DocumentIngestionApi

Method	HTTP request	Description
create_ingest_from_azure_blob_storage_job	POST /ingest/azure-blob-storage/job	Creates a job to add files from the Azure Blob Storage into a collection.
create_ingest_from_file_system_job	POST /ingest/file-system/job	Creates a jove to add files from the local system into a collection.
create_ingest_from_gcs_job	POST /ingest/gcs/job	Creates a job to add files from the Google Cloud Storage into a collection.
create_ingest_from_plain_text_job	POST /ingest/plain-text/job	Creates a job to add plain text to a collection.
create_ingest_from_s3_job	POST /ingest/s3/job	Creates a job to add files from the AWS S3 storage into a collection.
create_ingest_from_website_job	POST /ingest/website/job	Creates a job to crawl and ingest a URL into a collection.
create_ingest_upload_job	POST /uploads/{upload-id}/ingest/job	Creates a job to ingest uploaded document
ingest_from_azure_blob_storage	POST /ingest/azure-blob-storage	Adds files from the Azure Blob Storage into a collection.
ingest_from_file_system	POST /ingest/file-system	Adds files from the local system into a collection.
ingest_from_gcs	POST /ingest/gcs	Adds files from the Google Cloud Storage into a collection.
ingest_from_plain_text	POST /ingest/plain-text	Adds plain text to a collection.
ingest_from_s3	POST /ingest/s3	Adds files from the AWS S3 storage into a collection.
ingest_from_website	POST /ingest/website	Crawls and ingest a URL into a collection.
ingest_upload	POST /uploads/{upload-id}/ingest	Ingest uploaded document
upload_file	PUT /uploads

create_ingest_from_azure_blob_storage_job

JobDetails create_ingest_from_azure_blob_storage_job(collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, ingest_from_azure_blob_storage_body=ingest_from_azure_blob_storage_body)

Creates a job to add files from the Azure Blob Storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_azure_blob_storage_body import IngestFromAzureBlobStorageBody
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    ingest_from_azure_blob_storage_body = h2ogpte.rest.IngestFromAzureBlobStorageBody() # IngestFromAzureBlobStorageBody |  (optional)

    try:
        # Creates a job to add files from the Azure Blob Storage into a collection.
        api_response = api_instance.create_ingest_from_azure_blob_storage_job(collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, ingest_from_azure_blob_storage_body=ingest_from_azure_blob_storage_body)
        print("The response of DocumentIngestionApi->create_ingest_from_azure_blob_storage_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_azure_blob_storage_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
ingest_from_azure_blob_storage_body	IngestFromAzureBlobStorageBody		[optional]

Return type

JobDetails

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_from_file_system_job

JobDetails create_ingest_from_file_system_job(collection_id, ingest_from_file_system_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)

Creates a jove to add files from the local system into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_file_system_body import IngestFromFileSystemBody
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_file_system_body = h2ogpte.rest.IngestFromFileSystemBody() # IngestFromFileSystemBody | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)

    try:
        # Creates a jove to add files from the local system into a collection.
        api_response = api_instance.create_ingest_from_file_system_job(collection_id, ingest_from_file_system_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)
        print("The response of DocumentIngestionApi->create_ingest_from_file_system_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_file_system_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_file_system_body	IngestFromFileSystemBody
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]

Return type

JobDetails

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_from_gcs_job

JobDetails create_ingest_from_gcs_job(collection_id, ingest_from_gcs_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)

Creates a job to add files from the Google Cloud Storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_gcs_body import IngestFromGcsBody
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_gcs_body = h2ogpte.rest.IngestFromGcsBody() # IngestFromGcsBody | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)

    try:
        # Creates a job to add files from the Google Cloud Storage into a collection.
        api_response = api_instance.create_ingest_from_gcs_job(collection_id, ingest_from_gcs_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)
        print("The response of DocumentIngestionApi->create_ingest_from_gcs_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_gcs_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_gcs_body	IngestFromGcsBody
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]

Return type

JobDetails

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_from_plain_text_job

JobDetails create_ingest_from_plain_text_job(collection_id, file_name, body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions)

Creates a job to add plain text to a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    file_name = 'file_name_example' # str | String of the file name to use for the document.
    body = 'body_example' # str | The text that will ingested into a collection.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)

    try:
        # Creates a job to add plain text to a collection.
        api_response = api_instance.create_ingest_from_plain_text_job(collection_id, file_name, body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions)
        print("The response of DocumentIngestionApi->create_ingest_from_plain_text_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_plain_text_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
file_name	str	String of the file name to use for the document.
body	str	The text that will ingested into a collection.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]

