postgresml
MindsDB
postgresml | MindsDB | |
---|---|---|
23 | 78 | |
5,442 | 21,312 | |
1.8% | 1.5% | |
9.7 | 10.0 | |
4 days ago | 5 days ago | |
Rust | Python | |
MIT License | 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.
postgresml
- PostgresML
-
[P] pgml-chat: A command-line tool for deploying low-latency knowledge-based chatbots
The Python client SDK is so small, because it's just a wrapper around the Rust client SDK: https://github.com/postgresml/postgresml/tree/master/pgml-sdks/rust/pgml. Currently we also support JS/Typescript SDKs as well, all generated from the same safe and efficient underlying Rust implementation, using some fancy Rust macros.
-
Pg_later: Asynchronous Queries for Postgres
I don't think you'd replace a materialized view with pg_later, but it might help you populate or update your materialized view if you are trying to do that asynchronously. pglater.exec() works with DDL too!
I use it a lot for long running queries when doing data science and machine learning work, and a lot of times when executing queries from a jupyter notebook or CLI. That way if my jupyter kernel dies, my query execution continues even if the network or my environment has an issue. I've started using it a bit more with https://github.com/postgresml/postgresml for model training tasks too, since those can be quite long running depending on the situation.
-
Replace pinecone.
PostgresML comes with pgvector as a vector database. The cool thing is it can run your models in the same memory space as a database extension. We’re also working on ggml support for huggingface transformers, but could use some help testing more LLMs for compatibility. https://github.com/postgresml/postgresml/pull/748
-
Python SDK for PostgresML with scalable LLM embedding memory and text generation
We've been working on a Python SDK[1] for PostgresML to make it easier for application developers to get the performance and scalability benefits of integrated memory for LLMs, by combining embedding generation, vector recall and LLM tasks from HuggingFace in a single database query.
This work builds on our previous efforts that give a 10x performance improvement from generating the LLM embedding[2] from input text along with tuning vector recall[3] in a single process to avoid excessive network transit.
We'd love your feedback on our roadmap[4] for this extension, if you have other use cases for an ML application database. So far, we've implemented our best practices for scalable vector storage to provide an example reference implementation for interacting with an ML application database based on Postgres.
[1]: https://github.com/postgresml/postgresml/tree/master/pgml-sd...
-
[P] Python SDK for PostgresML w/ scalable LLM embedding memory and text generation
We've been working on a Python SDK for PostgresML to make it easier for application developers to get the performance and scalability benefits of integrated memory for LLMs, by combining embedding generation, vector recall and LLM tasks from HuggingFace in a single database query.
-
Show HN: We unified LLMs, vector memory, ranking, pruning models in one process
Links:
[1]: https://huggingface.co/spaces/mteb/leaderboard
[2]: https://postgresml.org/blog/generating-llm-embeddings-with-o...
[3]: https://postgresml.org/blog/tuning-vector-recall-while-gener...
[4]: https://postgresml.org/blog/personalize-embedding-vector-sea...
Github: https://github.com/postgresml/postgresml
- Personalize embedding results with application data in your database
-
[P] We've unified LLMs w/ vector memory + reranking & pruning models in a single process for better performance
Github: https://github.com/postgresml/postgresml
-
How to store hugging face model in postgreSQL
I'd encourage you to do inference outside of PostgreSQL (use TF serving and make requests against it, or do batch inference), but if you're determined to do so, they have an extension that integrates with the transformers library and allows for calling models directly from SQL.
MindsDB
-
What’s the Difference Between Fine-tuning, Retraining, and RAG?
Check us out on GitHub.
-
How to Forecast Air Temperatures with AI + IoT Sensor Data
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
-
Fine-tuning a Mistral Language Model with Anyscale
MindsDB is an open-source AI platform for developers that connects AI/ML models with real-time data. It provides tools and automation to easily build and maintain personalized AI solutions.
- Vanna.ai: Chat with your SQL database
- FLaNK Weekly 08 Jan 2024
-
MindsDB Docker Extension: Build ML powered apps at a much faster pace
MindsDB combines both AI and SQL functions in one; users can create, train, optimize, and deploy ML models without the need for external tools. Data analysts can create and visualize forecasts without having to navigate the complexities of ML pipelines.MindsDB is open-source and works with well-known databases like MySQL, Postgres, Redit, Snowflakes, etc.
-
How Modern SQL Databases Are Changing Web Development - #4 Into the AI Era
Mindsdb is a good example. It abstracts everything related to an AI workflow as "virtual tables". For example, you can import OpenAI API as a "virtual table":
-
🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
Repo : https://github.com/mindsdb/mindsdb
-
AI-Powered Selection of Asset Management Companies using MindsDB and LlamaIndex
MindsDB is an AI Automation platform for building AI/ML powered features and applications. It works by connecting any data source with any AI/ML model or framework and automating how real-time data flows between them. MindsDB is integrated with LlamaIndex, which makes use of its data framework for connecting custom data sources to large language models. LlamaIndex data ingestion allows you to connect to data sources like PDF’s, webpages, etc., provides data indexing and a query interface that takes input prompts from your data and provides knowledge-augmented responses, thus making it easy to Q&A over documents and webpages.
-
Using Large Language Models inside your database with MindsDB
Now, imagine if you can deploy these highly trained models in your database to get insights, make predictions, understand your users, auto-generate content, and more. MindsDB makes this possible! MindsDB is an open-source AI database middleware that allows you to supercharge your databases by integrating various machine learning (ML) engines.
What are some alternatives?
Postico - Public issue tracking for Postico
tensorflow - An Open Source Machine Learning Framework for Everyone
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
CapRover - Scalable PaaS (automated Docker+nginx) - aka Heroku on Steroids
dskueb
scikit-learn - scikit-learn: machine learning in Python
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
lightwood - Lightwood is Legos for Machine Learning.
mosec - A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow