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jhamonjhamon
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‎.github/actions/post-merge-additions/process-additions.py

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print(notebook)
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pc = Pinecone()
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idx = pc.Index(name='arjun-project') # TODO: adjust for arjun project
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# TODO: add embeddings related to new notebook

‎.github/actions/post-merge-deletions/process-deletions.py

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pc = Pinecone()
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idx = pc.Index(name='arjun-project') # TODO: adjust for arjun project
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# TODO: remove embeddings related to deleted notebook

‎.github/actions/post-merge-modifications/process-modifications.py

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pc = Pinecone()
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idx = pc.Index(name='arjun-project') # TODO: adjust for arjun project
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# TODO: update embeddings to reflect new contents of notebook

‎learn/assistant/pinecone-assistant-getting-started copy 6.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "IpHsLVa0mAJe"
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},
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"source": [
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"# Pinecone Assistant Getting Started\n",
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"\n",
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"Welcome to the getting started Notebook for [Pinecone assistant](https://www.pinecone.io/blog/pinecone-assistant/)!"
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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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"id": "v6cdZP0ylfC5"
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},
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"outputs": [],
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"source": [
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"# Install Pinecone notebook utilities including the Pinecone Connect widget\n",
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"!pip install pinecone==6.0.2"
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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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"id": "HO8ass1Qmdgq"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"if not os.environ.get(\"PINECONE_API_KEY\"):\n",
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" from pinecone_notebooks.colab import Authenticate\n",
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" Authenticate()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "lixB5GGnmAHv"
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},
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"source": []
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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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"id": "unDcuyR2EaJr"
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},
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"outputs": [],
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"source": [
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"from google.colab import userdata\n",
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"from pinecone import Pinecone\n",
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"\n",
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"pc = Pinecone(api_key=os.environ.get('PINECONE_API_KEY') or userdata.get('PINECONE_API_KEY'))\n",
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"\n",
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"assistant_name = 'HelloPineconeAssistant'\n",
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"\n",
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"metadata = {\"author\": \"Jane Doe\", \"version\": \"1.0\"}\n",
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"\n",
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"assistant = pc.assistant.create_assistant(\n",
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" assistant_name=assistant_name,\n",
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" metadata=metadata,\n",
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" timeout=30 # Wait 30 seconds for assistant creation to complete.\n",
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")"
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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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"id": "HJ5fxLvY97yF"
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},
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"outputs": [],
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"source": [
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"# Get assistant status\n",
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"assistant_status = pc.assistant.describe_assistant(assistant_name=assistant_name)\n",
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"assistant_status"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nIyz6gYqAbHn"
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},
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"source": [
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"Upload the toys.txt file from the repository to Google Colab:\n",
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"\n",
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"1. Click the folder icon in Google Colab's left rail <--\n",
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"2. Click the upload button and use the file picker to select `toys.txt` from the subdirectory of https://github.com/pinecone-io/examples that contains this notebook\n",
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"3. Run the next cell to upload the toys.txt file to your Pinecone assistant\n"
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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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"id": "CVixRBQfEKpo"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# Target our existing Pinecone Assistant\n",
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"assistant = pc.assistant.Assistant(\n",
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" assistant_name=assistant_name,\n",
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")\n",
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"\n",
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"file_path = '/content/toys.txt'\n",
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"\n",
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"# Upload the file\n",
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"if os.path.exists(file_path):\n",
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" response = assistant.upload_file(\n",
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" file_path=file_path,\n",
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" timeout=None\n",
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" )\n",
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" print(f\"Uploaded {file_path}\")\n",
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" print(\"Response:\", response)\n",
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"else:\n",
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" print(f\"File not found: {file_path}\")"
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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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"id": "fqYUFoZ8N487"
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},
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"outputs": [],
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"source": [
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"# List uploaded files our assistant is aware of\n",
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"files = assistant.list_files()\n",
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"files"
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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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"id": "iKLZtk7BOomq"
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},
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"outputs": [],
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"source": [
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"# Chat with your Pinecone assistant, which automatically references\n",
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"# your uploaded documents in its responses\n",
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"from pinecone_plugins.assistant.models.chat import Message\n",
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"chat_context = [Message(content='Which toys teach STEM skills?')]\n",
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"response = assistant.chat_completions(messages=chat_context)\n",
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"print(response)"
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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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"id": "GQKf1l3rOFhy"
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},
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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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"colab": {
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"private_outputs": true,
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

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