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eva-master ======= 7dd70375 (release: merge staging into master (#1032)) ======= ======= <<<<<<< HEAD <<<<<<< HEAD ======= 54907d3e (release: merge staging into master (#1032)) ======= <<<<<<< HEAD georgia-tech-db-main ======= <<<<<<< HEAD eva-master ======= 7dd70375 (release: merge staging into master (#1032)) f028c383 (release: merge staging into master (#1032)) <<<<<<< HEAD eva-source ======= georgia-tech-db-main

EvaDB

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Database system for AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Database system for AI-powered apps

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Database system for AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Database system for AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Database system for AI-powered apps

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Bring AI inside your database system and build AI-powered apps

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Database system for AI-powered apps

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EvaDB forks EvaDB stars EvaDB pull-requests EvaDB Commits

Follow EvaDB

Join EvaDB Slack Community Follow evadb_ai EvaDB on Medium EvaDB Website

Launch EvaDB on Colab Roadmap Python Versions Supported License Coverage Status

EvaDB enables software developers to build AI apps in a few lines of code. Its powerful SQL API simplifies AI app development for both structured and unstructured data. EvaDB's benefits include:

🔮 Easy to connect the EvaDB query engine with your data sources, such as PostgreSQL or S3 buckets, and build AI-powered apps with SQL queries.
Structured Data Sources Unstructured Data Sources Application Data Sources
  • PostgreSQL
  • SQLite
  • MySQL
  • MariaDB
  • Clickhouse
  • Snowflake
  • Local filesystem
  • AWS S3 bucket
  • Github

More details on the supported data sources is available here.

🤝 Query your connected data with a pre-trained AI model from Hugging Face, OpenAI, YOLO, Stable Diffusion, etc.
Hugging Face OpenAI YOLO
  • Audio Classification
  • Automatic Speech Recognition
  • Text Classification
  • Summarization
  • Text2Text Generation
  • Text Generation
  • Image Classification
  • Image Segmentation
  • Image-to-Text
  • Object Detection
  • Depth Estimation
  • gpt-4
  • gpt-4-0314
  • gpt-4-32k
  • gpt-4-32k-0314
  • gpt-3.5-turbo
  • gpt-3.5-turbo-0301
  • yolov8n.pt
  • yolov8s.pt
  • yolov8m.pt
  • yolov8l.pt
  • yolov8x.pt

More details on the supported AI models is available here

🔧 Create or fine-tune AI models for regression, classification, and time series forecasting.
Regression Classification Time Series Forecasting
  • Ludwig
  • Sklearn
  • Xgboost
  • Ludwig
  • Xboost
  • Statsforecast
  • Neuralforecast

More details on the supported AutoML frameworks is available here.

💰 Faster AI queries thanks to AI-centric query optimizations such as caching, batching, and parallel processing.
  • Function result caching helps reuse results of expensive AI function calls.
  • LLM batching reduces token usage and dollars spent on LLM calls.
  • Parallel query processing saves money and time spent on running AI models by better utilizing CPUs and/or GPUs.
  • Query predicate re-ordering and predicate push-down accelerates queries over both structured and unstructured data.

More details on the optimizations in EvaDB is available here.


👋 Hey! If you're excited about our vision of bringing AI inside database systems, show some ❤️ by:

  • ⭐ starring our GitHub 🐙 Repo =======

    EvaDB forks EvaDB stars EvaDB pull-requests EvaDB Commits

    Follow EvaDB

    Join EvaDB Slack Community Follow evadb_ai EvaDB on Medium EvaDB Website

    Share EvaDB

    Follow _superAGI Share on Telegram Share on Reddit

    Launch EvaDB on Colab Roadmap Python Versions Supported License Coverage Status
    Open in Gitpod

    EvaDB enables software developers to build AI apps in a few lines of code. Its powerful SQL API simplifies AI app development for both structured and unstructured data. EvaDB's benefits include:

    • 🔮 Easy to connect EvaDB with your SQL database system and build AI-powered apps with SQL queries
    • 🤝 Query your data with a pre-trained AI model from Hugging Face, OpenAI, YOLO, PyTorch, and other AI frameworks
    • ⚡️ Faster queries thanks to AI-centric query optimization
    • 💰 Save money spent on running models by efficient CPU/GPU use
    • 🔧 Fine-tune your AI models to achieve better results

    👋 Hey! If you're excited about our vision of bringing AI inside database systems, show some ❤️ by:

