scriptoriumadmin/supermemory
1
0
Fork 0
mirror of https://github.com/supermemoryai/supermemory.git synced 2026-09-14 23:21:20 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge pull request #140 from supermemoryai/kush/experimental-thread

Browse source 
support threads and segregate chunks
This commit is contained in:
Dhravya Shah 2024-07-25 18:58:28 -05:00 • committed by GitHub
commit 417872c936
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
16 changed files with 571 additions and 232 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
.vscode/settings.json vendored
View file

@ -2,6 +2,6 @@
"css.validate": false,
"editor.quickSuggestions": {
"strings": true
},
"typescript.tsdk": "node_modules/typescript/lib"
},
"typescript.tsdk": "node_modules/typescript/lib"
}

235
apps/cf-ai-backend/src/helper.ts
View file

@ -1,5 +1,5 @@
import { Context } from "hono";
import { Env, vectorObj } from "./types";
import { Env, vectorObj, Chunks } from "./types";
import { CloudflareVectorizeStore } from "@langchain/cloudflare";
import { OpenAIEmbeddings } from "./utils/OpenAIEmbedder";
import { createOpenAI } from "@ai-sdk/openai";
@ -7,6 +7,7 @@ import { createGoogleGenerativeAI } from "@ai-sdk/google";
import { createAnthropic } from "@ai-sdk/anthropic";
import { z } from "zod";
import { seededRandom } from "./utils/seededRandom";
import { bulkInsertKv } from "./utils/kvBulkInsert";
export async function initQuery(
c: Context<{ Bindings: Env }>,
@ -135,7 +136,7 @@ export async function batchCreateChunksAndEmbeddings({
}: {
store: CloudflareVectorizeStore;
body: z.infer<typeof vectorObj>;
chunks: string[];
chunks: Chunks;
context: Context<{ Bindings: Env }>;
}) {
//! NOTE that we use #supermemory-web to ensure that
@ -150,15 +151,25 @@ export async function batchCreateChunksAndEmbeddings({
const allIds = await context.env.KV.list({ prefix: uuid });
let pageContent = "";
// If some chunks for that content already exist, we'll just update the metadata to include
// the user.
if (allIds.keys.length > 0) {
const savedVectorIds = allIds.keys.map((key) => key.name);
const vectors = await context.env.VECTORIZE_INDEX.getByIds(savedVectorIds);
const vectors = [];
//Search in a batch of 20
for (let i = 0; i < savedVectorIds.length; i += 20) {
const batch = savedVectorIds.slice(i, i + 20);
const batchVectors = await context.env.VECTORIZE_INDEX.getByIds(batch);
vectors.push(...batchVectors);
}
console.log(
vectors.map((vector) => {
return vector.id;
}),
);
// Now, we'll update all vector metadatas with one more userId and all spaceIds
const newVectors = vectors.map((vector) => {
console.log(JSON.stringify(vector.metadata));
vector.metadata = {
...vector.metadata,
[`user-${body.user}`]: 1,
@ -169,51 +180,183 @@ export async function batchCreateChunksAndEmbeddings({
return acc;
}, {}),
};
const content =
vector.metadata.content.toString().split("Content: ")[1] ||
vector.metadata.content;
pageContent += `<---chunkId: ${vector.id}\n${content}\n---->`;
return vector;
});
await context.env.VECTORIZE_INDEX.upsert(newVectors);
return pageContent; //Return the page content that goes to d1 db
}
// upsert in batch of 20
const results = [];
for (let i = 0; i < newVectors.length; i += 20) {
results.push(newVectors.slice(i, i + 20));
console.log(JSON.stringify(newVectors[1].id));
}
for (let i = 0; i < chunks.length; i++) {
const chunk = chunks[i];
const chunkId = `${uuid}-${i}`;
const newPageContent = `Title: ${body.title}\nDescription: ${body.description}\nURL: ${body.url}\nContent: ${chunk}`;
const docs = await store.addDocuments(
[
{
pageContent: newPageContent,
metadata: {
title: body.title?.slice(0, 50) ?? "",
description: body.description ?? "",
url: body.url,
type: body.type ?? "page",
content: newPageContent,
[sanitizeKey(`user-${body.user}`)]: 1,
