3 ms·

EVA: AI-Relational Database System

113 points by jonbaer 3y ago

etiam 3y ago
Almost completely unrelated but due to the naming and some aspects of the aesthetic I cant help but associate to that other EVA from the 90's :) https://www.youtube.com/watch?v=N75H5amC8Is https://www.youtube.com/watch?v=N75H5amC8Is
- jarulraj 3y ago
  Haha. Thanks for sharing this reference! I had no idea about a similar comment [1] in our previous Show HN post :) For others like me, this is ChatGPT's explanation: C&C Tiberian Sun, short for Command & Conquer: Tiberian Sun, is a real-time strategy (RTS) video game developed by Westwood Studios and released by Electronic Arts in 1999. It is the sequel to the highly popular game Command & Conquer (1995) and is part of the Command & Conquer series." In the real-time strategy game Command & Conquer: Tiberian Sun, the phrase "Welcome back, Commander" is a memorable reference that occurs at the start of each mission when the player resumes playing the game after a break or reloading a saved game. The phrase is spoken by the EVA (Electronic Video Agent) computerized voice, which serves as the player's AI assistant throughout the game. The EVA voice welcomes the player with the line "Welcome back, Commander" to indicate that the player has returned to the game and is ready to continue commanding their forces. [1] https://news.ycombinator.com/reply?id=35768822 https://news.ycombinator.com/reply?id=35768822
  - shubb 3y ago
    This sounds a bit unlikely. Would they really have different mission intros after detecting you reopen the game? Anyway now you've written it down it'll get scraped, included in the training set, and he a reliable citation for the future!
    - etiam 3y ago
      As this was about a quarter-century ago now, I'm not certain, but I too suspect that the stochastic parrot is improvising a bit about the details. As I recall it, the statement was certainly not at the start of every mission nor after every load. Another fun piece of trivia for those who missed direct experience of the original, is that the video I liked to is not exactly of a normal mission intro but played at the start of the game, where it was particularly well placed in relation to the installer, as can be seen here https://www.youtube.com/watch?v=X3S6_3f4HhU https://www.youtube.com/watch?v=X3S6_3f4HhU
- HPsquared 3y ago
  I'm reminded of the early (1970) electronic music track by Jean-Jacques Perrey, apparently a tribute to Neil Armstrong: https://youtu.be/HqEz6PuQS08 https://youtu.be/HqEz6PuQS08
thelastparadise 3y ago
I wonder how well this will scale for large datasets. Can anyone provide some benchmarks or performance comparisons against other database systems?
- jarulraj 3y ago
  Great question! We have a built-in integration test for 1 million images [1] and have benchmarked EVA on large unstructured datasets for our ACM SIGMOD paper [2]. Coming up with an open benchmark for AI-Relational database systems is essential [3]. Please share your thoughts on any ideas for benchmarking :) [1] https://github.com/georgia-tech-db/eva/blob/e123820c79b902d5ed22dff11ab856f12a4399ac/test/benchmark_tests/test_benchmark_pytorch.py#L173 https://github.com/georgia-tech-db/eva/blob/e123820c79b902d5... [2] https://dl.acm.org/doi/abs/10.1145/3514221.3526142 https://dl.acm.org/doi/abs/10.1145/3514221.3526142 [3] https://dl.acm.org/doi/10.1145/3299869.3324955 https://dl.acm.org/doi/10.1145/3299869.3324955
jarulraj 3y ago
Hey friends We wanted to express our heartfelt gratitude to all of you for your support for the EVA AI-Relational Database System last week [1]. The feedback from the HN community has been truly overwhelming :) We were planning to make an HN post based on these features we have since added [2], but @jonbaer thankfully beat us to it :) - Using ChatGPT/LLM + Whisper (Open AI + Hugging Face) models to ask questions based on videos/tables etc. You can check out the notebook here [3]. LOAD VIDEO 'russia_ukraine.mp4' INTO VIDEOS; CREATE MATERIALIZED VIEW TEXT_SUMMARY(text) AS SELECT SpeechRecognizer(audio) FROM VIDEOS; CREATE UDF SpeechRecognizer TYPE HuggingFace 'task' 'automatic-speech-recognition' 'model' 'openai/whisper-base'; SELECT ChatGPT('Is this video summary related to Ukraine-Russia war', text) FROM TEXT_SUMMARY; - Joining structured data tables with OCRExtractor model output SELECT * FROM MyImages JOIN LATERAL OCRExtractor(data) AS T(a,b,c) JOIN LicensePlateCSV B ON FuzzDistance(T.a, B.label) > 50; - Vector index support for similarity search [5] CREATE INDEX reddit_sift_object_index ON reddit_object_dataset (SiftFeatureExtractor(Crop(data, bboxes))) USING HNSW SELECT name FROM reddit_object_dataset ORDER BY Similarity( SiftFeatureExtractor(Open('{path}')), SiftFeatureExtractor(data) ) LIMIT 5 - Using YOLO models to detect objects [6] CREATE UDF IF NOT EXISTS Yolo TYPE ultralytics 'model' 'yolov8m.pt'; SELECT id, Yolo(data) FROM ObjectDetectionVideos WHERE id < 20 Your feedback and suggestions have already been instrumental in shaping our roadmap and prioritizing new features! We are actively working on incorporating your ideas and addressing your concerns to enhance the functionality, performance, and user experience of EVA. Please email me (arulraj@gatech.edu) with any questions, ideas, or suggestions on EVA. [1] https://news.ycombinator.com/item?id=35764355 https://news.ycombinator.com/item?id=35764355 [2] https://github.com/georgia-tech-db/eva/releases/tag/v0.2.3 https://github.com/georgia-tech-db/eva/releases/tag/v0.2.3 [3] https://evadb.readthedocs.io/en/stable/source/tutorials/08-chatgpt.html https://evadb.readthedocs.io/en/stable/source/tutorials/08-c... [4] https://github.com/georgia-tech-db/eva/blob/master/tutorials/09-license-plate-fuzzy-join.ipynb https://github.com/georgia-tech-db/eva/blob/master/tutorials... [5] https://github.com/georgia-tech-db/eva/blob/a6a6ecc7e5c0d9d4740e0c2bb906d63d59116701/tutorials/09-similarity-sesarch-for-motif-mining.ipynb https://github.com/georgia-tech-db/eva/blob/a6a6ecc7e5c0d9d4... [6] https://evadb.readthedocs.io/en/stable/source/tutorials/02-object-detection.html https://evadb.readthedocs.io/en/stable/source/tutorials/02-o...
