800 bg-white text-sky-950"
+            value={text}
+            onChange={(e) => setText(e.target.value)}
+            placeholder="Describe what you are looking for... "
+          ></textarea>
+          <button
+            className="w-full max-w-[250px] p-2 border border-sky-900 rounded bg-sky-950 text-white hover:bg-sky-700 hover:outline-none hover:ring-1 hover:ring-sky-700"
+            onClick={() =>
+              handleSearch(
+                text,
+                sortField,
+                sortDirection,
+                setResults,
+                setLoading,
+                setError,
+              )
+            }
+          >
+            Search
+          </button>
+          {loading && (
+            <ClipLoader color={"#ffffff"} loading={loading} size={70} />
+          )}
+          {error && <p className="text-red-500">{error}</p>}
+        </div>
 
-      <div className="w-full flex justify-center mt-6">
-        <button
-          className="w-full max-w-[250px] p-2 border border-gray-700 rounded bg-gray-600 text-white hover:bg-gray-700 focus:outline-none focus:ring-2 focus:ring-gray-700"
-          onClick={() => setInfoBoxVisible(!infoBoxVisible)}
-        >
-          {infoBoxVisible ? "Hide Info" : "How does this work?"}
-        </button>
-      </div>
+        <div className="w-full flex justify-center mt-6">
+          <button
+            className="w-full max-w-[250px] p-2 border border-sky-700 rounded bg-sky-800 text-white hover:bg-sky-700 focus:outline-none focus:ring-2 focus:ring-sky-700"
+            onClick={() => setInfoBoxVisible(!infoBoxVisible)}
+          >
+            {infoBoxVisible ? "Hide Info" : "How does this work?"}
+          </button>
+        </div>
 
-      <InfoBox infoBoxVisible={infoBoxVisible} />
+        <InfoBox infoBoxVisible={infoBoxVisible} />
 
-      {results.length > 0 && (
-        <div className="w-full flex justify-center mt-6">
-          <div className="w-11/12 bg-gray-800 p-6 rounded-lg shadow-lg flex flex-col items-center">
-            <SearchResultsTable
-              results={results}
-              sortField={sortField}
-              sortDirection={sortDirection}
-              onSort={handleSort}
-            />
+        {results.length > 0 && (
+          <div className="w-full flex justify-center mt-6">
+            <div className="w-full max-w-[1800px] bg-sky-900 p-6 rounded-lg shadow-lg flex flex-col items-center">
+              <SearchResultsTable
+                results={results}
+                sortField={sortField}
+                sortDirection={sortDirection}
+                onSort={handleSort}
+              />
+            </div>
           </div>
-        </div>
-      )}
-    </main>
+        )}
+      </main>
+    </div>
   );
 }
diff --git a/frontend/tailwind.config.ts b/frontend/tailwind.config.ts
index 7e4bd91..8576951 100644
--- a/frontend/tailwind.config.ts
+++ b/frontend/tailwind.config.ts
@@ -8,6 +8,31 @@ const config: Config = {
   ],
   theme: {
     extend: {
+      colors: {
+        sky: {
+          50: "#d9f0ff", // Darkened from #f0f9ff
+          100: "#c3e4fe", // Darkened from #e0f2fe
+          200: "#a3d4fd", // Darkened from #bae6fd
+          300: "#5cbdfc", // Darkened from #7dd3fc
+          400: "#2aa3f8", // Darkened from #38bdf8
+          500: "#0b8edc", // Darkened from #0ea5e9
+          600: "#026baa", // Darkened from #0284c7
+          700: "#015a89", // Darkened from #0369a1
+          800: "#054b6e", // Darkened from #075985
+          900: "#083857", // Darkened from #0c4a6e
+          950: "#062338", // Darkened from #082f49
+        },
+        orange: {
+          100: "#f8d5c7",
+          200: "#f1ac9a",
+          300: "#ea836d",
+          400: "#e35a40",
+          500: "#d77a61",
+          600: "#c45b3f",
+          700: "#b23a1b",
+          800: "#D18829", // Orange from logo
+        },
+      },
       backgroundImage: {
         "gradient-radial": "radial-gradient(var(--tw-gradient-stops))",
         "gradient-conic":
@@ -17,4 +42,5 @@ const config: Config = {
   },
   plugins: [],
 };
+
 export default config;
diff --git a/poetry.lock b/poetry.lock
index b00709e..4fc1922 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -11,9 +11,6 @@ files = [
     {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
-
 [[package]]
 name = "anyio"
 version = "4.4.0"
@@ -141,21 +138,6 @@ six = ">=1.12.0"
 astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
 test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
-[[package]]
-name = "astunparse"
-version = "1.6.3"
-description = "An AST unparser for Python"
-optional = false
-python-versions = "*"
-files = [
-    {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
-    {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
-]
-
-[package.dependencies]
-six = ">=1.6.1,<2.0"
-wheel = ">=0.23.0,<1.0"
-
 [[package]]
 name = "async-lru"
 version = "2.0.4"
@@ -239,23 +221,9 @@ files = [
     {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"},
 ]
 
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
 [package.extras]
 dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-optional = false
-python-versions = "*"
-files = [
-    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
-    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
-]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -546,63 +514,63 @@ test = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.5.3"
+version = "7.5.4"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "coverage-7.5.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:aea7da970f1feccf48be7335f8b2ca64baf9b589d79e05b9397a06696ce1a1ec"},
-    {file = "coverage-7.5.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:923b7b1c717bd0f0f92d862d1ff51d9b2b55dbbd133e05680204465f454bb286"},
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-    {file = "coverage-7.5.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8b7339180d00de83e930358223c617cc343dd08e1aa5ec7b06c3a121aec4e1d"},
-    {file = "coverage-7.5.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:25a5caf742c6195e08002d3b6c2dd6947e50efc5fc2c2205f61ecb47592d2d83"},
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 ]
 
 [package.dependencies]
@@ -786,13 +754,13 @@ wmi = ["wmi (>=1.5.1)"]
 
 [[package]]
 name = "email-validator"
-version = "2.1.2"
+version = "2.2.0"
 description = "A robust email address syntax and deliverability validation library."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "email_validator-2.1.2-py3-none-any.whl", hash = "sha256:d89f6324e13b1e39889eab7f9ca2f91dc9aebb6fa50a6d8bd4329ab50f251115"},
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 ]
 
 [package.dependencies]
@@ -887,18 +855,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.15.1"
+version = "3.15.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.15.1-py3-none-any.whl", hash = "sha256:71b3102950e91dfc1bb4209b64be4dc8854f40e5f534428d8684f953ac847fac"},
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+    {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"},
 ]
 
 [package.extras]
 docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
@@ -990,17 +958,16 @@ dev = ["flake8", "markdown", "twine", "wheel"]
 
