MindsDB releases new features, improvements, and bug fixes weekly. Here you’ll find information on the latest product updates.
Join our Slack community to interact with the core MindsDB team, ask questions, and share your thoughts.
We added the pgvector extension to the PostgreSQL handler that enables you to store vector embeddings and perform vector similarity search in PostgreSQL.
Here are the features that cover vector databases:
The Gmail authentication process has been updated. Upon connecting to Gmail from MindsDB Cloud, users are redirected to a new window to authenticate their Gmail account.
MindsDB, as an AI workflow automation platform, enables all users to automate the data and prediction flow between their data sources and applications by disabling the restriction on job execution. All users, including the non-paid users, can execute an unlimited number of jobs.
We already support bringing custom models to MindsDB. Now you can finetune your custom model within MindsDB.
The TimeGPT handler can now handle the finetune steps, token validation, and date features.
As we grow, we continuously improve and refine the available features. Here are some fixes made in this release:
The aforementioned features, integrations, and bug fixes are updated in the MindsDB documentation.
MindsDB supports the latest Python versions, including 3.10 and 3.11.
You can create triggers on the MongoDB data sources that allow you to execute predefined SQL code upon data updates.
We’ve got a new SQL syntax for chatbots: CREATE CHATBOT. It allows you to simply create a chatbot that includes the following features:
There is also a table that stores all chatbots and can be queried like this: SELECT * FROM chatbots.
The OpenAI integration offers an embedding mode. It allows you to get the predictions in the form of embeddings to improve the performance of the model, including its efficiency and accuracy.
You can connect MindsDB to SQL Alchemy, which we migrated from version 1.4 to 2.0.
You can not only connect models from the Hugging Face hub but also finetuned them in MindsDB. It includes Text classification, Zero shot text classification, Translation, and Summarization models available from MindsDB.
We improved the prediction process by running the tasks in a subprocess that prevents blocking the main process.
We’ve implemented numerous integrations, including AI frameworks and applications.
We strive to keep improving the features available at MindsDB. Here is a list of improvements made in this release:
Thanks to our community, we are able to identify and fix bugs quickly. Here are some the bug fixes made in this release:
The aforementioned features, integrations, and bug fixes are updated in the MindsDB documentation.
We have updated the MySQL handler configuration by adding the conn_attrs key, which is required for providing a partner name for the SingleStore data source.
The error messages have been improved for the following cases:
The documentation has been updated, including MS SQL Server, What is MindsDB, REST API, OpenAI, SurrealDB, Shopify, and Google BigQuery.
We are working on the chatbot feature, so you can easily create custom chatbots based on your data. Currently, it is available on local MindsDB installation.
The integrations available at MindsDB are regularly updated to match the speed of developments in AI. We've decoupled the LangChain and OpenAI handlers since they don't share much functionalities.
We've made several updates to Python SDK. These include improving creation and deployment of time series models, enabling the predict method to take a dict data type as an argument, supporting file upload feature, supporting dropping tables, and updating the README file.
The new version of mindsdb_sql is available. It includes updates to the CREATE TRIGGER command and various fixes related to creating database and making predictions.
Now you can use the Anthropic models within MindsDB. Find out more by following this link.
Lightwood, MindsDB's default AI engine, supports embedding mode, so you can return the predictions in the form of embeddings to speed up the process.
As MindsDB provides the RETRAIN and FINETUNE features, one model may have multiple versions. Now if you want to delete a model that has many versions, it is going to take less time thanks to the usage of threads.
MindsDB offers over 100 handlers, including data handlers and ML handlers. To see all available handlers, you can use the SHOW HANDLERS statement, defining the type as data or ml.
Lastly, the Frappe API handler was improved to show the error message.
MindsDB offers free access to all OpenAI models, with a slight change in the user verification process. In order to utilize the OpenAI models, users are now required to confirm their email address. To complete this verification, users need to navigate to the Settings section and click on the Confirm email button. By following the instructions outlined in the verification email, users can successfully confirm their email address and resume using the OpenAI models within MindsDB.
MindsDB has introduced the LightFM and Popularity Recommender handlers that filter given data based on popularity.
