OpenAI has unveiled a groundbreaking new feature, the Code Interpreter, accessible to all ChatGPT Plus users. Check out my experiments using the 2739 edition of BRPTO’s Patent Gazette

Code Interpreter is an innovative extension of ChatGPT, now available to all subscribers of the ChatGPT Plus service. This tool boasts the ability to execute code, work with uploaded files, analyze data, create charts, edit files, and carry out mathematical computations. The implications of this are profound, not just for academics and coders, but for anyone looking to streamline their research processes. Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

What is the Code Interpreter?

The Code Interpreter Plugin for ChatGPT is a multifaceted addition that provides the AI chatbot with the capacity to handle data and perform a broad range of tasks. This plugin equips ChatGPT with the ability to generate and implement code in natural language, thereby streamlining data evaluation, file conversions, and more. Pioneering users have experienced its effectiveness in activities like generating GIFs and examining musical preferences. The potential of the Code Interpreter Plugin is enormous, having the capability to revolutionize coding processes and unearth novel uses. By capitalizing on ChatGPT’s capabilities, users can harness the power of this plugin, sparking a voyage of discovery and creativity.

Professor Ethan Mollick from the Wharton School of the University of Pennsylvania shares his experiences with using the Code Interpreter

Artificial intelligence is rapidly revolutionizing every aspect of our lives, particularly in the world of data analytics and computational tasks. This transition was recently illuminated by Wharton Professor Ethan Mollick who commented, “Things that took me weeks to master in my PhD were completed in seconds by the AI.” This is not just a statement about time saved or operational efficiency, but it speaks volumes about the growing capabilities of AI technologies, specifically OpenAI’s new tool for ChatGPT – Code Interpreter.

Mollick, an early adopter of AI and an esteemed academic at the Wharton School of the University of Pennsylvania lauded Code Interpreter as the most significant application of AI in the sphere of complex knowledge work. Not only does it complete intricate tasks in record time, but Mollick also noticed fewer errors than those typically expected from human analysts.

One might argue that Code Interpreter transcends the traditional scope of AI assistants, which have primarily been limited to generating text responses. It leverages large language models, the AI technology underpinning ChatGPT, to provide a general-purpose toolbox for problem-solving.

Mollick commended Code Interpreter’s use of Python, a versatile programming language known for its application in software building and data analysis. He pointed out that it closes some of the gaps in language models as the output is not entirely text-based. The code is processed through Python, which promptly flags any errors.

In practice, when given a dataset on superheroes, Code Interpreter could clean and merge the data seamlessly, with an admirable effort to maintain accuracy. This process would have been an arduous task otherwise. Additionally, it allows a back-and-forth interaction during data visualization, accommodating various alterations and enhancements.

Remarkably, Code Interpreter doesn’t just perform pre-set analyses but recommends pertinent analytical approaches. For instance, it conducted predictive modeling to anticipate a hero’s potential powers based on other factors. Mollick was struck by the AI’s human-like reasoning about data, noting the AI’s observation that the powers were often visually noticeable as they derived from the comic book medium.

Beyond its technical capabilities, Code Interpreter democratizes access to complex data analysis, making it accessible to more people, thereby transforming the future of work. It saves time and reduces the tedium of repetitive tasks, enabling individuals to focus on more fulfilling, in-depth work.

Here are 10 examples of how you can use Code Interpreter for data analysis:

  1. Analyzing customer feedback data to identify trends and patterns.
  2. Creating interactive dashboards and reports for business intelligence purposes.
  3. Cleaning and transforming datasets for machine learning models.
  4. Extracting insights from social media data to inform marketing strategies.
  5. Generating charts and graphs to visualize sales data.
  6. Analyzing website traffic data to optimize the user experience.
  7. Creating custom functions and scripts for specific data analysis tasks.
  8. Performing statistical analysis on survey data.
  9. Automating repetitive data analysis tasks with Python scripts.
  10. Creating custom visualizations for presentations and reports.

How to use it? Follow my own experiments

Initially, you must upload a file of your choice. Following this, you are permitted to pose queries concerning it. The interpreter for the code will generate and run a Python script to address nearly all inquiries you have. The entire process is incredibly swift. I conducted a few trials using the XML file from BRTPTO’s 2739 Patent Gazette. The experience was truly incredible. There’s no need for any understanding of coding or Python. The code interpreter simply provides the results. If you wish, you can view the Python script.

