ag-chirag

RAG_IN_CONTEXT_PROMPT = """
Given a QUERY below, your task is to come up with a maximum of 25
STATISTICAL QUESTIONS that help in answering QUERY.

Here are the only forms of STATISTICAL QUESTIONS you can generate:

1. "What is $METRIC in $PLACE?"
2. "What is $METRIC in $PLACE $PLACE_TYPE?"
3. "How has $METRIC changed over time in $PLACE $PLACE_TYPE?"

ag-chirag

    @staticmethod
    def pretty_print(q2resp: dict[str, dg.base.DataCommonsCall]):
        markdown_output = "# Data Commons Response\n"
        for k, v in q2resp.items():
            markdown_output += f"**{k}**\n\n"
            markdown_output += f"{v.answer()}\n\n"
        return markdown_output

ag-chirag

class DataCommonsClient:
    def __init__(self):
        self.data_fetcher = dg.DataCommons(api_key=DC_API_KEY)

    def call_dc(self, questions: list[str]) -> dict[str, dg.base.DataCommonsCall]:

        try:
            q2resp = self.data_fetcher.calln(questions, self.data_fetcher.point)
        except Exception as e:
            logging.warning(e)

ag-chirag

class DataGemma:
   def __init__(self, model_id: str = "bartowski/datagemma-rag-27b-it-GGUF", model_file: str =  "datagemma-rag-27b-it-Q2_K.gguf"):
      self.generation_kwargs = {
         "max_tokens": 4096, # Max number of new tokens to generate
      }
      self.model_path = hf_hub_download(model_id, model_file)
      self.llm = Llama(
            self.model_path
      )
      self.name = "DataGemma"