...body.spaces?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {}),
},
},
],
{
ids: [chunkId],
},
await Promise.all(
results.map((result) => {
return context.env.VECTORIZE_INDEX.upsert(result);
}),
);
console.log("Docs added: ", docs);
await context.env.KV.put(chunkId, ourID);
pageContent += `<---chunkId: ${chunkId}\n${chunk}\n---->`;
return;
}
return pageContent; // Return the pageContent that goes to the d1 db
switch (chunks.type) {
case "tweet":
{
const commonMetaData = {
type: body.type ?? "tweet",
title: body.title,
description: body.description ?? "",
url: body.url,
[sanitizeKey(`user-${body.user}`)]: 1,
};
const spaceMetadata = body.spaces?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {});
const ids = [];
const preparedDocuments = chunks.chunks
.map((tweet, i) => {
return tweet.chunkedTweet.map((chunk) => {
const id = `${uuid}-${i}`;
ids.push(id);
const { tweetLinks, tweetVids, tweetId, tweetImages } =
tweet.metadata;
return {
pageContent: chunk,
metadata: {
links: tweetLinks,
videos: tweetVids,
tweetId: tweetId,
tweetImages: tweetImages,
...commonMetaData,
...spaceMetadata,
},
};
});
})
.flat();
const docs = await store.addDocuments(preparedDocuments, {
ids: ids,
});
console.log("these are the doucment ids", ids);
console.log("Docs added:", docs);
const { CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID } =
context.env;
await bulkInsertKv(
{ CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID },
{ chunkIds: ids, urlid: ourID },
);
}
break;
case "page":
{
const commonMetaData = {
type: body.type ?? "page",
title: body.title,
description: body.description ?? "",
url: body.url,
[sanitizeKey(`user-${body.user}`)]: 1,
};
const spaceMetadata = body.spaces?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {});
const ids = [];
const preparedDocuments = chunks.chunks.map((chunk, i) => {
const id = `${uuid}-${i}`;
ids.push(id);
return {
pageContent: chunk,
metadata: {
...commonMetaData,
...spaceMetadata,
},
};
});
const docs = await store.addDocuments(preparedDocuments, { ids: ids });
console.log("Docs added:", docs);
const { CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID } =
context.env;
await bulkInsertKv(
{ CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID },
{ chunkIds: ids, urlid: ourID },
);
}
break;
case "note":
{
const commonMetaData = {
type: body.type ?? "page",
description: body.description ?? "",
url: body.url,
[sanitizeKey(`user-${body.user}`)]: 1,
};
const spaceMetadata = body.spaces?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {});
const ids = [];
const preparedDocuments = chunks.chunks.map((chunk, i) => {
const id = `${uuid}-${i}`;
ids.push(id);
return {
pageContent: chunk,
metadata: {
...commonMetaData,
...spaceMetadata,
},
};
});
const docs = await store.addDocuments(preparedDocuments, { ids: ids });
console.log("Docs added:", docs);
const { CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID } =
context.env;
await bulkInsertKv(
{ CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID },
{ chunkIds: ids, urlid: ourID },
);
}
break;
case "image": {
const commonMetaData = {
type: body.type ?? "image",
title: body.title,
description: body.description ?? "",
url: body.url,
[sanitizeKey(`user-${body.user}`)]: 1,
};
const spaceMetadata = body.spaces?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {});
const ids = [];
const preparedDocuments = chunks.chunks.map((chunk, i) => {
const id = `${uuid}-${i}`;
ids.push(id);
return {
pageContent: chunk,
metadata: {
...commonMetaData,
...spaceMetadata,
},
};
});
const docs = await store.addDocuments(preparedDocuments, { ids: ids });
console.log("Docs added:", docs);
const { CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID } = context.env;
await bulkInsertKv(
{ CF_KV_AUTH_TOKEN, CF_ACCOUNT_ID, KV_NAMESPACE_ID },
{ chunkIds: ids, urlid: ourID },
);
}
}
return;
}

