Agent Platform'da Gemini API Kullanarak Çok Formatlı Veriyle Artırılmış Üretim (RAG) Reviews

Ramya R. · Reviewed 11 gün ago

Deepika M. · Reviewed 11 gün ago

easy

Vinothkumar K. · Reviewed 11 gün ago

Kannaparamathma v. · Reviewed 11 gün ago

prischilla r. · Reviewed 11 gün ago

Vaishnavi.s G. · Reviewed 11 gün ago

utils/intro_multimodal_rag_utils.pyにてバグがあり、get_similar_text_from_queryが実行できなかった。

実歩 藤. · Reviewed 11 gün ago

Vaishnavi M. · Reviewed 11 gün ago

維均 蕭. · Reviewed 11 gün ago

David L. · Reviewed 11 gün ago

NA

Aditya Sanjay S. · Reviewed 12 gün ago

maulidya n. · Reviewed 12 gün ago

Vadym S. · Reviewed 12 gün ago

good

kiruthika s. · Reviewed 12 gün ago

Iyappan G. · Reviewed 12 gün ago

Gayathri L. · Reviewed 12 gün ago

Gomathi D. · Reviewed 12 gün ago

Siva M. · Reviewed 12 gün ago

Good Lab

Tubagus Z. · Reviewed 12 gün ago

Sirisha G. · Reviewed 12 gün ago

Carlos Guadalupe L. · Reviewed 12 gün ago

Sruthi V. · Reviewed 12 gün 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 12 gün ago

033 P. · Reviewed 13 gün ago

Shanmathi S. · Reviewed 13 gün ago