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docs:Updated integration docs structure for chat/google_vertex_ai_palm (#16201)
Description: - checked that the doc chat/google_vertex_ai_palm is using new functions: invoke, stream etc. - added Gemini example - fixed wrong output in Sanskrit example Issue: https://github.com/langchain-ai/langchain/issues/15664 Dependencies: None Twitter handle: None
This commit is contained in:
Anastasiia Manokhina
GitHub
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ce595f0203
@ -18,6 +18,14 @@
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"\n",
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"Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
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"\n",
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"ChatVertexAI exposes all foundational models available in Google Cloud:\n",
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"\n",
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"- Gemini (`gemini-pro` and `gemini-pro-vision`)\n",
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"- PaLM 2 for Text (`text-bison`)\n",
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"- Codey for Code Generation (`codechat-bison`)\n",
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"\n",
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"For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
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"\n",
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"By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) customer data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
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"\n",
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"To use `Google Cloud Vertex AI` PaLM you must have the `langchain-google-vertexai` Python package installed and either:\n",
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@ -35,9 +43,7 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-google-vertexai"
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@ -45,7 +51,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -64,7 +70,7 @@
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"AIMessage(content=\" J'aime la programmation.\")"
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]
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},
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -98,7 +104,7 @@
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"AIMessage(content=\"J'aime la programmation.\")"
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]
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},
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"execution_count": 9,
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -123,7 +129,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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@ -132,7 +138,7 @@
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"AIMessage(content=' プログラミングが大好きです')"
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]
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},
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -159,28 +165,17 @@
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-06-17T21:09:25.423568Z",
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"iopub.status.busy": "2023-06-17T21:09:25.423213Z",
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"iopub.status.idle": "2023-06-17T21:09:25.429641Z",
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"shell.execute_reply": "2023-06-17T21:09:25.429060Z",
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"shell.execute_reply.started": "2023-06-17T21:09:25.423546Z"
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},
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"tags": []
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},
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"metadata": {},
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"source": [
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"## Code generation chat models\n",
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"You can now leverage the Codey API for code chat within Vertex AI. The model name is:\n",
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"- codechat-bison: for code assistance"
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"You can now leverage the Codey API for code chat within Vertex AI. The model available is:\n",
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"- `codechat-bison`: for code assistance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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@ -242,7 +237,7 @@
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" model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
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")\n",
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"\n",
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"message = chat.invoke(\"Write a Python function to identify all prime numbers\")\n",
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"message = chat.invoke(\"Write a Python function generating all prime numbers\")\n",
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"print(message.content)"
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]
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},
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@ -266,7 +261,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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@ -320,7 +315,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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@ -353,7 +348,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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@ -362,7 +357,7 @@
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"MyModel(name='Erick', age=27)"
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]
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},
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -389,7 +384,7 @@
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"source": [
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"## Asynchronous calls\n",
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"\n",
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"We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)"
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"We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)."
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]
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},
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{
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@ -414,10 +409,10 @@
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{
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"data": {
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"text/plain": [
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"AIMessage(content=' Why do you love programming?')"
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"AIMessage(content=' अहं प्रोग्रामनं प्रेमामि')"
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]
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},
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"execution_count": 6,
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"execution_count": null,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -428,6 +423,10 @@
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")\n",
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"\n",
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"chat = ChatVertexAI(\n",
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" model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5\n",
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")\n",
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"chain = prompt | chat\n",
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"\n",
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"asyncio.run(\n",
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@ -483,43 +482,15 @@
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" sys.stdout.write(chunk.content)\n",
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" sys.stdout.flush()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"environment": {
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"kernel": "python3",
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"name": "common-cpu.m108",
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"type": "gcloud",
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"uri": "gcr.io/deeplearning-platform-release/base-cpu:m108"
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"display_name": "",
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"name": ""
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.10"
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},
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"vscode": {
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"interpreter": {
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"hash": "cc99336516f23363341912c6723b01ace86f02e26b4290be1efc0677e2e2ec24"
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}
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"name": "python"
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}
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},
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"nbformat": 4,
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