LowLevel brainstorm

all_scenarios.py

import os
import json
#
# Low-level client: lowest possible call
#

from azure.core.pipeline.transport import HttpRequest, HttpResponse
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]
documents = [{"id": str(idx), "text": doc} for idx, doc in enumerate(documents)]

request = HttpRequest("POST", "/languages",
    headers={
        'Content-Type': 'application/json'
    },
    data=json.dumps({
        "documents": documents
    })
)

response : HttpResponse = text_analytics_client.request(request)
response.raise_for_status()

result = json.loads(response.text())
reviewed_docs = [doc for doc, error in zip(result['documents'], result['errors']) if not error['error']]



#
# Low-level client: lowest possible call low-level option
#

from azure.core.pipeline.transport import HttpResponse
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]
documents = [{"id": str(idx), "text": doc} for idx, doc in enumerate(documents)]

response : HttpResponse = text_analytics_client.lowlevel().post(
    "/languages/,
    headers={
        'Content-Type': 'application/json'
    },
    data=json.dumps({
        "documents": documents
    })
)
response.raise_for_status()

result = json.loads(response.text())
reviewed_docs = [doc for doc, error in zip(result['documents'], result['errors']) if not error['error']]


#
# Low-level client: with preparers
#

from azure.core.pipeline.transport import HttpResponse
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
from azure.ai.textanalytics.protocol import TextAnalyticsPreparers

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]
documents = [{"id": str(idx), "text": doc} for idx, doc in enumerate(documents)]

request = TextAnalyticsPreparers.prepare_languages(
    path_1="foo", # positional
    path_2="a/b/c",
    # **kwargs
    body=documents, # Follow Swagger requiredness
    header_path_x_ms_stroage_whatever='bar', # Follow Swagger requiredness
    api_version="3.1-preview", # Required
    model_version="v1",
    show_stats=True,
)
response : HttpResponse = text_analytics_client.lowlevel().request(request)
response.raise_for_status()

result = json.loads(response.text())
reviewed_docs = [doc for doc, error in zip(result['documents'], result['errors']) if not error['error']]


#
# High-level autorest client: sending JSON
#

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]
documents = [{"id": str(idx), "text": doc} for idx, doc in enumerate(documents)]

result = text_analytics_client.languages(documents)  # Might raise

reviewed_docs = [doc for doc, error in zip(result.documents, result.errors) if not error.error]

#
# High-level autorest client: sending models
#

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
from azure.ai.textanalytics.models import LanguageInput

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]
documents = [LanguageInput(id=str(idx), text=doc) for idx, doc in enumerate(documents)]

result = text_analytics_client.languages(documents)  # Might raise

reviewed_docs = [doc for doc, error in zip(result.documents, result.errors) if not error.error]

#
# High-level manual client
#

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
    logging_enable=True
)

documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]

result = text_analytics_client.detect_language(documents)  # Might raise

reviewed_docs = [doc for doc in result if not doc.is_error]

ta_sample.py

        from azure.core.credentials import AzureKeyCredential
        from azure.ai.textanalytics import TextAnalyticsClient
        from azure.ai.textanalytics.protocol.v3_1_preview import TextAnalyticsPreparers

        endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
        key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

        # I create a regular client as usual
        # This is setting my policies, transports, etc. specific to TextAnalytics
        text_analytics_client = TextAnalyticsClient(
            endpoint=endpoint,
            credential=AzureKeyCredential(key),
            logging_enable=True
        )

        documents = [
            """
            The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
            She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
            walking up the stairs :). Can't say enough good things about my experience!
            """,
            """
            最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
            """
        ]
        documents = [{"id": str(idx), "text": doc} for idx, doc in enumerate(documents)]

        # This is Swagger generated but produce a request, it's NOT doing a call.
        # This never fails, there is no client side validation, and it's completly optional if you
        # feel like creating the request yourself
        # The body here could be JSON dict (like this example), or could be a model from Swagger generation, Python will adapt
        # Low-level will disable flattening, making certain that there is only ONE body parameter.
        # All parameters will be optional.
        # For the sake of clarity, body parameters are always called "body" whatever the Swagger will name it.
        # This follow the kwargs only syntax we use for model for now
        # This method is then a query parameter / path parameter / header parameter as its core.
        # Preparers are static, meaning they won't include client level parameters (endpoint). They do include API version info though,
        # since it's imported from a API version module name.
        request = TextAnalyticsPreparers.prepare_languages(
            documents,
            model_version="v1",
            show_stats=True,
        )

        # You have full flexibility to change what you want into it
        request.headers["x-ms-sdk-value"] = "lowlevelprototype/1.0.0b1"

        # All calls are done through "invoke". Swagger generating method are just preparers and optional
        response = text_analytics_client.invoke(request)

        # This is a convenience layer to raise if status is >= 4xx.
        # This is Python convention: https://2.python-requests.org/en/master/user/quickstart/#response-status-codes
        # This raises a HttpResponseError from azure-core
        response.raise_for_status()

        # Since it's Python, you can always deserialize if you really want to, but it's a manual step
        from azure.ai.textanalytics.protocol.v3_1_preview.models import DocumentsOutput
        result: DocumentsOutput = DocumentsOutput.deserialize(response)


        # An high-level autorest generated code is then syntactic sugar, with some "requiredness" validation
        def high_level_languages_example(documents, model_version=None, show_stats=None): # Some can be required
            request = self.prepare_languages(documents, model_version, show_stats)
            response = client.invoke(request)
            response.raise_for_status()
            return DocumentsOutput.deserialize(response)

• The two layer architecture we recommend:
  o Pure generation: low-level generation + high level autorest generation
  o Handcrafted: low-level generation + handcrafted layer