knowledge-base-builder

Build a knowledge base by placing documents (markdown, PDF, text) in a knowledge directory, then run the builder to generate embeddings and configure vector storage. This enables semantic search queries to retrieve relevant information based on meaning rather than keyword matching.

Knowledge base ingestion supports markdown, PDF, and text files. The builder processes these formats through configurable chunking strategies before generating embeddings for vector storage and semantic retrieval.

RAG (Retrieval-Augmented Generation) retrieves semantically relevant documents from your knowledge base to ground agent responses in specific domain knowledge. This reduces hallucinations and ensures answers reflect your ingested documentation rather than general training data.

Yes. The builder supports configurable chunking strategies and retrieval parameters including top-k result limits and minimum relevance scores. This lets you balance retrieval breadth, precision, and performance for your specific use case.

The builder includes quality metrics to track accuracy and relevance of retrieved results. Monitor these metrics across updates to ensure your knowledge base maintains effectiveness as domain sources evolve.

Yes. The builder supports knowledge updates and versioning, allowing you to evolve your knowledge base as source documents change while maintaining retrieval consistency and quality tracking across versions.