Multimodal Retrieval Augmented Generation (RAG) using the Gemini API in Agent Platform Reviews

Sruthi V. · Reviewed 12 days ago

`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. · Reviewed 13 days ago

033 P. · Reviewed 13 days ago

Shanmathi S. · Reviewed 13 days ago

Varshini G. · Reviewed 13 days ago

Asra S. · Reviewed 13 days ago

Dathri Ramanvitha B. · Reviewed 13 days ago

Suruthi M. · Reviewed 13 days ago

Sivasakthi N. · Reviewed 13 days ago

Potarlanka kamal2007 G. · Reviewed 13 days ago

Mohan R. · Reviewed 13 days ago

Sruthi P. · Reviewed 13 days ago

Choppakatla V. · Reviewed 13 days ago

Sathiriyan R. · Reviewed 13 days ago

Ahmed S. · Reviewed 13 days ago

Aisyah N. · Reviewed 13 days ago

yogasri G. · Reviewed 14 days ago

VISHAL G. · Reviewed 14 days ago

Rahul E. · Reviewed 14 days ago

Moparthi L. · Reviewed 14 days ago

Ficou carregando mais de 10 minutos sem carregar o laboratório

Alexandre R. · Reviewed 14 days ago

GIRDHARI K. · Reviewed 15 days ago

Bhavana G. · Reviewed 15 days ago

Sunilkumar D. · Reviewed 15 days ago

Mohan P. · Reviewed 15 days ago