Bytewax is a popular choice to process and embedd real-time data streams from various data sources to any of the leading vector databases such as Qdrant, Pinecone, Elastic, Milvus, Feast, and many more.
GenAI
Ingest Real-Time Data into LLMs
Real-time data is now essential for any large language model. Many developers have discovered Bytewax, as a Python-native stream processor, as their go to solution to build real-time feature pipelines and generating embeddings, among other applications.
USE CASES FROM OUR COMMUNITY
Build LLMs with Real-time Data Capabilities
Bytewax has become an essential tool in the developer community to create real-time LLMs.
Feature Pipelines
Real-time Embedding Generation
Ingest and process continuous data streams from multiple sources, and generate real-time data embeddings. These embeddings update a vector database continuously, supporting GenAI models in tasks such as text generation, image synthesis, or code generation.
Content Generation
Dynamic and Contextual Content
Process real-time data streams to dynamically create tailored prompts for GenAI models. This allows for the real-time generation of personalized content, images, or other outputs, ensuring relevance to the current context and user needs.
Inference
Real-time Inference
Ingest and process multiple real-time data streams of different modalities (text, images, audio, video, etc.), fusing them together to create rich, multi-modal inputs for GenAI models, enabling more context-aware and comprehensive generation tasks.
Discover the Top Connectors for LLM Developers
Solution Architecture
Build Real-Time Feature Pipelines with Bytewax
BLOG POSTS
Get Inspired to Build with Bytewax
Community Voices
Hear from Our Community about Building LLMs with Bytewax
I loved & understood the power of building streaming applications. The only thing that stood in my way was, well... Java.
I don't have something with Java; it is a powerful language. However, building an ML application in Java + Python takes much time due to a more significant resistance to integrating the two.
...and that's where Bytewax π kicks in.
Paul IusztinSenior ML/MLOps Engineer
Python alone is not a language designed for speed π’, which makes it unsuitable for real-time processing. Because of this, real-time feature pipelines were usually writen with Java-based tools like Apache Spark or Apache Flink.
However, things are changing fast with the emergence of Rust π¦ and libraries like Bytewax π that expose a pure Python API on top of a highly-efficient language like Rust.
Soufiene YakoubiAI/DeepLearning/Computer Vision Researcher