 [[package]]
 name = "griffe"
-version = "0.46.0"
+version = "0.47.0"
 description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "griffe-0.46.0-py3-none-any.whl", hash = "sha256:7804a22aa981e442bcb3ff1365268cee590e40c6b68388075d3e1287b7d817ca"},
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+    {file = "griffe-0.47.0-py3-none-any.whl", hash = "sha256:07a2fd6a8c3d21d0bbb0decf701d62042ccc8a576645c7f8799fe1f10de2b2de"},
+    {file = "griffe-0.47.0.tar.gz", hash = "sha256:95119a440a3c932b13293538bdbc405bee4c36428547553dc6b327e7e7d35e5a"},
 ]
 
 [package.dependencies]
-astunparse = {version = ">=1.6", markers = "python_version < \"3.9\""}
 colorama = ">=0.4"
 
 [[package]]
@@ -1168,22 +1135,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "7.2.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
-    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
+    {file = "importlib_metadata-7.2.0-py3-none-any.whl", hash = "sha256:04e4aad329b8b948a5711d394fa8759cb80f009225441b4f2a02bd4d8e5f426c"},
+    {file = "importlib_metadata-7.2.0.tar.gz", hash = "sha256:3ff4519071ed42740522d494d04819b666541b9752c43012f85afb2cc220fcc6"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+test = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
 
 [[package]]
 name = "importlib-resources"
@@ -1263,42 +1230,40 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
 
 [[package]]
 name = "ipython"
-version = "8.12.3"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipython-8.12.3-py3-none-any.whl", hash = "sha256:b0340d46a933d27c657b211a329d0be23793c36595acf9e6ef4164bc01a1804c"},
-    {file = "ipython-8.12.3.tar.gz", hash = "sha256:3910c4b54543c2ad73d06579aa771041b7d5707b033bd488669b4cf544e3b363"},
+    {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"},
+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
 typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["black", "curio", "docrepr", "exceptiongroup", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "matplotlib", "pickleshare", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio (<0.22)", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"]
 
 [[package]]
 name = "isodate"
@@ -1412,11 +1377,9 @@ files = [
 attrs = ">=22.2.0"
 fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
 jsonschema-specifications = ">=2023.03.6"
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
@@ -1440,7 +1403,6 @@ files = [
 ]
 
 [package.dependencies]
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
 referencing = ">=0.31.0"
 
 [[package]]
@@ -1596,7 +1558,6 @@ files = [
 async-lru = ">=1.0.0"
 httpx = ">=0.25.0"
 importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
-importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
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 [[package]]
 name = "limits"
-version = "3.12.0"
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 python-versions = ">=3.8"
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@@ -2274,21 +2235,21 @@ files = [
 
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 description = "Python package for creating and manipulating graphs and networks"
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 [[package]]
 name = "nodeenv"
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 [[package]]
@@ -2635,17 +2613,6 @@ files = [
 [package.dependencies]
 ptyprocess = ">=0.5"
 
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 [[package]]
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@@ -2732,40 +2699,6 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
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+version = "3.7.1"
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 [package.extras]
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@@ -3229,17 +3163,6 @@ files = [
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 [package.extras]
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@@ -3853,88 +3775,90 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
 
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@@ -3979,18 +3903,18 @@ train = ["accelerate (>=0.20.3)", "datasets"]
 
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 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "shellingham"
@@ -4106,15 +4030,15 @@ mpmath = ">=1.1.0,<1.4.0"
 
 [[package]]
 name = "tbb"
-version = "2021.12.0"
+version = "2021.13.0"
 description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
 optional = false
 python-versions = "*"
 files = [
-    {file = "tbb-2021.12.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:f2cc9a7f8ababaa506cbff796ce97c3bf91062ba521e15054394f773375d81d8"},
-    {file = "tbb-2021.12.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:a925e9a7c77d3a46ae31c34b0bb7f801c4118e857d137b68f68a8e458fcf2bd7"},
-    {file = "tbb-2021.12.0-py3-none-win32.whl", hash = "sha256:b1725b30c174048edc8be70bd43bb95473f396ce895d91151a474d0fa9f450a8"},
-    {file = "tbb-2021.12.0-py3-none-win_amd64.whl", hash = "sha256:fc2772d850229f2f3df85f1109c4844c495a2db7433d38200959ee9265b34789"},
+    {file = "tbb-2021.13.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:a2567725329639519d46d92a2634cf61e76601dac2f777a05686fea546c4fe4f"},
+    {file = "tbb-2021.13.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:aaf667e92849adb012b8874d6393282afc318aca4407fc62f912ee30a22da46a"},
+    {file = "tbb-2021.13.0-py3-none-win32.whl", hash = "sha256:6669d26703e9943f6164c6407bd4a237a45007e79b8d3832fe6999576eaaa9ef"},
+    {file = "tbb-2021.13.0-py3-none-win_amd64.whl", hash = "sha256:3528a53e4bbe64b07a6112b4c5a00ff3c61924ee46c9c68e004a1ac7ad1f09c3"},
 ]
 
 [[package]]
@@ -4662,13 +4586,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.2.1"
+version = "2.2.2"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
-    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+    {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
+    {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
 ]
 
 [package.extras]
@@ -4749,13 +4673,13 @@ test = ["Cython (>=0.29.36,<0.30.0)", "aiohttp (==3.9.0b0)", "aiohttp (>=3.8.1)"
 
 [[package]]
 name = "virtualenv"
-version = "20.26.2"
+version = "20.26.3"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"},
-    {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"},
+    {file = "virtualenv-20.26.3-py3-none-any.whl", hash = "sha256:8cc4a31139e796e9a7de2cd5cf2489de1217193116a8fd42328f1bd65f434589"},
+    {file = "virtualenv-20.26.3.tar.gz", hash = "sha256:4c43a2a236279d9ea36a0d76f98d84bd6ca94ac4e0f4a3b9d46d05e10fea542a"},
 ]
 
 [package.dependencies]
@@ -5032,20 +4956,6 @@ files = [
     {file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"},
 ]
 