79
apps/cf-ai-backend/src/index.ts
View file

@ -1,7 +1,15 @@
import { z } from "zod";
import { Hono } from "hono";
import { CoreMessage, generateText, streamText, tool } from "ai";
import { chatObj, Env, vectorObj } from "./types";
import {
chatObj,
Chunks,
Env,
ImageChunks,
PageOrNoteChunks,
TweetChunks,
vectorObj,
} from "./types";
import {
batchCreateChunksAndEmbeddings,
deleteDocument,
@ -15,6 +23,8 @@ import { zValidator } from "@hono/zod-validator";
import chunkText from "./utils/chonker";
import { systemPrompt, template } from "./prompts/prompt1";
import { swaggerUI } from "@hono/swagger-ui";
import { chunkThread } from "./utils/chunkTweet";
import { chunkNote, chunkPage } from "./utils/chunkPageOrNotes";
const app = new Hono<{ Bindings: Env }>();
@ -59,42 +69,42 @@ app.get("/api/health", (c) => {
});
app.post("/api/add", zValidator("json", vectorObj), async (c) => {
const body = c.req.valid("json");
try {
const body = c.req.valid("json");
const { store } = await initQuery(c);
const { store } = await initQuery(c);
console.log(body.spaces);
console.log(body.spaces);
let chunks: TweetChunks | PageOrNoteChunks;
// remove everything in <raw> tags
const newPageContent = body.pageContent?.replace(/<raw>.*?<\/raw>/g, "");
// remove everything in <raw> tags
const newPageContent = body.pageContent?.replace(/<raw>.*?<\/raw>/g, "");
switch (body.type) {
case "tweet":
chunks = chunkThread(newPageContent);
break;
const chunks = chunkText(newPageContent, 1536);
case "page":
chunks = chunkPage(newPageContent);
break;
const chunksOf20 = chunks.reduce((acc, chunk, index) => {
if (index % 20 === 0) {
acc.push([chunk]);
} else {
acc[acc.length - 1].push(chunk);
case "note":
chunks = chunkNote(newPageContent);
break;
}
return acc;
}, [] as string[][]);
const accumChunkedInputs = [];
const promises = chunksOf20.map(async (chunkGroup) => {
const chunkedInput = await batchCreateChunksAndEmbeddings({
await batchCreateChunksAndEmbeddings({
store,
body,
chunks: chunkGroup,
chunks: chunks,
context: c,
});
accumChunkedInputs.push(chunkedInput);
});
await Promise.all(promises);
return c.json({ status: "ok", chunkedInput: accumChunkedInputs });
return c.json({ status: "ok" });
} catch (error) {
console.error("Error processing request:", error);
return c.json({ status: "error", message: error.message }, 500);
}
});
app.post(
@ -147,6 +157,13 @@ app.post(
);
const imageDescriptions = await Promise.all(imagePromises);
const chunks: ImageChunks = {
type: "image",
chunks: [
imageDescriptions,
...(body.text ? chunkText(body.text, 1536) : []),
].flat(),
};
await batchCreateChunksAndEmbeddings({
store,
@ -162,10 +179,7 @@ app.post(
pageContent: imageDescriptions.join("\n"),
title: "Image content from the web",
},
chunks: [
imageDescriptions,
...(body.text ? chunkText(body.text, 1536) : []),
].flat(),
chunks: chunks,
context: c,
});
@ -263,7 +277,7 @@ app.post(
// This is a "router". this finds out if the user wants to add a document, or chat with the AI to get a response.
const routerQuery = await generateText({
model: model,
system: `You are Supermemory chatbot. You can either add a document to the supermemory database, or return a chat response. Based on this query,
system: `You are Supermemory chatbot. You can either add a document to the supermemory database, or return a chat response. Based on this query,
You must determine what to do. Basically if it feels like a "question", then you should intiate a chat. If it feels like a "command" or feels like something that could be forwarded to the AI, then you should add a document.
You must also extract the "thing" to add and what type of thing it is.`,
prompt: `Question from user: ${query}`,
@ -289,7 +303,9 @@ app.post(
if ((task as string) === "add") {
// addString is the plaintext string that the user wants to add to the database
//chunk the note
let addString: string = addContent;
let vectorContent: Chunks = chunkNote(addContent);
if (thingToAdd === "page") {
// TODO: Sometimes this query hangs, and errors out. we need to do proper error management here.
@ -300,6 +316,7 @@ app.post(
});
addString = await response.text();
vectorContent = chunkPage(addString);
}
// At this point, we can just go ahead and create the embeddings!
@ -312,7 +329,7 @@ app.post(
pageContent: addString,
title: `${addString.slice(0, 30)}... (Added from chatbot)`,
},
chunks: chunkText(addString, 1536),
chunks: vectorContent,
context: c,
});