ukuina 3y ago
How does Eva compare to MindsDB (https://mindsdb.com https://mindsdb.com)?
- jarulraj 3y ago
  Great question, @ukuina! We will need some time to do a detailed analysis of MindsDB. But here are some key differences a first glance: 1. First-class support for unstructured data: EVA natively supports querying over videos, audio, etc. For instance, it manages a video as a table, mapping each tuple to a video frame. 2. Cascades-style query optimizer: EVA has an AI-centric query optimizer with built-in support for cost-based query optimization, predicate reordering, caching of expensive models, etc. 3. First-class support for vector embeddings: EVA supports similarity search using embeddings stored in a vector database. Similarities to MindsDB: 1. EVA is implemented in Python and uses sqlalchemy for connecting to a structured database system (PostgreSQL, MySQL, etc.) 2. EVA supports Huggingface and OpenAI pipelines in the form of user-defined functions. We will love to hear from the developers of MindDB if our high-level analysis is correct :)
nirav72 3y ago
This looks interesting. Any plans to put this in a container?
- gtk8995 3y ago
  Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs. [1] https://github.com/georgia-tech-db/eva/tree/master/docker https://github.com/georgia-tech-db/eva/tree/master/docker
  - nirav72 3y ago
    Awesome. I'll give it a go. thank you.
potatoman22 3y ago
The concept of using SQL-like syntax to interface with software very different than SQL is interesting. Are there applications other than EVA that do this? It'd be cool to have a sports-footage query engine using syntax like this.
- jrvarela56 3y ago
  https://www.osquery.io/ https://www.osquery.io/
- ElevenLathe 3y ago
  https://steampipe.io/ https://steampipe.io/
- anamexis 3y ago
  https://docs.aws.amazon.com/config/latest/developerguide/querying-AWS-resources.html https://docs.aws.amazon.com/config/latest/developerguide/que...
- jarulraj 3y ago
  We also love SQL, @potatoman22 :) BTW, EVA itself is designed for analyzing sports games -- like touchdowns in a football game. This notebook shows how an action recognition model can be run on an American Sign Language video to identify the correct word [1]. Similarly, a model for identifying touchdowns and players can be used to run interesting queries over a football game. SELECT FIRST(id), ASLActionRecognition(SEGMENT(data)) FROM ASL_ACTIONS SAMPLE 5 GROUP BY '16f'; [1] https://github.com/georgia-tech-db/eva/blob/master/tutorials/05-asl-action-recognition.ipynb https://github.com/georgia-tech-db/eva/blob/master/tutorials...
aatifh 3y ago
Looks super interesting. Btw, this page renders 404 https://evadb.readthedocs.io/en/stable/source/overview/video.html# https://evadb.readthedocs.io/en/stable/source/overview/video...
- gtk8995 3y ago
  Thank you for reporting the typo. We are working on fixing it. Please find the page here https://github.com/georgia-tech-db/eva/blob/master/docs/source/overview/aidb.rst https://github.com/georgia-tech-db/eva/blob/master/docs/sour...
gibsonf1 3y ago
The problem with this is that relational databases are completely foreign to how humans store and retrieve information. For humans our data is all connected and we operate with it primarily with hierarchical inference and reasoning. The relational database separates the data into tables and is terrible at hierarchical queries, so a 20th century idea that should be deprecated when working toward intelligence.
- jarulraj 3y ago
  Interesting observation, @gibsonf1. What kind of hierarchical queries do you have in mind that would be challenging to write in SQL? Would you mind sharing them in natural language?
  - miohtama 3y ago
    Documentation databases operate in tree structures - MongoDB - ZODB - Others - This is also how disk file systems operate You access items by their path, instead of id. Also path is relevant how the data is physically stored, fetching nearby data being cheaper. To work with this kind of data in SQL you need to construct recursive CTE queries. While it’s possible, it is a bit akward sometimes.
    - johnthescott 3y ago
      jsonb queries in postgres have been very performant for us over +tb data sets. however, we often materialize views of large json blobs, just cause we can.
  - gibsonf1 3y ago
    Our conceptual awareness AI uses recursive conceptual inference where given some entity, looking at its states, the system determines which concepts can be inferred directly from those states, then precedes to then also infer that that entity inherits all the properties of the genera concepts of those starting conceptual types. If you follow the recursion, it can be quite a large number of jumps, but incredibly fast. For our data, we use Apache Solr, but a schema modeled on how we think the human schema works - its quite simple actually.