-[[package]]
-name = "wheel"
-version = "0.43.0"
-description = "A built-package format for Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"},
-    {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"},
-]
-
-[package.extras]
-test = ["pytest (>=6.0.0)", "setuptools (>=65)"]
-
 [[package]]
 name = "wrapt"
 version = "1.16.0"
@@ -5142,5 +5052,5 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 
 [metadata]
 lock-version = "2.0"
-python-versions = ">=3.8,<4.0"
-content-hash = "37d93dd135cc2322805eca294629bfc4414851c85c813caf127736c17be30067"
+python-versions = ">=3.9,<4.0"
+content-hash = "de83ca89b79716cfc21e06d916d81963401155a517238f9ff066b31ef349a160"
diff --git a/pypi_bigquery.sql b/pypi_bigquery.sql
index 0dec4b7..a72b31f 100644
--- a/pypi_bigquery.sql
+++ b/pypi_bigquery.sql
@@ -5,11 +5,11 @@ WITH recent_downloads AS (
   FROM
     `bigquery-public-data.pypi.file_downloads`
   WHERE
-    DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 28 DAY) AND CURRENT_DATE()
+    DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY) AND CURRENT_DATE()
   GROUP BY
     project
   HAVING
-    download_count >= 250
+    download_count >= 100
 )
 SELECT
   rd.project AS name,
diff --git a/pypi_scout/api/data_loader.py b/pypi_scout/api/data_loader.py
new file mode 100644
index 0000000..c4ea031
--- /dev/null
+++ b/pypi_scout/api/data_loader.py
@@ -0,0 +1,65 @@
+import logging
+from typing import Tuple
+
+import polars as pl
+
+from pypi_scout.config import Config, StorageBackend
+from pypi_scout.utils.blob_io import BlobIO
+
+
+class ApiDataLoader:
+    def __init__(self, config: Config):
+        self.config = config
+
+    def load_dataset(self) -> Tuple[pl.DataFrame, pl.DataFrame]:
+        if self.config.STORAGE_BACKEND == StorageBackend.LOCAL:
+            df_packages, df_embeddings = self._load_local_dataset()
+        elif self.config.STORAGE_BACKEND == StorageBackend.BLOB:
+            df_packages, df_embeddings = self._load_blob_dataset()
+        else:
+            raise ValueError(f"Unexpected value found for STORAGE_BACKEND: {self.config.STORAGE_BACKEND}")  # noqa: TRY003
+
+        return df_packages, df_embeddings
+
+    def _load_local_dataset(self) -> Tuple[pl.DataFrame, pl.DataFrame]:
+        packages_dataset_path = self.config.DATA_DIR / self.config.DATASET_FOR_API_CSV_NAME
+        embeddings_dataset_path = self.config.DATA_DIR / self.config.EMBEDDINGS_PARQUET_NAME
+
+        logging.info(f"Reading packages dataset from `{packages_dataset_path}`...")
+        df_packages = pl.read_csv(packages_dataset_path)
+        self._log_packages_dataset_info(df_packages)
+
+        logging.info(f"Reading embeddings from `{embeddings_dataset_path}`...")
+        df_embeddings = pl.read_parquet(embeddings_dataset_path)
+        self._log_embeddings_dataset_info(df_embeddings)
+
+        return df_packages, df_embeddings
+
+    def _load_blob_dataset(self) -> Tuple[pl.DataFrame, pl.DataFrame]:
+        blob_io = BlobIO(
+            self.config.STORAGE_BACKEND_BLOB_ACCOUNT_NAME,
+            self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME,
+            self.config.STORAGE_BACKEND_BLOB_KEY,
+        )
+
+        logging.info(
+            f"Downloading `{self.config.DATASET_FOR_API_CSV_NAME}` from container `{self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}`..."
+        )
+        df_packages = blob_io.download_csv_to_df(self.config.DATASET_FOR_API_CSV_NAME)
+        self._log_packages_dataset_info(df_packages)
+
+        logging.info(
+            f"Downloading `{self.config.EMBEDDINGS_PARQUET_NAME}` from container `{self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}`..."
+        )
+        df_embeddings = blob_io.download_parquet_to_df(self.config.EMBEDDINGS_PARQUET_NAME)
+        self._log_embeddings_dataset_info(df_embeddings)
+
+        return df_packages, df_embeddings
+
+    def _log_packages_dataset_info(self, df_packages: pl.DataFrame) -> None:
+        logging.info(f"Finished loading the `packages` dataset. Number of rows in dataset: {len(df_packages):,}")
+        logging.info(df_packages.describe())
+
+    def _log_embeddings_dataset_info(self, df_embeddings: pl.DataFrame) -> None:
+        logging.info(f"Finished loading the `embeddings` dataset. Number of rows in dataset: {len(df_embeddings):,}")
+        logging.info(df_embeddings.describe())
diff --git a/pypi_scout/api/main.py b/pypi_scout/api/main.py
index 5e2ad64..5e70afb 100644
--- a/pypi_scout/api/main.py
+++ b/pypi_scout/api/main.py
@@ -11,11 +11,11 @@
 from slowapi.util import get_remote_address
 from starlette.requests import Request
 
-from pypi_scout.api.utils import load_dataset
+from pypi_scout.api.data_loader import ApiDataLoader
 from pypi_scout.config import Config
+from pypi_scout.embeddings.simple_vector_database import SimpleVectorDatabase
 from pypi_scout.utils.logging import setup_logging
 from pypi_scout.utils.score_calculator import calculate_score
-from pypi_scout.vector_database import VectorDatabaseInterface
 
 # Setup logging
 setup_logging()
@@ -40,16 +40,12 @@
     allow_headers=["*"],
 )
 
-# Load dataset and initialize model and vector database interface
-df = load_dataset(config)
+data_loader = ApiDataLoader(config)
+df_packages, df_embeddings = data_loader.load_dataset()
+
 model = SentenceTransformer(config.EMBEDDINGS_MODEL_NAME)
 
-vector_database_interface = VectorDatabaseInterface(
-    pinecone_token=config.PINECONE_TOKEN,
-    pinecone_index_name=config.PINECONE_INDEX_NAME,
-    embeddings_model=model,
-    pinecone_namespace=config.PINECONE_NAMESPACE,
-)
+vector_database = SimpleVectorDatabase(embeddings_model=model, df_embeddings=df_embeddings)
 
 
 class QueryModel(BaseModel):
@@ -83,8 +79,8 @@ async def search(query: QueryModel, request: Request):
         raise HTTPException(status_code=400, detail="top_k cannot be larger than 100.")
 
     logging.info(f"Searching for similar projects. Query: '{query.query}'")
-    df_matches = vector_database_interface.find_similar(query.query, top_k=query.top_k * 2)
-    df_matches = df_matches.join(df, how="left", on="name")
+    df_matches = vector_database.find_similar(query.query, top_k=query.top_k * 2)
+    df_matches = df_matches.join(df_packages, how="left", on="name")
     logging.info(
         f"Fetched the {len(df_matches)} most similar projects. Calculating the weighted scores and filtering..."
     )
@@ -110,5 +106,5 @@ async def search(query: QueryModel, request: Request):
         df_matches = df_matches.head(query.top_k)
 
