84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
from models.llm_message import LLMSystemMessage, LLMUserMessage
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from models.presentation_layout import SlideLayoutModel
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from models.presentation_outline_model import SlideOutlineModel
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from services.llm_client import LLMClient
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from utils.llm_provider import get_model
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from utils.schema_utils import add_field_in_schema, remove_fields_from_schema
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system_prompt = """
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Generate structured slide based on provided outline, follow mentioned steps and notes and provide structured output.
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# Steps
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1. Analyze the outline.
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2. Generate structured slide based on the outline.
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3. Generate speaker note that is simple, clear, concise and to the point.
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# Notes
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- Slide body should not use words like "This slide", "This presentation".
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- Rephrase the slide body to make it flow naturally.
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- Provide prompt to generate image on "__image_prompt__" property.
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- Provide query to search icon on "__icon_query__" property.
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- Only use markdown to highlight important points.
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- Make sure to follow language guidelines.
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- Speaker note should be normal text, not markdown.
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**Strictly follow the max and min character limit for every property in the slide.**
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"""
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def get_user_prompt(outline: str, language: str):
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return f"""
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## Icon Query And Image Prompt Language
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English
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## Slide Content Language
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{language}
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## Slide Outline
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{outline}
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"""
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def get_messages(outline: str, language: str):
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return [
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LLMSystemMessage(
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content=system_prompt,
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),
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LLMUserMessage(
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content=get_user_prompt(outline, language),
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),
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]
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async def get_slide_content_from_type_and_outline(
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slide_layout: SlideLayoutModel, outline: SlideOutlineModel, language: str
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):
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client = LLMClient()
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model = get_model()
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response_schema = remove_fields_from_schema(
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slide_layout.json_schema, ["__image_url__", "__icon_url__"]
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)
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response_schema = add_field_in_schema(
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response_schema,
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{
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"__speaker_note__": {
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"type": "string",
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"minLength": 100,
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"maxLength": 250,
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"description": "Speaker note for the slide",
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}
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},
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True,
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)
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response = await client.generate_structured(
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model=model,
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messages=get_messages(
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outline.content,
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language,
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),
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response_format=response_schema,
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strict=False,
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)
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return response
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