Merge branch 'main' of https://github.com/Dhravya/supermemory into new-uinew-ui

9 files changed, 942 insertions, 136 deletions

diff --git a/apps/cf-ai-backend/package.json b/apps/cf-ai-backend/package.json
index b60ea18b..342fb7ff 100644
--- a/apps/cf-ai-backend/package.json
+++ b/apps/cf-ai-backend/package.json
@@ -1,15 +1,15 @@
 {
-  "name": "cf-ai-backend",
-  "version": "0.0.0",
-  "private": true,
-  "scripts": {
-    "deploy": "wrangler deploy",
-    "dev": "wrangler dev",
-    "start": "wrangler dev"
-  },
-  "devDependencies": {
-    "@cloudflare/workers-types": "^4.20240222.0",
-    "typescript": "^5.0.4",
-    "wrangler": "^3.0.0"
-  }
+	"name": "cf-ai-backend",
+	"version": "0.0.0",
+	"private": true,
+	"scripts": {
+		"deploy": "wrangler deploy",
+		"dev": "wrangler dev",
+		"start": "wrangler dev"
+	},
+	"devDependencies": {
+		"@cloudflare/workers-types": "^4.20240222.0",
+		"typescript": "^5.0.4",
+		"wrangler": "^3.0.0"
+	}
 }
diff --git a/apps/cf-ai-backend/pnpm-lock.yaml b/apps/cf-ai-backend/pnpm-lock.yaml
new file mode 100644
index 00000000..5c85a415
--- /dev/null
+++ b/apps/cf-ai-backend/pnpm-lock.yaml
@@ -0,0 +1,789 @@
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+      '@cloudflare/workerd-linux-arm64': 1.20240208.0
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+    dev: true
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+  /[email protected](@cloudflare/[email protected]):
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+    peerDependenciesMeta:
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+      mustache: 4.2.0
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+    dev: true
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+    dev: true
diff --git a/apps/cf-ai-backend/src/OpenAIEmbedder.ts b/apps/cf-ai-backend/src/OpenAIEmbedder.ts
index 35c36c74..e227d1e3 100644
--- a/apps/cf-ai-backend/src/OpenAIEmbedder.ts
+++ b/apps/cf-ai-backend/src/OpenAIEmbedder.ts
@@ -1,4 +1,4 @@
-import { AiTextGenerationOutput } from "@cloudflare/ai/dist/ai/tasks/text-generation";
+import { AiTextGenerationOutput } from '@cloudflare/ai/dist/ai/tasks/text-generation';
 
 interface OpenAIEmbeddingsParams {
 	apiKey: string;
@@ -15,7 +15,7 @@ export class OpenAIEmbeddings {
 	}
 
 	async embedDocuments(texts: string[]): Promise<number[][]> {
-		const responses = await Promise.all(texts.map(text => this.embedQuery(text)));
+		const responses = await Promise.all(texts.map((text) => this.embedQuery(text)));
 		return responses;
 	}
 
@@ -24,18 +24,18 @@ export class OpenAIEmbeddings {
 			method: 'POST',
 			headers: {
 				'Content-Type': 'application/json',
-				'Authorization': `Bearer ${this.apiKey}`
+				Authorization: `Bearer ${this.apiKey}`,
 			},
 			body: JSON.stringify({
 				input: text,
-				model: this.modelName
-			})
+				model: this.modelName,
+			}),
 		});
 
-		const data = await response.json() as {
+		const data = (await response.json()) as {
 			data: {
-				embedding: number[]
-			}[]
+				embedding: number[];
+			}[];
 		};
 
 		return data.data[0].embedding;
diff --git a/apps/cf-ai-backend/src/index.ts b/apps/cf-ai-backend/src/index.ts
index f55c465b..ccaf06bd 100644
--- a/apps/cf-ai-backend/src/index.ts
+++ b/apps/cf-ai-backend/src/index.ts
@@ -1,15 +1,9 @@
-import type {
-	VectorizeIndex,
-	Fetcher,
-	Request,
-} from "@cloudflare/workers-types";
-
-import {
-	CloudflareVectorizeStore,
-} from "@langchain/cloudflare";
-import { OpenAIEmbeddings } from "./OpenAIEmbedder";
-import { GoogleGenerativeAI } from "@google/generative-ai";
-import routeMap from "./routes";
+import type { VectorizeIndex, Fetcher, Request } from '@cloudflare/workers-types';
+
+import { CloudflareVectorizeStore } from '@langchain/cloudflare';
+import { OpenAIEmbeddings } from './OpenAIEmbedder';
+import { GoogleGenerativeAI } from '@google/generative-ai';
+import routeMap from './routes';
 
 function isAuthorized(request: Request, env: Env): boolean {
 	return request.headers.get('X-Custom-Auth-Key') === env.SECURITY_KEY;
@@ -32,7 +26,7 @@ export default {
 
 		const genAI = new GoogleGenerativeAI(env.GOOGLE_AI_API_KEY);
 