     logging.info(f"Returning the {len(df_matches)} best matches.")
-
+    df_matches = df_matches.select(["name", "similarity", "summary", "weekly_downloads"])
     return SearchResponse(matches=df_matches.to_dicts(), warning=warning, warning_message=warning_message)
diff --git a/pypi_scout/api/utils.py b/pypi_scout/api/utils.py
deleted file mode 100644
index efa70a4..0000000
--- a/pypi_scout/api/utils.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import logging
-import sys
-
-import polars as pl
-
-from pypi_scout.config import Config, StorageBackend
-from pypi_scout.utils.blob_io import BlobIO
-
-
-def load_dataset(config: Config) -> pl.DataFrame:
-    dataset_path = config.DATA_DIR / config.DATASET_FOR_API_CSV_NAME
-
-    if dataset_path.exists():
-        logging.info(f"Found local dataset. Reading dataset from `{dataset_path}`...")
-        df = pl.read_csv(dataset_path)
-
-    elif config.STORAGE_BACKEND == StorageBackend.BLOB:
-        logging.info(
-            f"Downloading `{config.DATASET_FOR_API_CSV_NAME}` from container `{config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}`..."
-        )
-        blob_io = BlobIO(
-            config.STORAGE_BACKEND_BLOB_ACCOUNT_NAME,
-            config.STORAGE_BACKEND_BLOB_CONTAINER_NAME,
-            config.STORAGE_BACKEND_BLOB_KEY,
-        )
-        df = blob_io.download_csv(config.DATASET_FOR_API_CSV_NAME)
-        logging.info("Finished downloading.")
-
-    else:
-        logging.error(
-            f"Dataset {dataset_path} not found, and config.StorageBackend is not `BLOB` so can't download the dataset from Azure. Terminating."
-        )
-        sys.exit(1)
-
-    logging.info(f"Finished loading the processed dataset. Number of rows: {len(df):,}")
-    logging.info(f"The highest weekly downloads in the dataset: {df['weekly_downloads'].max():,}")
-    logging.info(f"The lowest weekly downloads in the dataset: {df['weekly_downloads'].min():,}")
-    return df
diff --git a/pypi_scout/config.py b/pypi_scout/config.py
index 671c054..57cb849 100644
--- a/pypi_scout/config.py
+++ b/pypi_scout/config.py
@@ -1,5 +1,5 @@
 import os
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from enum import Enum
 from pathlib import Path
 
@@ -11,23 +11,11 @@ class StorageBackend(Enum):
 
 @dataclass
 class Config:
-    # Name of the Pinecone index used for storing vector representations of the package descriptions.
-    PINECONE_INDEX_NAME = "pypi"
-
-    # Namespace within the Pinecone index to logically separate data.
-    PINECONE_NAMESPACE = "ns1"
-
-    # API token for authenticating with Pinecone. Should be set as an environment variable.
-    PINECONE_TOKEN: str = field(default_factory=lambda: os.getenv("PINECONE_TOKEN"))
-
     # Name of the model used for generating vector embeddings from text.
     # See https://sbert.net/docs/sentence_transformer/pretrained_models.html for available models.
     EMBEDDINGS_MODEL_NAME = "all-mpnet-base-v2"
 
-    # Dimension of the vector embeddings produced by the model. Should match the output of the model above.
-    EMBEDDINGS_DIMENSION = 768
-
-    # Boolean to overwrite existing files. e.g. re-download the raw dataset, upload processed dataset to blob, etc.
+    # Boolean to overwrite raw data file if it already exists
     OVERWRITE: bool = True
 
     # Directory where dataset files are stored.
@@ -43,22 +31,26 @@ class Config:
     # For example; it needs the name, weekly downloads, and the summary, but not the (cleaned) description.
     DATASET_FOR_API_CSV_NAME = "dataset_for_api.csv"
 
+    # Filename for the dataset that contains the minimal data that the API needs.
+    # For example; it needs the name, weekly downloads, and the summary, but not the (cleaned) description.
+    EMBEDDINGS_PARQUET_NAME = "embeddings.parquet"
+
     # Google Drive file ID for downloading the raw dataset.
-    GOOGLE_FILE_ID = "1huR7-VD3AieBRCcQyRX9MWbPLMb_czjq"
+    GOOGLE_FILE_ID = "1IDJvCsq1gz0yUSXgff13pMl3nUk7zJzb"
 
     # Number of top results to return for a query.
-    N_RESULTS_TO_RETURN = 30
+    N_RESULTS_TO_RETURN = 40
 
     # Fraction of the dataset to include in the vector database. This value determines the portion of top packages
     # (sorted by weekly downloads) to include. Increase this value to include a larger portion of the dataset, up to 1.0 (100%).
     # For reference, a value of 0.25 corresponds to including all PyPI packages with at least approximately 650 weekly downloads
-    FRAC_DATA_TO_INCLUDE = 0.25
+    FRAC_DATA_TO_INCLUDE = 1
 
     # Weights for the combined score calculation. Higher WEIGHT_SIMILARITY prioritizes
     # relevance based on text similarity, while higher WEIGHT_WEEKLY_DOWNLOADS prioritizes
     # packages with more weekly downloads.
-    WEIGHT_SIMILARITY = 0.6
-    WEIGHT_WEEKLY_DOWNLOADS = 0.4
+    WEIGHT_SIMILARITY = 0.5
+    WEIGHT_WEEKLY_DOWNLOADS = 0.5
 
     # Storage backend configuration. Can be either StorageBackend.LOCAL or StorageBackend.BLOB.
     # If StorageBackend.BLOB, the processed dataset will be uploaded to Blob, and the backend API
@@ -70,9 +62,6 @@ class Config:
     STORAGE_BACKEND_BLOB_KEY: str | None = None
 
     def __post_init__(self) -> None:
-        if not self.PINECONE_TOKEN:
-            raise OSError("PINECONE_TOKEN not found in environment variables")  # noqa: TRY003
-
         if os.getenv("STORAGE_BACKEND") == "BLOB":
             self.STORAGE_BACKEND = StorageBackend.BLOB
             self.STORAGE_BACKEND_BLOB_ACCOUNT_NAME = os.getenv("STORAGE_BACKEND_BLOB_ACCOUNT_NAME")
diff --git a/pypi_scout/data/description_cleaner.py b/pypi_scout/data/description_cleaner.py
index 5bfa167..5f34c8c 100644
--- a/pypi_scout/data/description_cleaner.py
+++ b/pypi_scout/data/description_cleaner.py
@@ -25,7 +25,7 @@ def clean(self, df: pl.DataFrame, input_col: str, output_col: str) -> pl.DataFra
         Returns:
             pl.DataFrame: The modified DataFrame with the cleaned text.
         """
-        df = df.with_columns(pl.col(input_col).apply(self._clean_text).alias(output_col))
+        df = df.with_columns(pl.col(input_col).map_elements(self._clean_text, return_dtype=pl.String).alias(output_col))
         return df
 
