Knowledge extraction is the automated pipeline that turns menus, policies, hours, and FAQs from a venue's raw documents into structured embedded chunks ready for agent recall.
Last verified: April 2026
What is Knowledge Extraction?
Knowledge extraction is the automated pipeline that turns menus, policies, hours, and FAQs from a venue's raw documents into structured embedded chunks ready for agent recall.
The pipeline walks uploaded files and optionally scrapes the venue website through Firecrawl, extracts plain text, splits it on paragraph and heading boundaries with a 200-character overlap, and writes the chunks to VenueChunk with their embeddings. A final pass heuristically tags chunks by type (menu, policy, hours, faq) so the venue agent can bias retrieval per question.
FAQ
Knowledge Extraction FAQ.
Common questions about Knowledge Extraction in the AGNT platform.
Knowledge extraction is the automated pipeline that turns menus, policies, hours, and FAQs from a venue's raw documents into structured embedded chunks ready for agent recall.
People also ask.
Related terms
Knowledge Pack
A knowledge pack is a venue's documentation bundle — menus, policies, hours, FAQs — ingested and chunked into VenueChunk records with pgvector embeddings for retrieval-augmented generation.
Venue Soul
The Venue Soul is the AI model of a venue's identity — voice, vibe, rules, recommendations — that every response the venue agent gives is built from.
pgvector
pgvector is the PostgreSQL extension that adds a vector data type and approximate-nearest-neighbour search, letting AGNT store embeddings next to relational rows in one ACID transaction.
Authority sources
Share as social post
What is Knowledge Extraction? Knowledge extraction is the automated pipeline that turns menus, policies, hours, and FAQs from a venue's raw documents into structured embedded chunks ready for agent recall. https://agntdot.com/glossary/knowledge-extraction
256 / 280 chars
See it in action.
Now you know what Knowledge Extraction means. Try the live scan demo or read the developer docs to go deeper.