-		const model = genAI.getGenerativeModel({ model: "gemini-pro" });
+		const model = genAI.getGenerativeModel({ model: 'gemini-pro' });
 
 		const url = new URL(request.url);
 		const path = url.pathname;
diff --git a/apps/cf-ai-backend/src/routes.ts b/apps/cf-ai-backend/src/routes.ts
index 841f107a..a8a3249a 100644
--- a/apps/cf-ai-backend/src/routes.ts
+++ b/apps/cf-ai-backend/src/routes.ts
@@ -1,14 +1,20 @@
 import { CloudflareVectorizeStore } from '@langchain/cloudflare';
 import * as apiAdd from './routes/add';
-import * as apiQuery from "./routes/query"
-import * as apiAsk from "./routes/ask"
-import * as apiChat from "./routes/chat"
+import * as apiQuery from './routes/query';
+import * as apiAsk from './routes/ask';
+import * as apiChat from './routes/chat';
 import { OpenAIEmbeddings } from './OpenAIEmbedder';
 import { GenerativeModel } from '@google/generative-ai';
 import { Request } from '@cloudflare/workers-types';
 
-
-type RouteHandler = (request: Request, store: CloudflareVectorizeStore, embeddings: OpenAIEmbeddings, model: GenerativeModel, env: Env, ctx?: ExecutionContext) => Promise<Response>;
+type RouteHandler = (
+	request: Request,
+	store: CloudflareVectorizeStore,
+	embeddings: OpenAIEmbeddings,
+	model: GenerativeModel,
+	env: Env,
+	ctx?: ExecutionContext,
+) => Promise<Response>;
 
 const routeMap = new Map<string, Record<string, RouteHandler>>();
 
diff --git a/apps/cf-ai-backend/src/routes/add.ts b/apps/cf-ai-backend/src/routes/add.ts
index 9b05e9f0..b9a6da8f 100644
--- a/apps/cf-ai-backend/src/routes/add.ts
+++ b/apps/cf-ai-backend/src/routes/add.ts
@@ -1,36 +1,38 @@
-import { Request } from "@cloudflare/workers-types";
-import { type CloudflareVectorizeStore } from "@langchain/cloudflare";
+import { Request } from '@cloudflare/workers-types';
+import { type CloudflareVectorizeStore } from '@langchain/cloudflare';
 
 export async function POST(request: Request, store: CloudflareVectorizeStore) {
-	const body = await request.json() as {
-		pageContent: string,
-		title?: string,
-		description?: string,
-		space?: string,
-		url: string,
-		user: string
+	const body = (await request.json()) as {
+		pageContent: string;
+		title?: string;
+		description?: string;
+		space?: string;
+		url: string;
+		user: string;
 	};
 
 	if (!body.pageContent || !body.url) {
-		return new Response(JSON.stringify({ message: "Invalid Page Content" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid Page Content' }), { status: 400 });
 	}
-	const newPageContent = `Title: ${body.title}\nDescription: ${body.description}\nURL: ${body.url}\nContent: ${body.pageContent}`
+	const newPageContent = `Title: ${body.title}\nDescription: ${body.description}\nURL: ${body.url}\nContent: ${body.pageContent}`;
 
-
-	await store.addDocuments([
-		{
-			pageContent: newPageContent,
-			metadata: {
-				title: body.title ?? "",
-				description: body.description ?? "",
-				space: body.space ?? "",
-				url: body.url,
-				user: body.user,
+	await store.addDocuments(
+		[
+			{
+				pageContent: newPageContent,
+				metadata: {
+					title: body.title ?? '',
+					description: body.description ?? '',
+					space: body.space ?? '',
+					url: body.url,
+					user: body.user,
+				},
 			},
+		],
+		{
+			ids: [`${body.url}-${body.user}`],
 		},
-	], {
-		ids: [`${body.url}`]
-	})
+	);
 