     def _clean_text(self, text: str) -> str:
diff --git a/pypi_scout/data/raw_data_reader.py b/pypi_scout/data/raw_data_reader.py
index f0de2ba..a3a6eef 100644
--- a/pypi_scout/data/raw_data_reader.py
+++ b/pypi_scout/data/raw_data_reader.py
@@ -21,9 +21,13 @@ def read(self):
             DataFrame: The processed dataframe.
         """
         df = pl.read_csv(self.raw_dataset)
-        df = df.with_columns(weekly_downloads=(pl.col("number_of_downloads") / 4).round().cast(pl.Int32))
+        df = df.with_columns(weekly_downloads=pl.col("number_of_downloads").cast(pl.Int32))
         df = df.drop("number_of_downloads")
         df = df.unique(subset="name")
-        df = df.filter(~pl.col("description").is_null())
+        df = df.filter(~(pl.col("description").is_null() & pl.col("summary").is_null()))
         df = df.sort("weekly_downloads", descending=True)
+        df = df.with_columns(
+            summary=pl.col("summary").fill_null(""),
+            description=pl.col("description").fill_null(""),
+        )
         return df
diff --git a/pypi_scout/embeddings/embeddings_creator.py b/pypi_scout/embeddings/embeddings_creator.py
new file mode 100644
index 0000000..a61d7cc
--- /dev/null
+++ b/pypi_scout/embeddings/embeddings_creator.py
@@ -0,0 +1,60 @@
+import logging
+
+import polars as pl
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+
+
+class VectorEmbeddingCreator:
+    def __init__(
+        self,
+        embeddings_model: SentenceTransformer,
+        embedding_column_name: str = "embeddings",
+        batch_size: int = 128,
+    ):
+        """
+        Initializes the VectorEmbeddingCreator with a SentenceTransformer model, embedding column name, and batch size.
+
+        Args:
+            embeddings_model (SentenceTransformer): The SentenceTransformer model to generate embeddings.
+            embedding_column_name (str, optional): The name of the column to store embeddings. Defaults to 'embeddings'.
+            batch_size (int, optional): The size of batches to process at a time. Defaults to 128.
+        """
+        self.model = embeddings_model
+        self.embedding_column_name = embedding_column_name
+        self.batch_size = batch_size
+
+    def add_embeddings(self, df: pl.DataFrame, text_column: str) -> pl.DataFrame:
+        """
+        Adds embeddings to the DataFrame based on the specified text column.
+
+        Args:
+            df (pl.DataFrame): The Polars DataFrame to which embeddings will be added.
+            text_column (str): The column name containing text to generate embeddings for.
+
+        Returns:
+            pl.DataFrame: The DataFrame with an additional column containing embeddings.
+        """
+        logging.info("Splitting DataFrame into batches...")
+        df_chunks = self._split_dataframe_in_batches(df, batch_size=self.batch_size)
+        all_embeddings = []
+
+        logging.info("Generating embeddings...")
+        for chunk in tqdm(df_chunks, desc="Generating embeddings", unit="batch"):
+            embeddings = self._generate_embeddings(chunk, text_column)
+            all_embeddings.extend(embeddings)
+
+        df = df.with_columns(pl.Series(self.embedding_column_name, all_embeddings))
+        return df
+
+    def _generate_embeddings(self, chunk: pl.DataFrame, text_column: str) -> list:
+        embeddings = self.model.encode(list(chunk[text_column]), show_progress_bar=False)
+        return embeddings
+
+    @staticmethod
+    def _split_dataframe_in_batches(df: pl.DataFrame, batch_size: int) -> list:
+        """
+        Splits a Polars DataFrame into batches.
+        """
+        n_chunks = (df.height + batch_size - 1) // batch_size
+        return [df.slice(i * batch_size, batch_size) for i in range(n_chunks)]
diff --git a/pypi_scout/embeddings/simple_vector_database.py b/pypi_scout/embeddings/simple_vector_database.py
new file mode 100644
index 0000000..7812b76
--- /dev/null
+++ b/pypi_scout/embeddings/simple_vector_database.py
@@ -0,0 +1,55 @@
+import numpy as np
+import polars as pl
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+
+
+class SimpleVectorDatabase:
+    def __init__(
+        self,
+        embeddings_model: SentenceTransformer,
+        df_embeddings: pl.DataFrame,
+        embedding_column: str = "embeddings",
+        processed_column: str = "embeddings_array",
+    ):
+        """
+        Initializes the SimpleVectorDatabase with a SentenceTransformer model and a DataFrame containing embeddings.
+
+        Args:
+            embeddings_model (SentenceTransformer): The SentenceTransformer model to generate embeddings.
+            df_embeddings (pl.DataFrame): The Polars DataFrame containing the initial embeddings.
+            embedding_column (str, optional): The name of the column containing the original embeddings. Defaults to 'embeddings'.
+        """
+        self.embeddings_model = embeddings_model
+        self.df_embeddings = df_embeddings
+        self.embedding_column = embedding_column
+        self.embeddings_matrix = self._create_embeddings_matrix()
+
+    def find_similar(self, query: str, top_k: int = 25) -> pl.DataFrame:
+        """
+        Finds the top_k most similar vectors in the database for a given query.
+
+        Args:
+            query (str): The query string to find similar vectors for.
+            top_k (int, optional): The number of similar vectors to retrieve. Defaults to 25.
+
+        Returns:
+            pl.DataFrame: A Polars DataFrame containing the most similar vectors and their similarity scores.
+        """
+        query_embedding = self.embeddings_model.encode(query, show_progress_bar=False)
+
+        similarities = cosine_similarity([query_embedding], self.embeddings_matrix)[0]
+
+        top_k_indices = np.argsort(similarities)[::-1][:top_k]
+        top_k_scores = similarities[top_k_indices]
+        df_best_matches = self.df_embeddings[top_k_indices]
+
+        df_best_matches = df_best_matches.with_columns(pl.Series("similarity", top_k_scores))
+        df_best_matches = df_best_matches.drop(self.embedding_column)
+
+        return df_best_matches
+
+    def _create_embeddings_matrix(self) -> np.ndarray:
+        return np.stack(
+            self.df_embeddings[self.embedding_column].apply(lambda x: np.array(x, dtype=np.float32)).to_numpy()
+        )
diff --git a/pypi_scout/scripts/create_vector_embeddings.py b/pypi_scout/scripts/create_vector_embeddings.py
new file mode 100644
index 0000000..870b019
--- /dev/null
+++ b/pypi_scout/scripts/create_vector_embeddings.py
@@ -0,0 +1,45 @@
+import logging
+from pathlib import Path
+
+import polars as pl
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+
+from pypi_scout.config import Config
+from pypi_scout.embeddings.embeddings_creator import VectorEmbeddingCreator
+from pypi_scout.utils.logging import setup_logging
+
+
+def read_processed_dataset(path_to_processed_dataset: Path):
+    logging.info("📂 Reading the processed dataset...")
+    df = pl.read_csv(path_to_processed_dataset)
+    logging.info(f"📊 Number of rows in the processed dataset: {len(df):,}")
+    return df
+
+
+def write_parquet(df: pl.DataFrame, processed_dataset_path: Path):
+    logging.info(f"Storing dataset in {processed_dataset_path}...")
+    df.write_parquet(processed_dataset_path)
+    logging.info("✅ Done!")
+
+
+def create_vector_embeddings():
+    setup_logging()
+    load_dotenv()
+
+    config = Config()
+    df = read_processed_dataset(config.DATA_DIR / config.PROCESSED_DATASET_CSV_NAME)
+    df = df.with_columns(
+        summary_and_description_cleaned=pl.concat_str(pl.col("summary"), pl.lit(" - "), pl.col("description_cleaned"))
+    )
+    df = VectorEmbeddingCreator(embeddings_model=SentenceTransformer(config.EMBEDDINGS_MODEL_NAME)).add_embeddings(
+        df, text_column="summary_and_description_cleaned"
+    )
+
+    df = df.select("name", "embeddings").unique(subset="name")
+    write_parquet(df, config.DATA_DIR / config.EMBEDDINGS_PARQUET_NAME)
+
+
+if __name__ == "__main__":
+    setup_logging()
+    create_vector_embeddings()
diff --git a/pypi_scout/scripts/setup.py b/pypi_scout/scripts/setup.py
index 795e201..7145162 100644
--- a/pypi_scout/scripts/setup.py
+++ b/pypi_scout/scripts/setup.py
@@ -1,37 +1,27 @@
-import argparse
 import logging
 