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    • ⭐ starring our GitHub 🐙 Repo =======
    • 🐙 giving a ⭐ on our EvaDB repo on Github >>>>>>> 8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD >>>>>>> eva-source ======= <<<<<<< HEAD >>>>>>> bf18bc80 (Bump v0.3.4+ dev) ======= ======= >>>>>>> 22e78346 (Bump v0.3.4+ dev) >>>>>>> 922824b7 (Bump v0.3.4+ dev)
    • ⭐ starring our GitHub 🐙 Repo ======= >>>>>>> 22e78346 (Bump v0.3.4+ dev)
    • 🐙 giving a ⭐ on our EvaDB repo on Github >>>>>>> 8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD >>>>>>> georgia-tech-db-main =======
    • ⭐ starring our GitHub 🐙 Repo >>>>>>> 40a10ce1 (Bump v0.3.4+ dev) =======
    • ⭐ starring our GitHub 🐙 Repo >>>>>>> 6d6a14c8 (Bump v0.3.4+ dev) <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD ======= ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 922824b7 (Bump v0.3.4+ dev) ======= ======= >>>>>>> 2170a7a9 (Bump v0.3.4+ dev) ======= >>>>>>> c5f43c65 (Bump v0.3.4+ dev)
    • ⭐ starring our GitHub 🐙 Repo =======
    • 🐙 giving a ⭐ on our EvaDB repo on Github >>>>>>> 8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD >>>>>>> georgia-tech-db-main >>>>>>> f028c383 (release: merge staging into master (#1032)) ======= =======
    • ⭐ starring our GitHub 🐙 Repo >>>>>>> 40a10ce1 (Bump v0.3.4+ dev) >>>>>>> 2170a7a9 (Bump v0.3.4+ dev) ======= >>>>>>> 22e78346 (Bump v0.3.4+ dev) <<<<<<< HEAD >>>>>>> eva-source ======= <<<<<<< HEAD =======
    • ⭐ starring our GitHub 🐙 Repo =======
    • 🐙 giving a ⭐ on our EvaDB repo on Github >>>>>>> 8c5b63dc (release: merge staging into master (#1032)) >>>>>>> a9124e1e (release: merge staging into master (#1032)) ======= =======
    • ⭐ starring our GitHub 🐙 Repo >>>>>>> 40a10ce1 (Bump v0.3.4+ dev) >>>>>>> c5f43c65 (Bump v0.3.4+ dev) ======= >>>>>>> ae08f806 (Bump v0.3.4+ dev) ======= ======= >>>>>>> f028c383 (release: merge staging into master (#1032)) <<<<<<< HEAD >>>>>>> 54907d3e (release: merge staging into master (#1032)) ======= ======= =======
    • ⭐ starring our GitHub 🐙 Repo >>>>>>> 40a10ce1 (Bump v0.3.4+ dev) >>>>>>> 2170a7a9 (Bump v0.3.4+ dev) >>>>>>> bf18bc80 (Bump v0.3.4+ dev) ======= >>>>>>> 922824b7 (Bump v0.3.4+ dev) >>>>>>> georgia-tech-db-main
    • 📟 joining our Slack Community
    • 🐦 following us on Twitter
    • 📝 following us on Medium
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    We would love to learn about your AI app. Please complete this 1-minute form: https://v0fbgcue0cm.typeform.com/to/BZHZWeZm

    Quick Links

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    Quick Links

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    Documentation

    You can find the complete documentation of EvaDB at evadb.ai/docs 📚✨🚀

    Why EvaDB

    In the world of AI, we've reached a stage where many AI tasks that were traditionally handled by AI or ML engineers can now be automated. EvaDB enables software developers with the ability to perform advanced AI tasks without needing to delve into the intricate details.

    EvaDB covers many AI applications, including regression, classification, image recognition, question answering, and many other generative AI applications. EvaDB targets 99% of AI problems that are often repetitive and can be automated with a simple function call in an SQL query. Until now, there is no comprehensive open-source framework for bringing AI into an existing SQL database system with a principled AI optimization framework, and that's where EvaDB comes in.

    Our target audience is software developers who may not necessarily have a background in AI but require AI capabilities to solve specific problems. We target programmers who write simple SQL queries inside their CRUD apps. With EvaDB, it is possible to easily add AI features to these apps by calling built-in AI functions in the queries.

    How does EvaDB work

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    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • ======= >>>>>>> georgia-tech-db-main =======
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • <<<<<<< HEAD ======= >>>>>>> c5f43c65 (Bump v0.3.4+ dev) ======= ======= >>>>>>> efdfee93 (Update README.md)
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> 08f14335 (Update README.md) <<<<<<< HEAD >>>>>>> 710cd748 (Update README.md) ======= =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> f5a7c929 (Update README.md) >>>>>>> efdfee93 (Update README.md) ======= =======
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • >>>>>>> 54907d3e (release: merge staging into master (#1032)) ======= =======
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> 7cac771f (Bump v0.3.4+ dev) >>>>>>> bf18bc80 (Bump v0.3.4+ dev)
    • Write SQL queries with AI functions to get inference results:
      • Pick a pre-trained AI model from Hugging Face, Open AI, Ultralytics, PyTorch, and built-in AI frameworks for generative AI, NLP, and vision applications;
      • or pick from a variety of state-of-the-art ML engines for classic ML use-cases (classification, regression, etc.);
      • or bring your custom model built with any AI/ML framework using `CREATE FUNCTION`.

    Follow the getting started guide to get on-boarded as fast as possible.

    Illustrative Queries

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    • Get insights about Github stargazers using GPT4. <<<<<<< HEAD =======
    • Get insights about Github stargazers using GPT4.

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    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image.
    CREATE INDEX reddit_sift_image_index
    ON reddit_dataset (SiftFeatureExtractor(data))
    USING FAISS
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SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD

    8c5b63dc (release: merge staging into master (#1032)) =======

    40a10ce1 (Bump v0.3.4+ dev) <<<<<<< HEAD

    <<<<<<< HEAD You can find the complete documentation of EvaDB at evadb.ai/docs 📚✨🚀

    Why EvaDB

    In the world of AI, we've reached a stage where many AI tasks that were traditionally handled by AI or ML engineers can now be automated. EvaDB enables software developers with the ability to perform advanced AI tasks without needing to delve into the intricate details.

    EvaDB covers many AI applications, including regression, classification, image recognition, question answering, and many other generative AI applications. EvaDB targets 99% of AI problems that are often repetitive and can be automated with a simple function call in an SQL query. Until now, there is no comprehensive open-source framework for bringing AI into an existing SQL database system with a principled AI optimization framework, and that's where EvaDB comes in.

    Our target audience is software developers who may not necessarily have a background in AI but require AI capabilities to solve specific problems. We target programmers who write simple SQL queries inside their CRUD apps. With EvaDB, it is possible to easily add AI features to these apps by calling built-in AI functions in the queries.