-	return new Response(JSON.stringify({ message: "Document Added" }), { status: 200 });
+	return new Response(JSON.stringify({ message: 'Document Added' }), { status: 200 });
 }
diff --git a/apps/cf-ai-backend/src/routes/ask.ts b/apps/cf-ai-backend/src/routes/ask.ts
index 1c48dde8..267c1513 100644
--- a/apps/cf-ai-backend/src/routes/ask.ts
+++ b/apps/cf-ai-backend/src/routes/ask.ts
@@ -1,18 +1,18 @@
-import { GenerativeModel } from "@google/generative-ai";
-import { OpenAIEmbeddings } from "../OpenAIEmbedder";
-import { CloudflareVectorizeStore } from "@langchain/cloudflare";
-import { Request } from "@cloudflare/workers-types";
+import { GenerativeModel } from '@google/generative-ai';
+import { OpenAIEmbeddings } from '../OpenAIEmbedder';
+import { CloudflareVectorizeStore } from '@langchain/cloudflare';
+import { Request } from '@cloudflare/workers-types';
 
 export async function POST(request: Request, _: CloudflareVectorizeStore, embeddings: OpenAIEmbeddings, model: GenerativeModel, env?: Env) {
-	const body = await request.json() as {
-		query: string
+	const body = (await request.json()) as {
+		query: string;
 	};
 
 	if (!body.query) {
-		return new Response(JSON.stringify({ message: "Invalid Page Content" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid Page Content' }), { status: 400 });
 	}
 
-	const prompt = `You are an agent that answers a question based on the query. don't say 'based on the context'.\n\n Context:\n${body.query} \nAnswer this question based on the context. Question: ${body.query}\nAnswer:`
+	const prompt = `You are an agent that answers a question based on the query. don't say 'based on the context'.\n\n Context:\n${body.query} \nAnswer this question based on the context. Question: ${body.query}\nAnswer:`;
 	const output = await model.generateContentStream(prompt);
 
 	const response = new Response(
@@ -22,14 +22,14 @@ export async function POST(request: Request, _: CloudflareVectorizeStore, embedd
 				for await (const chunk of output.stream) {
 					const chunkText = await chunk.text();
 					console.log(chunkText);
-					const encodedChunk = converter.encode("data: " + JSON.stringify({ "response": chunkText }) + "\n\n");
+					const encodedChunk = converter.encode('data: ' + JSON.stringify({ response: chunkText }) + '\n\n');
 					controller.enqueue(encodedChunk);
 				}
-				const doneChunk = converter.encode("data: [DONE]");
+				const doneChunk = converter.encode('data: [DONE]');
 				controller.enqueue(doneChunk);
 				controller.close();
-			}
-		})
+			},
+		}),
 	);
 	return response;
 }
diff --git a/apps/cf-ai-backend/src/routes/chat.ts b/apps/cf-ai-backend/src/routes/chat.ts
index 95788d03..75e298b8 100644
--- a/apps/cf-ai-backend/src/routes/chat.ts
+++ b/apps/cf-ai-backend/src/routes/chat.ts
@@ -1,28 +1,28 @@
-import { Content, GenerativeModel } from "@google/generative-ai";
-import { OpenAIEmbeddings } from "../OpenAIEmbedder";
-import { CloudflareVectorizeStore } from "@langchain/cloudflare";
-import { Request } from "@cloudflare/workers-types";
+import { Content, GenerativeModel } from '@google/generative-ai';
+import { OpenAIEmbeddings } from '../OpenAIEmbedder';
+import { CloudflareVectorizeStore } from '@langchain/cloudflare';
+import { Request } from '@cloudflare/workers-types';
 
 export async function POST(request: Request, _: CloudflareVectorizeStore, embeddings: OpenAIEmbeddings, model: GenerativeModel, env?: Env) {
 	const queryparams = new URL(request.url).searchParams;
-	const query = queryparams.get("q");
-	const topK = parseInt(queryparams.get("topK") ?? "5");
-	const user = queryparams.get("user")
-	const spaces = queryparams.get("spaces")
-	const spacesArray = spaces ? spaces.split(",") : undefined
+	const query = queryparams.get('q');
+	const topK = parseInt(queryparams.get('topK') ?? '5');
+	const user = queryparams.get('user');
+	const spaces = queryparams.get('spaces');
+	const spacesArray = spaces ? spaces.split(',') : undefined;
 
-	const sourcesOnly = (queryparams.get("sourcesOnly") ?? "false")
+	const sourcesOnly = queryparams.get('sourcesOnly') ?? 'false';
 
 	if (!user) {
-		return new Response(JSON.stringify({ message: "Invalid User" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid User' }), { status: 400 });
 	}
 
 	if (!query) {
-		return new Response(JSON.stringify({ message: "Invalid Query" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid Query' }), { status: 400 });
 	}
 	const filter: VectorizeVectorMetadataFilter = {
-		user
-	}
+		user,
+	};
 
 	const responses: VectorizeMatches = { matches: [], count: 0 };
 
