Retrieval-Augmented Generation (RAG) Multimodal menggunakan Gemini API di Agent Platform Ulasan

Sruthi V. · Diulas 12 hari lalu

`get_cosine_score` in intro_multimodal_rag_utils.py needs to be updated to: ''' def get_cosine_score( dataframe: pd.DataFrame, column_name: str, input_text_embed: np.ndarray ) -> float: """ Calculates the cosine similarity between the user query embedding and the dataframe embedding for a specific column. Args: dataframe: The pandas DataFrame containing the data to compare against. column_name: The name of the column containing the embeddings to compare with. input_text_embed: The NumPy array representing the user query embedding. Returns: The cosine similarity score (rounded to two decimal places) between the user query embedding and the dataframe embedding. """ return round(np.dot(dataframe[column_name], input_text_embed), 2) '''

Israel U. · Diulas 13 hari lalu

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Sivasakthi N. · Diulas 13 hari lalu

Potarlanka kamal2007 G. · Diulas 13 hari lalu

Mohan R. · Diulas 13 hari lalu

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Ficou carregando mais de 10 minutos sem carregar o laboratório

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