evadb
SQLite
evadb | SQLite | |
---|---|---|
27 | 40 | |
2,578 | 5,575 | |
0.9% | - | |
9.5 | 0.0 | |
16 days ago | about 13 hours ago | |
Python | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
evadb
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Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community
Hey friends!
We have built an app for getting insights about your favorite GitHub community using large language models.
The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.
This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.
Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!
We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.
We’ve been working on this app for a month now and are excited to open source it today :)
Some useful links:
* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...
* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/
* EvaDB - https://github.com/georgia-tech-db/evadb
Please let us know what you think!
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Language Model UXes in 2027
The discord link seems to be not working. Just a heads up.
The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....
Once the function is loaded, it can be used in queries in this way:
SELECT id, Yolo(data)
- EvaDB: Bring AI to your Database System
- Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
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Gorilla: Large Language Model Connected with APIs
Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?
[1] https://github.com/georgia-tech-db/eva
- PrivateGPT in SQL
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Eva AI-Relational Database System
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
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This week's top indie A.I projects, launches and resources
EVA AI-Relational Database System; build simpler and faster AI-powered apps
- Show HN: EVA – AI-Relational Database System
SQLite
- Show HN: Roast my SQLite encryption at-rest
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A SQLite extension that brings column-oriented tables to SQLite
If you are into alternative storage engines for SQLite, there is also an LSM (Log-Structured Merge-tree) extension in the main repository that is not announced nor documented but seems to work. It’s based on the SQLite 4 project.
https://github.com/sqlite/sqlite/tree/master/ext/lsm1
https://www.charlesleifer.com/blog/lsm-key-value-storage-in-...
- SQLite License
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Ask HN: Where do I find good code to read?
The sqlite code base is really well done. Lots of documentation.
https://github.com/sqlite/sqlite
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Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
Especially the VM part: https://github.com/spandanb/learndb-py/blob/master/learndb/v...
Compare it with this: https://github.com/sqlite/sqlite/blob/master/src/vdbe.c
That's said, I'm curious how complete this LearnDB is. SQLite is hard to read not only it's old but also it covers a lot of SQL and following SQL spec makes hings complicated. SQLite has great test suite so it's nice if you run the suit against this implementation.
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SQLite Begin Concurrent
Correct, see the github mirror[1]. I don't know how well supported that feature is compared to main branch. If it was completely stable, then it would have already landed in the main stable branch. Clarity about the roadmap of that branch would be nice.
1. https://github.com/sqlite/sqlite/tree/begin-concurrent
- Why sqlite3 temp files were renamed 'etilqs_*' (2006)
- SQLite builds for WASI since 3.41.0
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SQLite VS sqlite_blaster - a user suggested alternative
2 projects | 17 Mar 2023
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Stop Saying “Technical Debt”
Including comprehensive comments, documentation and tests in a codebase takes time and effort.
Failing to do so creates code that is very difficult to maintain or for someone new to the codebase to understand.
However, time and effort may not be what the organization wants to pay for, and individuals may view their own incomprehensible code as something like job security, as they can't be replaced by someone else easily.
As an example of complicated code that's still well-documented, the open-source sqlite code is a good example, about 1/4 of the B-tree file is comments, every time a variable is defined there's a short note explaining what it's used for, every function has a comment header that's comprehensive, such that someone new to the codebase could construct a map of how it all works fairly quickly. It's a good model for how to avoid the problem:
https://github.com/sqlite/sqlite/blob/master/src/btree.c
What are some alternatives?
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
sqlcipher - SQLCipher is a standalone fork of SQLite that adds 256 bit AES encryption of database files and other security features.
emdash - 📚🧙♂️ Wisdom indexer — use AI to organize text snippets so you can actually remember & learn from what you read
LevelDB - LevelDB is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
RocksDB - A library that provides an embeddable, persistent key-value store for fast storage.
MindsDB - The platform for customizing AI from enterprise data
sqlite_orm - ❤️ SQLite ORM light header only library for modern C++
gpt-json - Structured and typehinted GPT responses in Python
bolt
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
phpMyAdmin - A web interface for MySQL and MariaDB