Powerful Features for Modern RAG
Everything you need to build production-ready RAG applications
Vector Database Integration
Connect with popular vector databases like Pinecone, Weaviate, and Milvus for efficient similarity search and retrieval.
Multi-Model Support
Integrate with leading LLMs including OpenAI, Anthropic, and open-source models for flexible RAG implementations.
Advanced Retrieval
Leverage sophisticated retrieval algorithms including semantic search, hybrid search, and re-ranking for better results.
Intelligent Chunking
Automatically optimize document chunking and processing for improved context retrieval and relevance.
Comprehensive RAG Pipeline
End-to-end RAG pipeline capabilities for building production applications
Document Processing
Process and index various document types including PDFs, Word documents, and web pages.
- Automatic text extraction
- Document chunking optimization
- Metadata preservation
- Multi-language support
Vector Operations
Comprehensive vector database operations for efficient information retrieval.
- Real-time vectorization
- Bulk indexing support
- Vector search optimization
- Automatic index management
Query Processing
Advanced query processing and result optimization capabilities.
- Query vectorization
- Context window optimization
- Result re-ranking
- Response generation
RAG Use Cases
Discover how organizations are leveraging RAG to enhance their AI applications
Enterprise Search
Build intelligent search systems across your organization's documents, knowledge bases, and internal content.
Customer Support
Create AI-powered support systems that leverage your documentation and knowledge base for accurate responses.
Research Assistant
Develop research tools that can analyze and retrieve information from large document collections.
Content Generation
Generate accurate, context-aware content by leveraging your existing documentation and knowledge.