30
apps/cf-ai-backend/src/types.ts
View file

@ -1,5 +1,6 @@
import { sourcesZod } from "@repo/shared-types";
import { z } from "zod";
import { ThreadTweetData } from "./utils/chunkTweet";
export type Env = {
VECTORIZE_INDEX: VectorizeIndex;
@ -7,6 +8,9 @@ export type Env = {
SECURITY_KEY: string;
OPENAI_API_KEY: string;
GOOGLE_AI_API_KEY: string;
CF_KV_AUTH_TOKEN: string;
KV_NAMESPACE_ID: string;
CF_ACCOUNT_ID: string;
MY_QUEUE: Queue<TweetData[]>;
KV: KVNamespace;
MYBROWSER: unknown;
@ -23,6 +27,32 @@ export interface TweetData {
saveToUser: string;
}
interface BaseChunks {
type: "tweet" | "page" | "note" | "image";
}
export interface TweetChunks extends BaseChunks {
type: "tweet";
chunks: Array<ThreadTweetData>;
}
export interface PageOrNoteChunks extends BaseChunks {
type: "page" | "note";
chunks: string[];
}
export interface ImageChunks extends BaseChunks {
type: "image";
chunks: string[];
}
export type Chunks = TweetChunks | PageOrNoteChunks | ImageChunks;
export interface KVBulkItem {
key: string;
value: string;
base64: boolean;
}
export const contentObj = z.object({
role: z.string(),
parts: z

13
apps/cf-ai-backend/src/utils/chunkPageOrNotes.ts Normal file
View file

@ -0,0 +1,13 @@
import chunkText from "./chonker";
import { PageOrNoteChunks } from "../types";
export function chunkPage(pageContent: string): PageOrNoteChunks {
const chunks = chunkText(pageContent, 1536);
return { type: "page", chunks: chunks };
}
export function chunkNote(noteContent: string): PageOrNoteChunks {
const chunks = chunkText(noteContent, 1536);
return { type: "note", chunks: chunks };
}

40
apps/cf-ai-backend/src/utils/chunkTweet.ts Normal file
View file

@ -0,0 +1,40 @@
import { TweetChunks } from "../types";
import chunkText from "./chonker";
interface Tweet {
id: string;
text: string;
links: Array<string>;
images: Array<string>;
videos: Array<string>;
}
interface Metadata {
tweetId: string;
tweetLinks: any[];
tweetVids: any[];
tweetImages: any[];
}
export interface ThreadTweetData {
chunkedTweet: string[];
metadata: Metadata;
}
export function chunkThread(threadText: string): TweetChunks {
const thread = JSON.parse(threadText);
const chunkedTweets = thread.map((tweet: Tweet) => {
const chunkedTweet = chunkText(tweet.text, 1536);
const metadata = {
tweetId: tweet.id,
tweetLinks: tweet.links,
tweetVids: tweet.videos,
tweetImages: tweet.images,
};
return { chunkedTweet, metadata };
});
return { type: "tweet", chunks: chunkedTweets };
}

43
apps/cf-ai-backend/src/utils/kvBulkInsert.ts Normal file
View file

@ -0,0 +1,43 @@
import { KVBulkItem } from "../types";
export const bulkInsertKv = async (
credentials: {
CF_KV_AUTH_TOKEN: string;
KV_NAMESPACE_ID: string;
CF_ACCOUNT_ID: string;
},
keyData: {
chunkIds: Array<string>;
urlid: string;
},
) => {
const data: Array<KVBulkItem> = keyData.chunkIds.map((chunkId) => ({
key: chunkId,
value: keyData.urlid,
base64: false,
}));
try {
const response = await fetch(
`https://api.cloudflare.com/client/v4/accounts/${credentials.CF_ACCOUNT_ID}/storage/kv/namespaces/${credentials.KV_NAMESPACE_ID}/bulk`,
{
method: "PUT",
headers: {
Authorization: `Bearer ${credentials.CF_KV_AUTH_TOKEN}`,
"Content-Type": "application/json",
},
body: JSON.stringify(data),
},
);
if (!response.ok) {
throw new Error(
`can't insert bulk to kv because ${response.status} ${response.statusText} ${JSON.stringify(response.body)}`,
);
}
return await response.json();
} catch (e) {
//dosomething
throw e;
}
};