By selecting the “Show Work” option, the Python script will become visible to you.

He executed the work flawlessly!

This is the screen of the CSV file.

Here is the Python code generated by the Code Interpreter to create the CSV file:

import pandas as pd
import xml.etree.ElementTree as ET

# Load the XML file
tree = ET.parse("/mnt/data/Patente_2739_04072023.xml")
root = tree.getroot()

# Function to extract data from XML into a dictionary
def extract_data(root):
    data = []
    for despacho in root.findall('despacho'):
        row = {}
        row['despacho_codigo'] = despacho.find('codigo').text
        row['despacho_titulo'] = despacho.find('titulo').text
        row['despacho_comentario'] = despacho.find('comentario').text if despacho.find('comentario') is not None else ""
        processo_patente = despacho.find('processo-patente')
        row['processo_numero'] = processo_patente.find('numero').text
        row['processo_data_deposito'] = processo_patente.find('data-deposito').text if processo_patente.find('data-deposito') is not None else ""
        row['processo_concessao'] = processo_patente.find('concessao').text if processo_patente.find('concessao') is not None else ""
        titulars = processo_patente.findall('titular-lista/titular')
        row['titular_nome_completo'] = "; ".join([titular.find('nome-completo').text for titular in titulars])
        row['titular_endereco_pais'] = "; ".join([titular.find('endereco/pais/sigla').text for titular in titulars])
    return data

# Extract data from XML
data = extract_data(root)

# Convert to DataFrame
df = pd.DataFrame(data)

# Save DataFrame to CSV
df.to_csv('/mnt/data/patente.csv', index=False)

df.head()  # Display the first few rows of the DataFrame

# Extract distinct application/patent numbers
distinct_application_numbers = df['processo_numero'].unique()

# Convert to DataFrame
df_distinct_application_numbers = pd.DataFrame(distinct_application_numbers, columns=['processo_numero'])

# Save DataFrame to CSV
df_distinct_application_numbers.to_csv('/mnt/data/distinct_application_numbers.csv', index=False)

df_distinct_application_numbers.head()  # Display the first few rows of the DataFrame

This video can demonstrate the capabilities of the Code Interpreter.

You can find more information on the official Open AI site by clicking here.


Code Interpreter is a powerful tool that is making data analysis accessible for everyone with ChatGPT Plus. By allowing users to run code snippets within their chat sessions, it enables them to perform a wide range of data analysis tasks quickly and easily. Whether you’re analyzing customer feedback data or creating custom visualizations for presentations and reports, Code Interpreter has something to offer everyone.

Code Interpreter invites us to consider how we can leverage such advancements across various sectors impacted by AI. Indeed, Code Interpreter signifies the dawn of a new era in artificial intelligence and computational capabilities. So why not give it a try today?

That’s it for today!


Wharton professor sees future of work in new ChatGPT tool | Fortune

Asking questions via chat to the BRPTO’s Basic Manual for Patent Protection PDF, using LangChain, Pinecone, and Open AI

Have you ever wanted to search through your PDF files and find the most relevant information quickly and easily? If you have a lot of PDF documents, such as books, articles, reports, or manuals, you might find it hard to locate the information you need without opening each file and scanning through the pages. Wouldn’t it be nice if you could type in a query and get the best matches from your PDF collection?

In this blog post, I will show you how to build a simple but powerful PDF search engine using LangChain, Pinecone, and Open AI. By combining these tools, we can create a system that can:

  1. Extract text and metadata from PDF files.
  2. Embed the text into vector representations using a language model.
  3. Index and query the vectors using a vector database.
  4. Generate natural language responses using the “text-embedding-ada-002” model from Open AI.

What is LangChain?

LangChain is a framework for developing applications powered by language models. It provides modular abstractions for the components necessary to work with language models, such as data loaders, prompters, generators, and evaluators. It also has collections of implementations for these components and use-case-specific chains that assemble these components in particular ways to accomplish a specific task.

Prompts: This part allows you to create adaptable instructions using templates. It can adjust to different language learning models based on the size of the conversation window and input factors like conversation history, search results, previous answers, and more.