Return type

JobDetails

HTTP request headers

Content-Type: text/plain
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_from_s3_job

JobDetails create_ingest_from_s3_job(collection_id, ingest_from_s3_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)

Creates a job to add files from the AWS S3 storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_s3_body import IngestFromS3Body
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_s3_body = h2ogpte.rest.IngestFromS3Body() # IngestFromS3Body | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)

    try:
        # Creates a job to add files from the AWS S3 storage into a collection.
        api_response = api_instance.create_ingest_from_s3_job(collection_id, ingest_from_s3_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)
        print("The response of DocumentIngestionApi->create_ingest_from_s3_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_s3_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_s3_body	IngestFromS3Body
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]

Return type

JobDetails

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_from_website_job

JobDetails create_ingest_from_website_job(collection_id, ingest_from_website_body, follow_links=follow_links, max_depth=max_depth, max_documents=max_documents, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)

Creates a job to crawl and ingest a URL into a collection.

Creates a job to crawl and ingest a URL into a collection. The web page or document linked from this URL will be imported.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_website_body import IngestFromWebsiteBody
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_website_body = h2ogpte.rest.IngestFromWebsiteBody() # IngestFromWebsiteBody | 
    follow_links = False # bool | Whether to import all web pages linked from this URL will be imported. External links will be ignored. Links to other pages on the same domain will be followed as long as they are at the same level or below the URL you specify. Each page will be transformed into a PDF document. (optional) (default to False)
    max_depth = -1 # int | Max depth of recursion when following links, only when follow_links is `true`. Max_depth of 0 means don't follow any links, max_depth of 1 means follow only top-level links, etc. Use -1 for automatic (system settings). (optional) (default to -1)
    max_documents = 56 # int | Max number of documents when following links, only when follow_links is `true`. Use None for automatic (system defaults). Use -1 for max (system limit). (optional)
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)

    try:
        # Creates a job to crawl and ingest a URL into a collection.
        api_response = api_instance.create_ingest_from_website_job(collection_id, ingest_from_website_body, follow_links=follow_links, max_depth=max_depth, max_documents=max_documents, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)
        print("The response of DocumentIngestionApi->create_ingest_from_website_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_from_website_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_website_body	IngestFromWebsiteBody
follow_links	bool	Whether to import all web pages linked from this URL will be imported. External links will be ignored. Links to other pages on the same domain will be followed as long as they are at the same level or below the URL you specify. Each page will be transformed into a PDF document.	[optional] [default to False]
max_depth	int	Max depth of recursion when following links, only when follow_links is `true`. Max_depth of 0 means don’t follow any links, max_depth of 1 means follow only top-level links, etc. Use -1 for automatic (system settings).	[optional] [default to -1]
max_documents	int	Max number of documents when following links, only when follow_links is `true`. Use None for automatic (system defaults). Use -1 for max (system limit).	[optional]
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]

Return type

JobDetails

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

create_ingest_upload_job

JobDetails create_ingest_upload_job(upload_id, collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)

Creates a job to ingest uploaded document

Creates a job to ingest uploaded document identified to a given collection

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.job_details import JobDetails
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    upload_id = 'upload_id_example' # str | Id of uploaded document
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)

    try:
        # Creates a job to ingest uploaded document
        api_response = api_instance.create_ingest_upload_job(upload_id, collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check)
        print("The response of DocumentIngestionApi->create_ingest_upload_job:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->create_ingest_upload_job: %s\n" % e)

Parameters

Name	Type	Description	Notes
upload_id	str	Id of uploaded document
collection_id	str	String id of the collection to add the ingested documents into.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]

Return type

JobDetails

HTTP request headers

Content-Type: Not defined
Accept: application/json

HTTP response details

Status code	Description	Response headers
201	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_azure_blob_storage

ingest_from_azure_blob_storage(collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout, ingest_from_azure_blob_storage_body=ingest_from_azure_blob_storage_body)

Adds files from the Azure Blob Storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_azure_blob_storage_body import IngestFromAzureBlobStorageBody
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)
    ingest_from_azure_blob_storage_body = h2ogpte.rest.IngestFromAzureBlobStorageBody() # IngestFromAzureBlobStorageBody |  (optional)

    try:
        # Adds files from the Azure Blob Storage into a collection.
        api_instance.ingest_from_azure_blob_storage(collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout, ingest_from_azure_blob_storage_body=ingest_from_azure_blob_storage_body)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_azure_blob_storage: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]
ingest_from_azure_blob_storage_body	IngestFromAzureBlobStorageBody		[optional]

