Knowledge Graph

The Knowledge Graph allows you to store, version, and govern the operational knowledge your agents need: firm rules, mappings, approval criteria, exception history, and process documentation. You organise documents in namespaces and folders, assign each agent the corpora they may search, and agents pull approved, versioned context on each turn instead of re-stating the same logic in every chat. Namespace boundaries keep regulated corpora segregated, so retrieval stays least-privilege and focused on the domain you intend.

KG and Skills share the same workspace filesystem explorer in Flow (upload, rename, move, download, drag-and-drop). Knowledge Graph uses an amber accent; Skills uses sky. File bytes live in one classified store; KG processing metadata overlays Knowledge Graph sources.

What is the Knowledge Graph?

A context store for operational knowledge: firm rules, mappings, approvals, exception history, and process documentation. Documents are namespace-scoped with version history; agents retrieve approved context instead of rediscovering logic on every run.

Knowledge Graph

Build a knowledge graph to model your firm's standards, rules, and decisions.

How agents get context

Before each chat or automation turn, Fontana automatically builds context from the Knowledge Graph. It runs vector search over the namespaces you assigned to that agent, ranks documents by similarity to the current message, and injects full text for the best matches above your configured threshold. You do not paste rules or procedures into every prompt; the agent starts each turn with approved, versioned knowledge already in place.

This pre-turn pass is fast, scoped to the corpora you permit, and bounded so only likely-relevant articles enter the thread before the model responds.

Agentic search

Automatic injection covers the strongest matches up front; agentic search lets agents improve results during the work itself. As the task develops, agents can explore the Knowledge Graph again: run semantic searches with vector similarity, inspect ranked summaries and scores, and pull full document text only for hits that support the current step. They can also retrieve exact articles with @slug references when a specific governed document applies.

Exploration stays inside the namespaces and permissions you configured, so agents can deepen context without loading your entire corpus into every conversation.

Embeddings and retrieval

Knowledge Graph documents are indexed and embedded as 1536-dimensional vectors (text-embedding-3-small). Both automatic pre-turn injection and agentic search rely on this index; injection uses the agent’s kbInjectionScoreThreshold (minimum similarity score; default 0.35).

What are embeddings?

Dense vector representations of text used for semantic search. Fontana stores 1536-dimensional vectors on Knowledge Graph documents and workflow templates; only title, description, and vectorSource are sent to the embedding provider, not full document bodies.

When you upload or save documents, Fontana parses structured files (CSV, TSV, JSON) and prose (Markdown, text) into governed records, indexes them for search, and builds a relationship graph linking cross-references, semantically similar neighbours, and folder structure within each namespace.

Processing Status and Queue

In Flow at Knowledge Graph, the Status and Queue tabs show ingestion progress for uploads and folder sync. For each active file you can see pipeline phase (parsing, chunking, embedding, extraction), elapsed time, the approved model in use, and file size. When a worker stalls past the platform limit, the job fails closed with an error on that file so you can re-ingest or restart the queue; silent stuck Running counts without detail are not the intended state.

Chunking and entity extraction use the approved Agent model you configure for Knowledge Graph (global default, with optional namespace, folder, or file overrides). That modelId routes through the same Vault gateway resolution as chat, so Bedrock, Anthropic, OpenRouter, and other approved providers use the matching keys for the selected model.

Knowledge Graph Document content types

Structured documents use CSV, TSV, PSV, or JSON. Unstructured prose and Markdown (YAML frontmatter) use text. In Flow on a Knowledge Graph document card, the Content section offers type-aware views: Monaco Raw (syntax highlighted by file type) on every text file, plus Preview for Markdown, Form and Table for JSON, and Table for CSV, TSV, and PSV.

Data type	Usage
Text / Markdown	Unstructured prose and Markdown articles with YAML frontmatter. Preview and Raw editors in the document card.
CSV	Comma-delimited tabular sources. Table and Raw views; parsed for structured agent retrieval.
TSV	Tab-delimited tabular sources. Table and Raw views; parsed for structured agent retrieval.
PSV	Pipe-delimited tabular sources. Table and Raw views; parsed for structured agent retrieval.
JSON	Structured JSON document bodies. Form, Table, and Raw views with schema-aware row editing when the body is tabular.

Knowledge Graph namespaces

Knowledge Graph documents are organised in three levels: a namespace (top-level corpus boundary), a slash-path folder within that namespace, and individual documents addressed by path. In Flow at Knowledge Graph, you create, rename, and delete namespaces from the sidebar (the memory namespace is reserved). Folders and files support rename and move within a namespace; each document carries version history.

Each agent searches only the namespaces you assign in Admin → Agents. Retrieval and relationship expansion stay inside those boundaries, which keeps regulated corpora segregated and agent answers focused on the domain you intend.