rag-services provides a collection of containerized RESTful microservices designed to streamline retrieval-augmented generation (RAG) applications. It includes modular components for document storage, vector indexing, embedding generation, LLM inference, and orchestration. Developers can plug in popular vector databases and language model providers, creating highly customizable and scalable RAG pipelines. Fully open-source, rag-services simplifies deployment and management of AI assistants in cloud-native, production environments.
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rag-services
rag-services provides a collection of containerized RESTful microservices designed to streamline retrieval-augmented generation (RAG) applications. It includes modular components for document storage, vector indexing, embedding generation, LLM inference, and orchestration. Developers can plug in popular vector databases and language model providers, creating highly customizable and scalable RAG pipelines. Fully open-source, rag-services simplifies deployment and management of AI assistants in cloud-native, production environments.
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What is rag-services?
rag-services is an extensible platform that breaks down RAG pipelines into discrete microservices. It offers a document store service, a vector index service, an embedder service, multiple LLM inference services, and an orchestrator service to coordinate workflows. Each component exposes REST APIs, allowing you to mix and match databases and model providers. With Docker and Docker Compose support, you can deploy locally or in Kubernetes clusters. The framework enables scalable, fault-tolerant RAG solutions for chatbots, knowledge bases, and automated document Q&A.
Who will use rag-services?
- AI/ML Engineers
- Backend Developers
- Data Scientists
- Enterprises building RAG applications
How to use the rag-services?
- Step1: Clone the repository from GitHub.
- Step2: Copy and customize the .env configuration for vector DB and LLM endpoints.
- Step3: Build and start all services via Docker Compose.
- Step4: Ingest documents through the document store API and generate embeddings.
- Step5: Send user queries to the orchestrator endpoint for RAG-enabled responses.
Platform
- Linux
- Mac
- Windows
rag-services's Core Features & Benefits
The Core Features
- Document storage service
- Vector indexing and search
- Embedding generation
- Multiple LLM inference endpoints
- Workflow orchestration API
The Benefits
- Modular, microservices architecture
- Scalable and fault-tolerant
- Flexible integration with various DBs and LLMs
- Cloud-native deployment with Docker
- Fully open-source and extensible
rag-services's Main Use Cases & Applications
- Knowledge base question answering
- Customer support chatbots
- Internal document search
- Automated report summarization
FAQs of rag-services
rag-services Company Information
rag-services Reviews
5/5
rag-services's Main Competitors and alternatives?
LangChain
Haystack
LlamaIndex
RAGStack
Pelorus.RAG
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