Return type

void (empty response body)

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_file_system

ingest_from_file_system(collection_id, ingest_from_file_system_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)

Adds files from the local system into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_file_system_body import IngestFromFileSystemBody
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_file_system_body = h2ogpte.rest.IngestFromFileSystemBody() # IngestFromFileSystemBody | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Adds files from the local system into a collection.
        api_instance.ingest_from_file_system(collection_id, ingest_from_file_system_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_file_system: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_file_system_body	IngestFromFileSystemBody
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_gcs

ingest_from_gcs(collection_id, ingest_from_gcs_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)

Adds files from the Google Cloud Storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_gcs_body import IngestFromGcsBody
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_gcs_body = h2ogpte.rest.IngestFromGcsBody() # IngestFromGcsBody | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Adds files from the Google Cloud Storage into a collection.
        api_instance.ingest_from_gcs(collection_id, ingest_from_gcs_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_gcs: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_gcs_body	IngestFromGcsBody
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_plain_text

ingest_from_plain_text(collection_id, file_name, body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, timeout=timeout)

Adds plain text to a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    file_name = 'file_name_example' # str | String of the file name to use for the document.
    body = 'body_example' # str | The text that will ingested into a collection.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Adds plain text to a collection.
        api_instance.ingest_from_plain_text(collection_id, file_name, body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_plain_text: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
file_name	str	String of the file name to use for the document.
body	str	The text that will ingested into a collection.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: text/plain
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_s3

ingest_from_s3(collection_id, ingest_from_s3_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)

Adds files from the AWS S3 storage into a collection.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_s3_body import IngestFromS3Body
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_s3_body = h2ogpte.rest.IngestFromS3Body() # IngestFromS3Body | 
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Adds files from the AWS S3 storage into a collection.
        api_instance.ingest_from_s3(collection_id, ingest_from_s3_body, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_s3: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_s3_body	IngestFromS3Body
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_from_website

ingest_from_website(collection_id, ingest_from_website_body, follow_links=follow_links, max_depth=max_depth, max_documents=max_documents, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)

Crawls and ingest a URL into a collection.

Crawls and ingest a URL into a collection. The web page or document linked from this URL will be imported.

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.ingest_from_website_body import IngestFromWebsiteBody
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    ingest_from_website_body = h2ogpte.rest.IngestFromWebsiteBody() # IngestFromWebsiteBody | 
    follow_links = False # bool | Whether to import all web pages linked from this URL will be imported. External links will be ignored. Links to other pages on the same domain will be followed as long as they are at the same level or below the URL you specify. Each page will be transformed into a PDF document. (optional) (default to False)
    max_depth = -1 # int | Max depth of recursion when following links, only when follow_links is `true`. Max_depth of 0 means don't follow any links, max_depth of 1 means follow only top-level links, etc. Use -1 for automatic (system settings). (optional) (default to -1)
    max_documents = 56 # int | Max number of documents when following links, only when follow_links is `true`. Use None for automatic (system defaults). Use -1 for max (system limit). (optional)
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Crawls and ingest a URL into a collection.
        api_instance.ingest_from_website(collection_id, ingest_from_website_body, follow_links=follow_links, max_depth=max_depth, max_documents=max_documents, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_from_website: %s\n" % e)

Parameters

Name	Type	Description	Notes
collection_id	str	String id of the collection to add the ingested documents into.
ingest_from_website_body	IngestFromWebsiteBody
follow_links	bool	Whether to import all web pages linked from this URL will be imported. External links will be ignored. Links to other pages on the same domain will be followed as long as they are at the same level or below the URL you specify. Each page will be transformed into a PDF document.	[optional] [default to False]
max_depth	int	Max depth of recursion when following links, only when follow_links is `true`. Max_depth of 0 means don’t follow any links, max_depth of 1 means follow only top-level links, etc. Use -1 for automatic (system settings).	[optional] [default to -1]
max_documents	int	Max number of documents when following links, only when follow_links is `true`. Use None for automatic (system defaults). Use -1 for max (system limit).	[optional]
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: application/json
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

ingest_upload

ingest_upload(upload_id, collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)

