I already have a working Python script that builds a graph for my Markdown-based knowledge system. It lives at system/graph/build.py.
Right now, the script does roughly this:
Discovers all .md files.
Parses YAML frontmatter to build a node registry:
▫ each node has at least: id, optional type, file, aliases, metadata.
Parses inline links [id](#) / [Label](#) from Markdown bodies.
Extracts structured edges from YAML metadata (starting with metadata.author / metadata.authors via a RELATION_FIELDS mapping).
Resolves edges using id_map / alias_map.
Writes JSON artifacts under generated/graph/ (e.g. nodes.json, edges.json, unresolved.json).
Writes a Markdown diagnostics report under reports/graph-report.md (missing nodes, orphans, nodes without type, etc.).
I do NOT want you to rewrite this script from scratch.
I want you to extend the existing script to add a graph enrichment step that creates “type index” nodes and edges. The idea:
Every node has a type field in its frontmatter (e.g. book, author, movie, concept, etc.).
For each distinct type, I want a virtual “index node” whose id is <type>-list. Examples:
▫ type: author → index node author-list
▫ type: book → index node book-list
These index nodes do not correspond to .md files; they are generated by the system.
Each index node should:
▫ appear in the nodes output (e.g. nodes.json) with something like:
⁃ id: "author-list"
⁃ type: "index"
⁃ optionally index_of_type: "author"
▫ have edges from the index node to all nodes of that type, e.g.:
⁃ { "from": "author-list", "to": "cleyton-cabral", "type": "contains" }
⁃ { "from": "book-list", "to": "the-idiot", "type": "contains" }
Key constraints and requirements:
Keep the current node/edge building, unresolved detection, and report generation intact. You are adding an enrichment layer on top.
Conceptually the pipeline should be:
▫ parse Markdown → build base nodes
▫ extract inline + metadata edges
▫ resolve edges
▫ enrich graph with type index nodes + edges
▫ write JSON outputs + report
Implementation details for the enrichment:
▫ Work from the in-memory nodes and edges structures that the script already uses (or whatever the current internal representation is).
▫ Group nodes by type. Nodes without a type should simply be ignored by this enrichment.
▫ For each distinct type_name:
⁃ Create (or reuse if already present) an index node with:
▪ id = f"{type_name}-list"
▪ type = "index"
▪ optional helper field like index_of_type = type_name
⁃ For each node of that type, append a new edge:
▫ Make sure you don’t create duplicate index nodes or duplicate contains edges if the script is run multiple times in-memory.
Integration into existing outputs:
▫ Ensure the new index nodes are included in generated/graph/nodes.json.
▫ Ensure the new contains edges are included in generated/graph/edges.json using the same schema as other edges (from, to, type, optionally source).
▫ These virtual nodes should NOT appear in unresolved.json (they are not unresolved; they are generated).
▫ They also shouldn’t be treated as “missing type” in the report (since they have type: "index").
Code style and structure:
▫ Add one or more helper functions instead of stuffing everything into main:
⁃ e.g. def add_type_index_nodes_and_edges(nodes, edges): ...
▫ Use clear, descriptive names, and keep the existing style of the file.
▫ Add concise comments explaining:
⁃ what type index nodes are,
⁃ why they are generated,
⁃ where in the pipeline the enrichment happens.
No API breaking changes:
▫ Keep current function signatures and external behavior unless absolutely necessary.
▫ Any new data fields added to node or edge objects should be additive and safe for downstream consumers (e.g. future HTML generator reading graph.json).
Please show only the modified/added parts of build.py first (so I can see the diff), and then, if helpful, show the full updated file. Keep comments and inline explanations in the code so it’s easy to understand later.