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|
import { Context } from "hono";
import { Env, vectorObj } from "./types";
import { CloudflareVectorizeStore } from "@langchain/cloudflare";
import { OpenAIEmbeddings } from "./utils/OpenAIEmbedder";
import { createOpenAI } from "@ai-sdk/openai";
import { createGoogleGenerativeAI } from "@ai-sdk/google";
import { createAnthropic } from "@ai-sdk/anthropic";
import { z } from "zod";
import { seededRandom } from "./utils/seededRandom";
export async function initQuery(
c: Context<{ Bindings: Env }>,
model: string = "gpt-4o",
) {
const embeddings = new OpenAIEmbeddings({
apiKey: c.env.OPENAI_API_KEY,
modelName: "text-embedding-3-small",
});
const store = new CloudflareVectorizeStore(embeddings, {
index: c.env.VECTORIZE_INDEX,
});
let selectedModel:
| ReturnType<ReturnType<typeof createOpenAI>>
| ReturnType<ReturnType<typeof createGoogleGenerativeAI>>
| ReturnType<ReturnType<typeof createAnthropic>>;
switch (model) {
case "claude-3-opus":
const anthropic = createAnthropic({
apiKey: c.env.ANTHROPIC_API_KEY,
});
selectedModel = anthropic.chat("claude-3-opus-20240229");
console.log("Selected model: ", selectedModel);
break;
case "gemini-1.5-pro":
const googleai = createGoogleGenerativeAI({
apiKey: c.env.GOOGLE_AI_API_KEY,
});
selectedModel = googleai.chat("models/gemini-1.5-pro-latest");
console.log("Selected model: ", selectedModel);
break;
case "gpt-4o":
default:
const openai = createOpenAI({
apiKey: c.env.OPENAI_API_KEY,
});
selectedModel = openai.chat("gpt-4o");
break;
}
return { store, model: selectedModel };
}
export async function deleteDocument({
url,
user,
c,
store,
}: {
url: string;
user: string;
c: Context<{ Bindings: Env }>;
store: CloudflareVectorizeStore;
}) {
const toBeDeleted = `${url}#supermemory-web`;
const random = seededRandom(toBeDeleted);
const uuid =
random().toString(36).substring(2, 15) +
random().toString(36).substring(2, 15);
const allIds = await c.env.KV.list({ prefix: uuid });
if (allIds.keys.length > 0) {
const savedVectorIds = allIds.keys.map((key) => key.name);
const vectors = await c.env.VECTORIZE_INDEX.getByIds(savedVectorIds);
// We don't actually delete document directly, we just remove the user from the metadata.
// If there's no user left, we can delete the document.
const newVectors = vectors.map((vector) => {
delete vector.metadata[`user-${user}`];
// Get count of how many users are left
const userCount = Object.keys(vector.metadata).filter((key) =>
key.startsWith("user-"),
).length;
// If there's no user left, we can delete the document.
// need to make sure that every chunk is deleted otherwise it would be problematic.
if (userCount === 0) {
store.delete({ ids: savedVectorIds });
void Promise.all(savedVectorIds.map((id) => c.env.KV.delete(id)));
return null;
}
return vector;
});
// If all vectors are null (deleted), we can delete the KV too. Otherwise, we update (upsert) the vectors.
if (newVectors.every((v) => v === null)) {
await c.env.KV.delete(uuid);
} else {
await c.env.VECTORIZE_INDEX.upsert(newVectors.filter((v) => v !== null));
}
}
}
function sanitizeKey(key: string): string {
if (!key) throw new Error("Key cannot be empty");
// Remove or replace invalid characters
let sanitizedKey = key.replace(/[.$"]/g, "_");
// Ensure key does not start with $
if (sanitizedKey.startsWith("$")) {
sanitizedKey = sanitizedKey.substring(1);
}
return sanitizedKey;
}
export async function batchCreateChunksAndEmbeddings({
store,
body,
chunks,
context,
}: {
store: CloudflareVectorizeStore;
body: z.infer<typeof vectorObj>;
chunks: string[];
context: Context<{ Bindings: Env }>;
}) {
//! NOTE that we use #supermemory-web to ensure that
//! If a user saves it through the extension, we don't want other users to be able to see it.
// Requests from the extension should ALWAYS have a unique ID with the USERiD in it.
// I cannot stress this enough, important for security.
const ourID = `${body.url}#supermemory-web`;
const random = seededRandom(ourID);
const uuid =
random().toString(36).substring(2, 15) +
random().toString(36).substring(2, 15);
const allIds = await context.env.KV.list({ prefix: uuid });
// 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);
// Now, we'll update all vector metadatas with one more userId and all spaceIds
const newVectors = vectors.map((vector) => {
vector.metadata = {
...vector.metadata,
[`user-${body.user}`]: 1,
// For each space in body, add the spaceId to the vector metadata
...(body.spaces ?? [])?.reduce((acc, space) => {
acc[`space-${body.user}-${space}`] = 1;
return acc;
}, {}),
};
return vector;
});
await context.env.VECTORIZE_INDEX.upsert(newVectors);
return;
}
for (let i = 0; i < chunks.length; i++) {
const chunk = chunks[i];
const chunkId = `${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],
},
);
console.log("Docs added: ", docs);
await context.env.KV.put(chunkId, ourID);
}
}
|