    How does EvaDB work

      >>>>>>> a9124e1e (release: merge staging into master (#1032))
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> georgia-tech-db-main >>>>>>> 7cac771f (Bump v0.3.4+ dev)
    • Write SQL queries with AI functions to get inference results:
      • Pick a pre-trained AI model from Hugging Face, Open AI, Ultralytics, PyTorch, and built-in AI frameworks for generative AI, NLP, and vision applications;
      • or pick from a variety of state-of-the-art ML engines for classic ML use-cases (classification, regression, etc.);
      • or bring your custom model built with any AI/ML framework using `CREATE FUNCTION`.

    Follow the getting started guide to get on-boarded as fast as possible.

    Illustrative Queries

    • Get insights about Github stargazers using GPT4.
    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image. <<<<<<< HEAD ======= =======
    • Run the MNIST Image Classification model to obtain digit labels for each frame in the video. =======

    b250207e (Update README.md)

    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.

    <<<<<<< HEAD

    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images.

    eva-master =======

    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image.

    b250207e (Update README.md) 6f08d5a3 (Update README.md) =======

    • Run the MNIST Image Classification model to obtain digit labels for each frame in the video.
    SELECT MnistImageClassifier(data).label FROM mnist_video;
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images.

    54907d3e (release: merge staging into master (#1032))

    CREATE INDEX reddit_sift_image_index
    ON reddit_dataset (SiftFeatureExtractor(data))
    USING FAISS
<<<<<<< HEAD
<<<<<<< HEAD
=======
<<<<<<< HEAD
<<<<<<< HEAD
=======
=======
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> b250207e (Update README.md)
>>>>>>> 6f08d5a3 (Update README.md)
>>>>>>> 304c4a34 (Update README.md)
>>>>>>> georgia-tech-db-main

=======

>>>>>>> 6d6a14c8 (Bump v0.3.4+ dev)
=======
    • Retrieve the top-5 most similar images for the given image using the index.
    >>>>>>> 54907d3e (release: merge staging into master (#1032))
SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    54907d3e (release: merge staging into master (#1032)) georgia-tech-db-main

    8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD georgia-tech-db-main =======

    40a10ce1 (Bump v0.3.4+ dev) <<<<<<< HEAD ======= ======= 54907d3e (release: merge staging into master (#1032)) ======= bf18bc80 (Bump v0.3.4+ dev) georgia-tech-db-main

    <<<<<<< HEAD You can find the complete documentation of EvaDB at evadb.ai/docs 📚✨🚀

    Why EvaDB

    In the world of AI, we've reached a stage where many AI tasks that were traditionally handled by AI or ML engineers can now be automated. EvaDB enables software developers with the ability to perform advanced AI tasks without needing to delve into the intricate details.

    EvaDB covers many AI applications, including regression, classification, image recognition, question answering, and many other generative AI applications. EvaDB targets 99% of AI problems that are often repetitive and can be automated with a simple function call in an SQL query. Until now, there is no comprehensive open-source framework for bringing AI into an existing SQL database system with a principled AI optimization framework, and that's where EvaDB comes in.

    Our target audience is software developers who may not necessarily have a background in AI but require AI capabilities to solve specific problems. We target programmers who write simple SQL queries inside their CRUD apps. With EvaDB, it is possible to easily add AI features to these apps by calling built-in AI functions in the queries.

    How does EvaDB work

      >>>>>>> 7dd70375 (release: merge staging into master (#1032))
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> 08f14335 (Update README.md) =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • >>>>>>> f5a7c929 (Update README.md)
    • Write SQL queries with AI functions to get inference results:
      • Pick a pre-trained AI model from Hugging Face, Open AI, Ultralytics, PyTorch, and built-in AI frameworks for generative AI, NLP, and vision applications;
      • or pick from a variety of state-of-the-art ML engines for classic ML use-cases (classification, regression, etc.);
      • or bring your custom model built with any AI/ML framework using `CREATE FUNCTION`.

    Follow the getting started guide to get on-boarded as fast as possible.

    Illustrative Queries

    • Get insights about Github stargazers using GPT4. <<<<<<< HEAD
    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image.
    CREATE INDEX reddit_sift_image_index
    ON reddit_dataset (SiftFeatureExtractor(data))
    USING FAISS
<<<<<<< HEAD
<<<<<<< HEAD
=======
<<<<<<< HEAD
>>>>>>> eva-source
=======
>>>>>>> georgia-tech-db-main

=======

>>>>>>> 6d6a14c8 (Bump v0.3.4+ dev)
SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD <<<<<<< HEAD

    922824b7 (Bump v0.3.4+ dev) georgia-tech-db-main ======= 065f25fb (release: merge staging into master (#1032)) ======= 8da6decc (Bump v0.3.4+ dev) <<<<<<< HEAD ======= <<<<<<< HEAD ======= ======= 065f25fb (release: merge staging into master (#1032)) 66bd4f55 (release: merge staging into master (#1032)) ======= 922824b7 (Bump v0.3.4+ dev) georgia-tech-db-main

    8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= ======= <<<<<<< HEAD eva-source ======= ======= <<<<<<< HEAD georgia-tech-db-main eva-master

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD

    =======

    georgia-tech-db-main 28d8bad1 (release: merge staging into master (#1032)) =======

    40a10ce1 (Bump v0.3.4+ dev)

    <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD

    eva-source ======= georgia-tech-db-main eva-master =======

    <<<<<<< HEAD

    065f25fb (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD ======= 28d8bad1 (release: merge staging into master (#1032)) eva-source ======= 28d8bad1 (release: merge staging into master (#1032)) georgia-tech-db-main You can find the complete documentation of EvaDB at evadb.ai/docs 📚✨🚀

    Why EvaDB

    In the world of AI, we've reached a stage where many AI tasks that were traditionally handled by AI or ML engineers can now be automated. EvaDB enables software developers with the ability to perform advanced AI tasks without needing to delve into the intricate details.