Models: This part serves as a bridge to connect with most third-party language learning models. It has connections to roughly 40 public language learning models, chat, and text representation models.

Memory: This allows the language learning models to remember the conversation history.

Indexes: Indexes are methods to arrange documents so that language learning models can interact with them effectively. This part includes helpful functions for dealing with documents and connections to different database systems for storing vectors (numeric representations of text).

Agents: Some applications don’t just need a set sequence of calls to language learning models or other tools, but possibly an unpredictable sequence based on the user’s input. In these sequences, there’s an agent that has access to a collection of tools. Depending on the user’s input, the agent can decide which tool – if any – to use.

Chains: Using a language learning model on its own is fine for some simple applications, but more complex ones need to link multiple language learning models, either with each other or with other experts. LangChain offers a standard interface for these chains, as well as some common chain setups for easy use.

With LangChain, you can build applications that can:

  • Connect a language model to other sources of data, such as documents, databases, or APIs
  • Allow a language model to interact with its environments, such as chatbots, agents, or generators
  • Optimize the performance and quality of a language model using feedback and reinforcement learning

Some examples of applications that you can build with LangChain are:

  • Question answering over specific documents
  • Chatbots that can access external knowledge or services
  • Agents that can perform tasks or solve problems using language models
  • Generators that can create content or code using language models

You can learn more about LangChain from their documentation or their GitHub repository. You can also find tutorials and demos in different languages, such as Chinese, Japanese, or English.

What is Pinecone?

Pinecone is a vector database for vector search. It makes it easy to build high-performance vector search applications by managing and searching through vector embeddings in a scalable and efficient way. Vector embeddings are numerical representations of data that capture their semantic meaning and similarity. For example, you can embed text into vectors using a language model, such that similar texts have similar vectors.

With Pinecone, you can create indexes that store your vector embeddings and metadata, such as document titles or authors. You can then query these indexes using vectors or keywords, and get the most relevant results in milliseconds. Pinecone also handles all the infrastructure and algorithmic complexities behind the scenes, ensuring you get the best performance and results without any hassle.

Some examples of applications that you can build with Pinecone are:

  • Semantic search: Find documents or products that match the user’s intent or query
  • Recommendations: Suggest items or content that are similar or complementary to the user’s preferences or behavior
  • Anomaly detection: Identify outliers or suspicious patterns in data
  • Generation: Create new content or code that is similar or related to the input

You can learn more about Pinecone from their website or their blog. You can also find pricing details and sign up for a free account here.

Presenting the Python code and explaining its functionality

This code is divided into two parts:

This stage involves preparing the PDF document for querying
This stage pertains to executing queries on the PDF

Below is the Python script that I’ve developed which can be also executed in Google Colab at this link.

# Install the dependencies
pip install langChain
pip install OpenAI
pip install pinecone-client
pip install tiktoken
pip install pypdf
# Provide your OpenAI API key and define the embedding model
embed_model = "text-embedding-ada-002"

# Provide your Pinecone API key and specify the environment

# Import the required modules
import openai, langchain, pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import UnstructuredPDFLoader, OnlinePDFLoader, PyPDFLoader

# Define a text splitter to handle the 4096 token limit of OpenAI
text_splitter = RecursiveCharacterTextSplitter(
    # We set a small chunk size for demonstration
    chunk_size = 2000,
    chunk_overlap  = 0,
    length_function = len,

# Initialize Pinecone with your API key and environment
        api_key = PINECONE_API_KEY,
        environment = PINECONE_ENV

# Define the index name for Pinecone
index_name = 'pine-search'

# Create an OpenAI embedding object with your API key
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)

# Set up an OpenAI LLM model
llm = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)

# Define a PDF loader and load the file
loader = PyPDFLoader("")

# Use the text splitter to split the loaded file content into manageable chunks
book_texts = text_splitter.split_documents(file_content)

# Check if the index exists in Pinecone
if index_name not in pinecone.list_indexes():
    print("Index does not exist: ", index_name)

# Create a Pinecone vector search object from the text chunks
book_docsearch = Pinecone.from_texts([t.page_content for t in book_texts], embeddings, index_name = index_name)

# Define your query
query = "Como eu faço para depositar uma patente no Brasil?"

# Use the Pinecone vector search to find documents similar to the query
docs = book_docsearch.similarity_search(query)

# Set up a QA chain with the LLM model and the selected chain type
chain = load_qa_chain(llm, chain_type="stuff")

# Run the QA chain with the found documents and your query to get the answer, question=query)

Below is the application I developed for real-time evaluation of the PDF Search Engine

You can examine the web application that I’ve designed, enabling you to carry out real-time tests of the PDF search engine. This app provides you with the facility to pose questions about the data contained within BRPTO’S Basic Manual for Patent Protection. Click here to launch the application.


