Cleanup

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# prepare to use standard Python libraries
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import sys
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import time
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# prepare to use the SDK
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# and an exception that the SDK throws when authorization fails
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from aihub import AIHub
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from aihub.exceptions import UnauthorizedException
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# authorize the SDK
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client = AIHub(api_root="PASTE YOUR API ROOT HERE",
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               api_key="PASTE YOUR API KEY HERE",
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               ib_context="PASTE YOUR IB-CONTEXT HERE")
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print("creating an empty batch")
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try:
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    # make an empty batch with a specific name in a specific workspace
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    create_batch_resp = client.batches.create(
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        name="receipt batch",
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        workspace="SDK-Tutorial")
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except UnauthorizedException:
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    # exit the program while printing a user-friendly error message and
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    # instructions on how to fix the problem
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    sys.exit("ERROR: SDK not authorized. "
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             "Are the API ROOT, API KEY, and IB-Context values correct?")
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# store batch_id in an easy-to-read variable, since we'll use it several times
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batch_id = create_batch_resp.id
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print("uploading two files to the batch")
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# upload the first file to the batch
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client.batches.add_file(batch_id=batch_id,
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                        file_name="receipt-a.jpg",
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                        file=open("PATH/ON/YOUR/COMPUTER/TO/receipt-1.jpg", "rb"))
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# upload a second file to the batch
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client.batches.add_file(batch_id=batch_id,
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                        file_name="receipt-b.jpg",
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                        file=open("PATH/ON/YOUR/COMPUTER/TO/receipt-2.jpg", "rb"))
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print("running the app")
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# trigger an app run, specifying which app, who wrote it, and which batch it should process
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run_resp = client.apps.runs.create(app_name="Meal Receipt",
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                                   owner="Instabase",
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                                   batch_id=batch_id)
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print("checking the app status until it finishes")
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while True:  # loop until explicitly told to leave the loop
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    time.sleep(3)  # pause a few seconds between each app status check
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    status_resp = client.apps.runs.status(run_resp.id)  # get the app status
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    print(f"status: {status_resp.status}")  # update the user on the app status
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    if status_resp.status not in ["PENDING", "RUNNING"]:  # these statuses mean the app is still running
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        break  # the app is done, so stop looping
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print("fetching the app results")
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results_resp = client.apps.runs.results(run_resp.id)  # get the app results
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for file in results_resp.files:  # iterate across all processed files
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    print(f"file name: {file.original_file_name}")
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    for document in file.documents:  # iterate across all documents in a file
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        for field in document.fields:  # iterate across all fields in a document
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            print(f"{field.field_name}: {field.value}")  # print the field name and value
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        print("---")  # visual separator between files
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print(f"deleting batch with ID {batch_id}")
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# submit an asynchronous request to delete the whole batch
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delete_batch_resp = client.batches.delete(batch_id)
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# repeatedly check status of delete batch job
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while True:
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    # pause to let the delete batch job make progress
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    time.sleep(3)
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    # check status of the delete batch job
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    status_resp = client.jobs.status(delete_batch_resp.job_id)
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    print(f"delete batch status: {status_resp.state}")
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    # break out of the loop if the delete batch job no longer running
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    if status_resp.state not in ["RUNNING", "PENDING"]:
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        break
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# print number of app runs
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list_runs_resp = client.apps.runs.list()
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num_app_runs = len(list_runs_resp.runs)
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print(f"number of app runs: {num_app_runs}")
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print(f"deleting app run with ID {run_resp.id}")
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delete_run_resp = client.apps.runs.delete(run_resp.id)
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# repeatedly check status of the job that deletes the output directory
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while True:
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    time.sleep(3)
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    status_resp = client.jobs.status(delete_run_resp.delete_output_dir_job_id)
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    print(f"delete output dir status: {status_resp.state}")
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    if status_resp.state not in ["RUNNING", "PENDING"]:
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        break
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# print number of app runs
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list_runs_resp = client.apps.runs.list()
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num_app_runs = len(list_runs_resp.runs)
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print(f"number of app runs: {num_app_runs}")

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# prepare to use standard Python libraries
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import sys
3
import time
4
5
# prepare to use the SDK
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from aihub import AIHub
