Love @langchain 🦜⛓️? Love DocArray? Langchain just launched their new integrations hub and DocArray is right there in the mix - twice! The best part: You can even give us a ❤️ integrations.langchain.com/v…

⚡️Deserialize DocVec directly to numpy, torch, or tf When you deserialize a DocVec from Protobuf, you can now choose if its tensors should be loaded to numpy, pytorch, or tensorflow:

💡 Easily check if a Document is already in a DocumentIndex Just use the `in` keyword to see if a Document has already been indexed.

🔍 Nested search for InMemoryExactNNIndex with subindexes You can now use find_subindex() to search through Documents that are in a DocList inside of another Document!

The Tensor coercion feature is a must-have and allows you to seamlessly work with different tensor backends (numpy, @PyTorch , @TensorFlow ) at the same time, thanks to @pydantic validation.

Last but not least, save that precious memory by using quantization, allowing vector search even on puny low-level machines

Want to move from your old vector database to Qdrant? Just swap out a line or two of code

Next, Qdrant can incorporate filtering directly into the vector search phase. No need for that pre- or post-filtering!

First up, with DocArray + Qdrant you can experiment locally first, without needing to run a server. You can move to on-prem or cloud when you're ready

DocArray's @qdrant_engine support keeps getting better and better. Let's dive into the benefits in this thread!

DocArray v0.31.0 was just released 🎉 New: An in-memory Document Index! ⚡️ Also, 11 bug fixes 😬🐞, 9 documentation improvements 📗, and 2 small breaking changes 💥 github.com/docarray/docarray…

Thanks for all the enthusiasm of #PyConUS #PyCon2023 Python developers!

to sum up: as we move into the ML world, consider DocArray for talking to your database, and validation. Type hinting is eating the Python world. DocArray embraces this wave of types and apply it to the ML/AI world. @atomicflndr @pycon #PyConUS2023