8
apps/web/app/(auth)/onboarding/page.tsx
View file

@ -24,7 +24,7 @@ export default function Home() {
useEffect(() => {
const updateDb = async () => {
await completeOnboarding();
}
};
if (currStep > 3) {
updateDb().then(() => {
push("/home?q=what%20is%20supermemory");
@ -389,7 +389,7 @@ function Navbar() {
const handleSkip = async () => {
await completeOnboarding();
router.push("/home?q=what%20is%20supermemory");
}
};
return (
<div className="flex items-center justify-between p-4 fixed top-0 left-0 w-full">
@ -399,7 +399,9 @@ function Navbar() {
className="hover:brightness-125 duration-200 size-12"
/>
<button className="text-sm" onClick={handleSkip}>Skip</button>
<button className="text-sm" onClick={handleSkip}>
Skip
</button>
</div>
);
}

13
apps/web/app/(dash)/header/signOutButton.tsx
View file

@ -5,13 +5,18 @@ export default function SignOutButton() {
return (
<form
action={async () => {
"use server"
await signOut()
"use server";
await signOut();
}}
>
<Button variant="ghost" size="sm" type="submit" className="text-[#7D8994]">
<Button
variant="ghost"
size="sm"
type="submit"
className="text-[#7D8994]"
>
Sign Out
</Button>
</form>
);
}
}

42
apps/web/app/actions/doers.ts
View file

@ -88,7 +88,7 @@ export const createSpace = async (
}
};
const typeDecider = (content: string) => {
const typeDecider = (content: string): "page" | "tweet" | "note" => {
// if the content is a URL, then it's a page. if its a URL with https://x.com/user/status/123, then it's a tweet. else, it's a note.
// do strict checking with regex
if (content.match(/https?:\/\/(x\.com|twitter\.com)\/[\w]+\/[\w]+\/[\d]+/)) {
@ -199,6 +199,7 @@ export const createMemory = async (input: {
let pageContent = input.content;
let metadata: Awaited<ReturnType<typeof getMetaData>>;
let vectorData: string;
if (!(await limit(data.user.id, type))) {
return {
@ -217,7 +218,7 @@ export const createMemory = async (input: {
},
});
pageContent = await response.text();
vectorData = pageContent;
try {
metadata = await getMetaData(input.content);
} catch (e) {
@ -227,8 +228,42 @@ export const createMemory = async (input: {
};
}
} else if (type === "tweet") {
//Request the worker for the entire thread
let thread: string;
let errorOccurred: boolean = false;
try {
const cf_thread_endpoint = process.env.THREAD_CF_WORKER;
const authKey = process.env.THREAD_CF_AUTH;
const threadRequest = await fetch(cf_thread_endpoint, {
method: "POST",
headers: {
"Content-Type": "application/json",
Authorization: authKey,
},
body: JSON.stringify({ url: input.content }),
});
if (threadRequest.status !== 200) {
throw new Error(
`Failed to fetch the thread: ${input.content}, Reason: ${threadRequest.statusText}`,
);
}
thread = await threadRequest.text();
} catch (e) {
console.log("[THREAD FETCHING SERVICE] Failed to fetch the thread", e);
errorOccurred = true;
}
const tweet = await getTweetData(input.content.split("/").pop() as string);
pageContent = tweetToMd(tweet);
console.log("THis ishte page content!!", pageContent);
//@ts-ignore
vectorData = errorOccurred ? pageContent : thread;
metadata = {
baseUrl: input.content,
description: tweet.text.slice(0, 200),
@ -237,6 +272,7 @@ export const createMemory = async (input: {
};
} else if (type === "note") {
pageContent = input.content;
vectorData = pageContent;
noteId = new Date().getTime();
metadata = {
baseUrl: `https://supermemory.ai/note/${noteId}`,
@ -263,7 +299,7 @@ export const createMemory = async (input: {
{
method: "POST",
body: JSON.stringify({
pageContent,
pageContent: vectorData,
title: metadata.title,
description: metadata.description,
url: metadata.baseUrl,