+from pypi_scout.scripts.create_vector_embeddings import create_vector_embeddings
 from pypi_scout.scripts.download_raw_dataset import download_raw_dataset
 from pypi_scout.scripts.process_raw_dataset import process_raw_dataset
-from pypi_scout.scripts.setup_pinecone import setup_pinecone
 from pypi_scout.scripts.upload_processed_datasets import upload_processed_datasets
-from pypi_scout.scripts.upsert_data import upsert_data
 from pypi_scout.utils.logging import setup_logging
 
 
-def main(no_upsert):
+def main():
     setup_logging()
 
-    logging.info("\n\nSETTING UP PINECONE -------------\n")
-    setup_pinecone()
-
     logging.info("\n\nDOWNLOADING RAW DATASET -------------\n")
     download_raw_dataset()
 
     logging.info("\n\nPROCESSING RAW DATASET -------------\n")
     process_raw_dataset()
 
+    logging.info("\n\nCREATING VECTOR EMBEDDINGS -------------\n")
+    create_vector_embeddings()
+
     logging.info("\n\nUPLOADING PROCESSED DATASETS -------------\n")
     upload_processed_datasets()
-    if not no_upsert:
-        logging.info("\n\nUPSERTING DATA TO PINECONE -------------\n")
-        upsert_data()
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run the setup script with optional flags.")
-    parser.add_argument("--no-upsert", action="store_true", help="If set, do not upsert data to the Pinecone database.")
-
-    args = parser.parse_args()
-
-    main(no_upsert=args.no_upsert)
+    main()
diff --git a/pypi_scout/scripts/setup_pinecone.py b/pypi_scout/scripts/setup_pinecone.py
deleted file mode 100644
index 4f3b1f2..0000000
--- a/pypi_scout/scripts/setup_pinecone.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import logging
-
-from dotenv import load_dotenv
-from pinecone import Pinecone, ServerlessSpec
-from pinecone.core.client.exceptions import PineconeApiException
-
-from pypi_scout.config import Config
-from pypi_scout.utils.logging import setup_logging
-
-
-def setup_pinecone():
-    """
-    This script sets up a Pinecone index for storing embeddings.
-
-    It loads the environment variables from a .env file, creates a Pinecone client,
-    and creates an index with the specified name, dimension, metric, and serverless specification.
-    """
-
-    load_dotenv()
-    config = Config()
-
-    logging.info("🔗 Connecting to Pinecone..")
-    pc = Pinecone(api_key=config.PINECONE_TOKEN)
-
-    try:
-        logging.info("Creating Pinecone index..")
-        pc.create_index(
-            name=config.PINECONE_INDEX_NAME,
-            dimension=config.EMBEDDINGS_DIMENSION,
-            metric="dotproduct",
-            spec=ServerlessSpec(cloud="aws", region="us-east-1"),
-        )
-        logging.info("✅ Pinecone index created successfully.")
-    except PineconeApiException as e:
-        if e.status == 409:
-            logging.warning(f"🔹 Pinecone index '{config.PINECONE_INDEX_NAME}' already exists.")
-        else:
-            logging.exception("❌ An error occurred while creating the Pinecone index.")
-
-
-if __name__ == "__main__":
-    setup_logging()
-    setup_pinecone()
diff --git a/pypi_scout/scripts/upload_processed_datasets.py b/pypi_scout/scripts/upload_processed_datasets.py
index 4d94043..e55b51f 100644
--- a/pypi_scout/scripts/upload_processed_datasets.py
+++ b/pypi_scout/scripts/upload_processed_datasets.py
@@ -1,7 +1,5 @@
 import logging
-from pathlib import Path
 
-import polars as pl
 from dotenv import load_dotenv
 
 from pypi_scout.config import Config, StorageBackend
@@ -9,45 +7,6 @@
 from pypi_scout.utils.logging import setup_logging
 
 
-class CsvToBlobUploader:
-    def __init__(self, config: Config):
-        self.config = config
-        self.blob_io = BlobIO(
-            config.STORAGE_BACKEND_BLOB_ACCOUNT_NAME,
-            config.STORAGE_BACKEND_BLOB_CONTAINER_NAME,
-            config.STORAGE_BACKEND_BLOB_KEY,
-        )
-        self.overwrite = config.OVERWRITE
-
-    def read_csv(self, path_to_csv: Path) -> pl.DataFrame:
-        logging.info(f"📂 Reading the dataset from {path_to_csv}...")
-        df = pl.read_csv(path_to_csv)
-        logging.info(f"📊 Number of rows in the dataset {path_to_csv.name}: {len(df):,}")
-        return df
-
-    def upload_csv_to_blob(self, df: pl.DataFrame, csv_name: str):
-        if self.blob_io.exists(csv_name):
-            if not self.overwrite:
-                logging.info(
-                    f"🔹  Dataset {csv_name} already exists in container '{self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}'! Skipping upload."
-                )
-                return
-            else:
-                logging.info(
-                    f"⤵️  Dataset {csv_name} already exists in container '{self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}', but overwrite is enabled. Overwriting..."
-                )
-
-        logging.info(f"Uploading {csv_name} to blob container {self.config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}...")
-        self.blob_io.upload_csv(df, csv_name)
-        logging.info("✅ Done!")
-
-    def process_and_upload_datasets(self, dataset_names: list[str]):
-        for dataset_name in dataset_names:
-            csv_path = self.config.DATA_DIR / dataset_name
-            df = self.read_csv(csv_path)
-            self.upload_csv_to_blob(df, dataset_name)
-
-
 def upload_processed_datasets():
     load_dotenv()
     config = Config()
@@ -58,10 +17,19 @@ def upload_processed_datasets():
         )
         return
 
-    dataset_names = [config.PROCESSED_DATASET_CSV_NAME, config.DATASET_FOR_API_CSV_NAME]
+    file_names = [config.PROCESSED_DATASET_CSV_NAME, config.DATASET_FOR_API_CSV_NAME, config.EMBEDDINGS_PARQUET_NAME]
+
+    blob_io = BlobIO(
+        config.STORAGE_BACKEND_BLOB_ACCOUNT_NAME,
+        config.STORAGE_BACKEND_BLOB_CONTAINER_NAME,
+        config.STORAGE_BACKEND_BLOB_KEY,
+    )
+
+    for file_name in file_names:
+        logging.info(f"💫 Uploading {file_name} to blob container `{config.STORAGE_BACKEND_BLOB_CONTAINER_NAME}`...")
+        blob_io.upload_local_file(config.DATA_DIR / file_name, file_name)
 
-    uploader = CsvToBlobUploader(config)
-    uploader.process_and_upload_datasets(dataset_names)
+    logging.info("✅ Done!")
 
