import { vectorStore, VectorStoreMetadata } from "../vector-store.js";
import { getDocsFileContent } from "./content.js";
import { embed } from "ai";
import { openai } from "@ai-sdk/openai";
const MIN_SCORE = process.env.MIN_SCORE ? parseFloat(process.env.MIN_SCORE) : 0.4;
if (!process.env.OPENAI_API_KEY) {
throw new Error("OPENAI_API_KEY is not set");
}
export async function queryVectorStore(query: string) {
console.log(`Querying vector store with query: ${query}`);
const queryEmbedding = await embed({
model: openai.embedding("text-embedding-3-small"),
value: query,
maxRetries: 3,
});
const results = await vectorStore.query({
indexName: "docs" as any,
queryVector: queryEmbedding.embedding,
topK: 5,
minScore: MIN_SCORE,
} as any);
console.log(`Retrieved ${results.length} results`);
const contents = await Promise.all(results.map(async (result) => {
const metadata = result.metadata as VectorStoreMetadata;
// const fullDocPage = await getDocsFileContent(metadata.webPath);
// const { body } = fullDocPage;
const fullDocPage = metadata.content;
return {
path: `${metadata.webPath}`,
score: parseFloat(result.score.toFixed(3)),
title: metadata.title,
description: metadata.description?? "",
content: fullDocPage,
};
}));
// Remove contents with duplicate paths
const uniqueContents = contents.filter((content, index, self) =>
index === self.findIndex((t) => t.path === content.path)
);
return uniqueContents;
}