26
apps/web/app/api/chat/route.ts
View file

@ -48,22 +48,22 @@ export async function POST(req: NextRequest) {
// Only allow 5 requests per hour for each user, something lke this but this one is bad because chathistory.userid doesnt exist, we have to do a join and get it from the threads table
const result = await db
.select({
count: sql<number>`count(*)`.mapWith(Number),
})
.from(chatHistoryDb)
.innerJoin(chatThreads, eq(chatHistoryDb.threadId, chatThreads.id))
.where(
and(
eq(chatThreads.userId, session.user.id),
gt(chatHistoryDb.createdAt, lastHour)
)
)
.execute();
.select({
count: sql<number>`count(*)`.mapWith(Number),
})
.from(chatHistoryDb)
.innerJoin(chatThreads, eq(chatHistoryDb.threadId, chatThreads.id))
.where(
and(
eq(chatThreads.userId, session.user.id),
gt(chatHistoryDb.createdAt, lastHour),
),
)
.execute();
if (result[0]?.count && result[0]?.count >= 5) {
// return new Response(`Too many requests ${result[0]?.count}`, { status: 429 });
console.log(result[0]?.count)
console.log(result[0]?.count);
} else {
console.log("count", result);
}

7
apps/web/app/api/store/route.ts
View file

@ -30,7 +30,12 @@ const createMemoryFromAPI = async (input: {
count: sql<number>`count(*)`.mapWith(Number),
})
.from(storedContent)
.where(and(gt(storedContent.savedAt, last2Hours), eq(storedContent.userId, input.userId)))
.where(
and(
gt(storedContent.savedAt, last2Hours),
eq(storedContent.userId, input.userId),
),
);
if (numberOfItemsSavedInLast2Hours[0]!.count >= 20) {
return {

5
apps/web/cf-env.d.ts vendored
View file

@ -18,10 +18,13 @@ declare global {
CLOUDFLARE_DATABASE_ID: string;
CLOUDFLARE_D1_TOKEN: string;
THREAD_CF_WORKER: string;
THREAD_CF_AUTH: string;
MOBILE_TRUST_TOKEN: string;
RATELIMITER: {
limit: ({key: string}) => {success: boolean}
limit: ({ key: string }) => { success: boolean };
};
}
}

4
apps/web/server/db/schema.ts
View file

@ -212,7 +212,9 @@ export const chatHistory = createTable(
answer: text("answerParts"), // Single answer part as string
answerSources: text("answerSources"), // JSON stringified array of objects
answerJustification: text("answerJustification"),
createdAt: int("createdAt", { mode: "timestamp" }).notNull().default(new Date()),
createdAt: int("createdAt", { mode: "timestamp" })
.notNull()
.default(new Date()),
},
(history) => ({
threadIdx: index("chatHistory_thread_idx").on(history.threadId),

