Castorice-LLM-Service is a high-performance microservice framework for deploying and managing large language models. It offers unified HTTP APIs for chat, completion, and embeddings, supports backends like OpenAI, Azure, Vertex AI, and local models, and integrates with vector databases for retrieval-augmented generation. Key features include request batching, caching, streaming responses, role-based access control, and metrics tracking for easy monitoring and scaling.
Castorice-LLM-Service is a high-performance microservice framework for deploying and managing large language models. It offers unified HTTP APIs for chat, completion, and embeddings, supports backends like OpenAI, Azure, Vertex AI, and local models, and integrates with vector databases for retrieval-augmented generation. Key features include request batching, caching, streaming responses, role-based access control, and metrics tracking for easy monitoring and scaling.
Castorice-LLM-Service provides a standardized HTTP interface to interact with various large language model providers out of the box. Developers can configure multiple backends—including cloud APIs and self-hosted models—via environment variables or config files. It supports retrieval-augmented generation through seamless vector database integration, enabling context-aware responses. Features such as request batching optimize throughput and cost, while streaming endpoints deliver token-by-token responses. Built-in caching, RBAC, and Prometheus-compatible metrics help ensure secure, scalable, and observable deployment on-premises or in the cloud.
Who will use Castorice-LLM-Service?
AI developers
Data scientists
DevOps engineers
Startups building LLM-powered applications
Enterprises deploying generative AI services
How to use the Castorice-LLM-Service?
Step1: Clone the repository from GitHub to your local machine.
Step2: Install dependencies via pip or build the Docker image.
Step3: Configure provider credentials and vector DB settings in the .env file.
Step4: Launch the service using docker-compose or the provided startup script.
Step5: Use the unified HTTP endpoints (/chat, /complete, /embed) in your application.
Platform
mac
windows
linux
Castorice-LLM-Service's Core Features & Benefits
The Core Features
Unified HTTP API for chat, completion, and embeddings
Multi-model backend support (OpenAI, Azure, Vertex AI, local models)
Vector database integration for retrieval-augmented generation
Request batching and caching
Streaming token-by-token responses
Role-based access control
Prometheus-compatible metrics export
The Benefits
Easy integration with existing applications
Scalable and cost-efficient request handling
Interoperable across cloud and on-premises environments
Improved response relevance via RAG
Secure and observable service with RBAC and metrics
Castorice-LLM-Service's Main Use Cases & Applications
Building conversational chatbots with context retrieval
rag-services is an open-source microservices framework enabling scalable retrieval-augmented generation pipelines with vector storage, LLM inference, and orchestration.