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dab 2025-05-06 17:12:54 -05:00
commit 8c6f336cad
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2
.gitignore vendored Normal file
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node_modules
nodeapp/config.json

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# embedwith

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import React, { StrictMode, useState } from 'react'
import { createRoot } from 'react-dom/client'
import { useUnstrictEffect as useEffect } from './useUnstrictEffect'
const ApiTest = function ({ path, method, params }) {
const [ result, setResult ] = useState(null)
useEffect(() => {
let endpoint_full
let body
if (method === 'GET') {
endpoint_full = path + '?' + new URLSearchParams({
p: JSON.stringify(params),
}).toString()
}
else if (method === 'POST') {
endpoint_full = path
body = JSON.stringify(params)
}
else {
throw `bad method : ${method}`
}
fetch(endpoint_full, { method, body }).then(async (resp) => {
const resp_json = await resp.json()
setResult(resp_json)
return
})
return () => {
return
}
}, [])
return (
<div>
{JSON.stringify(result)}
</div>
)
}
const App = function () {
//let jsx_inner = <ApiTest path={'/stash'} method={'POST'} params={{ collection_name: 'misc', documents: ['hello', 'world'], wait_for_result: true }} />
let jsx_inner = <ApiTest path={'/search'} method={'POST'} params={{ collection_name: 'misc', qtext: 'hello!', limit: 10 }} />
return (
<div style={{ height: '100%', width: '100%', overflow: 'auto', backgroundColor: '#000', color: '#FFF', fontFamily: 'monospace', fontSize: `${2*8}px`, lineHeight: `${2*8}px` }}>
{jsx_inner}
</div>
)
}
const render_app = function () {
const elem_appstyles = document.createElement('style')
elem_appstyles.innerHTML = `
html, body {
height: 100%;
width: 100%;
overflow: hidden;
margin: 0px;
padding: 0px;
border: 0px;
}
`.trim()
document.head.appendChild(elem_appstyles)
const elem_root = document.createElement('div')
elem_root.style = 'height:100%; width:100%;'
document.body.appendChild(elem_root)
const func_render_app = () => {
const react_root = createRoot(elem_root)
react_root.render((
<StrictMode>
<App />
</StrictMode>
))
return
}
func_render_app()
return {}
}
export { render_app }

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{
"lockfileVersion": 1,
"workspaces": {
"": {
"dependencies": {
"motion": "^12.9.7",
"react": "^19.1.0",
"react-dom": "^19.1.0",
},
},
},
"packages": {
"framer-motion": ["framer-motion@12.9.7", "", { "dependencies": { "motion-dom": "^12.9.6", "motion-utils": "^12.9.4", "tslib": "^2.4.0" }, "peerDependencies": { "@emotion/is-prop-valid": "*", "react": "^18.0.0 || ^19.0.0", "react-dom": "^18.0.0 || ^19.0.0" }, "optionalPeers": ["@emotion/is-prop-valid", "react", "react-dom"] }, "sha512-Eo5TYU6sEPPy82GDx32PJm++G+AkBCrzxtEQOWLnpQX896Q3LFrsYhMZ5YO5ct4wL7wyHU6hqlrpYXeexKAevg=="],
"motion": ["motion@12.9.7", "", { "dependencies": { "framer-motion": "^12.9.7", "tslib": "^2.4.0" }, "peerDependencies": { "@emotion/is-prop-valid": "*", "react": "^18.0.0 || ^19.0.0", "react-dom": "^18.0.0 || ^19.0.0" }, "optionalPeers": ["@emotion/is-prop-valid", "react", "react-dom"] }, "sha512-Vh5NJxec7lZ4iNZXEhdweJpKwP2gjgevL/4Dx+dfMc7ABPMnLJaBUkz91TG/ZONhz/GUAy0Mw3Xd5rYtnxNRmw=="],
"motion-dom": ["motion-dom@12.9.6", "", { "dependencies": { "motion-utils": "^12.9.4" } }, "sha512-IK9pm5zU8BIp3FCoUGF3T7AHVLVOlXxlwco/bIbcnpBtyYb2gDQhdOzUh2KSDJVjYl1MZ9vdq8tnFTTahX2lfg=="],
"motion-utils": ["motion-utils@12.9.4", "", {}, "sha512-BW3I65zeM76CMsfh3kHid9ansEJk9Qvl+K5cu4DVHKGsI52n76OJ4z2CUJUV+Mn3uEP9k1JJA3tClG0ggSrRcg=="],
"react": ["react@19.1.0", "", {}, "sha512-FS+XFBNvn3GTAWq26joslQgWNoFu08F4kl0J4CgdNKADkdSGXQyTCnKteIAJy96Br6YbpEU1LSzV5dYtjMkMDg=="],
"react-dom": ["react-dom@19.1.0", "", { "dependencies": { "scheduler": "^0.26.0" }, "peerDependencies": { "react": "^19.1.0" } }, "sha512-Xs1hdnE+DyKgeHJeJznQmYMIBG3TKIHJJT95Q58nHLSrElKlGQqDTR2HQ9fx5CN/Gk6Vh/kupBTDLU11/nDk/g=="],
"scheduler": ["scheduler@0.26.0", "", {}, "sha512-NlHwttCI/l5gCPR3D1nNXtWABUmBwvZpEQiD4IXSbIDq8BzLIK/7Ir5gTFSGZDUu37K5cMNp0hFtzO38sC7gWA=="],
"tslib": ["tslib@2.8.1", "", {}, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="],
}
}