248
package.json
View file

@ -1,126 +1,126 @@
{
"name": "supermemory-ai",
"private": true,
"type": "module",
"scripts": {
"build": "turbo build",
"dev": "turbo dev",
"lint": "turbo lint",
"format": "prettier --write \"**/*.{ts,tsx,md}\"",
"deploy": "turbo deploy"
},
"devDependencies": {
"@clack/prompts": "^0.7.0",
"@cloudflare/next-on-pages": "1",
"@cloudflare/workers-types": "^4.20240614.0",
"@repo/eslint-config": "*",
"@repo/shared-types": "*",
"@repo/tailwind-config": "*",
"@repo/typescript-config": "*",
"@repo/ui": "*",
"@tailwindcss/typography": "^0.5.13",
"@types/turndown": "^5.0.4",
"autoprefixer": "^10.4.19",
"drizzle-kit": "0.21.2",
"eslint-plugin-next-on-pages": "^1.11.3",
"lint-staged": "^15.2.5",
"postcss": "^8.4.38",
"prettier": "^3.3.3",
"readline-sync": "^1.4.10",
"tailwindcss": "^3.4.3",
"tailwindcss-animate": "^1.0.7",
"turbo": "2.0.3",
"vercel": "^34.2.0"
},
"engines": {
"node": ">=18"
},
"packageManager": "yarn@1.22.21",
"workspaces": [
"apps/*",
"packages/*"
],
"dependencies": {
"@ai-sdk/anthropic": "^0.0.15",
"@ai-sdk/google": "^0.0.15",
"@ai-sdk/openai": "^0.0.14",
"@auth/drizzle-adapter": "^1.1.0",
"@aws-sdk/client-s3": "^3.577.0",
"@aws-sdk/s3-request-presigner": "^3.577.0",
"@babel/plugin-transform-runtime": "^7.24.7",
"@cloudflare/puppeteer": "^0.0.11",
"@headlessui/react": "^2.0.4",
"@heroicons/react": "^2.1.4",
"@hono/swagger-ui": "^0.2.2",
"@hookform/resolvers": "^3.4.2",
"@iarna/toml": "^2.2.5",
"@langchain/cloudflare": "^0.0.6",
"@million/lint": "^1.0.0-rc.81",
"@mozilla/readability": "^0.5.0",
"@radix-ui/react-accordion": "^1.1.2",
"@radix-ui/react-dropdown-menu": "^2.1.1",
"@radix-ui/react-icons": "^1.3.0",
"@radix-ui/react-label": "^2.0.2",
"@radix-ui/react-popover": "^1.1.1",
"@radix-ui/react-progress": "^1.0.3",
"@radix-ui/react-scroll-area": "^1.0.5",
"@radix-ui/react-select": "^2.0.0",
"@radix-ui/react-separator": "^1.0.3",
"@radix-ui/react-slot": "^1.1.0",
"@radix-ui/react-switch": "^1.1.0",
"@radix-ui/react-tabs": "^1.0.4",
"@radix-ui/react-toast": "^1.1.5",
"@radix-ui/react-tooltip": "^1.1.2",
"@tldraw/assets": "^2.2.0",
"@types/jsonwebtoken": "^9.0.6",
"@types/react-responsive-masonry": "^2.1.3",
"@types/readline-sync": "^1.4.8",
"ai": "^3.1.14",
"aws4fetch": "^1.0.18",
"cheerio": "^1.0.0-rc.12",
"compromise": "^14.13.0",
"crypto-browserify": "^3.12.0",
"drizzle-orm": "0.30.0",
"eslint-config-turbo": "^2.0.6",
"framer-motion": "^11.2.6",
"geist": "^1.3.0",
"google-auth-library": "^9.11.0",
"grammy": "^1.25.1",
"http": "^0.0.1-security",
"https": "^1.0.0",
"jsonwebtoken": "^9.0.2",
"katex": "^0.16.10",
"lucide-react": "^0.379.0",
"million": "^3.1.11",
"next-app-theme": "^0.1.10",
"next-auth": "^5.0.0-beta.18",
"next-themes": "^0.3.0",
"random-js": "^2.1.0",
"react-dropzone": "^14.2.3",
"react-hook-form": "^7.51.5",
"react-layout-masonry": "^1.1.0",
"react-markdown": "^9.0.1",
"react-responsive-masonry": "^2.2.1",
"react-tweet": "^3.2.1",
"react-use-measure": "^2.1.1",
"react-web-share": "^2.0.2",
"rehype-highlight": "^7.0.0",
"rehype-katex": "^7.0.0",
"remark-gfm": "^4.0.0",
"remark-math": "^6.0.0",
"sonner": "^1.5.0",
"tailwind-scrollbar": "^3.1.0",
"tldraw": "^2.1.4",
"turndown": "^7.2.0",
"uploadthing": "^6.10.4",
"vaul": "^0.9.1",
"zod": "^3.23.8"
},
"trustedDependencies": [
"core-js-pure",
"es5-ext"
],
"lint-staged": {
"**/*": "prettier --write --ignore-unknown"
}
"name": "supermemory-ai",
"private": true,
"type": "module",
"scripts": {
"build": "turbo build",
"dev": "turbo dev",