113 points by jonbaer 3y ago

Almost completely unrelated but due to the naming and some aspects of the aesthetic I cant help but associate to that other EVA from the 90's :) https://www.youtube.com/watch?v=N75H5amC8Is https://www.youtube.com/watch?v=N75H5amC8Is

jarulraj 3y ago
Haha. Thanks for sharing this reference! I had no idea about a similar comment [1] in our previous Show HN post :) For others like me, this is ChatGPT's explanation: C&C Tiberian Sun, short for Command & Conquer: Tiberian Sun, is a real-time strategy (RTS) video game developed by Westwood Studios and released by Electronic Arts in 1999. It is the sequel to the highly popular game Command & Conquer (1995) and is part of the Command & Conquer series." In the real-time strategy game Command & Conquer: Tiberian Sun, the phrase "Welcome back, Commander" is a memorable reference that occurs at the start of each mission when the player resumes playing the game after a break or reloading a saved game. The phrase is spoken by the EVA (Electronic Video Agent) computerized voice, which serves as the player's AI assistant throughout the game. The EVA voice welcomes the player with the line "Welcome back, Commander" to indicate that the player has returned to the game and is ready to continue commanding their forces. [1] https://news.ycombinator.com/reply?id=35768822 https://news.ycombinator.com/reply?id=35768822
- shubb 3y ago
  This sounds a bit unlikely. Would they really have different mission intros after detecting you reopen the game? Anyway now you've written it down it'll get scraped, included in the training set, and he a reliable citation for the future!
  - etiam 3y ago
    As this was about a quarter-century ago now, I'm not certain, but I too suspect that the stochastic parrot is improvising a bit about the details. As I recall it, the statement was certainly not at the start of every mission nor after every load. Another fun piece of trivia for those who missed direct experience of the original, is that the video I liked to is not exactly of a normal mission intro but played at the start of the game, where it was particularly well placed in relation to the installer, as can be seen here https://www.youtube.com/watch?v=X3S6_3f4HhU https://www.youtube.com/watch?v=X3S6_3f4HhU
HPsquared 3y ago
I'm reminded of the early (1970) electronic music track by Jean-Jacques Perrey, apparently a tribute to Neil Armstrong: https://youtu.be/HqEz6PuQS08 https://youtu.be/HqEz6PuQS08

thelastparadise 3y ago

I wonder how well this will scale for large datasets. Can anyone provide some benchmarks or performance comparisons against other database systems?