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# authorize the SDK
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client = AIHub(api_root="PASTE YOUR API ROOT HERE",
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               api_key="PASTE YOUR API KEY HERE",
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               ib_context="PASTE YOUR IB-CONTEXT HERE")
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print("making a conversation with one document")
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create_conversation_resp = client.conversations.create(
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    name="Airbus conversation",
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    description="Analyze Airbus technical specs",
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    org="PASTE YOUR IB-CONTEXT HERE",
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    workspace="SDK-Tutorial",
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    files=["PATH/ON/YOUR/COMPUTER/TO/A330_specs.pdf"])
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print("checking the first document's processing status until it finishes")
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# repeatedly check whether the one document has been processed, only
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# leaving the loop when AI Hub reports that processing is done
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while True:
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    time.sleep(3)  # wait 3 seconds between status checks
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    status_resp = client.conversations.status(create_conversation_resp.id)
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    print(f"status of processing the first document: {status_resp.state}")
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    if status_resp.state == "COMPLETE":
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        break
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# get the single document's ID from the last response to a status check
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first_document_id = status_resp.documents[0].id
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print("sending a question to the first document")
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# send a synchronous question about the one document,
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# and print the answer
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converse_resp = client.conversations.converse(
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    conversation_id=create_conversation_resp.id,
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    question="What airplane family are these specs for?",
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    document_ids=[first_document_id])
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print(f"answer: {converse_resp.answer}")
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print("uploading the second document to the conversation")
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add_documents_resp = client.conversations.add_documents(
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    conversation_id=create_conversation_resp.id,
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    files=["PATH/ON/YOUR/COMPUTER/TO/A350_specs.pdf"])
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print("checking the second document's processing status until it finishes")
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# repeatedly check whether the second document has been processed, only
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# leaving the loop when AI Hub reports that processing is done
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while True:
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    time.sleep(3)  # wait 3 seconds between status checks
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    status_resp = client.conversations.status(create_conversation_resp.id)
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    print(f"status of processing second document: {status_resp.state}")
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    if status_resp.state == "COMPLETE":
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        break
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# indicate which AI Hub conversation or chatbot you want to
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# asynchronously send a question to
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source_app = {"type": "CONVERSE",
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              "id": create_conversation_resp.id}
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# "query", "prompt", and "question" are all synonyms in this context
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query = "List all Airbus planes by ascending wingspan."
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print("sending a question to both documents")
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# send an asynchronous question that AI Hub will use both of the
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# conversation's documents to answer
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run_query_resp = client.queries.run(query=query,
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                                    source_app=source_app)
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print("checking the status of the query processing until AI Hub has an answer")
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# repeatedly check whether AI Hub has generated an answer to
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# your question, only leaving the loop when an answer is available
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while True:
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    time.sleep(3)  # wait 3 seconds between status checks
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    query_status_resp = client.queries.status(query_id=run_query_resp.query_id)
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    print(f"query status: {query_status_resp.status}")
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    # if it generated an answer, status will be "COMPLETE"
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    if query_status_resp.status == "COMPLETE":
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        break
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    # if something went wrong, status will be "FAILED" and the program should exit
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    if query_status_resp.status == "FAILED":
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        sys.exit("ERROR: AI Hub couldn't process this query. Check the logs.")
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# print all the answers (there might be just one) that are stored in the response
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# to the last status query
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for result in query_status_resp.results:
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    print(f"answer: {result.response}")
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# delete the first document (A330_specs.pdf) from the conversation
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client.conversations.delete_documents(
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    conversation_id=create_conversation_resp.id,
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    ids=[first_document_id])
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# print number of conversations
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list_conversations_resp = client.conversations.list()
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num_conversations = len(list_conversations_resp.conversations)
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print(f"number of conversations: {num_conversations}")
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# delete the conversation and all remaining documents
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client.conversations.delete(create_conversation_resp.id)
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# print number of conversations
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list_conversations_resp = client.conversations.list()
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num_conversations = len(list_conversations_resp.conversations)
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print(f"number of conversations: {num_conversations}")