In this blog post, I have shown you how to build a simple but powerful PDF search engine using LangChain, Pinecone, and Open AI. This system can help you find the most relevant information from your PDF files in a fast and easy way. You can also extend this system to handle other types of documents, such as images, audio, or video, by using different data loaders and language models.

I hope you enjoyed this tutorial and learned something new. If you have any questions or feedback, please feel free to leave a comment below or contact me here. Thank you for reading!

That’s it for today!


GoodAITechnology/LangChain-Tutorials (

INPI – Instituto Nacional da Propriedade Industrial — Instituto Nacional da Propriedade Industrial (

Chatting with your Enterprise data privately and securely through the use of Azure Cognitive Search and Azure Open AI

In an age where data is power, businesses are constantly looking for ways to leverage their vast enterprise data stores. One promising avenue lies in the intersection of AI and search technologies, specifically through the use of Azure Cognitive Search and Azure Open AI. These tools provide powerful ways to converse with enterprise data privately and securely.

Enterprise data can take various forms, from structured database datasets to unstructured documents, emails, and files. Some examples are data about the company’s benefits, internal policies, job descriptions, roles, and much more.

What is Azure Cognitive Search?

Azure Cognitive Search is a cloud-based service provided by Microsoft Azure that enables developers to build sophisticated search experiences into custom applications. It integrates with other Azure Cognitive Services to enable AI-driven content understanding through capabilities such as natural language processing, entity recognition, image analysis, and more.

Here are some of the key benefits of Azure Cognitive Search:

  1. Fully Managed: Azure Cognitive Search is fully managed, meaning you don’t have to worry about infrastructure setup, maintenance, or scaling. You just need to focus on the development of your application.
  2. Rich Search Experiences: It allows for the creation of rich search experiences, including auto-complete, geospatial search, filtering, and faceting.
  3. AI-Enhanced Search Capabilities: When combined with other Azure Cognitive Services, Azure Cognitive Search can provide advanced search features. For example, it can extract key phrases, detect languages, identify entities, and more. It can even index and search unstructured data, like text within documents or images.
  4. Scalability and Performance: Azure Cognitive Search can automatically scale to handle large volumes of data and high query loads. It provides fast, efficient search across large datasets.
  5. Data Integration: It can pull in data from a variety of sources, including Azure SQL Database, Azure Cosmos DB, Azure Blob Storage, and more.
  6. Security: Azure Cognitive Search supports data encryption at rest and in transit. It also integrates with Azure Active Directory for identity and access management.
  7. Developer Friendly: It provides a simple, RESTful API and integrates with popular programming languages and development frameworks. This makes it easier for developers to embed search functionality into applications.
  8. Indexing: The service provides robust indexing capabilities, allowing you to index data from a variety of sources and formats. This allows for a more comprehensive search experience for end-users.

In summary, Azure Cognitive Search can provide powerful, intelligent search capabilities for your applications, allowing users to find the information they need quickly and easily.

What is Azure Open AI?

Azure OpenAI Service is a platform that provides REST API access to OpenAI’s powerful language models, including GPT-3, GPT-4, Codex, and Embeddings. It can be used for tasks such as content generation, summarization, semantic search, and natural language-to-code translation.

