Search results for "embeddings"

getModelBuilder

@bluemsn

An interactive, runnable TypeScript val by bluemsn

4

0

Script

async () => {

const { OpenAIEmbeddings } = await import(

"npm:langchain/embeddings/openai"

return new OpenAIEmbeddings(args);

matches({ type: "llm", provider: "huggingface" }),

await import("npm:@huggingface/inference");

const { HuggingFaceInferenceEmbeddings } = await import(

"npm:langchain/embeddings/hf"

return new HuggingFaceInferenceEmbeddings(args);

// Return function to prevent "Serialization Error"

Test00_getModelBuilder

@lisazz

241219 練習從 webup 而來

1 7

0

Script

async () => {

const { OpenAIEmbeddings } = await import("https://esm.sh/langchain/embeddings/openai");

return new OpenAIEmbeddings(args);

matches({ type: "llm", provider: "huggingface" }),

await import("https://esm.sh/@huggingface/inference");

const { HuggingFaceInferenceEmbeddings } = await import("https://esm.sh/langchain/embeddings/hf");

return new HuggingFaceInferenceEmbeddings(args);

// 5. 返回函數以防止序列化錯誤

createQdrantCollection

@wilt

* Create a new Qdrant collection in an existing cluster with the given name * Uses recommended values for OpenAPI embeddings

1

Script

import { fetch } from "https://esm.town/v/std/fetch";

* Create a new Qdrant collection in an existing cluster with the given name

* Uses recommended values for OpenAPI embeddings

export async function createQdrantCollection(args: {

collectionName: string;

SidebarToArchive

@yawnxyz

Sidebar.io Archiver Sidebar's been one of my biggest design resources for the last decade. Since sidebar.io's break announcement this morning (https://sidebar.io/break/) I've set out to mess around with using Val.town to archive sidebar. The Google Sheet can be found here: https://docs.google.com/spreadsheets/d/1RghvMfPTR5xvHMAk1pKYuH2pFYYKOJ6bH0LLkHNZiuc/edit?usp=sharing Initially I was just piping data to Google Sheets but that's slow, wasteful and kind of dumb... instead I just wrapped Hono around the code to provide a download the CSV to browser.

1

HTTP

try {

// let embeddings = await modelProvider.gen({

// embed: true,

// value: JSON.stringify(link)

// console.log(`Adding link ${counter}:`, link.title, link.url, embeddings);

console.log(`Adding link ${counter}:`, link.title, link.url);

data: JSON.stringify(link),

// embeddings: embeddings.embedding.join(','),

} finally {

runAgent

@jacoblee93

An interactive, runnable TypeScript val by jacoblee93

1

Script

-----Header tweet-----

@LangChainAI 🦜🔗 JS/TS 0.0.83 out with loads of @GoogleAI and PaLM!

💬 Google Vertex AI chat model + embeddings

🔎 Google Custom Search tool

⛓️ API chain: make API calls from docs

topHNThreadByHour

@elsif_maj

// set at Thu Nov 30 2023 14:22:53 GMT+0000 (Coordinated Universal Time)

6

0

Email

// set at Thu Nov 30 2023 14:22:53 GMT+0000 (Coordinated Universal Time)

's autonomous vehicle deployment","Top thread on Hackernews for 10:00 is: Embeddings: What they are and why they matter","Top

CohereEmbed

@rlesser

An interactive, runnable TypeScript val by rlesser

2

0

Script

import { CohereCommon } from "https://esm.town/v/rlesser/CohereCommon";

export let CohereEmbed = (key: string, texts: Array<string>, options = {}) => {

// see endpoint options here: https://docs.cohere.ai/reference/embed

const defaultOptions = {

truncate: "END",

return CohereCommon(key, "embed", {

...defaultOptions,

...options,

texts,

embedSelf

@tgrecojs

* This val embeds itself in the web page created by hitting it's Web Endpoint!

0

HTTP

* This val embeds itself in the web page created by hitting it's Web Endpoint!

export let embedSelf = async (request: Request): Promise<Response> => {

return new Response(

<style>

:root { background: indianred; font-family: sans-serif; }

</style>

<iframe

id="val_embedded"

embedSelf

@neverstew

* This val embeds itself in the web page created by hitting it's Web Endpoint!

1

0

HTTP

* This val embeds itself in the web page created by hitting it's Web Endpoint!

export let embedSelf = async (request: Request): Promise<Response> => {

return new Response(

<style>

:root { background: "white"; font-family: sans-serif; }

</style>

<iframe

id="val_embedded"

title="Embedded Val Example"

embed

@pomdtr

An interactive, runnable TypeScript val by pomdtr

0

Script

const script = document.querySelector(`[src="https://esm.town/v/pomdtr/embed"]`);

if (!script) {

throw new Error("could not find script");

const wrapperID = script.getAttribute("data-element-id");

const wrapper = document.getElementById(wrapperID);

const code = wrapper.textContent;

const iframe = document.createElement("iframe", { is: "iframe" });

iframe.src = `https://easrng-playground.web.val.run?code=${encodeURIComponent(code)}`;

iframe.style.border = "none";

iframe.style.width = "500px";

lessons

@petermillspaugh

An interactive, runnable TypeScript val by petermillspaugh

7

0

Script

export const lessons = {

0: retrievalPractice,

1: spacedRepetition,

2: interleaving,

3: variedPractice,

4: elaborationAndReflection,

5: encodingAndConsolidation,

6: generation,

7: growthMindset,

8: yourBrain,

embed_demo

@pomdtr

An interactive, runnable TypeScript val by pomdtr

0

HTTP

const html = `

<h3 style="text-align: center;">Welcome to my website</h3>

console.log("hello world!")

</div>

export default function(req: Request) {

return new Response(

html,

encodingAndConsolidation

@petermillspaugh

@jsxImportSource https://esm.sh/react

1

0

Script

/** @jsxImportSource https://esm.sh/react */

* Note: this lesson is a work in progress 👷‍♂️

const TITLE = "Encoding & consolidation";

// TODO: better prompt

const FILL_BLANK = <>Where are memories stored in your brain?</>;

const CONTENT = (

<h2>Memory traces</h2>

<p>

A memory trace is a metaphorical/theoretical term for the brain’s representation of some knowledge or memory. The

“trace” includes synaptic connections between neurons. Also known as an “engram”.