Model catalog to have code examples for text completion along with Vision examples

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Are u sure, here is what I found for Claude 3.5

from transforms.api import transform, Output
from palantir_models.transforms import GenericVisionCompletionLanguageModelInput
from palantir_models.models import GenericVisionCompletionLanguageModel
from transforms.mediasets import MediaSetInput
from language_model_service_api.languagemodelservice_api_completion_v3 import (
    GenericVisionCompletionRequest,
    GenericChatCompletionResponse
)
from pyspark.sql import functions as F
from pyspark.sql.types import StringType
from language_model_service_api.languagemodelservice_api import (
    ChatMessageRole,
    GenericMessageContent,
    GenericMessage,
    MediaSetReference,
    MediaTransformation
)


@transform(
    image_input=MediaSetInput("Media set rid or path"),
    model=GenericVisionCompletionLanguageModelInput("ri.language-model-service..language-model.anthropic-claude-3-5-sonnet"),
    output=Output("Output dataset rid or path"),
)
def compute_generic(ctx, image_input, model: GenericVisionCompletionLanguageModel, output):
    media_set_rid = image_input.get_media_set_rid()
    image_references = image_input.list_media_items_by_path_with_media_reference(ctx)
    prompt = "Describe this image for me."

    def get_llm_response(media_item_rid):
        prompt_content = GenericMessageContent(text=prompt)
        request: GenericVisionCompletionRequest = GenericVisionCompletionRequest([
            GenericMessage(contents=[
                prompt_content,
                GenericMessageContent(
                        media_set_reference=MediaSetReference(
                            media_set_rid=media_set_rid,
                            media_item_rid=media_item_rid,
                            transformation=MediaTransformation.IMAGE_TO_BASE64_STRING
                        )
                    )
            ], role=ChatMessageRole.USER),
        ], max_tokens=200, temperature=0.8)
        response: GenericChatCompletionResponse = model.create_vision_completion(request)
        return response.completion

    get_llm_response_udf = F.udf(get_llm_response, StringType())

    output_df = image_references.withColumn('llm_response', get_llm_response_udf(F.col('mediaItemRid')))

    column_typeclasses = {'mediaReference': [{'kind': 'reference', 'name': 'media_reference'}]}
    output.write_dataframe(output_df, column_typeclasses=column_typeclasses)

Not sure where is the text completion part here in the code.

response: GenericChatCompletionResponse = model.create_vision_completion(request)