    EvaDB covers many AI applications, including regression, classification, image recognition, question answering, and many other generative AI applications. EvaDB targets 99% of AI problems that are often repetitive and can be automated with a simple function call in an SQL query. Until now, there is no comprehensive open-source framework for bringing AI into an existing SQL database system with a principled AI optimization framework, and that's where EvaDB comes in.

    Our target audience is software developers who may not necessarily have a background in AI but require AI capabilities to solve specific problems. We target programmers who write simple SQL queries inside their CRUD apps. With EvaDB, it is possible to easily add AI features to these apps by calling built-in AI functions in the queries.

    How does EvaDB work

      <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • ======= <<<<<<< HEAD <<<<<<< HEAD
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • ======= >>>>>>> georgia-tech-db-main =======
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • ======= <<<<<<< HEAD <<<<<<< HEAD >>>>>>> eva-source <<<<<<< HEAD
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • ======= ======= <<<<<<< HEAD
    • Connect EvaDB to your SQL and vector database systems with the `CREATE DATABASE` and `CREATE INDEX` statements.
      • ======= >>>>>>> 66bd4f55 (release: merge staging into master (#1032)) >>>>>>> georgia-tech-db-main
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • >>>>>>> eva-master =======
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
      • >>>>>>> 065f25fb (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 28d8bad1 (release: merge staging into master (#1032)) >>>>>>> eva-source ======= >>>>>>> 28d8bad1 (release: merge staging into master (#1032)) >>>>>>> georgia-tech-db-main
    • Write SQL queries with AI functions to get inference results:
      • Pick a pre-trained AI model from Hugging Face, Open AI, Ultralytics, PyTorch, and built-in AI frameworks for generative AI, NLP, and vision applications;
      • or pick from a variety of state-of-the-art ML engines for classic ML use-cases (classification, regression, etc.);
      • or bring your custom model built with any AI/ML framework using `CREATE FUNCTION`.

    Follow the getting started guide to get on-boarded as fast as possible.

    Illustrative Queries

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    =======

    georgia-tech-db-main ======= <<<<<<< HEAD <<<<<<< HEAD 28d8bad1 (release: merge staging into master (#1032)) <<<<<<< HEAD eva-source ======= georgia-tech-db-main

    • Get insights about Github stargazers using GPT4.
    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image. <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD =======

    eva-source ======= georgia-tech-db-main =======

    • Run the MNIST Image Classification model to obtain digit labels for each frame in the video. =======

    b250207e (Update README.md)

    SELECT name, country, email, programming_languages, social_media, GPT4(prompt,topics_of_interest)
FROM gpt4all_StargazerInsights;

--- Prompt to GPT-4
You are given 10 rows of input, each row is separated by two new line characters.
Categorize the topics listed in each row into one or more of the following 3 technical areas - Machine Learning, Databases, and Web development. If the topics listed are not related to any of these 3 areas, output a single N/A. Do not miss any input row. Do not add any additional text or numbers to your output.
The output rows must be separated by two new line characters. Each input row must generate exactly one output row. For example, the input row [Recommendation systems, Deep neural networks, Postgres] must generate only the output row [Machine Learning, Databases].
The input row [enterpreneurship, startups, venture capital] must generate the output row N/A.

    <<<<<<< HEAD

    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images.

    eva-master =======

    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. Return the top-5 similar images for a given image.

    b250207e (Update README.md) <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD 6f08d5a3 (Update README.md) ======= eva-source ======= 6f08d5a3 (Update README.md) ======= georgia-tech-db-main =======

    • Run the MNIST Image Classification model to obtain digit labels for each frame in the video.
    SELECT MnistImageClassifier(data).label FROM mnist_video;
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images.

    065f25fb (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD ======= ======= georgia-tech-db-main 28d8bad1 (release: merge staging into master (#1032))

    CREATE INDEX reddit_sift_image_index
    ON reddit_dataset (SiftFeatureExtractor(data))
    USING FAISS
<<<<<<< HEAD
=======
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> b250207e (Update README.md)
>>>>>>> 6f08d5a3 (Update README.md)

=======

>>>>>>> 6d6a14c8 (Bump v0.3.4+ dev)
SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD <<<<<<< HEAD

    =======

    30d7834d (release: merge staging into master (#1032)) ======= ae08f806 (Bump v0.3.4+ dev) georgia-tech-db-main

    8c5b63dc (release: merge staging into master (#1032))

    <<<<<<< HEAD You can find the complete documentation of EvaDB at evadb.ai/docs 📚✨🚀

    Why EvaDB

    In the world of AI, we've reached a stage where many AI tasks that were traditionally handled by AI or ML engineers can now be automated. EvaDB enables software developers with the ability to perform advanced AI tasks without needing to delve into the intricate details.

    EvaDB covers many AI applications, including regression, classification, image recognition, question answering, and many other generative AI applications. EvaDB targets 99% of AI problems that are often repetitive and can be automated with a simple function call in an SQL query. Until now, there is no comprehensive open-source framework for bringing AI into an existing SQL database system with a principled AI optimization framework, and that's where EvaDB comes in.

    Our target audience is software developers who may not necessarily have a background in AI but require AI capabilities to solve specific problems. We target programmers who write simple SQL queries inside their CRUD apps. With EvaDB, it is possible to easily add AI features to these apps by calling built-in AI functions in the queries.

