firattamur / llmdantic Goto Github PK

Is your feature request related to a problem? Please describe.
The tool is fantastic but it would be great to integrate its functionality with Agent frameworks like CrewAI or Autogen to control the outputs of the agents within the framework. This could potentially be game changing.

Describe the solution you'd like
Autogen and CrewAI allow you to create tools specific to an agent. We should be able to easily create an agent tool to check on outputs and ensure consistency of the agent in getting it's tasks done.

Describe alternatives you've considered
Currently my attempts are to use pydantic structures to achieve this but it doesn't seem to work well.

• Add badges for code coverage, code quality, python version etc.

Description

Hey, seems like a fun library and tried to use example that's similar to one that's in readme (would be nice to have a script/notebook to check out library). I used AzureChatOpenAI as a llm.

The result (LLMdanticResult) have field with None and result's text have dict that's filled.
text - {"rewritten": "Miód to ulubiony przysmak Kubusia Puchatka."} - Honey is Winnie the Pooh's favorite treat.
output - rewritten=None

To Reproduce
Steps to reproduce the behavior:

# setup
llm = AzureChatOpenAI(
    api_key=AZURE_API_KEY,
    api_version=AZURE_API_VERSION,
    azure_endpoint=RESOURCE_ENDPOINT,
    azure_deployment=AZURE_DEPLOY,
    temperature=0,
    verbose=True,
)
class RewriteInput(BaseModel):
    text: str


class RewriteOutput(BaseModel):
    rewritten: str

    @field_validator("rewritten")
    def rewritten_text_must_be_in_polish(cls, v) -> bool:
        """ Rewritten text must be in polish language """
        ...

    @field_validator("rewritten")
    def rewritten_text_must_not_be_empty(cls, v) -> bool:
        """Rewritten text cannot be empty """
        ...

    @field_validator("rewritten")
    def rewritten_text_must_be_short(cls, v) -> bool:
        """Rewritten must be less than 100 words """
        ...

config: LLMdanticConfig = LLMdanticConfig(
    objective="Rewrite the polish text and convey the same meaning but with different phrasing and structure to ensure it is fresh and engaging.",
    inp_schema=RewriteInput,
    out_schema=RewriteOutput, 
    retry=3,
    verbose=True,
)

llmdantic = LLMdantic(llm=llm, config=config)

# polish for Winnie the Pooh likes honey
data = RewriteInput(text="Kubuś Puchatek lubi miód.")
result = llmdantic.invoke(data)
print(result.text)
# output: {"rewritten": "Miód to ulubiony przysmak Kubusia Puchatka."} - Honey is Winnie the Pooh's favorite treat.
print(result.output)
# output: rewritten=None

Expected behavior
Output should have filled fields from llm

• Add references to langchain and pydantic.
• Add prompt template and example prompts generated with input.
• Add use case such Translation, Summarization etc.
• Add a flow of how package works.

• Add workflow to run tests when merge to develop and main.
• Add workflow to publish when merge to main.