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21 changes: 21 additions & 0 deletions CHANGELOG.md
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## [0.3.7] - 2023-09-30

* PR #1239: release 0.3.7
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* PR #986: docs: Update README.md
* PR #983: doc: fix nav bar

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## [0.3.3] - 2023-08-29

* PR #983: doc: fix nav bar
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<p align="center"><i><b>Database system for AI-powered apps</b></i></p>
=======
<p align="center"><i><b>Bring AI inside your database system and build AI-powered apps</b></i></p>
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<ul>
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<li> 🐙 giving a ⭐ on our <a href="https://github.com/georgia-tech-db/evadb">EvaDB repo on Github</a>
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<li> ⭐ starring our <a href="https://github.com/georgia-tech-db/evadb">GitHub 🐙 Repo</a>
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<li> ⭐ starring our <a href="https://github.com/georgia-tech-db/evadb">GitHub 🐙 Repo</a>
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<li> 🐙 giving a ⭐ on our <a href="https://github.com/georgia-tech-db/evadb">EvaDB repo on Github</a>
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<li> ⭐ starring our <a href="https://github.com/georgia-tech-db/evadb">GitHub 🐙 Repo</a>
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<li> 📟 joining our <a href="https://evadb.ai/community">Slack Community</a>
<li> 🐦 following us on <a href="https://twitter.com/evadb_ai">Twitter</a>
<li> 📝 following us on <a href="https://medium.com/evadb-blog">Medium</a>
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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

👋 Hey! If you're excited about our vision of bringing AI inside database systems, show some ❤️ by:
<ul>
<li> 🐙 giving a ⭐ on our <a href="https://github.com/georgia-tech-db/evadb">EvaDB repo on Github</a>
<li> 📟 joining our <a href="https://evadb.ai/community">Slack Community</a>
<li> 🐦 following us on <a href="https://twitter.com/evadb_ai">Twitter</a>
<li> 🐦 following us on <a href="https://medium.com/evadb-blog">Medium</a>
</ul>

## Quick Links

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<li>Connect EvaDB to your database system with the `CREATE DATABASE` statement.</li>
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<li>Connect EvaDB to your SQL and vector database systems with the <a href="https://evadb.readthedocs.io/en/stable/source/reference/databases/postgres.html">`CREATE DATABASE`</a> and <a href="https://evadb.readthedocs.io/en/stable/source/reference/evaql/create.html#create-index">`CREATE INDEX`</a> statements.</li>
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<li>Write SQL queries with AI functions to get inference results:</li>
<ul>
<li>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;</li>
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## Illustrative Queries

* Run the MNIST Image Classification model to obtain digit labels for each frame in the video.
* Get insights about Github stargazers using GPT4.

```sql
SELECT MnistImageClassifier(data).label FROM mnist_video;
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.
* 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.

```sql
CREATE INDEX reddit_sift_image_index
ON reddit_dataset (SiftFeatureExtractor(data))
USING FAISS
```

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

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* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/13-privategpt.html">PrivateGPT</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/08-chatgpt.html">ChatGPT-based Video Question Answering</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/12-query-pdf.html">Querying PDF Documents</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/02-object-detection.html">Analysing Traffic Flow with YOLO</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/03-emotion-analysis.html">Examining Emotions of Movie</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/07-object-segmentation-huggingface.html">Image Segmentation with Hugging Face</a>
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* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/sentiment-analysis.html">Sentiment Analysis using LLM within PostgreSQL</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/question-answering.html">ChatGPT-based Video Question Answering</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/text-summarization.html">Text Summarization on PDF Documents</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/object-detection.html">Analysing Traffic Flow with YOLO</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/emotion-analysis.html">Examining Emotions of Movie</a>
* 🔮 <a href="https://evadb.readthedocs.io/en/stable/source/usecases/image-search.html">Image Similarity Search</a>
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<details>

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* Store the text returned by a Speech Recognition model on the audio component of a video in a table.
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* Get a transcript from a video stored in a table using a Speech Recognition model. Then, ask questions on the extracted transcript using ChatGPT.
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```sql
CREATE TABLE text_summary AS
SELECT SpeechRecognizer(audio) FROM ukraine_video;
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```

* Run ChatGPT on the `text` column in a table.
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SELECT ChatGPT('Is this video summary related to Ukraine russia war', text)
FROM text_summary;
```
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* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
```sql
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* Train an ML model using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine to predict a column in a table.
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* Train a classic ML model for predicting a column using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
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* Train a classic ML model for prediction using the <a href="https://ludwig.ai/latest/">Ludwig AI</a> engine.
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```sql
CREATE FUNCTION IF NOT EXISTS PredictHouseRent FROM
( SELECT * FROM HomeRentals )
(SELECT * FROM HomeRentals)
TYPE Ludwig
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'predict' 'rental_price'
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2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)
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2. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem)
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2. AI Frameworks (Transform Unstructured Data to Structured Data, Unstructured data includes PDFs, images, podcasts, etc. stored on cloud buckets or local filesystem)
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2. AI Frameworks (Transform Unstructured Data to Structured Data; Unstructured data includes PDFs, text, images, etc. stored locally or on the cloud)
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3. Vector Database Systems (Feature Embeddings)

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