    How does EvaDB work

    <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 30d7834d (release: merge staging into master (#1032)) ======= >>>>>>> 304c4a34 (Update README.md) >>>>>>> georgia-tech-db-main
    • Connect EvaDB to your database system with the `CREATE DATABASE` statement.
    • Write SQL queries with AI functions to get inference results:
      • Pick a pre-trained AI model from Hugging Face, Open AI, Ultralytics, PyTorch, and built-in AI frameworks for generative AI, NLP, and vision applications;
      • or pick from a variety of state-of-the-art ML engines for classic ML use-cases (classification, regression, etc.);
      • or bring your custom model built with any AI/ML framework using `CREATE FUNCTION`.

    Follow the getting started guide to get on-boarded as fast as possible. <<<<<<< HEAD

    <<<<<<< HEAD <<<<<<< HEAD

    georgia-tech-db-main =======

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD

    =======

    <<<<<<< HEAD

    304c4a34 (Update README.md) ======= efdfee93 (Update README.md) georgia-tech-db-main

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.
    CREATE TABLE text_summary AS
    SELECT SpeechRecognizer(audio) FROM ukraine_video;
SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
    FROM text_summary;
    • Train a classic ML model for prediction using the Ludwig AI engine.
    CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
PREDICT 'rental_price'
TIME_LIMIT 120;

    6f08d5a3 (Update README.md) <<<<<<< HEAD ======= <<<<<<< HEAD ======= 30d7834d (release: merge staging into master (#1032)) ======= 304c4a34 (Update README.md) georgia-tech-db-main

    Illustrative Queries

    • Run the MNIST Image Classification model to obtain digit labels for each frame in the video.

    <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD <<<<<<< HEAD

    30d7834d (release: merge staging into master (#1032)) ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    SELECT MnistImageClassifier(data).label FROM mnist_video;
    • Build a vector index on the feature embeddings returned by the SIFT Feature Extractor on a collection of Reddit images. <<<<<<< HEAD

    eva-source ======= georgia-tech-db-main

    CREATE INDEX reddit_sift_image_index
    ON reddit_dataset (SiftFeatureExtractor(data))
    USING FAISS
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> b250207e (Update README.md)
=======
=======
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
=======
=======
=======
>>>>>>> 065f25fb (release: merge staging into master (#1032))
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
<<<<<<< HEAD

    eva-source

    ======= <<<<<<< HEAD

    30d7834d (release: merge staging into master (#1032)) ======= 66bd4f55 (release: merge staging into master (#1032))

    
* Retrieve the top-5 most similar images for the given image using the index.

```sql
>>>>>>> georgia-tech-db-main
SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    66bd4f55 (release: merge staging into master (#1032)) ======= 922824b7 (Bump v0.3.4+ dev) georgia-tech-db-main ======= 40a10ce1 (Bump v0.3.4+ dev) ======= 065f25fb (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD ======= 922824b7 (Bump v0.3.4+ dev) georgia-tech-db-main ======= ======= 40a10ce1 (Bump v0.3.4+ dev) 8da6decc (Bump v0.3.4+ dev) <<<<<<< HEAD ======= <<<<<<< HEAD ======= 30d7834d (release: merge staging into master (#1032)) ======= ======= 40a10ce1 (Bump v0.3.4+ dev) ae08f806 (Bump v0.3.4+ dev) ======= 66bd4f55 (release: merge staging into master (#1032)) ======= 922824b7 (Bump v0.3.4+ dev) georgia-tech-db-main

    More Illustrative Queries

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    eva-source

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.
    CREATE TABLE text_summary AS
    SELECT SpeechRecognizer(audio) FROM ukraine_video;
SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
    FROM text_summary;
    • Train a classic ML model for prediction using the Ludwig AI engine.
    CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
PREDICT 'rental_price'
TIME_LIMIT 120;

    Architecture of EvaDB

    EvaDB's AI-centric query optimizer takes a query as input and generates a query plan. The query engine takes the query plan and hits the relevant backends to efficiently process the query: 1. SQL Database Systems (Structured Data) 2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud) 3. Vector Database Systems (Feature Embeddings)

    ======= <<<<<<< HEAD <<<<<<< HEAD

    065f25fb (release: merge staging into master (#1032))

    
* Retrieve the top-5 most similar images for the given image using the index.

```sql
SELECT name FROM reddit_dataset ORDER BY
    Similarity(
        SiftFeatureExtractor(Open('reddit-images/g1074_d4mxztt.jpg')),
        SiftFeatureExtractor(data)
    )
    LIMIT 5

    Illustrative Apps

    Here are some illustrative AI apps built using EvaDB (each notebook can be opened on Google Colab):

    <<<<<<< HEAD <<<<<<< HEAD

    40a10ce1 (Bump v0.3.4+ dev) ======= 065f25fb (release: merge staging into master (#1032)) ======= ======= 40a10ce1 (Bump v0.3.4+ dev) 8da6decc (Bump v0.3.4+ dev)

    =======

    eva-source

    More Illustrative Queries

    <<<<<<< HEAD

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.
    CREATE TABLE text_summary AS
    SELECT SpeechRecognizer(audio) FROM ukraine_video;
SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
    FROM text_summary;
    • Train a classic ML model for prediction using the Ludwig AI engine.
    CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
PREDICT 'rental_price'
TIME_LIMIT 120;

    Architecture of EvaDB

    EvaDB's AI-centric query optimizer takes a query as input and generates a query plan. The query engine takes the query plan and hits the relevant backends to efficiently process the query: 1. SQL Database Systems (Structured Data) 2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud) 3. Vector Database Systems (Feature Embeddings)

    =======

    georgia-tech-db-main

    More Illustrative Queries

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. <<<<<<< HEAD =======
    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. Run ChatGPT on the text column in a table.