@@ -34,12 +34,12 @@ export async function POST(request: Request, _: CloudflareVectorizeStore, embedd
 
 			const resp = await env!.VECTORIZE_INDEX.query(queryAsVector, {
 				topK,
-				filter
+				filter,
 			});
 
 			if (resp.count > 0) {
-				responses.matches.push(...resp.matches)
-				responses.count += resp.count
+				responses.matches.push(...resp.matches);
+				responses.count += resp.count;
 			}
 		}
 	} else {
@@ -47,13 +47,13 @@ export async function POST(request: Request, _: CloudflareVectorizeStore, embedd
 		const resp = await env!.VECTORIZE_INDEX.query(queryAsVector, {
 			topK,
 			filter: {
-				user
-			}
+				user,
+			},
 		});
 
 		if (resp.count > 0) {
-			responses.matches.push(...resp.matches)
-			responses.count += resp.count
+			responses.matches.push(...resp.matches);
+			responses.count += resp.count;
 		}
 	}
 
@@ -61,27 +61,36 @@ export async function POST(request: Request, _: CloudflareVectorizeStore, embedd
 	// 	return new Response(JSON.stringify({ message: "No Results Found" }), { status: 404 });
 	// }
 
-	const highScoreIds = responses.matches.filter(({ score }) => score > 0.35).map(({ id }) => id)
+	const highScoreIds = responses.matches.filter(({ score }) => score > 0.35).map(({ id }) => id);
 
-	if (sourcesOnly === "true") {
+	if (sourcesOnly === 'true') {
 		return new Response(JSON.stringify({ ids: highScoreIds }), { status: 200 });
 	}
 
-	const vec = await env!.VECTORIZE_INDEX.getByIds(highScoreIds)
+	const vec = await env!.VECTORIZE_INDEX.getByIds(highScoreIds);
 
-	const preparedContext = vec.map(({ metadata }) => `Website title: ${metadata!.title}\nDescription: ${metadata!.description}\nURL: ${metadata!.url}\nContent: ${metadata!.text}`).join("\n\n");
+	const preparedContext = vec
+		.map(
+			({ metadata }) =>
+				`Website title: ${metadata!.title}\nDescription: ${metadata!.description}\nURL: ${metadata!.url}\nContent: ${metadata!.text}`,
+		)
+		.join('\n\n');
 
-	const body = await request.json() as {
-		chatHistory?: Content[]
+	const body = (await request.json()) as {
+		chatHistory?: Content[];
 	};
 
 	const defaultHistory = [
 		{
-			role: "user",
-			parts: [{ text: `You are an agent that summarizes a page based on the query. don't say 'based on the context'. I expect you to be like a 'Second Brain'. you will be provided with the context (old saved posts) and questions. Answer accordingly. Answer in markdown format` }],
+			role: 'user',
+			parts: [
+				{
+					text: `You are an agent that summarizes a page based on the query. don't say 'based on the context'. I expect you to be like a 'Second Brain'. you will be provided with the context (old saved posts) and questions. Answer accordingly. Answer in markdown format`,
+				},
+			],
 		},
 		{
-			role: "model",
+			role: 'model',
 			parts: [{ text: "Ok, I am a personal assistant, and will act as a second brain to help with user's queries." }],
 		},
 	] as Content[];
@@ -100,14 +109,14 @@ export async function POST(request: Request, _: CloudflareVectorizeStore, embedd
 				const converter = new TextEncoder();
 				for await (const chunk of output.stream) {
 					const chunkText = await chunk.text();
-					const encodedChunk = converter.encode("data: " + JSON.stringify({ "response": chunkText }) + "\n\n");
+					const encodedChunk = converter.encode('data: ' + JSON.stringify({ response: chunkText }) + '\n\n');
 					controller.enqueue(encodedChunk);
 				}
-				const doneChunk = converter.encode("data: [DONE]");
+				const doneChunk = converter.encode('data: [DONE]');
 				controller.enqueue(doneChunk);
 				controller.close();
-			}
-		})
+			},
+		}),
 	);
 	return response;
 }
diff --git a/apps/cf-ai-backend/src/routes/query.ts b/apps/cf-ai-backend/src/routes/query.ts
index be237d7d..cd5295c5 100644
--- a/apps/cf-ai-backend/src/routes/query.ts
+++ b/apps/cf-ai-backend/src/routes/query.ts
@@ -1,59 +1,65 @@
-import { GenerativeModel } from "@google/generative-ai";
-import { OpenAIEmbeddings } from "../OpenAIEmbedder";
-import { CloudflareVectorizeStore } from "@langchain/cloudflare";
-import { Request } from "@cloudflare/workers-types";
+import { GenerativeModel } from '@google/generative-ai';
+import { OpenAIEmbeddings } from '../OpenAIEmbedder';
+import { CloudflareVectorizeStore } from '@langchain/cloudflare';
+import { Request } from '@cloudflare/workers-types';
 