 
 if __name__ == "__main__":
diff --git a/pypi_scout/scripts/upsert_data.py b/pypi_scout/scripts/upsert_data.py
deleted file mode 100644
index fef0a42..0000000
--- a/pypi_scout/scripts/upsert_data.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import logging
-
-import polars as pl
-from dotenv import load_dotenv
-from sentence_transformers import SentenceTransformer
-
-from pypi_scout.config import Config
-from pypi_scout.utils.logging import setup_logging
-from pypi_scout.vector_database import VectorDatabaseInterface
-
-
-def upsert_data():
-    """
-    Upserts data from a processed dataset CSV into a vector database.
-    """
-    load_dotenv()
-    config = Config()
-
-    logging.info("Reading the processed dataset...")
-    df = pl.read_csv(config.DATA_DIR / config.PROCESSED_DATASET_CSV_NAME)
-    logging.info("Number of rows in the dataset: %s", len(df))
-
-    logging.info("🔗 Connecting to the vector database..")
-    vector_database_interface = VectorDatabaseInterface(
-        pinecone_token=config.PINECONE_TOKEN,
-        pinecone_index_name=config.PINECONE_INDEX_NAME,
-        embeddings_model=SentenceTransformer(config.EMBEDDINGS_MODEL_NAME),
-        pinecone_namespace=config.PINECONE_NAMESPACE,
-    )
-
-    logging.info("⬆️  Upserting data into the vector database..")
-    logging.info("This can take a while...")
-    logging.info("If this really takes too long, consider lowering the value of `FRAC_DATA_TO_INCLUDE` in config.py.")
-    df = df.with_columns(
-        summary_and_description_cleaned=pl.concat_str(pl.col("summary"), pl.lit(" - "), pl.col("description_cleaned"))
-    )
-    vector_database_interface.upsert_polars(df, key_column="name", text_column="summary_and_description_cleaned")
-    logging.info("✅ Done!")
-
-
-if __name__ == "__main__":
-    setup_logging()
-    upsert_data()
diff --git a/pypi_scout/utils/blob_io.py b/pypi_scout/utils/blob_io.py
index 74eaa5e..a5fd4af 100644
--- a/pypi_scout/utils/blob_io.py
+++ b/pypi_scout/utils/blob_io.py
@@ -1,10 +1,15 @@
 import tempfile
-from io import BytesIO
+from enum import Enum
 
 import polars as pl
 from azure.storage.blob import BlobServiceClient
 
 
+class Format(Enum):
+    CSV = "csv"
+    PARQUET = "parquet"
+
+
 class BlobIO:
     def __init__(self, account_name: str, container_name: str, account_key: str):
         self.account_name = account_name
@@ -15,32 +20,33 @@ def __init__(self, account_name: str, container_name: str, account_key: str):
         )
         self.container_client = self.service_client.get_container_client(container_name)
 
-    def upload_csv(self, data_frame: pl.DataFrame, blob_name: str) -> None:
-        csv_buffer = BytesIO()
-        data_frame.write_csv(csv_buffer)
-        csv_buffer.seek(0)  # Reset buffer position to the beginning
-        blob_client = self.container_client.get_blob_client(blob_name)
-        blob_client.upload_blob(csv_buffer, overwrite=True)
-
-    def upload_local_csv(self, local_file_path: str, blob_name: str) -> None:
+    def upload_local_file(self, local_file_path: str, blob_name: str) -> None:
         with open(local_file_path, "rb") as data:
             blob_client = self.container_client.get_blob_client(blob_name)
             blob_client.upload_blob(data, overwrite=True)
 
-    def download_csv(self, blob_name: str) -> pl.DataFrame:
+    def download_csv_to_df(self, blob_name: str):
+        return self._download_as_df(blob_name, Format.CSV)
+
+    def download_parquet_to_df(self, blob_name: str):
+        return self._download_as_df(blob_name, Format.PARQUET)
+
+    def _download_as_df(self, blob_name: str, format: Format) -> pl.DataFrame:  # noqa: A002
+        """
+        //TODO: Improve by not reading into a file first.
+        """
         blob_client = self.container_client.get_blob_client(blob_name)
         download_stream = blob_client.download_blob()
 
-        # Create a temporary file
         with tempfile.NamedTemporaryFile(delete=True) as temp_file:
-            # Download the blob content into the temporary file
             temp_file.write(download_stream.readall())
             temp_file.flush()
 
-            # Read the CSV using Polars
-            df = pl.read_csv(temp_file.name)
+            if format == Format.CSV:
+                return pl.read_csv(temp_file.name)
 
-        return df
+            if format == Format.PARQUET:
+                return pl.read_parquet(temp_file.name)
 
