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Source channel @githubtrending · Post #14706 · May 14

#python#agents#knowledge_graph#llm#llm_agent#rag#search#search_agent#vector_database Airweave is a tool that helps make information from apps and databases easily accessible to AI agents. It connects over 100 data sources with minimal coding, allowing for fast data synchronization and semantic search. This means you can quickly turn app data into useful knowledge for AI agents, making them smarter and more efficient. It's especially helpful for tasks like customer support or generating reports, as it ensures AI agents have the most accurate and up-to-date information. https://github.com/airweave-ai/airweave

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djangoproject

@djangoproject · Post #274 · 03/18/2017, 01:48 AM

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https://github.com/riga/tfdeploy Google's TensorFlow framework is taking off big-time now that it's at a full 1.0 release. One common question about it: How can I make use of the models I train in TensorFlow without using TensorFlow itself? #Tfdeploy is a partial answer to that question. It exports a trained TensorFlow model to "a simple #NumPy-based callable," meaning the model can be used in Python with Tfdeploy and the the NumPy math-and-stats library as the only dependencies. Most of the operations you can perform in TensorFlow can also be performed in Tfdeploy, and you can extend the behaviors of the library by way of standard Python metaphors (such as overloading a class). Now the bad news: Tfdeploy doesn't support GPU acceleration, if only because NumPy doesn't do that. Tfdeploy's creator suggests using the gNumPy project as a possible replacement. #Machine_learning

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