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.

Connector Hub

Discover the Top Connectors for LLM Developers

Connector

Azure AI Search

Sink Premium
Redis
Connector

Redis

Sink & Source Premium
Redpanda
Connector

Redpanda

Sink & Source Open source
Weaviate
Connector

Weaviate

Sink Premium
Apache Kafka
Connector

Apache Kafka

Sink & Source Open source
Clickhouse
Connector

Clickhouse

Sink Premium
Solution Architecture

Build Real-Time Feature Pipelines with Bytewax

GenAI Real Time Feature Pipeline 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.

Community projects

Check how the GenAI community is using Bytewax

hands-on-llms
This iconic course teaches you to design, train, and deploy a real-time financial advisor LLM system based on Alpaca News, Bytewax, LangChain, and other tools based on the leading 3-pipeline FTI (Feature, Training, Inference) architecture.
3142MIT LicenseView on GitHub
resume
Resumify is an AI assistant that uses different source inputs like LinkedIn or GitHub, ingest them, and then output a tailored resume and cover letter personalized to the job description and your experience. Built with MongoDB, RabbitMQ, Bytewax and Qdrant.
GutenbergV2
Automated system that grades GitHub repositories by evaluating code quality, documentation, commit history, and project activity. It converts this data into actionable insights using stream processing, microservices, and local large language models (LLMs), ensuring the system is scalable and efficient.
2MIT LicenseView on GitHub
spanda-platform-backend
Spanda.ai specializes in delivering human-centered AI solutions and hands-on AI training. They focus on enabling organizations to integrate AI into their operations, improve efficiency, and foster innovation through tailored educational programs and collaborative research. Bytewax is used to generate real-time embeddings with Weaviate's Verba.