You can learn more about the LightFM Recommender handler by following this link and about the Popularity Recommender handler by following this link.
The process cache feature helps shorten the model training time. By starting processes that initialize the available ML handlers beforehand, we make them ready for the incoming tasks. So, when a user creates a model using the Lightwood ML engine, it picks up the process with the initialized Lightwood handler, assuming that such a process is ready and available.
MindsDB integrates with the Shopify application. We added support for the INSERT statement so that users can insert customer data into their Slack accounts.
Currently, MindsDB offers Slack and Rocket Chat integrations for creating chatbots. When using local MindsDB installation (via pip or Docker) or a managed instance (MindsDB Pro), you can utilize the REST API endpoints to create and work with chatbots.
Here are the available REST API endpoints:
Additionally, we have developed a real-time Slack chat handler that implements an interface for sending and receiving Slack messages.
MindsDB has upgraded its capabilities with the introduction of web crawler integration, empowering users to retrieve valuable data from websites effortlessly. By integrating this functionality, users can now harness the power of MindsDB to extract and utilize website data within their AI-powered applications.
You can learn more about how to use it by following this link.
MindsDB integrates with MediaWiki, providing users with the power to effortlessly query its pages and leverage their content in the development of AI-powered applications.
You can learn more about how to use it by following this link.
MindsDB has enhanced its Gmail handler by introducing the UPDATE and DELETE methods, expanding its functionality beyond fetching and writing emails to include the capability to update and delete them as well.
We have recently made some updates to the Writer handler, addressing any missing dependencies and ensuring a seamless experience for users.
The Llama Index handler has undergone a refactoring process to optimize its functionality and utilize appropriate abstractions. This refactoring is part of our ongoing efforts to enhance the handler, with more improvements planned for the future.
As a MindsDB user, you may already be aware that the presence of the target column is crucial in the training dataset. To ensure data integrity, we have implemented a check that raises an exception if the target column is missing from the training dataset. This enhancement aims to provide a more robust and error-free training process.
In this release, we have addressed the issues related to missing dependencies for the google_calendar, langchain, and llamaindex handlers.
Furthermore, the introduction of UPDATE and DELETE statements enables you to manipulate the data stored in your connected data sources.
We have also implemented a custom SQL function called json_extract(), which facilitates the extraction of JSON values by specifying a key from a JSON object.
In addition to these updates, we have made miscellaneous documentation improvements to enhance the overall user experience.
The PayPal handler has been updated to include support for querying payments.
Additionally, a new ML handler called Llama Index has been introduced, providing the ability to create models based on it.
MindsDB released new exciting integrations handlers, including Rocket Chat, Frappe, and Shopify. Here are the details:
In pursuit of delivering the best quality product, MindsDB maintains a commitment to continuous improvement. Here are the latest enhancements:
MindsDB continuously enhances the user experience by consistently improving their content and documentation. Here are the latest updates:
MindsDB uses the Lightwood ML engine by default unless specified differently in the CREATE MODEL statement. As such, we keep improving it. Now, the Lightwood engine is at version 23.5.1.0. Learn more by following the Lightwood docs.
We’ve also made updates to the LangChain ML engine.
The support for Python version 3.7 is dropped. Currently, MindsDB requires Python 3.8 or 3.9.
The REST API endpoints are now well-documented. You can use it to manage databases, tables, models, views, and more. Check out our REST API docs here.
We’ve created a cache for data handlers that keeps connections opened during TTL time from the handler's last use.
As MindsDB grows, we continuously update the documentation. We’ve made a major restructure to our docs, including dividing them into sections for better readability.
We’ve got a lot of new integrations coming up. Let’s look at the ones released this week.
This integration enables organizations and users on GitLab to use ML for issue estimations, labeling, projects recommendation, automation of comments on GitLab issues, proofreading of READMEs, and more.
You can find out more about GitLab integration and its usage here.
The Hugging Face Inference API lets you easily integrate NLP, audio, and computer vision models deployed for inference via simple API calls.
You can find out more about Hugging Face Inference API integration and its usage here.