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import { render_app } from './App.jsx'
const async_main = async function () {
const ref_renderapp = render_app()
return
}
let after_page_ready
after_page_ready = async function () {
async_main().then(()=>{}).catch(console.error)
return
}
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', after_page_ready)
}
else {
after_page_ready()
}

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bunapp/package.json Normal file
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{
"dependencies": {
"motion": "^12.9.7",
"react": "^19.1.0",
"react-dom": "^19.1.0"
}
}

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bunapp/useUnstrictEffect.js Normal file
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import React, { useEffect, useRef } from 'react'
const useUnstrictEffect = function (og_func, og_deps) {
const ref_obj = {}
const ref_teardown = useRef()
useEffect(() => {
const id_timeout = setTimeout(() => {
// do the original thing //
const func_teardown = og_func()
ref_teardown.current = () => {
func_teardown()
return
}
return
}, 1)
return () => {
if (ref_teardown.current) {
ref_teardown.current()
ref_teardown.current = undefined
}
else {
// immediate Unmount = STOP og_func from ever running
clearTimeout(id_timeout)
}
return
}
}, [ ...og_deps ])
return ref_obj
}
export { useUnstrictEffect }

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nodeapp/milvus_utils.js Normal file
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import { MilvusClient } from '@zilliz/milvus2-sdk-node'
const async_get_client = async () => {
const address = process.env['MILVUS_HOST']
console.info('connecting...')
const client = new MilvusClient({
address,
})
await client.connectPromise
console.info('connected .')
return client
}
export { async_get_client }

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nodeapp/package-lock.json generated Normal file
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{
"name": "mount",
"lockfileVersion": 3,
"requires": true,
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"ioredis": "^5.6.1"
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"ansi-styles": "^4.0.0",
"string-width": "^4.1.0",
"strip-ansi": "^6.0.0"
},
"engines": {
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},
"funding": {
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}
}

6
nodeapp/package.json Normal file
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{
"dependencies": {
"@zilliz/milvus2-sdk-node": "^2.5.8",
"ioredis": "^5.6.1"
}
}