    b250207e (Update README.md) =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    f5a7c929 (Update README.md) ======= 7dd70375 (release: merge staging into master (#1032)) =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    7cac771f (Bump v0.3.4+ dev) ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= bf18bc80 (Bump v0.3.4+ dev) ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT. ======= =======

    2170a7a9 (Bump v0.3.4+ dev) <<<<<<< HEAD ======= 28d8bad1 (release: merge staging into master (#1032)) ======= <<<<<<< HEAD <<<<<<< HEAD ======= 28d8bad1 (release: merge staging into master (#1032)) ======= bf18bc80 (Bump v0.3.4+ dev) ======= ======= 28d8bad1 (release: merge staging into master (#1032)) 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. <<<<<<< HEAD <<<<<<< HEAD

    c63abee7 (release: merge staging into master (#1032)) ======= ======= f028c383 (release: merge staging into master (#1032)) =======

    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. Run ChatGPT on the text column in a table.

    b250207e (Update README.md) <<<<<<< HEAD 6f08d5a3 (Update README.md) ======= =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    f5a7c929 (Update README.md) <<<<<<< HEAD d40331e4 (Update README.md) ======= ======= 7dd70375 (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD ======= bf18bc80 (Bump v0.3.4+ dev) georgia-tech-db-main f028c383 (release: merge staging into master (#1032)) ======= =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    7cac771f (Bump v0.3.4+ dev) <<<<<<< HEAD <<<<<<< HEAD 2170a7a9 (Bump v0.3.4+ dev) ======= eva-source ======= <<<<<<< HEAD <<<<<<< HEAD ======= 66bd4f55 (release: merge staging into master (#1032)) 2170a7a9 (Bump v0.3.4+ dev) ======= =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT. =======
    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. <<<<<<< HEAD <<<<<<< HEAD

    c63abee7 (release: merge staging into master (#1032)) <<<<<<< HEAD 065f25fb (release: merge staging into master (#1032)) 28d8bad1 (release: merge staging into master (#1032)) =======

    • Store the text returned by a Speech Recognition model on the audio component of a video in a table.

    a9124e1e (release: merge staging into master (#1032)) =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    c5f43c65 (Bump v0.3.4+ dev) georgia-tech-db-main =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT. =======
    • Store the text returned by a Speech Recognition model on the audio component of a video in a table.

    c63abee7 (release: merge staging into master (#1032)) <<<<<<< HEAD 065f25fb (release: merge staging into master (#1032)) <<<<<<< HEAD ======= 28d8bad1 (release: merge staging into master (#1032)) eva-source ======= 30d7834d (release: merge staging into master (#1032)) ======= ======= =======

    • Store the text returned by a Speech Recognition model on the audio component of a video in a table. Run ChatGPT on the text column in a table.

    b250207e (Update README.md) <<<<<<< HEAD 6f08d5a3 (Update README.md) <<<<<<< HEAD 304c4a34 (Update README.md) ======= ======= =======

    • Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.

    f5a7c929 (Update README.md) d40331e4 (Update README.md) efdfee93 (Update README.md) ======= f028c383 (release: merge staging into master (#1032)) 54907d3e (release: merge staging into master (#1032)) ======= 2170a7a9 (Bump v0.3.4+ dev) bf18bc80 (Bump v0.3.4+ dev) ======= c63abee7 (release: merge staging into master (#1032)) 065f25fb (release: merge staging into master (#1032)) 28d8bad1 (release: merge staging into master (#1032)) 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    CREATE TABLE text_summary AS
    SELECT SpeechRecognizer(audio) FROM ukraine_video;
<<<<<<< HEAD
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>>>>>>> 065f25fb (release: merge staging into master (#1032))
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>>>>>>> f028c383 (release: merge staging into master (#1032))
>>>>>>> 54907d3e (release: merge staging into master (#1032))
=======
>>>>>>> 304c4a34 (Update README.md)
=======
>>>>>>> bf18bc80 (Bump v0.3.4+ dev)
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
=======
>>>>>>> f028c383 (release: merge staging into master (#1032))
=======
>>>>>>> 2170a7a9 (Bump v0.3.4+ dev)
=======
<<<<<<< HEAD
=======
>>>>>>> 065f25fb (release: merge staging into master (#1032))
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
>>>>>>> eva-source
=======
=======
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
=======
>>>>>>> 065f25fb (release: merge staging into master (#1032))
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
=======
<<<<<<< HEAD
=======
>>>>>>> a9124e1e (release: merge staging into master (#1032))
=======
=======
>>>>>>> 30d7834d (release: merge staging into master (#1032))
=======
>>>>>>> bf18bc80 (Bump v0.3.4+ dev)
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
    • Run ChatGPT on the text column in a table.
    <<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
>>>>>>> 7dd70375 (release: merge staging into master (#1032))
=======
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
=======
>>>>>>> c63abee7 (release: merge staging into master (#1032))
>>>>>>> 065f25fb (release: merge staging into master (#1032))
=======
<<<<<<< HEAD
=======
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>>>>>>> c63abee7 (release: merge staging into master (#1032))
=======
>>>>>>> 6f08d5a3 (Update README.md)
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=======
=======
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
>>>>>>> 7dd70375 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> f028c383 (release: merge staging into master (#1032))
=======
=======
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
<<<<<<< HEAD
>>>>>>> 2170a7a9 (Bump v0.3.4+ dev)
=======
=======
>>>>>>> c63abee7 (release: merge staging into master (#1032))
>>>>>>> 065f25fb (release: merge staging into master (#1032))
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
=======
>>>>>>> a9124e1e (release: merge staging into master (#1032))
=======
>>>>>>> c5f43c65 (Bump v0.3.4+ dev)
=======
>>>>>>> c63abee7 (release: merge staging into master (#1032))
>>>>>>> 30d7834d (release: merge staging into master (#1032))
=======
>>>>>>> 304c4a34 (Update README.md)
=======
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
>>>>>>> c63abee7 (release: merge staging into master (#1032))
=======
>>>>>>> 6f08d5a3 (Update README.md)
=======
=======
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
>>>>>>> 7dd70375 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> f028c383 (release: merge staging into master (#1032))
<<<<<<< HEAD
=======
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>>>>>>> 54907d3e (release: merge staging into master (#1032))
=======
>>>>>>> georgia-tech-db-main
=======
=======
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
<<<<<<< HEAD
>>>>>>> 2170a7a9 (Bump v0.3.4+ dev)
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=======
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>>>>>>> bf18bc80 (Bump v0.3.4+ dev)
=======
>>>>>>> georgia-tech-db-main
=======
=======
>>>>>>> c63abee7 (release: merge staging into master (#1032))
>>>>>>> 065f25fb (release: merge staging into master (#1032))
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> eva-source
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
    FROM text_summary;

