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This is an example of using the platform via REST. For more sample code, including any dependencies this file has, please have a look at our GitHub examples repository. This file can be found in that repo at python/vectara-rest/

"""Simple example of using the Vectara REST API for indexing.

import json
import logging
import requests

def _get_index_request_json(customer_id: int, corpus_id: int):
""" Returns some example data to index. """
document = {}
document["document_id"] = "doc-id-2"
# Note that the document ID must be unique for a given corpus
document["title"] = "Another example Title"
document["metadata_json"] = json.dumps(
"book-name": "Another example title",
"collection": "Mathematics",
"author": "Example Author"
sections = []
section = {}
section["text"] = "The answer to the ultimate question of life, the universe, and everything is 42."
document["section"] = sections

request = {}
request['customer_id'] = customer_id
request['corpus_id'] = corpus_id
request['document'] = document

return json.dumps(request)

def index_document(customer_id: int, corpus_id: int, idx_address: str, jwt_token: str):
""" Indexes content to the corpus.
customer_id: Unique customer ID in vectara platform.
corpus_id: ID of the corpus to which data needs to be indexed.
idx_address: Address of the indexing server. e.g.,
jwt_token: A valid Auth token.

(response, True) in case of success and returns (error, False) in case of failure.


post_headers = {
"Authorization": f"Bearer {jwt_token}",
"customer-id": f"{customer_id}"
response =
data=_get_index_request_json(customer_id, corpus_id),

if response.status_code != 200:
logging.error("REST upload failed with code %d, reason %s, text %s",
return response, False
return response, True