Hello, I'm glad you find it useful. I aimed to create something that would serve a purpose. If you can provide me details about use case you are trying to solve, I may add a feature to llmdantic to support it. Right now:
After initialize llmdantic you can get the prompt by running the following command:
"""
from llmdantic import LLMdantic, LLMdanticConfig
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
config: LLMdanticConfig = LLMdanticConfig(
objective="Summarize the text",
inp_schema=SummarizeInput,
out_schema=SummarizeOutput,
retries=3,
)
llmdantic = LLMdantic(llm=llm, config=config)
input_data: SummarizeInput = SummarizeInput(
text="The quick brown fox jumps over the lazy dog."
)
prompt: str = llmdantic.prompt(input_data)
"""
But here you need to provide a langchain llm model. If you do not want to use langchain llm model, you can use the following code:
"""
from llmdantic.prompts.prompt_builder import LLMPromptBuilder
from llmdantic.output_parsers.output_parser import LLMOutputParser
output_parser: LLMOutputParser = LLMOutputParser(pydantic_object=SummarizeOutput)
prompt_builder = LLMPromptBuilder(
objective="Summarize the text",
inp_model=SummarizeInput,
out_model=SummarizeOutput,
parser=output_parser,
)
data: SummarizeInput = SummarizeInput(text="Some text to summarize")
prompt = prompt_builder.build_template()
print(prompt.format(input=data.model_dump()))
"""
But here still we use langchain for the prompt building. If you any questions, feel free to ask I will be happy to help you.