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nodeapp/run.js Normal file
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import http from 'http'
import url from 'url'
import crypto from 'crypto'
import child_process from 'node:child_process'
import util from 'node:util'
const execFile = util.promisify(child_process.execFile)
import { DataType } from '@zilliz/milvus2-sdk-node'
// init a single Milvus client for this process lifecycle
import { async_get_client } from './milvus_utils.js'
const client = await async_get_client()
process.on('exit', (code) => {
client.closeConnection()
return
})
import fs from 'fs'
import path from 'path'
const CONFIG = JSON.parse(fs.readFileSync(path.resolve(import.meta.dirname, './config.json'), 'utf8'))
const { MODELKEY_SERVER_MAP } = CONFIG
const PORT = 8801
const HOST = '0.0.0.0'
const get_siteroot_html = function () {return `
<!DOCTYPE html>
<html lang=en>
<head>
<meta charset=utf-8>
<link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg'/>">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>${'embedwith'}</title>
</head>
<body>
<script type="text/javascript" src="/index.js">
</script>
</body>
</html>
`.trim()}
const get_siteroot_js = async () => {
const { stdout } = await execFile("/home/user/bun-linux-x64/bun", ["build", "index.js"], { cwd: '/home/user/mount/bunapp', encoding : 'utf8' })
const str_output = stdout.trim()
return str_output
}
const async_get_embeddings_for_model = async (model_key, documents) => {
if (!(model_key in MODELKEY_SERVER_MAP)) {
console.warn(`no matching model_key: ${model_key}`)
return []
}
const { endpoint } = MODELKEY_SERVER_MAP[model_key]
// direct pass, no pre-processing
const endpoint_content = [ ...documents ]
const resp = await fetch(endpoint, {
method: 'POST',
body: JSON.stringify({ content: endpoint_content }),
})
const resp_json = await resp.json()
return [ ...resp_json ]
}
const async_get_request_body = (req) => {
return new Promise((resolve) => {
const lst_chunks = []
req.on("data", function (chunk) {
lst_chunks.push(chunk)
})
req.on("end", function () {
const all_bytes = []
for (let chunk of lst_chunks) {
for (let int_byte of chunk) {
all_bytes.push(int_byte)
}
}
resolve(new Uint8Array(all_bytes))
return
})
return
})
}
const ensure_collection_exists = async (collection_name, model_dim) => {
const collections = await client.listCollections()
if (collections.collection_names.indexOf(collection_name) === -1) {
console.info(`creating collection : ${collection_name} ...`)
const schema = [
{
name: 'id',
description: 'ID field',
data_type: DataType.VarChar,
max_length: 32,
is_primary_key: true,
autoID: false,
},
{
name: 'vector',
description: 'Vector field',
data_type: DataType.FloatVector,
dim: model_dim,
},
]
const result = await client.createCollection({
collection_name,
fields: schema,
})
if (result.error_code === 'Success') {
console.info(`created collection : ${collection_name} .`)
//
console.info(`creating index ...`)
const r2 = await client.createIndex({
collection_name,
field_name: 'vector',
index_name: 'myindex_A',
})
if (r2.error_code === 'Success') {
console.info('created index .')
}
else {
console.error('failed to create index .')
}
//
}
else {
console.info('failed to create collection.')
}
}
else {
//console.info(`collection exists already: ${collection_name}`)
}
return
}
const get_modelkey_from_original = (og_collectionname, model_key) => `${og_collectionname}_${model_key}`
const async_get_documents_embeddings_then_insert_into_collection = async (model_key, model_dim, og_collectionname, documents) => {
// make stable key for model-specific Collection
const collection_name = get_modelkey_from_original(og_collectionname, model_key)
await ensure_collection_exists(collection_name, model_dim)
const result = await async_get_embeddings_for_model(model_key, documents)
const fields_data = result.map(({ index, embedding }) => {
const vector = embedding[0]
const og_document = documents[index]
// create stable key (used as primary key) for doc-specific Embedding
const md5_doc = crypto.createHash('md5').update(og_document).digest('hex')
return { id: md5_doc, vector }
})
//console.info(`upserting data : ${fields_data.length} ...`)
const result_upsert = await client.upsert({
collection_name,
fields_data
})
if (result_upsert.status.error_code === 'Success') {