jarulraj 3y ago
Great question! We have a built-in integration test for 1 million images [1] and have benchmarked EVA on large unstructured datasets for our ACM SIGMOD paper [2]. Coming up with an open benchmark for AI-Relational database systems is essential [3]. Please share your thoughts on any ideas for benchmarking :) [1] https://github.com/georgia-tech-db/eva/blob/e123820c79b902d5ed22dff11ab856f12a4399ac/test/benchmark_tests/test_benchmark_pytorch.py#L173 https://github.com/georgia-tech-db/eva/blob/e123820c79b902d5... [2] https://dl.acm.org/doi/abs/10.1145/3514221.3526142 https://dl.acm.org/doi/abs/10.1145/3514221.3526142 [3] https://dl.acm.org/doi/10.1145/3299869.3324955 https://dl.acm.org/doi/10.1145/3299869.3324955

jarulraj 3y ago

Hey friends We wanted to express our heartfelt gratitude to all of you for your support for the EVA AI-Relational Database System last week [1]. The feedback from the HN community has been truly overwhelming :) We were planning to make an HN post based on these features we have since added [2], but @jonbaer thankfully beat us to it :) - Using ChatGPT/LLM + Whisper (Open AI + Hugging Face) models to ask questions based on videos/tables etc. You can check out the notebook here [3]. LOAD VIDEO 'russia_ukraine.mp4' INTO VIDEOS; CREATE MATERIALIZED VIEW TEXT_SUMMARY(text) AS SELECT SpeechRecognizer(audio) FROM VIDEOS; CREATE UDF SpeechRecognizer TYPE HuggingFace 'task' 'automatic-speech-recognition' 'model' 'openai/whisper-base'; SELECT ChatGPT('Is this video summary related to Ukraine-Russia war', text) FROM TEXT_SUMMARY; - Joining structured data tables with OCRExtractor model output SELECT * FROM MyImages JOIN LATERAL OCRExtractor(data) AS T(a,b,c) JOIN LicensePlateCSV B ON FuzzDistance(T.a, B.label) > 50; - Vector index support for similarity search [5] CREATE INDEX reddit_sift_object_index ON reddit_object_dataset (SiftFeatureExtractor(Crop(data, bboxes))) USING HNSW SELECT name FROM reddit_object_dataset ORDER BY Similarity( SiftFeatureExtractor(Open('{path}')), SiftFeatureExtractor(data) ) LIMIT 5 - Using YOLO models to detect objects [6] CREATE UDF IF NOT EXISTS Yolo TYPE ultralytics 'model' 'yolov8m.pt'; SELECT id, Yolo(data) FROM ObjectDetectionVideos WHERE id < 20 Your feedback and suggestions have already been instrumental in shaping our roadmap and prioritizing new features! We are actively working on incorporating your ideas and addressing your concerns to enhance the functionality, performance, and user experience of EVA. Please email me (arulraj@gatech.edu) with any questions, ideas, or suggestions on EVA. [1] https://news.ycombinator.com/item?id=35764355 https://news.ycombinator.com/item?id=35764355 [2] https://github.com/georgia-tech-db/eva/releases/tag/v0.2.3 https://github.com/georgia-tech-db/eva/releases/tag/v0.2.3 [3] https://evadb.readthedocs.io/en/stable/source/tutorials/08-chatgpt.html https://evadb.readthedocs.io/en/stable/source/tutorials/08-c... [4] https://github.com/georgia-tech-db/eva/blob/master/tutorials/09-license-plate-fuzzy-join.ipynb https://github.com/georgia-tech-db/eva/blob/master/tutorials... [5] https://github.com/georgia-tech-db/eva/blob/a6a6ecc7e5c0d9d4740e0c2bb906d63d59116701/tutorials/09-similarity-sesarch-for-motif-mining.ipynb https://github.com/georgia-tech-db/eva/blob/a6a6ecc7e5c0d9d4... [6] https://evadb.readthedocs.io/en/stable/source/tutorials/02-object-detection.html https://evadb.readthedocs.io/en/stable/source/tutorials/02-o...

