import { removeObjectEmptyFields } from "https://deno.land/x/windmill_helpers@v1.1.1/mod.ts";

import { PineconeClient } from "npm:@pinecone-database/pinecone";

import { QueryOperationRequest } from "npm:@pinecone-database/pinecone/0.0.12/dist/pinecone-generated-ts-fetch/index.js";

/**

 *

 * @param topK The number of results to return for each query.

 *

 * @param vector _(Conditionally Optional)_ The query vector. This should be the same length as the dimension

 * of the index being queried.

 * **Each query request can contain only one of the parameters "id" or "vector".**

 *

 * @param id _(Conditionally Optional)_ The unique ID of the vector to be used as a query vector.

 * **Each query request can contain only one of the parameters "vector" or "id".**

 *

 * @param namespace _(Optional)_ The namespace to query.

 *

 * @param includeValues _(Optional)_ Indicates whether vector values are included in the response.

 * Defaults to `false`.

 *

 * @param includeMetadata _(Optional)_ Indicates whether metadata is included in the response as well as the ids.

 * Defaults to `false`.

 *

 * @param filter _(Optional)_ The filter to apply. You can use vector metadata to limit your search.

 * See https://www.pinecone.io/docs/metadata-filtering/.

 */

type Pinecone = {

  apiKey: string;

  environment: string;

};

export async function main(

  auth: Pinecone,

  index_name: string,

  topK: number,

  vector?: number[],

  id?: string,

  namespace?: string,

  include_values?: boolean,

  include_metadata?: boolean,

  filter?: object,

  raw?: boolean,

) {

  const client = new PineconeClient();

  await client.init(auth);

  const index = client.Index(index_name);

  const queryRequest: QueryOperationRequest = removeObjectEmptyFields({

    topK,

    vector,

    id,

    namespace,

    includeValues: include_values,

    includeMetadata: include_metadata,

    filter,

  });

  return await index[raw ? "queryRaw" : "query"]({ queryRequest });

}