 export async function GET(request: Request, _: CloudflareVectorizeStore, embeddings: OpenAIEmbeddings, model: GenerativeModel, env?: Env) {
 	const queryparams = new URL(request.url).searchParams;
-	const query = queryparams.get("q");
-	const topK = parseInt(queryparams.get("topK") ?? "5");
-	const user = queryparams.get("user")
-	const space = queryparams.get("space")
+	const query = queryparams.get('q');
+	const topK = parseInt(queryparams.get('topK') ?? '5');
+	const user = queryparams.get('user');
+	const space = queryparams.get('space');
 
-	const sourcesOnly = (queryparams.get("sourcesOnly") ?? "false")
+	const sourcesOnly = queryparams.get('sourcesOnly') ?? 'false';
 
 	if (!user) {
-		return new Response(JSON.stringify({ message: "Invalid User" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid User' }), { status: 400 });
 	}
 
 	if (!query) {
-		return new Response(JSON.stringify({ message: "Invalid Query" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'Invalid Query' }), { status: 400 });
 	}
 
 	const filter: VectorizeVectorMetadataFilter = {
-		user
-	}
+		user,
+	};
 
 	if (space) {
-		filter.space
+		filter.space;
 	}
 
 	const queryAsVector = await embeddings.embedQuery(query);
 
 	const resp = await env!.VECTORIZE_INDEX.query(queryAsVector, {
 		topK,
-		filter
+		filter,
 	});
 
 	if (resp.count === 0) {
-		return new Response(JSON.stringify({ message: "No Results Found" }), { status: 404 });
+		return new Response(JSON.stringify({ message: 'No Results Found' }), { status: 404 });
 	}
 
-	const highScoreIds = resp.matches.filter(({ score }) => score > 0.3).map(({ id }) => id)
+	const highScoreIds = resp.matches.filter(({ score }) => score > 0.3).map(({ id }) => id);
 
-	if (sourcesOnly === "true") {
+	if (sourcesOnly === 'true') {
 		return new Response(JSON.stringify({ ids: highScoreIds }), { status: 200 });
 	}
 
-	const vec = await env!.VECTORIZE_INDEX.getByIds(highScoreIds)
+	const vec = await env!.VECTORIZE_INDEX.getByIds(highScoreIds);
 
 	if (vec.length === 0 || !vec[0].metadata) {
-		return new Response(JSON.stringify({ message: "No Results Found" }), { status: 400 });
+		return new Response(JSON.stringify({ message: 'No Results Found' }), { status: 400 });
 	}
 
-	const preparedContext = vec.slice(0, 3).map(({ metadata }) => `Website title: ${metadata!.title}\nDescription: ${metadata!.description}\nURL: ${metadata!.url}\nContent: ${metadata!.text}`).join("\n\n");
+	const preparedContext = vec
+		.slice(0, 3)
+		.map(
+			({ metadata }) =>
+				`Website title: ${metadata!.title}\nDescription: ${metadata!.description}\nURL: ${metadata!.url}\nContent: ${metadata!.text}`,
+		)
+		.join('\n\n');
 
-	const prompt = `You are an agent that summarizes a page based on the query. Be direct and concise, don't say 'based on the context'.\n\n Context:\n${preparedContext} \nAnswer this question based on the context. Question: ${query}\nAnswer:`
+	const prompt = `You are an agent that summarizes a page based on the query. Be direct and concise, don't say 'based on the context'.\n\n Context:\n${preparedContext} \nAnswer this question based on the context. Question: ${query}\nAnswer:`;
 	const output = await model.generateContentStream(prompt);
 
 	const response = new Response(
@@ -62,14 +68,14 @@ export async function GET(request: Request, _: CloudflareVectorizeStore, embeddi
 				const converter = new TextEncoder();
 				for await (const chunk of output.stream) {
 					const chunkText = await chunk.text();
-					const encodedChunk = converter.encode("data: " + JSON.stringify({ "response": chunkText }) + "\n\n");
+					const encodedChunk = converter.encode('data: ' + JSON.stringify({ response: chunkText }) + '\n\n');
 					controller.enqueue(encodedChunk);
 				}
-				const doneChunk = converter.encode("data: [DONE]");
+				const doneChunk = converter.encode('data: [DONE]');
 				controller.enqueue(doneChunk);
 				controller.close();
-			}
-		})
+			},
+		}),
 	);
 	return response;
 }