evadb
Lark
evadb | Lark | |
---|---|---|
27 | 35 | |
2,578 | 4,510 | |
0.9% | 1.6% | |
9.5 | 7.5 | |
16 days ago | 24 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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
Lark
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Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
Lark supports, and recommends, writing and storing the grammar in a .lark file. We have syntax highlighting support in all major IDEs, and even in github itself. For example, here is Lark's built-in grammar for Python: https://github.com/lark-parser/lark/blob/master/lark/grammar...
You can also test grammars "live" in our online IDE: https://www.lark-parser.org/ide/
The rationale is that it's more terse and has less visual clutter than a DSL over Python, which makes it easier to read and write.
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Oops, I wrote yet another SQLAlchemy alternative (looking for contributors!)
First, let me introduce myself. My name is Erez. You may know some of the Python libraries I wrote in the past: Lark, Preql and Data-diff.
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Hey guys, have any of you tried creating your own language using Python? I'm interested in giving it a shot and was wondering if anyone has any tips or resources to recommend. Thanks in advance!
It's not super maintained but you might enjoy building something with ppci, Pure Python Compiler Infrastructure. It has some front-ends and some back-ends. There's also PeachPy for an assembler. People like using Lark for parsing, I hear.
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Is it possible to propagate higher level constructs (+, *) to the generated parse tree in an LR-style parser?
lark, a parsing library where I am somewhat involved has a really nice solution to this: Rules starting with _ are inlined in a post processing step.
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can you create your own program language in python, if yes how?
Lark is a good library to assist with this.
- Lark a Python lexer/parser library
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Create your own scripting language in Python with Sly
If I may ask, did you consider Lark, and if so, why wasn't it fit for your purposes?
- Creating a language with Python.
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Not Your Grandfather’s Perl
A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.
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Earley Parsing Explained
I made a solid attempt at an Earley parser framework of my own, but apparently to get the most reliable performance from Earley parsing you need to implement Joop Leo's improvement for right-recursive grammars, which nobody has been able to adequately explain to me. I've read Kegler's open letter to Vaillant, I've tried to read other implementations, I've even tried to beat my head against the original academic paper, but I don't have the background knowledge to make sense of it all.
What are some alternatives?
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
pyparsing - Python library for creating PEG parsers [Moved to: https://github.com/pyparsing/pyparsing]
emdash - 📚🧙♂️ Wisdom indexer — use AI to organize text snippets so you can actually remember & learn from what you read
PLY - Python Lex-Yacc
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
pydantic - Data validation using Python type hints
MindsDB - The platform for customizing AI from enterprise data
sqlparse - A non-validating SQL parser module for Python
gpt-json - Structured and typehinted GPT responses in Python
Atoma - Atom, RSS and JSON feed parser for Python 3
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
Construct - Construct: Declarative data structures for python that allow symmetric parsing and building