diff --git a/UseCases/Chat_with_docs_GenAI/Chat_with_docs_VantageDB_GenAI_Python.ipynb b/UseCases/Chat_with_docs_GenAI/Chat_with_docs_VantageDB_GenAI_Python.ipynb index dc6fb457..72b3d0b7 100644 --- a/UseCases/Chat_with_docs_GenAI/Chat_with_docs_VantageDB_GenAI_Python.ipynb +++ b/UseCases/Chat_with_docs_GenAI/Chat_with_docs_VantageDB_GenAI_Python.ipynb @@ -129,6 +129,8 @@ "metadata": {}, "outputs": [], "source": [ + "%%capture\n", + "\n", "!pip install --upgrade -r requirements.txt --quiet" ] }, @@ -449,7 +451,7 @@ "metadata": {}, "source": [ "
\n", - "

Please be patient: Generating embeddings for 1500+ document contents may take up to 2 to 4 minutes. It is depends on number of APMS in the database. Since the volume of data is large and the machine is small, going through the below code could take up to 4 minutes.

\n", + "

Please be patient: Generating embeddings for 600+ document contents may take up to 2 to 4 minutes. It is depends on number of APMS in the database. Since the volume of data is large and the machine is small, going through the below code could take up to 4 minutes.

\n", "
" ] }, @@ -545,8 +547,8 @@ "outputs": [], "source": [ "# store the embeddings if you're generating for new document for speed up in next run\n", - "df = tdf_text_embeddings.to_pandas().reset_index()\n", - "df.to_parquet('./embeddings/df_SmartTraveller_International_txt_emb_200_30.parquet.gzip',compression='gzip')" + "# df = tdf_text_embeddings.to_pandas().reset_index()\n", + "# df.to_parquet('./embeddings/df_SmartTraveller_International_txt_emb_200_30.parquet.gzip',compression='gzip')" ] }, { diff --git a/UseCases/Chat_with_docs_GenAI/requirements.txt b/UseCases/Chat_with_docs_GenAI/requirements.txt index a223ed5b..09f810b7 100644 --- a/UseCases/Chat_with_docs_GenAI/requirements.txt +++ b/UseCases/Chat_with_docs_GenAI/requirements.txt @@ -7,6 +7,8 @@ tiktoken pypdf PyMuPDF panel==1.3.4 +google-cloud-aiplatform +openai pyopenssl cryptography tdapiclient