adeleke5140

#!/bin/bash

DIR="${1:-.}"
CRF="${2:-28}"

if ! command -v ffmpeg &> /dev/null; then
  echo "Error: ffmpeg is not installed or in PATH"
fi

if [ ! -d "$DIR" ]; then

adeleke5140

title	description
Storing Embeddings in A Vector Database | Mastra Docs	Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.

import { Tabs } from "nextra/components";

Storing Embeddings in A Vector Database

After generating embeddings, you need to store them in a database that supports vector similarity search. Mastra provides a consistent interface for storing and querying embeddings across different vector databases.

adeleke5140

---
title: "Storing Embeddings in A Vector Database | Mastra Docs"
description: Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.
---

import { Tabs } from "nextra/components";

## Storing Embeddings in A Vector Database

After generating embeddings, you need to store them in a database that supports vector similarity search. Mastra provides a consistent interface for storing and querying embeddings across different vector databases.

adeleke5140

---
title: "Storing Embeddings in A Vector Database | Mastra Docs"
description: Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.
---

import { Tabs } from "nextra/components";

## Storing Embeddings in A Vector Database

After generating embeddings, you need to store them in a database that supports vector similarity search. Mastra provides a consistent interface for storing and querying embeddings across different vector databases.

adeleke5140

---
title: "Storing Embeddings in A Vector Database | Mastra Docs"
description: Guide on vector storage options in Mastra, including embedded and dedicated vector databases for similarity search.
---

import { Tabs } from "nextra/components";

## Storing Embeddings in A Vector Database

After generating embeddings, you need to store them in a database that supports vector similarity search. Mastra provides a consistent interface for storing and querying embeddings across different vector databases.

adeleke5140

.editor-group-watermark > .letterpress{
  background-image: url("https://github.com/Aikoyori/ProgrammingVTuberLogos/blob/main/VSCode/VSCode.png?raw=true");
  opacity: 0.9
}

adeleke5140

const MultiSelect = ({
  options,
  inputId,
  name,
  inputPlaceholder,
  ...rest
}: MultiSelectProps) => {
  return (
    <Select
      {...rest}

adeleke5140

const Notifications = () => {
  const { container, ref } = useModalParent<HTMLDivElement>();
  return (
    <div ref={ref}>
    // rest of code
        <Popover.Portal container={container}>
          // rest of code
        </Popover/>
    </div>
  )

adeleke5140

import axios, { CreateAxiosDefaults } from 'axios'
import { refreshToken as refreshTokenAdapter} from './session'


export const getJSONContentHeaders = () => {
  "Content-Type": "application/json",
  Accept: "application/json"
}

adeleke5140

const blob = b64toBlob(imageSrc.replace(/^data:image\/jpeg;base64,/, ""));
const file = new File([blob], "profile.jpeg", { type: "image/jpeg" });
const blobURl = URL.createObjectURL(file);