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Edits history of script submission #340 for ' Query Index (pinecone)'

  • deno
    import { removeObjectEmptyFields } from "https://deno.land/x/[email protected]/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 });
}

    Submitted by hugo697 399 days ago

  • deno
    import { removeObjectEmptyFields } from "https://deno.land/x/[email protected]/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 });
}

    Submitted by admin 1031 days ago

  • deno
    import { removeObjectEmptyFields } from "https://deno.land/x/[email protected]/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 });
}

    Submitted by admin 1034 days ago

  • deno
    import { Resource } from "https://deno.land/x/[email protected]/mod.ts";
import { removeObjectEmptyFields } from "https://deno.land/x/[email protected]/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/.
 */
export async function main(
  auth: Resource<"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 });
}

    Submitted by adam186 1149 days ago

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