knowledge-base-server

To index documents for semantic search, you ingest content into the system, which preprocesses text through chunking and embeddings-based search to enable fast, context-aware retrieval across your knowledge base.

Analyzing meeting transcripts involves indexing the text to extract entities and track decision history, allowing you to perform contextual reasoning and surface relevant past decisions through semantic search queries.

Yes, you can build a knowledge graph from indexed documents by using entity extraction to identify relationships, then querying and visualizing the resulting graph for contextual information retrieval across teams.

Embeddings-based search works by converting document chunks into vector representations, enabling the system to match semantic queries against indexed content and surface intelligent insights through scalable contextual reasoning.

Configurable pipelines and security controls are required for document indexing to manage preprocessing steps and ensure secure access, providing scalable and safe contextual reasoning across your indexed knowledge base.

Limitations of using a knowledge graph for meeting analysis include the dependency on accurate entity extraction and chunking quality, as poor input data or preprocessing can hinder effective semantic search and decision history tracking.