    <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD

    065f25fb (release: merge staging into master (#1032)) ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= 54907d3e (release: merge staging into master (#1032)) ======= bf18bc80 (Bump v0.3.4+ dev) ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main ======= d40331e4 (Update README.md) ======= f028c383 (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD georgia-tech-db-main ======= 2170a7a9 (Bump v0.3.4+ dev) ======= ======= 065f25fb (release: merge staging into master (#1032)) 28d8bad1 (release: merge staging into master (#1032)) <<<<<<< HEAD eva-source ======= <<<<<<< HEAD ======= 30d7834d (release: merge staging into master (#1032)) ======= 304c4a34 (Update README.md) ======= ======= d40331e4 (Update README.md) efdfee93 (Update README.md) ======= 54907d3e (release: merge staging into master (#1032)) ======= ======= 2170a7a9 (Bump v0.3.4+ dev) bf18bc80 (Bump v0.3.4+ dev) ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    • Train a classic ML model for prediction using the Ludwig AI engine.
    CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
PREDICT 'rental_price'
TIME_LIMIT 120;
=======
* Train an ML model using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine to predict a column in a table.
<<<<<<< HEAD
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=======
* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
=======
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=======
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=======
>>>>>>> 304c4a34 (Update README.md)
=======
>>>>>>> 54907d3e (release: merge staging into master (#1032))
=======
>>>>>>> bf18bc80 (Bump v0.3.4+ dev)
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
>>>>>>> georgia-tech-db-main
=======
* Train a classic ML model for predicting a column using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
>>>>>>> 6f08d5a3 (Update README.md)

```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> c63abee7 (release: merge staging into master (#1032))
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 53dafecf (feat: sync master staging (#1050))
=======
<<<<<<< HEAD
=======
<<<<<<< HEAD
<<<<<<< HEAD
>>>>>>> georgia-tech-db-main
=======
=======
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
=======
<<<<<<< HEAD
* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
>>>>>>> 2170a7a9 (Bump v0.3.4+ dev)
>>>>>>> eva-source
=======
<<<<<<< HEAD
=======
=======
=======
>>>>>>> bf18bc80 (Bump v0.3.4+ dev)
=======
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
>>>>>>> 7cac771f (Bump v0.3.4+ dev)
>>>>>>> 2170a7a9 (Bump v0.3.4+ dev)
>>>>>>> georgia-tech-db-main

```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> 7dd70375 (release: merge staging into master (#1032))
<<<<<<< HEAD
<<<<<<< HEAD
=======
<<<<<<< HEAD
>>>>>>> f028c383 (release: merge staging into master (#1032))
=======
>>>>>>> eva-source
=======
>>>>>>> f028c383 (release: merge staging into master (#1032))
=======
>>>>>>> georgia-tech-db-main
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 5d9d82f0 (feat: sync master staging (#1050))
<<<<<<< HEAD
<<<<<<< HEAD
=======
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>>>>>>> 9fe75f29 (feat: sync master staging (#1050))
=======
>>>>>>> eva-source
=======
>>>>>>> 9fe75f29 (feat: sync master staging (#1050))
=======
=======

```sql
<<<<<<< HEAD
<<<<<<< HEAD
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> c63abee7 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 065f25fb (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
=======
=======
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 53dafecf (feat: sync master staging (#1050))
>>>>>>> 2eef5e8f (feat: sync master staging (#1050))
>>>>>>> 70850a8b (feat: sync master staging (#1050))
=======
* Train an ML model using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine to predict a column in a table.
=======
* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
>>>>>>> c5f43c65 (Bump v0.3.4+ dev)

```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
(SELECT * FROM HomeRentals)
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> a9124e1e (release: merge staging into master (#1032))
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> b87af508 (feat: sync master staging (#1050))
>>>>>>> georgia-tech-db-main
=======

```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> c63abee7 (release: merge staging into master (#1032))
<<<<<<< HEAD
<<<<<<< HEAD
>>>>>>> 065f25fb (release: merge staging into master (#1032))
<<<<<<< HEAD
=======
<<<<<<< HEAD
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
=======
>>>>>>> eva-source
=======
>>>>>>> 30d7834d (release: merge staging into master (#1032))
=======
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 53dafecf (feat: sync master staging (#1050))
>>>>>>> 03a6c555 (feat: sync master staging (#1050))
=======