The security and safety of enterprise data is a top priority for Azure OpenAI. Here are some key points on how it ensures safety:

  • The Azure OpenAI Service is fully controlled by Microsoft and does not interact with any services operated by OpenAI. Your prompts (inputs) and completions (outputs), your embeddings, and your training data are not available to other customers, OpenAI, or used to improve OpenAI models, any Microsoft or 3rd party products or services, or to automatically improve Azure OpenAI models for your use in your resource. Your fine-tuned Azure OpenAI models are available exclusively for your use.
  • The service processes different types of data including prompts and generated content, augmented data included with prompts, and training & validation data.
  • When generating completions, images, or embeddings, the service evaluates the prompt and completion data in real-time to check for harmful content types. The models are stateless, meaning no prompts or generations are stored in the model, and prompts and generations are not used to train, retrain, or improve the base models.
  • With the “on your data” feature, the service retrieves relevant data from a configured data store and augments the prompt to produce generations that are grounded with your data. The data remains stored in the data source and location you designate. No data is copied into the Azure OpenAI service.
  • Training data uploaded for fine-tuning is stored in the Azure OpenAI resource in the customer’s Azure tenant. It can be double encrypted at rest and can be deleted by the customer at any time. This data is not used to train, retrain, or improve any Microsoft or 3rd party base models.
  • Azure OpenAI includes both content filtering and abuse monitoring features to reduce the risk of harmful use of the service. To detect and mitigate abuse, Azure OpenAI stores all prompts and generated content securely for up to thirty (30) days.
  • The data store where prompts and completions are stored is logically separated by customer resources. Prompts and generated content are stored in the Azure region where the customer’s Azure OpenAI service resource is deployed, within the Azure OpenAI service boundary. Human reviewers can only access the data when it has been flagged by the abuse monitoring system.
  • Customers who meet additional Limited Access eligibility criteria and attest to specific use cases can apply to modify the Azure OpenAI content management features. Suppose Microsoft approves a customer’s request to change abuse monitoring. In that case, Microsoft does not store any prompts and completions associated with the approved Azure subscription for which abuse monitoring is configured.

In conclusion, Azure OpenAI takes numerous measures to ensure that your enterprise data is kept secure and confidential while using its service.

Revolutionize your Enterprise Data with ChatGPT: step by step how to create your own Enterprise Chat

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure Open AI Service to access the ChatGPT model (gpt-35-turbo), and Azure Cognitive Search for data indexing and retrieval.

The repo includes sample data so it’s ready to try end-to-end. In this sample application, we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions, and roles.


  • Chat and Q&A interfaces
  • Explores various options to help users evaluate the trustworthiness of responses with citations, tracking of source content, etc.
  • Shows possible approaches for data preparation, prompt construction, and orchestration of interaction between model (ChatGPT) and retriever (Cognitive Search)
  • Settings directly in the UX to tweak the behavior and experiment with options
Chat screen

Getting Started

IMPORTANT: In order to deploy and run this example, you’ll need an Azure subscription with access enabled for the Azure OpenAI service. You can request access here. You can also visit here to get some free Azure credits to get you started.

AZURE RESOURCE COSTS by default this sample will create Azure App Service and Azure Cognitive Search resources that have a monthly cost, as well as Form Recognizer resource that has cost per document page. You can switch them to free versions of each of them if you want to avoid this cost by changing the parameters file under the infra folder (though there are some limits to consider; for example, you can have up to 1 free Cognitive Search resource per subscription, and the free Form Recognizer resource only analyzes the first 2 pages of each document.)


To Run Locally

  • Azure Developer CLI
  • Python 3+
    • Important: Python and the pip package manager must be in the path in Windows for the setup scripts to work.
    • Important: Ensure you can run python --version from the console. On Ubuntu, you might need to run sudo apt install python-is-python3 to link python to python3.
  • Node.js
  • Git
  • Powershell 7+ (pwsh) – For Windows users only.
    • Important: Ensure you can run pwsh.exe from a PowerShell command. If this fails, you likely need to upgrade PowerShell.

NOTE: Your Azure Account must have Microsoft.Authorization/roleAssignments/write permissions, such as User Access Administrator or Owner.


Project Initialization

  1. Create a new folder and switch to it in the terminal
  2. Run azd auth login
  3. Run azd init -t azure-search-openai-demo
    • For the target location, the regions that currently support the models used in this sample are East US or South Central US. For an up-to-date list of regions and models, check here
    • note that this command will initialize a git repository and you do not need to clone this repository

Starting from scratch:

Execute the following command, if you don’t have any pre-existing Azure services and want to start from a fresh deployment.

  1. Run azd up – This will provision Azure resources and deploy this sample to those resources, including building the search index based on the files found in the ./data folder.
  2. After the application has been successfully deployed you will see a URL printed to the console. Click that URL to interact with the application in your browser.