//console.info('upsert succeeded .')
}
else {
console.warn(['upsert failed', { collection_name }])
}
return { finished: true }
}
const LOADED_COLLECTIONS = new Set()
const ensure_collection_loaded = async (internal_collectionname) => {
if (!LOADED_COLLECTIONS.has(internal_collectionname)) {
console.info(`loading collection : ${internal_collectionname} ...`)
await client.loadCollection({ collection_name: internal_collectionname })
console.info('loaded collection .')
LOADED_COLLECTIONS.add(internal_collectionname)
}
return
}
const get_searchresults_from_params = async (j_params) => {
const { qtext, collection_name, limit } = j_params
const async_getresult = async (model_key) => {
const embed_result = await async_get_embeddings_for_model(model_key, [ qtext ])
const vector = embed_result[0].embedding[0]
const internal_collectionname = get_modelkey_from_original(collection_name, model_key)
await ensure_collection_loaded(internal_collectionname)
const search_result = await client.search({
collection_name: internal_collectionname,
data: vector,
limit,
consistency_level: 'Eventually',
output_fields: ['id'],
})
return { model_key, search_result }
}
const lst_promises = []
for (let model_key of Object.keys(MODELKEY_SERVER_MAP)) {
lst_promises.push(async_getresult(model_key))
}
const results = await Promise.all(lst_promises)
return results
}
const wrap_handler_error = async (async_handlerfunc) => {
let result
let did_error = false
try {
result = await async_handlerfunc()
}
catch (err) {
console.error(err)
did_error = true
}
return [ did_error, result ]
}
const func_dohandle = async (request, res) => {
const { method, headers, body } = request
const url_parts = url.parse(request.url)
const pathname = url_parts.pathname
let func_handle_res
if (method === 'GET' && pathname === '/') {
const str_html = await get_siteroot_html()
func_handle_res = () => {
res.writeHead(200, { 'Content-Type': 'text/html' })
res.write(str_html)
res.end()
}
}
else if (method === 'GET' && pathname === '/index.js') {
const str_js = await get_siteroot_js()
func_handle_res = () => {
res.writeHead(200, { 'Content-Type': 'text/javascript' })
res.write(str_js)
res.end()
}
}
else if (pathname === '/search') {
let j_params
if (method === 'GET') {
const searchParams = new URLSearchParams(url_parts.query)
const str_jparams = searchParams.get('p')
j_params = JSON.parse(str_jparams)
}
else if (method === 'POST') {
const body = await async_get_request_body(request)
j_params = JSON.parse(new TextDecoder().decode(body))
}
if (j_params !== undefined) {
const results = await get_searchresults_from_params(j_params)
func_handle_res = () => {
res.writeHead(200, { 'Content-Type': 'application/json' })
res.write(JSON.stringify({ results }))
res.end()
}
}
// else: fallback to default Not Found
}
else if (method === 'POST' && pathname === '/stash') {
const body = await async_get_request_body(request)
const j_body = JSON.parse(new TextDecoder().decode(body))
const { collection_name, documents, wait_for_result } = j_body
const lst_promises = []
for (let [ model_key, { dim } ] of Object.entries(MODELKEY_SERVER_MAP)) {
const model_dim = dim
lst_promises.push(async_get_documents_embeddings_then_insert_into_collection(model_key, model_dim, collection_name, documents))
}
let results_out = null
if (wait_for_result === true) {
try {
results_out = await Promise.all(lst_promises)
}
catch (err) {
console.error(err)
}
}
else {
Promise.all(lst_promises).then(()=>{}).catch(console.error)
}
func_handle_res = () => {
res.writeHead(200, { 'Content-Type': 'application/json' })
res.write(JSON.stringify({ results: results_out }))
res.end()
}
}
return { func_handle_res }
}
const server = http.createServer(async (request, res) => {
const [ did_error, handler_result ] = await wrap_handler_error(func_dohandle.bind(null, request, res))
if (did_error) {
res.writeHead(404)
res.end()
}
else if (handler_result.func_handle_res === undefined) {
res.writeHead(404)
res.end()
}
else {
handler_result.func_handle_res()
}
return
})
server.listen(PORT, HOST, () => {
console.log(`Server is running on http://${HOST}:${PORT}`)
return
})
function do_shutdown() {
console.log('graceful shutdown')
server.close(() => {
console.info('server closed.')
return
})
return
}
process.on('SIGINT', do_shutdown)