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import weave | |
from ..retrieval import SimilarityMetric | |
from .llm_client import LLMClient | |
class MedQAAssistant(weave.Model): | |
"""Cuming""" | |
llm_client: LLMClient | |
retriever: weave.Model | |
top_k_chunks: int = 2 | |
retrieval_similarity_metric: SimilarityMetric = SimilarityMetric.COSINE | |
def predict(self, query: str) -> str: | |
retrieved_chunks = self.retriever.predict( | |
query, top_k=self.top_k_chunks, metric=self.retrieval_similarity_metric | |
) | |
retrieved_chunks = [chunk["text"] for chunk in retrieved_chunks] | |
system_prompt = """ | |
You are a medical expert. You are given a query and a list of chunks from a medical document. | |
""" | |
return self.llm_client.predict( | |
system_prompt=system_prompt, user_prompt=retrieved_chunks | |
) | |