PulpGen is a modular, open-source framework designed for developers to assemble high-performance LLM applications. It integrates vector database backends, retrieval-augmented generation, streaming responses, and batch processing pipelines into a unified toolkit, enabling rapid prototyping and production-grade deployments.
PulpGen is a modular, open-source framework designed for developers to assemble high-performance LLM applications. It integrates vector database backends, retrieval-augmented generation, streaming responses, and batch processing pipelines into a unified toolkit, enabling rapid prototyping and production-grade deployments.
PulpGen provides a unified, configurable platform to build advanced LLM-based applications. It offers seamless integrations with popular vector stores, embedding services, and LLM providers. Developers can define custom pipelines for retrieval-augmented generation, enable real-time streaming outputs, batch process large document collections, and monitor system performance. Its extensible architecture allows plug-and-play modules for cache management, logging, and auto-scaling, making it ideal for AI-powered search, question-answering, summarization, and knowledge management solutions.
Who will use PulpGen?
AI researchers
Software developers
Data engineers
Enterprises building LLM applications
How to use the PulpGen?
Step1: Clone the PulpGen repository from GitHub.
Step2: Install dependencies with pip or Docker.
Step3: Configure your preferred vector store and LLM provider in the YAML settings.
Step4: Define a retrieval-augmented generation pipeline in code or config.
Step5: Run the server or CLI to start processing requests.
Step6: Send queries via the REST API or Python SDK and receive generated responses.
Platform
mac
windows
linux
PulpGen's Core Features & Benefits
The Core Features
Integration with multiple vector database backends