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)
: 0.25;
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}`);
console.log("Creating embeddings...");
let queryEmbedding: Awaited<ReturnType<typeof embed>>["embedding"] = [];
let results: Awaited<ReturnType<typeof vectorStore.query>> = [];
try {
const queryEmbeddingResult = await embed({
model: openai.embedding("text-embedding-3-small"),
value: query,
maxRetries: 3,
});
queryEmbedding = queryEmbeddingResult.embedding;
} catch (error) {
console.error("Error creating embeddings:", error);
throw error;
}
try {
results = await vectorStore.query({
indexName: "docs" as any,
queryVector: queryEmbedding,
topK: 5,
minScore: MIN_SCORE,
} as any);
} catch (error) {
console.error("Error querying vector store:", error);
throw error;
}
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;
}
