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AI RAG Pipeline Builder

Visually build Retrieval-Augmented Generation (RAG) systems, supporting document chunking, vector embedding, retrieval strategy configuration, quickly build enterprise knowledge base Q&A systems

RAG Pipeline Builder Interface

Visual RAG builder coming soon

Features

  • ✓ Visual RAG workflow orchestration, supporting full pipeline configuration: document loading, chunking, embedding, retrieval, generation
  • ✓ Smart document chunking strategies, supporting paragraph, sentence, semantic chunking while preserving context integrity
  • ✓ Supports multiple vector databases: Pinecone, Weaviate, Chroma, Milvus, Qdrant and more
  • ✓ Advanced retrieval strategies: hybrid search (vector+keyword), reranking, context compression, query rewriting
  • ✓ Built-in evaluation metrics: retrieval accuracy, generation quality, hallucination detection for continuous RAG optimization

How to Use

  1. Upload knowledge base documents (PDF, Word, TXT, web pages, etc.)
  2. Configure document chunking strategy and vector embedding model
  3. Select vector database and configure retrieval strategy
  4. Connect LLM generation model and test Q&A performance

FAQ

What is a RAG system?

RAG (Retrieval-Augmented Generation) enhances LLM response quality by retrieving from external knowledge bases, reducing hallucinations and improving accuracy and timeliness.

What document formats are supported?

Supports PDF, Word, Excel, PPT, TXT, Markdown, HTML, web URLs and other common formats. Also supports importing from databases and APIs.

How to choose the right vector database?

Choose based on data scale and use case: small scale (<1M vectors) recommend Chroma; medium scale recommend Pinecone, Qdrant; large enterprise scale recommend Milvus, Weaviate.

How to improve RAG system accuracy?

Optimize document chunking strategy, use hybrid retrieval, add reranking steps, optimize prompt templates, regularly update knowledge base. System provides evaluation metrics for continuous optimization.

Can I deploy to production?

Yes. Export as Docker Compose configuration, Kubernetes YAML, or directly generate Python/Node.js code for easy production deployment.