Ingest uploaded document

Ingests uploaded document identified to a given collection

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    upload_id = 'upload_id_example' # str | Id of uploaded document
    collection_id = 'collection_id_example' # str | String id of the collection to add the ingested documents into.
    gen_doc_summaries = False # bool | Whether to auto-generate document summaries (uses LLM). (optional) (default to False)
    gen_doc_questions = False # bool | Whether to auto-generate sample questions for each document (uses LLM). (optional) (default to False)
    audio_input_language = 'auto' # str | Language of audio files. (optional) (default to 'auto')
    ocr_model = 'auto' # str | Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made). (optional) (default to 'auto')
    tesseract_lang = 'tesseract_lang_example' # str | Which language to use when using ocr_model=\"tesseract\". (optional)
    keep_tables_as_one_chunk = True # bool | When tables are identified by the table parser the table tokens will be kept in a single chunk. (optional)
    chunk_by_page = True # bool | Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`. (optional)
    handwriting_check = True # bool | Check pages for handwriting. Will use specialized models if handwriting is found. (optional)
    timeout = 300 # int | Timeout in seconds (optional) (default to 300)

    try:
        # Ingest uploaded document
        api_instance.ingest_upload(upload_id, collection_id, gen_doc_summaries=gen_doc_summaries, gen_doc_questions=gen_doc_questions, audio_input_language=audio_input_language, ocr_model=ocr_model, tesseract_lang=tesseract_lang, keep_tables_as_one_chunk=keep_tables_as_one_chunk, chunk_by_page=chunk_by_page, handwriting_check=handwriting_check, timeout=timeout)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->ingest_upload: %s\n" % e)

Parameters

Name	Type	Description	Notes
upload_id	str	Id of uploaded document
collection_id	str	String id of the collection to add the ingested documents into.
gen_doc_summaries	bool	Whether to auto-generate document summaries (uses LLM).	[optional] [default to False]
gen_doc_questions	bool	Whether to auto-generate sample questions for each document (uses LLM).	[optional] [default to False]
audio_input_language	str	Language of audio files.	[optional] [default to ‘auto’]
ocr_model	str	Which method to use to extract text from images using AI-enabled optical character recognition (OCR) models. docTR is best for Latin text, PaddleOCR is best for certain non-Latin languages, Tesseract covers a wide range of languages. Mississippi works well on handwriting. - `auto` - Automatic will auto-select the best OCR model for every page. - `off` - Disable OCR for speed, but all images will then be skipped (also no image captions will be made).	[optional] [default to ‘auto’]
tesseract_lang	str	Which language to use when using ocr_model="tesseract".	[optional]
keep_tables_as_one_chunk	bool	When tables are identified by the table parser the table tokens will be kept in a single chunk.	[optional]
chunk_by_page	bool	Each page will be a chunk. `keep_tables_as_one_chunk` will be ignored if this is `true`.	[optional]
handwriting_check	bool	Check pages for handwriting. Will use specialized models if handwriting is found.	[optional]
timeout	int	Timeout in seconds	[optional] [default to 300]

Return type

void (empty response body)

HTTP request headers

Content-Type: Not defined
Accept: application/json

HTTP response details

Status code	Description	Response headers
204	Successful operation	-
401	Unauthorized - Invalid or missing API key	-

upload_file

UploadedFile upload_file(file=file)

Uploads file to H2OGPTe instance

Example

Bearer Authentication (bearerAuth):

import h2ogpte.rest
from h2ogpte.rest.models.uploaded_file import UploadedFile
from h2ogpte.rest.rest import ApiException
from pprint import pprint

# Defining the host is optional and defaults to https://h2ogpte.genai.h2o.ai/api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = h2ogpte.rest.Configuration(
    host = "https://h2ogpte.genai.h2o.ai/api/v1"
)

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure Bearer authorization: bearerAuth
configuration = h2ogpte.rest.Configuration(
    access_token = os.environ["BEARER_TOKEN"]
)

# Enter a context with an instance of the API client
with h2ogpte.rest.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = h2ogpte.rest.DocumentIngestionApi(api_client)
    file = None # bytearray |  (optional)

    try:
        api_response = api_instance.upload_file(file=file)
        print("The response of DocumentIngestionApi->upload_file:\n")
        pprint(api_response)
    except Exception as e:
        print("Exception when calling DocumentIngestionApi->upload_file: %s\n" % e)

Parameters

Name	Type	Description	Notes
file	bytearray		[optional]

Return type

UploadedFile

HTTP request headers

Content-Type: multipart/form-data
Accept: application/json

HTTP response details

Status code	Description	Response headers
200	Successful operation	-
401	Unauthorized - Invalid or missing API key	-