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Build RAG workflows with document ingestion, hybrid search, and agentic answers.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ragx" yet — see the docs or source repo.
Use ragx to ingest our product manuals, FAQs, and technical docs, build a searchable knowledge base, and answer: "What access control methods does the enterprise edition support?" Include citations.
An accurate answer grounded in the ingested documents, with relevant excerpts or source citations.
Use ragx to search the uploaded project documents for "API rate limiting" and "error retry mechanisms," then summarize the most relevant results by topic.
A cross-document summary of the most relevant search results, organized by topic for quick reference.
Use ragx to list all documents in the current knowledge base, flag duplicates or outdated content, and suggest which files should be updated, removed, or re-ingested.
A document inventory with issue flags and maintenance recommendations to keep the knowledge base clean and usable.
Index documents and retrieve relevant context for better LLM responses.
Let AI securely query private local documents with persistent memory.
Turn unstructured documents into a searchable knowledge base for AI agents.
Retrieve relevant context and metadata from Qdrant using natural language queries.
Index PDFs into Qdrant and enable semantic search and RAG document QA.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.