For detailed information click here on my GitHub and follow a video from Microsoft talking about the example solution.

You can look at the Chat App that I’ve developed, which I will make available for you to test for a few days.

Firstly, it’s important to understand that you have the ability to replace the PDF files within the “./data” directory with your own business data.

If you wish to examine these files first to gain insights into the types of questions you can make in the chat to test, please click here.

Regrettably, the demo app had to be deactivated due to Azure expenses. If you’d like it to be reactivated, please click here to contact me. Thank you.

You’re able to query any content found within the enterprise PDF files located in the “./data” directory. The chat will respond with citations from the respective PDFs, and you have the option to click through and verify the information directly from the source PDF.


The vast universe of enterprise data, spanning from structured database datasets to unstructured documents, emails, and files, holds a wealth of insights that can drive an organization’s growth and success. Azure Cognitive Search and Azure OpenAI serve as powerful tools that make this data readily accessible, private, and secure. By leveraging these technologies, businesses can tap into the full potential of their internal data, from understanding the intricacies of their benefits and policies to defining roles and job descriptions more effectively. With a future powered by AI and machine learning, the conversations we can have with our data are only just beginning. This is more than just a technological shift; it’s a new era of informed decision-making, driven by data that’s within our reach. This solution provides an array of opportunities to assist businesses in leveraging their corporate data and disseminating it amongst their employees. This method simplifies comprehension, fostering organizational growth and enhancing the company culture. Should you require additional details on this topic, please do not hesitate to reach out to me.

That’s it for today!

The Future of Data Analytics: An Introduction to Microsoft Fabric

Microsoft Fabric, launched on May 24-25 of 2023 at the Microsoft Build event, is an end-to-end data and analytics platform that combines Microsoft’s OneLake data lake, Power BI, Azure Synapse, and Azure Data Factory into a unified software as a service (SaaS) platform. It’s a one-stop solution designed to serve various data professionals including data engineers, data warehousing professionals, data scientists, data analysts, and business users, enabling them to collaborate effectively within the platform to foster a healthy data culture across their organizations​​.

What are the Microsoft Fabric key features?

Data Factory – Microsoft’s Azure Data Factory is a powerful tool that combines the simplicity of Power Query with Azure Data Factory’s scale. It provides over 200 native connectors for data linkage from on-premises and cloud-based sources. Data Factory enables the scheduling and orchestration of notebooks and Spark jobs.

Data Engineering – Leveraging the extensive capabilities of Spark, data engineering in Microsoft Fabric provides premier authoring experiences and facilitates large-scale data transformations. It plays a crucial role in democratizing data through the lakehouse model. Moreover, integration with

Data Science – The data science capability in Microsoft Fabric aids in building, deploying, and operationalizing machine learning models within the Fabric framework. It interacts with Azure Machine Learning for built-in experiment tracking and model registry, empowering data scientists to enhance organizational data with predictions that business analysts can incorporate into their BI reports, thereby transitioning from descriptive to predictive insights.

Data Warehouse – The data warehousing component of Microsoft Fabric offers top-tier SQL performance and scalability. It features a full separation of computing and storage for independent scaling and native data storage in the open Delta Lake format.

Real-Time Analytics – Observational data, acquired from diverse sources like apps, IoT devices, human interactions, and more, represents the fastest-growing data category. This semi-structured, high-volume data, often in JSON or Text format with varying schemas, presents challenges for conventional data warehousing platforms. However, Microsoft Fabric’s Real-Time Analytics offers a superior solution for analyzing such data.

Power BI – Recognised as a leading Business Intelligence platform worldwide, Power BI in Microsoft Fabric enables business owners to access all Fabric data swiftly and intuitively for data-driven decision-making.

What are the Advantages of Microsoft Fabric?

Unified Platform: Microsoft Fabric provides a unified platform for different data analytics workloads such as data integration, engineering, warehousing, data science, real-time analytics, and business intelligence. This can foster a well-functioning data culture across the organization as data engineers, warehousing professionals, data scientists, data analysts, and business users can collaborate within Fabric​​.

Multi-cloud Support: Fabric is designed with a multi-cloud approach in mind, with support for data in Amazon S3 and (soon) Google Cloud Platform. This means that users are not restricted to using data only from Microsoft’s ecosystem, providing flexibility​.

