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, a their go to solution to build real-time feature pipelines and generating embeddings, among other applications.


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.


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.

Connector Hub

Discover the Top Connectors for LLM Developers

Solution Architecture

Build Real-Time Feature Pipelines with Bytewax

LLM Architecture 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, Hopsworks FS, Milvus, Feast, and many more.

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 🐝 kicks in.

Python alone is not a language designed for speed 🐒, 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 πŸ¦€ and libraries like Bytewax 🐝 that expose a pure Python API on top of a highly-efficient language like Rust.

I don’t see myself switching bytewax with another tool, anytime soon πŸ˜… - its got everything a dev needs when processing streams.