knowledge-base-management

To build a searchable knowledge base, upload your documents to this processor which automatically extracts metadata, generates hierarchical structures, and indexes content for context-rich retrieval.

LLM-powered document processing uses DeepSeek features for naming, summarization, and sectioning. It includes robust rule-based fallback methods to ensure content extraction continues if the LLM is unavailable.

Yes, you can configure per-library settings for centralized knowledge-base management. The system supports configurable options across multiple libraries to orchestrate document processing, hierarchy creation, and indexing.

Document processing works without an active LLM connection by falling back to rule-based methods. If DeepSeek features are unavailable, the system automatically generates hierarchical structures and extracts content using predefined rules.

The best way to search retrieved documents is using the advanced search feature, which provides hierarchical disclosure and context-aware results directly from the centralized knowledge base generated during processing.

Yes, you need python-dotenv and requests installed to run the knowledge-base-management scripts. These dependencies facilitate environment configuration and HTTP requests required for LLM orchestration and document processing.