run-cfai.ts

import type { } from "npm:@cloudflare/workers-types@4.20240524.0";

const CF_API_TOKEN = Deno.env.get('CLOUDFLARE_AI_API_TOKEN')!;
const CF_ACCOUNT_ID = Deno.env.get('CLOUDFLARE_ACCOUNT_ID')!;
const INDEX_NAME = 'embeddings-index';

interface EmbeddingResponse {
  result: {
    shape: number[];
    data: number[][];
  },
  success: boolean;
  errors: any[];
  messages: any[];
}

async function runCfAi(model: string, args: any) {
  const endpoint = `https://api.cloudflare.com/client/v4/accounts/${CF_ACCOUNT_ID}/ai/run/${model}`;
  return fetch(
    endpoint,
    {
      headers: {
        'Authorization': `Bearer ${CF_API_TOKEN}`,
        'Content-Type': 'application/json',
      },
      method: "POST",
      body: JSON.stringify(args),
    }
  ).then((res) => res.json());
}

async function getEmbeddingVectors(args: { text: string[] }): Promise<EmbeddingResponse> {
  return await runCfAi('@cf/baai/bge-base-en-v1.5', args) as any;
}

async function runCfVectorize(method: string, args: any, { ndjson = true }: {
  ndjson?: boolean,
} = {}) {
  const endpoint = `https://api.cloudflare.com/client/v4/accounts/${CF_ACCOUNT_ID}/vectorize/indexes/${INDEX_NAME}/${method}`;
  return fetch(
    endpoint,
    {
      headers: {
        'Authorization': `Bearer ${CF_API_TOKEN}`,
        'Content-Type': ndjson ? 'application/x-ndjson' : 'application/json',
      },
      method: "POST",
      body: ndjson ? args.map((arg: any) => JSON.stringify(arg)).join('\n') : JSON.stringify(args),
    }
  ).then((res) => res.json());
}

async function upsertVectors(vectors: VectorizeVector[]) {
  return runCfVectorize('upsert', vectors, { ndjson: true });
}

async function insertVectors(vectors: VectorizeVector[]) {
  return runCfVectorize('insert', vectors, { ndjson: true });
}

async function queryVectors(
  vectors: number[],
  options: VectorizeQueryOptions
): Promise<VectorizeMatches> {
  return runCfVectorize('query', {
    ...options,
    vector: vectors,
  }, { ndjson: false }) as any;
}

const texts = [
  'This is a story about an orange cloud',
  'This is a story about a llama',
  'This is a story about a hugging emoji'
];

const res = await getEmbeddingVectors({
  text: texts,
});

const vectors: VectorizeVector[] = [];
res.result.data.forEach((vector, id) => {
  vectors.push({
    id: id.toString(),
    values: vector,
    metadata: { title: texts[id] }
  });
});

const userQuery = 'orange cloud';
const queryVector: EmbeddingResponse = await getEmbeddingVectors({
  text: [userQuery],
});

const query = queryVector.result.data[0];
const matches = await queryVectors(query, { topK: 3, returnValues: false, returnMetadata: false });

console.log(matches);