It enables MindsDB users to retrieve search results from Google directly within their ML models. Users can incorporate external data from Google Search to improve their model accuracy and predictions.
You can find out more about Google Search integration and its usage here.
This integration enables users to analyze their Google Fit data with the help of ML models in MindsDB.
You can find out more about Google Fit integration and its usage here.
This integration enables users to leverage ML capabilities for sales predictions, inventory management, product recommendations, and other automation tasks.
You can find out more about Google Content API integration and its usage here.
The Google Books integration with MindsDB enables users to use ML for book recommendations, reading history analysis, and other automation tasks based on their Google Books activity.
You can find out more about Google Books integration and its usage here.
This integration enables Quickbooks users to use the power of machine learning for generating expense reports, risk assessments, accounts payable, and many other use cases.
You can find out more about Quickbooks integration and its usage here.
This integration enables Sendinblue users to use ML for email campaign predictions, lead scoring, and other automation tasks in their Sendinblue account.
You can find out more about Sendinblue integration and its usage here.
The integration of MindsDB and YouTube lets users query for videos’ comments and analyze them using ML models.
You can find out more about YouTube integration and its usage here.
With this integration, users can easily connect MindsDB to their Slack workspace and use powerful AI capabilities to enhance team communication and collaboration.
You can find out more about Slack integration and its usage here.
This integration enables MindsDB users to gather data from HackerNews, including news articles, comments, and user profiles, for machine learning tasks such as sentiment analysis, content recommendation, and data analysis.
You can find out more about HackerNews integration and its usage here.
This integration enables users to access their Strava data, including activities, segments, and athlete information, through MindsDB, opening up possibilities for fitness-related automation, analysis, and insights.
You can find out more about Strava integration and its usage here.
This integration enables users to retrieve news articles from various sources, filter by keywords, and perform other operations using ML for news analysis, recommendation, and automation purposes.
You can find out more about NewsAPI integration and its usage here.
This integration enables users of SAP MaxDB to connect it to MindsDB as a data source. It is a high-performance, scalable, and reliable relational database management system that supports a wide range of applications.
You can find out more about SAP MaxDB integration and its usage here.
We’ve got new REST API endpoints for databases and views.
You can now fetch all databases connected to MindsDB using the GET /api/databases endpoint. We provide the POST and PUT endpoints to create or update a database. And to delete a database, you can call the DELETE /api/databases/<database_name> endpoint, passing your database name.
The same goes for views. You can fetch, create, update, or delete them using the REST API endpoints. All these endpoints include a project name where the view resides. For example, to get a single view, you can call the GET /api/projects/<project_name>/views/<view_name> endpoint. To learn more about MindsDB projects, check out our docs here.
We’ve got a lot of new integrations coming up. Let’s look at the ones released this week.
The integration with Reddit allows users to automate tasks, such as sentiment analysis, community trend analysis, and user behavior analysis, utilizing machine learning models.
Check out our docs here to learn how to connect your Reddit account to MindsDB.
Thanks to the integration with Plaid, users can apply ML for financial predictions, transaction categorization, and other financial automation tasks.
.png)
Check out our docs here to learn how to connect your Plaid account to MindsDB.
With the Google Calendar integration, you can now leverage the power of ML for smarter scheduling, event recommendations, and other automation tasks.
.png)
Check out our docs here to learn how to connect your Google Calendar to MindsDB.
The integration with Confluence enables users to create, collaborate, and organize their work, utilizing the power of ML.
.png)
Check out our docs here to learn how to connect your Confluence account to MindsDB.
MindsDB offers over 70 integrations with various data sources. Here is another one: InfluxDB is an open-source time series database that you can now connect to MindsDB.
.png)
Check out our docs here to learn how to connect your InfluxDB database to MindsDB.
We’ve implemented numerous new features, product improvements, and bug fixes. Read along to see the overview.
Stay tuned for more exciting updates in the future!
Here are the new features, product improvements, and bug fixes released this month.
We would like to thank our community for ongoing support, and if you are new to MindsDB, please feel free to try it without installation using our free demo environment.
.webp)
.webp)
.webp)