"lint": "turbo lint",
"format": "prettier --write \"**/*.{ts,tsx,md}\"",
"deploy": "turbo deploy"
},
"devDependencies": {
"@clack/prompts": "^0.7.0",
"@cloudflare/next-on-pages": "1",
"@cloudflare/workers-types": "^4.20240614.0",
"@repo/eslint-config": "*",
"@repo/shared-types": "*",
"@repo/tailwind-config": "*",
"@repo/typescript-config": "*",
"@repo/ui": "*",
"@tailwindcss/typography": "^0.5.13",
"@types/turndown": "^5.0.4",
"autoprefixer": "^10.4.19",
"drizzle-kit": "0.21.2",
"eslint-plugin-next-on-pages": "^1.11.3",
"lint-staged": "^15.2.5",
"postcss": "^8.4.38",
"prettier": "^3.3.3",
"readline-sync": "^1.4.10",
"tailwindcss": "^3.4.3",
"tailwindcss-animate": "^1.0.7",
"turbo": "2.0.3",
"vercel": "^34.2.0"
},
"engines": {
"node": ">=18"
},
"packageManager": "yarn@1.22.21",
"workspaces": [
"apps/*",
"packages/*"
],
"dependencies": {
"@ai-sdk/anthropic": "^0.0.15",
"@ai-sdk/google": "^0.0.15",
"@ai-sdk/openai": "^0.0.14",
"@auth/drizzle-adapter": "^1.1.0",
"@aws-sdk/client-s3": "^3.577.0",
"@aws-sdk/s3-request-presigner": "^3.577.0",
"@babel/plugin-transform-runtime": "^7.24.7",
"@cloudflare/puppeteer": "^0.0.11",
"@headlessui/react": "^2.0.4",
"@heroicons/react": "^2.1.4",
"@hono/swagger-ui": "^0.2.2",
"@hookform/resolvers": "^3.4.2",
"@iarna/toml": "^2.2.5",
"@langchain/cloudflare": "^0.0.6",
"@million/lint": "^1.0.0-rc.81",
"@mozilla/readability": "^0.5.0",
"@radix-ui/react-accordion": "^1.1.2",
"@radix-ui/react-dropdown-menu": "^2.1.1",
"@radix-ui/react-icons": "^1.3.0",
"@radix-ui/react-label": "^2.0.2",
"@radix-ui/react-popover": "^1.1.1",
"@radix-ui/react-progress": "^1.0.3",
"@radix-ui/react-scroll-area": "^1.0.5",
"@radix-ui/react-select": "^2.0.0",
"@radix-ui/react-separator": "^1.0.3",
"@radix-ui/react-slot": "^1.1.0",
"@radix-ui/react-switch": "^1.1.0",
"@radix-ui/react-tabs": "^1.0.4",
"@radix-ui/react-toast": "^1.1.5",
"@radix-ui/react-tooltip": "^1.1.2",
"@tldraw/assets": "^2.2.0",
"@types/jsonwebtoken": "^9.0.6",
"@types/react-responsive-masonry": "^2.1.3",
"@types/readline-sync": "^1.4.8",
"ai": "^3.1.14",
"aws4fetch": "^1.0.18",
"cheerio": "^1.0.0-rc.12",
"compromise": "^14.13.0",
"crypto-browserify": "^3.12.0",
"drizzle-orm": "0.30.0",
"eslint-config-turbo": "^2.0.6",
"framer-motion": "^11.2.6",
"geist": "^1.3.0",
"google-auth-library": "^9.11.0",
"grammy": "^1.25.1",
"http": "^0.0.1-security",
"https": "^1.0.0",
"jsonwebtoken": "^9.0.2",
"katex": "^0.16.10",
"lucide-react": "^0.379.0",
"million": "^3.1.11",
"next-app-theme": "^0.1.10",
"next-auth": "^5.0.0-beta.18",
"next-themes": "^0.3.0",
"random-js": "^2.1.0",
"react-dropzone": "^14.2.3",
"react-hook-form": "^7.51.5",
"react-layout-masonry": "^1.1.0",
"react-markdown": "^9.0.1",
"react-responsive-masonry": "^2.2.1",
"react-tweet": "^3.2.1",
"react-use-measure": "^2.1.1",
"react-web-share": "^2.0.2",
"rehype-highlight": "^7.0.0",
"rehype-katex": "^7.0.0",
"remark-gfm": "^4.0.0",
"remark-math": "^6.0.0",
"sonner": "^1.5.0",
"tailwind-scrollbar": "^3.1.0",
"tldraw": "^2.1.4",
"turndown": "^7.2.0",
"uploadthing": "^6.10.4",
"vaul": "^0.9.1",
"zod": "^3.23.8"
},
"trustedDependencies": [
"core-js-pure",
"es5-ext"
],
"lint-staged": {
"**/*": "prettier --write --ignore-unknown"
}
}

6
packages/ui/types.d.ts vendored
View file

@ -1,4 +1,4 @@
declare module "*.svg" {
const content: React.FunctionComponent<React.SVGAttributes<SVGElement>>;
export default content;
}
const content: React.FunctionComponent<React.SVGAttributes<SVGElement>>;
export default content;
}