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docs: remove session and connection in llm notebook (#821)
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‎notebooks/generative_ai/large_language_models.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Prerequisites\n",
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"Create session and define a BQ connection which we already created and allowlisted. "
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"## Define the model"
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"/usr/local/google/home/garrettwu/src/bigframes/bigframes/session/__init__.py:1762: UserWarning: No explicit location is set, so using location US for the session.\n",
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" return Session(context)\n"
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"/usr/local/google/home/garrettwu/src/bigframes/bigframes/ml/llm.py:589: DefaultLocationWarning: No explicit location is set, so using location US for the session.\n",
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" self.session = session or bpd.get_global_session()\n"
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"source": [
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"session = bigframes.pandas.get_global_session()\n",
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"connection = f\"{session.bqclient.project}.us.bigframes-default-connection\""
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"## Define the model"
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"model = GeminiTextGenerator(session=session, connection_name=connection)"
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"model = GeminiTextGenerator()"
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"/usr/local/google/home/garrettwu/src/bigframes/bigframes/core/__init__.py:108: PreviewWarning: Interpreting JSON column(s) as StringDtype. This behavior may change in future versions.\n",
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" warnings.warn(\n"
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" <tbody>\n",
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" <th>0</th>\n",
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" <td>**BigQuery**\n",
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" <td>## BigQuery: A Serverless Data Warehouse\n",
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"\n",
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"**Definition:**\n",
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"\n",
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"BigQuery is a s...</td>\n",
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"BigQ...</td>\n",
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" <td>[{\"category\":1,\"probability\":1,\"probability_sc...</td>\n",
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" <td>What is BigQuery?</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>**BigQuery Machine Learning (BQML)**\n",
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" <td>## BigQuery Machine Learning (BQML)\n",
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"BQML is ...</td>\n",
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"BQML is a...</td>\n",
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" <td>BigQuery DataFrame is a Python DataFrame imple...</td>\n",
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" <td>## What is BigQuery DataFrame?\n",
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"\n",
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"**BigQuery Dat...</td>\n",
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" <td>[{\"category\":1,\"probability\":1,\"probability_sc...</td>\n",
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" <td>What is BigQuery DataFrame?</td>\n",
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" ml_generate_text_llm_result \\\n",
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"0 **BigQuery**\n",
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"0 ## BigQuery: A Serverless Data Warehouse\n",
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"\n",
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"**Definition:**\n",
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"BigQ... \n",
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"1 ## BigQuery Machine Learning (BQML)\n",
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"BigQuery is a s... \n",
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"1 **BigQuery Machine Learning (BQML)**\n",
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"BQML is a... \n",
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"2 ## What is BigQuery DataFrame?\n",
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"BQML is ... \n",
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"2 BigQuery DataFrame is a Python DataFrame imple... \n",
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"**BigQuery Dat... \n",
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" ml_generate_text_rai_result ml_generate_text_status \\\n",
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" ml_generate_text_rai_result ml_generate_text_status \\\n",
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"0 [{\"category\":1,\"probability\":1,\"probability_sc... \n",
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"1 [{\"category\":1,\"probability\":1,\"probability_sc... \n",
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"2 [{\"category\":1,\"probability\":1,\"probability_sc... \n",
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" prompt \n",
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"0 What is BigQuery? \n",
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"1 What is BQML? \n",
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"2 What is BigQuery DataFrame? "
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"text/plain": [
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"'**BigQuery**\\n\\n**Definition:**\\n\\nBigQuery is a serverless, highly scalable, cloud-based data warehouse and analytics platform offered by Google Cloud.\\n\\n**Key Features:**\\n\\n* **Massive Scalability:** Can handle large datasets (petabytes or more) with fast query execution.\\n* **Elastic:** Automatically scales compute resources based on workload requirements.\\n* **Serverless:** Users do not need to manage infrastructure or provision resources.\\n* **Flexible Data Loading:** Supports a wide range of data sources, including files, databases, and streaming data.\\n* **SQL-Based Querying:** Uses standard SQL syntax for querying and analyzing data.\\n* **Machine Learning Integration:** Provides built-in machine learning capabilities for predictive analytics and data exploration.\\n* **Real-Time Analysis:** Supports streaming data analysis and interactive dashboards.\\n* **Collaboration and Sharing:** Allows multiple users to access and analyze data in a collaborative environment.\\n* **Cost-Effective:** Pay-as-you-go pricing based on data scanned and compute resources used.\\n\\n**Applications:**\\n\\n* Data warehousing and analytics\\n* Business intelligence and reporting\\n* Data science and machine learning\\n* Data exploration and visualization\\n* Marketing analytics\\n* Fraud detection and risk management\\n\\n**Benefits:**\\n\\n* Rapid data analysis on large datasets\\n* Reduced infrastructure management overhead\\n* Increased agility and flexibility\\n* Enhanced collaboration and data sharing\\n* Cost-effective data storage and analytics'"
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"\"## BigQuery: A Serverless Data Warehouse\\n\\nBigQuery is a serverless, cloud-based data warehouse that enables scalable analysis of large datasets. It's a popular choice for businesses of all sizes due to its ability to handle petabytes of data and run complex queries quickly and efficiently. Let's delve into its key features:\\n\\n**Serverless Architecture:** BigQuery eliminates the need for server management, allowing you to focus on analyzing data. Google manages the infrastructure, scaling resources up or down automatically based on your needs.\\n\\n**Scalability:** BigQuery can handle massive datasets, scaling seamlessly as your data volume grows. It automatically distributes queries across its infrastructure, ensuring fast and efficient processing.\\n\\n**SQL-like Querying:** BigQuery uses a familiar SQL-like syntax, making it easy for data analysts and developers to learn and use. This allows them to leverage their existing SQL knowledge for data exploration and analysis.\\n\\n**Cost-Effectiveness:** BigQuery offers a pay-as-you-go pricing model, meaning you only pay for the resources you use. This makes it a cost-effective solution for businesses with varying data processing needs.\\n\\n**Integration with Google Cloud:** BigQuery integrates seamlessly with other Google Cloud services like Cloud Storage, Dataflow, and Machine Learning, enabling a comprehensive data processing and analysis workflow within the Google Cloud ecosystem.\\n\\n**Security and Reliability:** BigQuery offers robust security features and high availability, ensuring data protection and reliable access.\\n\\n**Use Cases:** BigQuery finds applications in various scenarios, including:\\n\\n* **Data Warehousing:** Store and analyze large amounts of structured and semi-structured data.\\n* **Business Intelligence:** Generate insights from data for informed decision-making.\\n* **Data Analytics:** Perform complex data analysis and extract valuable patterns.\\n* **Machine Learning:** Train and deploy machine learning models on large datasets.\\n\\n**Getting Started:** To get started with BigQuery, you can create a free trial account on Google Cloud Platform and explore its features. Numerous tutorials and documentation are available to help you learn and use BigQuery effectively.\\n\\n## Additional Resources:\\n\\n* **BigQuery Documentation:** https://cloud.google.com/bigquery/docs/\\n* **BigQuery Quickstart:** https://cloud.google.com/bigquery/docs/quickstarts/quickstart-console\\n* **BigQuery Pricing:** https://cloud.google.com/bigquery/pricing\\n\\nFeel free to ask if you have any further questions about BigQuery!\""
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