```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> 7dd70375 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> f028c383 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 54907d3e (release: merge staging into master (#1032))
=======
=======
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 5d9d82f0 (feat: sync master staging (#1050))
>>>>>>> 9fe75f29 (feat: sync master staging (#1050))
>>>>>>> f431fb09 (feat: sync master staging (#1050))
=======
CREATE UDF IF NOT EXISTS PredictHouseRent FROM
=======
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
>>>>>>> f75511e6 (feat: sync master staging (#1050))
( SELECT * FROM HomeRentals )
TYPE Ludwig
<<<<<<< HEAD
'predict' 'rental_price'
'time_limit' 120;
>>>>>>> c63abee7 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 065f25fb (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 28d8bad1 (release: merge staging into master (#1032))
<<<<<<< HEAD
>>>>>>> 66bd4f55 (release: merge staging into master (#1032))
=======
=======
>>>>>>> georgia-tech-db-main
=======
=======
PREDICT 'rental_price'
TIME_LIMIT 120;
>>>>>>> 53dafecf (feat: sync master staging (#1050))
>>>>>>> 2eef5e8f (feat: sync master staging (#1050))
<<<<<<< HEAD
<<<<<<< HEAD
=======
>>>>>>> 70850a8b (feat: sync master staging (#1050))
>>>>>>> eva-source
=======
>>>>>>> 70850a8b (feat: sync master staging (#1050))
>>>>>>> f75511e6 (feat: sync master staging (#1050))
>>>>>>> georgia-tech-db-main

    Architecture of EvaDB

    EvaDB's AI-centric query optimizer takes a query as input and generates a query plan. The query engine takes the query plan and hits the relevant backends to efficiently process the query: 1. SQL Database Systems (Structured Data) <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 065f25fb (release: merge staging into master (#1032)) ======= <<<<<<< HEAD <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> bf18bc80 (Bump v0.3.4+ dev) ======= >>>>>>> 66bd4f55 (release: merge staging into master (#1032)) >>>>>>> georgia-tech-db-main ======= >>>>>>> f028c383 (release: merge staging into master (#1032)) ======= >>>>>>> 2170a7a9 (Bump v0.3.4+ dev) <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD ======= >>>>>>> 66bd4f55 (release: merge staging into master (#1032)) >>>>>>> georgia-tech-db-main ======= ======= >>>>>>> 065f25fb (release: merge staging into master (#1032)) >>>>>>> 28d8bad1 (release: merge staging into master (#1032)) <<<<<<< HEAD >>>>>>> eva-source ======= <<<<<<< HEAD ======= >>>>>>> 30d7834d (release: merge staging into master (#1032)) ======= >>>>>>> efdfee93 (Update README.md) ======= ======= >>>>>>> f028c383 (release: merge staging into master (#1032)) >>>>>>> 54907d3e (release: merge staging into master (#1032)) ======= >>>>>>> bf18bc80 (Bump v0.3.4+ dev) ======= >>>>>>> 66bd4f55 (release: merge staging into master (#1032)) >>>>>>> georgia-tech-db-main 2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud) ======= 2. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem) >>>>>>> c63abee7 (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD 3. Vector Database Systems (Feature Embeddings)

    8c5b63dc (release: merge staging into master (#1032)) ======= <<<<<<< HEAD ======= <<<<<<< HEAD <<<<<<< HEAD <<<<<<< HEAD ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    d40331e4 (Update README.md) ======= 28d8bad1 (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD ======= 30d7834d (release: merge staging into master (#1032)) ======= =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    d40331e4 (Update README.md) efdfee93 (Update README.md) ======= 66bd4f55 (release: merge staging into master (#1032)) georgia-tech-db-main

    1. Vector Database Systems (Feature Embeddings)

    8c5b63dc (release: merge staging into master (#1032)) <<<<<<< HEAD ======= <<<<<<< HEAD eva-source eva-master =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem) =======
    2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    7cac771f (Bump v0.3.4+ dev) ======= eva-master =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem) <<<<<<< HEAD <<<<<<< HEAD =======
    2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    7cac771f (Bump v0.3.4+ dev) ======= 54907d3e (release: merge staging into master (#1032)) ======= =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    7cac771f (Bump v0.3.4+ dev) bf18bc80 (Bump v0.3.4+ dev) georgia-tech-db-main

    1. Vector Database Systems (Feature Embeddings)

    8c5b63dc (release: merge staging into master (#1032)) 7dd70375 (release: merge staging into master (#1032)) <<<<<<< HEAD <<<<<<< HEAD ======= f028c383 (release: merge staging into master (#1032)) eva-source ======= f028c383 (release: merge staging into master (#1032)) <<<<<<< HEAD =======

    1. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem) =======
    2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)

    c5f43c65 (Bump v0.3.4+ dev)

    1. Vector Database Systems (Feature Embeddings)

    8c5b63dc (release: merge staging into master (#1032)) a9124e1e (release: merge staging into master (#1032)) ======= 54907d3e (release: merge staging into master (#1032)) georgia-tech-db-main

    Architecture Diagram

    Community and Support

    <<<<<<< HEAD We would love to learn about your AI app. Please complete this 1-minute form: https://v0fbgcue0cm.typeform.com/to/BZHZWeZm

    If you run into any bugs or have any comments, you can reach us on our Slack Community 📟 or create a Github Issue 🐛.

    =======

    If you run into any bugs or have any comments, you can reach us on our Slack Community 📟 or create a Github Issue 🐛.

    8c5b63dc (release: merge staging into master (#1032)) Here is EvaDB's public roadmap 🛤️. We prioritize features based on user feedback, so we'd love to hear from you!

    Contributing

    We are a lean team on a mission to bring AI inside database systems! All kinds of contributions to EvaDB are appreciated 🙌 If you'd like to get involved, here's information on where we could use your help: contribution guide 🤗

    CI Status:

    CI Status Documentation Status

    Star History

    EvaDB Star History Chart

    License

    Copyright (c) Georgia Tech Database Group. Licensed under an Apache License.