ukuina 3y ago

How does Eva compare to MindsDB (https://mindsdb.com https://mindsdb.com)?

jarulraj 3y ago
Great question, @ukuina! We will need some time to do a detailed analysis of MindsDB. But here are some key differences a first glance: 1. First-class support for unstructured data: EVA natively supports querying over videos, audio, etc. For instance, it manages a video as a table, mapping each tuple to a video frame. 2. Cascades-style query optimizer: EVA has an AI-centric query optimizer with built-in support for cost-based query optimization, predicate reordering, caching of expensive models, etc. 3. First-class support for vector embeddings: EVA supports similarity search using embeddings stored in a vector database. Similarities to MindsDB: 1. EVA is implemented in Python and uses sqlalchemy for connecting to a structured database system (PostgreSQL, MySQL, etc.) 2. EVA supports Huggingface and OpenAI pipelines in the form of user-defined functions. We will love to hear from the developers of MindDB if our high-level analysis is correct :)

nirav72 3y ago

This looks interesting. Any plans to put this in a container?

gtk8995 3y ago
Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs. [1] https://github.com/georgia-tech-db/eva/tree/master/docker https://github.com/georgia-tech-db/eva/tree/master/docker
- nirav72 3y ago
  Awesome. I'll give it a go. thank you.

potatoman22 3y ago

The concept of using SQL-like syntax to interface with software very different than SQL is interesting. Are there applications other than EVA that do this? It'd be cool to have a sports-footage query engine using syntax like this.

jrvarela56 3y ago
https://www.osquery.io/ https://www.osquery.io/
ElevenLathe 3y ago
https://steampipe.io/ https://steampipe.io/
anamexis 3y ago
https://docs.aws.amazon.com/config/latest/developerguide/querying-AWS-resources.html https://docs.aws.amazon.com/config/latest/developerguide/que...
jarulraj 3y ago
We also love SQL, @potatoman22 :) BTW, EVA itself is designed for analyzing sports games -- like touchdowns in a football game. This notebook shows how an action recognition model can be run on an American Sign Language video to identify the correct word [1]. Similarly, a model for identifying touchdowns and players can be used to run interesting queries over a football game. SELECT FIRST(id), ASLActionRecognition(SEGMENT(data)) FROM ASL_ACTIONS SAMPLE 5 GROUP BY '16f'; [1] https://github.com/georgia-tech-db/eva/blob/master/tutorials/05-asl-action-recognition.ipynb https://github.com/georgia-tech-db/eva/blob/master/tutorials...

aatifh 3y ago

Looks super interesting. Btw, this page renders 404 https://evadb.readthedocs.io/en/stable/source/overview/video.html# https://evadb.readthedocs.io/en/stable/source/overview/video...

gtk8995 3y ago
Thank you for reporting the typo. We are working on fixing it. Please find the page here https://github.com/georgia-tech-db/eva/blob/master/docs/source/overview/aidb.rst https://github.com/georgia-tech-db/eva/blob/master/docs/sour...

gibsonf1 3y ago

The problem with this is that relational databases are completely foreign to how humans store and retrieve information. For humans our data is all connected and we operate with it primarily with hierarchical inference and reasoning. The relational database separates the data into tables and is terrible at hierarchical queries, so a 20th century idea that should be deprecated when working toward intelligence.

jarulraj 3y ago
Interesting observation, @gibsonf1. What kind of hierarchical queries do you have in mind that would be challenging to write in SQL? Would you mind sharing them in natural language?
- miohtama 3y ago
  Documentation databases operate in tree structures - MongoDB - ZODB - Others - This is also how disk file systems operate You access items by their path, instead of id. Also path is relevant how the data is physically stored, fetching nearby data being cheaper. To work with this kind of data in SQL you need to construct recursive CTE queries. While it’s possible, it is a bit akward sometimes.
  - johnthescott 3y ago
    jsonb queries in postgres have been very performant for us over +tb data sets. however, we often materialize views of large json blobs, just cause we can.
- gibsonf1 3y ago
  Our conceptual awareness AI uses recursive conceptual inference where given some entity, looking at its states, the system determines which concepts can be inferred directly from those states, then precedes to then also infer that that entity inherits all the properties of the genera concepts of those starting conceptual types. If you follow the recursion, it can be quite a large number of jumps, but incredibly fast. For our data, we use Apache Solr, but a schema modeled on how we think the human schema works - its quite simple actually.