Accessibility: Microsoft Fabric is currently available in public preview, and anyone can try the service without providing their credit card information. Starting from July 1, Fabric will be enabled for all Power BI tenants​.

AI Integration: The private preview of Copilot in Power BI will combine advanced generative AI with data, enabling users to simply describe the insights they need or ask a question about their data, and Copilot will analyze and pull the correct data into a report, turning data into actionable insights instantly​​.

Microsoft Fabric – Licensing and Pricing

Microsoft Fabric capacities are available for purchase in the Azure portal. These capacities provide the compute resources for all the experiences in Fabric from the Data Factory to ingest and transform to Data Engineering, Data Science, Data Warehouse, Real-Time Analytics, and all the way to Power BI for data visualization. A single capacity can power all workloads concurrently and does not need to be pre-allocated across the workloads. Moreover, a single capacity can be shared among multiple users and projects, without any limitations on the number of workspaces or creators that can utilize it.

To gain access to Microsoft Fabric, you have three options:

  1. Leverage your existing Power BI Premium subscription by turning on the Fabric preview switch. All Power BI Premium capacities can instantly power all the Fabric workloads with no additional action required. If you already have a Power BI Premium subscription, you can simply turn on the Fabric preview switch. This means you can enable Microsoft Fabric’s capabilities as part of your existing Power BI Premium subscription without having to do anything else. All the capacities you have with your Power BI Premium subscription can be used to power the full range of workloads in Microsoft Fabric. In other words, you can use your existing Power BI Premium resources to run all of the data and analytics tasks that Microsoft Fabric can handle.
  2. Start a Fabric trial if your tenant supports trials. If you’re not sure about committing to Microsoft Fabric yet, you can start a trial if your tenant (an instance of Azure Active Directory) supports it. A trial allows you to test the service before deciding to purchase. During the trial period, you can explore the full capabilities of Microsoft Fabric, such as data ingestion, data transformation, data engineering, data science, data warehouse operations, real-time analytics, and data visualization with Power BI.
  3. Purchase a Fabric pay-as-you-go capacity from the Azure portal. If you decide that Microsoft Fabric suits your needs and you don’t have a Power BI Premium subscription, you can directly purchase a Fabric capacity on a pay-as-you-go basis from the Azure portal. The pay-as-you-go model is flexible because it allows you to pay for only the compute and storage resources you use. Microsoft Fabric capacities come in different sizes, from F2 to F2048, representing 2 – 2048 Capacity Units (CU). Your bill will be determined by the amount of computing you provision (i.e., the size of the capacity you choose) and the amount of storage you use in OneLake, the data lake built into Microsoft Fabric. This model also allows you to easily scale your capacities up and down to adjust their computing power, and even pause your capacities when not in use to save on your bills​​.

Microsoft Fabric is a unified product for all your data and analytics workloads. Rather than provisioning and managing separate compute for each workload, with Fabric, your bill is determined by two variables: the amount of compute you provision and the amount of storage you use.

Follow the capacities that you can buy in the Azure portal:

Check out this video from Guy and Cube which breaks down the details on pricing and licensing.

How to activate the Microsoft Fabric Trial version?

Step 1

Login to Microsoft Power BI with your Developer Account

You will observe that asides from the OneLake icon at the top left, everything looks normal if you are familiar with Power BI Service.

Step 2

Enable Microsoft Fabric for your Tenant

Your Screen will Look like this

So far, we’ve only enabled Microsoft Fabric at the tenant level. This doesn’t give full access to Fabric resources as can be seen in the illustration below

So, Let’s upgrade the Power BI License to Microsoft Fabric Trial

For a smoother experience, You should create a new Workspace and add Microsoft Fabric Trial License as can be seen below

As you can see, while creating a new Workspace, you can now Assign Fabric Trial License to it. Upon creation, we are able to take full advantage of Microsoft Fabric

This video by Guy and Cube explains the steps for getting the Microsoft Fabric Trial.


Microsoft Fabric is currently in preview but already represents a significant advancement in the field of data and analytics, offering a unified platform that brings together various tools and services. It enables a smooth and collaborative experience for a variety of data professionals, fostering a data-driven culture within organizations. Let´s wait for the next steps from Microsoft.

That’s it for today!