     def exists(self, blob_name):
         blob_client = self.container_client.get_blob_client(blob_name)
diff --git a/pypi_scout/vector_database/__init__.py b/pypi_scout/vector_database/__init__.py
deleted file mode 100644
index 7bdf73d..0000000
--- a/pypi_scout/vector_database/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from __future__ import annotations
-
-from pypi_scout.vector_database.interface import VectorDatabaseInterface
-
-__all__ = ("VectorDatabaseInterface",)
diff --git a/pypi_scout/vector_database/interface.py b/pypi_scout/vector_database/interface.py
deleted file mode 100644
index 3293231..0000000
--- a/pypi_scout/vector_database/interface.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import logging
-
-import polars as pl
-from pinecone import Pinecone
-from sentence_transformers import SentenceTransformer
-from tqdm import tqdm
-
-
-class VectorDatabaseInterface:
-    """
-    A class that provides an interface for interacting with a vector database.
-
-    Args:
-        pinecone_token (str): The Pinecone API token.
-        pinecone_index_name (str): The name of the Pinecone index.
-        pinecone_namespace (str): The namespace for the Pinecone index.
-        embeddings_model (SentenceTransformer): The sentence transformer model for encoding text into embeddings.
-        batch_size (int, optional): The batch size for upserting data. Defaults to 250.
-    """
-
-    def __init__(
-        self,
-        pinecone_token: str,
-        pinecone_index_name: str,
-        pinecone_namespace: str,
-        embeddings_model: SentenceTransformer,
-        batch_size: int = 128,
-    ):
-        self.batch_size = batch_size
-        self.model = embeddings_model
-        logging.info("Connecting to Pinecone...")
-        pc = Pinecone(api_key=pinecone_token)
-        self.index = pc.Index(pinecone_index_name)
-        self.pinecone_namespace = pinecone_namespace
-        logging.info("Connection successful.")
-
-    def upsert_polars(self, df: pl.DataFrame, key_column: str, text_column: str):
-        """
-        Upserts the data from a Polars DataFrame into the vector database.
-
-        Args:
-            df (pl.DataFrame): The Polars DataFrame containing the data to be upserted.
-            key_column (str): The name of the column in the DataFrame containing the unique keys.
-            text_column (str): The name of the column in the DataFrame containing the text data.
-        """
-        df_chunks = self._split_dataframe_in_batches(df, batch_size=self.batch_size)
-        for chunk in tqdm(df_chunks, desc="Upserting batches", unit="batch"):
-            self._upsert_chunk(chunk, key_column, text_column)
-
-    def find_similar(self, query: str, top_k: int = 25) -> pl.DataFrame:
-        """
-        Finds similar vectors in the database for a given query.
-
-        Args:
-            query (str): The query string.
-            top_k (int, optional): The number of similar vectors to retrieve. Defaults to 25.
-
-        Returns:
-            pl.DataFrame: A Polars DataFrame containing the similar vectors and their similarity scores.
-        """
-        embeddings = self.model.encode(query, show_progress_bar=False)
-        matches = self.index.query(
-            namespace=self.pinecone_namespace, vector=embeddings.tolist(), top_k=top_k, include_values=False
-        )
-        return pl.from_dicts([{"name": x["id"], "similarity": x["score"]} for x in matches["matches"]])
-
-    def _upsert_chunk(self, chunk: pl.DataFrame, key_column: str, text_column: str) -> None:
-        embeddings = self.model.encode(list(chunk[text_column]), show_progress_bar=False)
-        vectors = [
-            {"id": project_name, "values": embedding} for project_name, embedding in zip(chunk[key_column], embeddings)
-        ]
-        self.index.upsert(vectors=vectors, namespace=self.pinecone_namespace, show_progress=False)
-
-    @staticmethod
-    def _split_dataframe_in_batches(df: pl.DataFrame, batch_size: int) -> pl.DataFrame:
-        """
-        Splits a Polars DataFrame into batches.
-        """
-        n_chunks = (df.height + batch_size - 1) // batch_size
-        return [df.slice(i * batch_size, batch_size) for i in range(n_chunks)]
diff --git a/pyproject.toml b/pyproject.toml
index aecb24e..7d51b9d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,13 +11,12 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<4.0"
+python = ">=3.9,<4.0"
 beautifulsoup4 = "^4.12.3"
 polars = "^0.20.31"
 sentence-transformers = "^3.0.1"
 lxml = "^5.2.2"
 python-dotenv = "^1.0.1"
-pinecone = "^4.0.0"
 tqdm = "^4.66.4"
 fastapi = "^0.111.0"
 pydantic = "^2.7.4"
@@ -26,6 +25,8 @@ gdown = "^5.2.0"
 azure-storage-blob = "^12.20.0"
 slowapi = "^0.1.9"
 starlette = "^0.37.2"
+numpy = "^2.0.0"
+scikit-learn = "^1.5.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.0"
diff --git a/requirements-cpu.txt b/requirements-cpu.txt
index 2c10e0e..bf9744c 100644
--- a/requirements-cpu.txt
+++ b/requirements-cpu.txt
@@ -5,7 +5,6 @@ polars==0.20.31
 sentence-transformers==3.0.1
 lxml==5.2.2
 python-dotenv==1.0.1
-pinecone-client==4.0.0
 tqdm==4.66.4
 fastapi==0.111.0
 pydantic==2.7.4
@@ -16,5 +15,6 @@ numpy==1.24.4
 azure-storage-blob==12.20.0
 slowapi==0.1.9
 starlette==0.37.2
+scikit-learn==1.5.0
 --index-url=https://download.pytorch.org/whl/cpu
 --extra-index-url=https://pypi.org/simple
diff --git a/static/architecture.png b/static/architecture.png
deleted file mode 100644
index 6b46e8b5a34ec70f5ca7931f14e8595e4b3e91f6..0000000000000000000000000000000000000000
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diff --git a/tests/test_vdb.py b/tests/test_vdb.py
index 61305fe..ea40271 100644
--- a/tests/test_vdb.py
+++ b/tests/test_vdb.py
@@ -4,51 +4,53 @@
 import polars as pl
 import pytest
 
-from pypi_scout.vector_database.interface import VectorDatabaseInterface
+from pypi_scout.embeddings.simple_vector_database import SimpleVectorDatabase
 
 
 @pytest.fixture
-def mock_pinecone(mocker):
-    mock_pinecone = mocker.patch("pypi_scout.vector_database.interface.Pinecone").return_value
-    mock_index = MagicMock()
-    mock_pinecone.Index.return_value = mock_index
-    return mock_pinecone, mock_index
+def mock_model():
+    # Mock the SentenceTransformer model
+    mock_model = MagicMock()
+    # Mock the encode method to return a fixed vector
+    mock_model.encode.return_value = np.array([0.5, 0.5, 0.5])
+    return mock_model
 
 
 @pytest.fixture
-def mock_model(mocker):
-    return mocker.patch("pypi_scout.vector_database.interface.SentenceTransformer").return_value
+def df_embeddings():
+    return pl.DataFrame(
+        {
+            "id": [1, 2, 3],
+            "text": ["Hello world", "Hi there", "Greetings"],
+            "embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]],
+        }
+    )
 
 
 @pytest.fixture
-def vdb(mock_pinecone, mock_model):
-    return VectorDatabaseInterface(
-        pinecone_token="fake_token",  # noqa: S106
-        pinecone_index_name="fake_index",
-        pinecone_namespace="fake_namespace",
-        embeddings_model=mock_model,
-        batch_size=2,
-    )
+def vector_db(mock_model, df_embeddings):
+    return SimpleVectorDatabase(embeddings_model=mock_model, df_embeddings=df_embeddings)
 
 
-def test_initialization(mock_pinecone, mock_model, vdb):
-    mock_pinecone[0].Index.assert_called_with("fake_index")
-    assert vdb.pinecone_namespace == "fake_namespace"
-    assert vdb.batch_size == 2
-    assert vdb.model == mock_model
+def test_embeddings_matrix_creation(vector_db):
+    expected_matrix = np.array(
+        [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]], dtype=np.float32
+    )  # Ensure the expected matrix has dtype float32
 
+    np.testing.assert_allclose(vector_db.embeddings_matrix, expected_matrix, rtol=1e-6, atol=1e-8)
 
-def test_find_similar(mock_model, vdb):
-    # Mock the model.encode method
-    mock_model.encode.return_value = np.array([0.1] * 768)
 
-    # Mock the Pinecone query method
-    vdb.index.query.return_value = {"matches": [{"id": "1", "score": 0.99}, {"id": "2", "score": 0.98}]}
+def test_find_similar(vector_db):
+    query = "Hello"
+    result = vector_db.find_similar(query, top_k=2)
 
-    result_df = vdb.find_similar(query="test query", top_k=2)
+    # Check that the result has the expected number of rows
+    assert result.shape[0] == 2
 
-    # Create expected DataFrame
-    expected_df = pl.DataFrame([{"name": "1", "similarity": 0.99}, {"name": "2", "similarity": 0.98}])
+    # Check that the similarity scores are between 0 and 1
+    assert result["similarity"].min() >= 0
+    assert result["similarity"].max() <= 1
 
-    # Check that the result matches the expected DataFrame
-    assert result_df.equals(expected_df)
+    # Check that the result contains the expected columns
+    expected_columns = ["id", "text", "similarity"]
+    assert set(result.columns) == set(expected_columns)