chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Reproducible Example
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A small document pipeline — parser, validator, processor, storage, API — with architecture notes and research notes. Seven files, two languages, clear call relationships between modules.
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Run graphify on it and you get a knowledge graph showing how the modules connect, which functions call which, and how the architecture notes relate to the code.
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## Input files
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```
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raw/
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├── parser.py — reads files, detects format, kicks off the pipeline
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├── validator.py — schema checks, calls processor for text normalization
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├── processor.py — keyword extraction, cross-reference detection
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├── storage.py — persists everything, maintains the index
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├── api.py — HTTP handlers that orchestrate the above four modules
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├── architecture.md — design decisions and module responsibilities
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└── notes.md — open questions and tradeoffs
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```
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## How to run
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```bash
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pip install graphifyy
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graphify install # Claude Code
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graphify install --platform codex # Codex
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graphify install --platform opencode # OpenCode
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graphify install --platform claw # OpenClaw
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```
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Then open your AI coding assistant in this directory and type:
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```
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/graphify ./raw
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```
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No PDF or image extraction — runs entirely on AST and markdown with no token cost for semantic extraction.
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## What to expect
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- `api.py` as a hub node connected to all four modules
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- `storage.py` as the highest-degree god node (everything reads and writes through it)
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- `parser.py` calling `validator.py` and `storage.py`
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- `architecture.md` and `notes.md` linked to the code modules they discuss
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- 2 communities: the four Python modules together, the two markdown files together (or api.py in its own cluster given high connectivity)
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## After it runs
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Ask questions from your AI coding assistant:
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- "what calls storage directly?"
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- "what is the shortest path between parser and processor?"
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- "which module has the most connections?"
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- "what does the architecture doc say about the storage design?"
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The graph lives in `graphify-out/` and persists across sessions.
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"""
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API module - exposes the document pipeline over HTTP.
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Thin layer over parser, validator, processor, and storage.
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"""
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from parser import batch_parse, parse_file
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from validator import validate_document, ValidationError
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from processor import process_and_save, enrich_document
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from storage import load_record, delete_record, list_records, load_index
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def handle_upload(paths: list) -> dict:
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"""
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Accept a list of file paths, run the full pipeline on each,
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and return a summary of what succeeded and what failed.
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"""
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results = batch_parse(paths)
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succeeded = [r for r in results if r["ok"]]
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failed = [r for r in results if not r["ok"]]
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return {
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"uploaded": len(succeeded),
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"failed": len(failed),
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"ids": [r["id"] for r in succeeded],
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"errors": failed,
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}
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def handle_get(record_id: str) -> dict:
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"""Fetch a document by ID and return it."""
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try:
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return load_record(record_id)
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except KeyError:
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return {"error": f"Record {record_id} not found"}
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def handle_delete(record_id: str) -> dict:
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"""Delete a document by ID."""
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deleted = delete_record(record_id)
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if deleted:
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return {"deleted": record_id}
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return {"error": f"Record {record_id} not found"}
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def handle_list() -> dict:
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"""List all document IDs in storage."""
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return {"records": list_records()}
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def handle_search(query: str) -> dict:
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"""
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Simple keyword search over the index.
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Returns documents whose keyword list overlaps with the query terms.
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"""
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terms = set(query.lower().split())
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index = load_index()
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matches = []
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for record_id, entry in index.items():
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keywords = set(entry.get("keywords", []))
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if terms & keywords:
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matches.append({
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"id": record_id,
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"title": entry.get("title", ""),
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"matched_keywords": list(terms & keywords),
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})
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return {"query": query, "results": matches}
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def handle_enrich(record_id: str) -> dict:
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"""Re-enrich a document to pick up new cross-references."""
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try:
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doc = load_record(record_id)
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except KeyError:
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return {"error": f"Record {record_id} not found"}
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try:
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validated = validate_document(doc)
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except ValidationError as e:
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return {"error": str(e)}
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enriched_id = process_and_save(validated)
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return {"enriched": enriched_id}
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# Document Pipeline Architecture
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This is a small document ingestion and search system. Files come in, get parsed and validated, keywords get extracted, cross-references get built, and everything ends up queryable via a simple API.
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## How data flows
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Raw files on disk go through four stages before they are searchable.
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**Parsing** reads the file, detects the format (markdown, JSON, plaintext), and converts it into a structured dict. The parser handles each format differently. Markdown gets title, sections, and links extracted. JSON gets loaded directly. Plaintext gets split into paragraphs.
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**Validation** checks that the parsed document has the required fields and a known format. It also normalizes text fields (lowercase, trim whitespace, strip control characters) using the processor before the document moves forward.
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**Processing** enriches the validated document with a keyword index and cross-references. Cross-references are built by comparing the document's keywords against every other document already in the index. If they share three or more keywords they get linked.
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**Storage** persists everything to disk as JSON files and maintains a flat index that maps record IDs to metadata. All other modules read and write through the storage interface so there is one source of truth.
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## Module responsibilities
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- parser.py: reads files, detects format, calls validate_document and save_parsed
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- validator.py: enforces schema, normalizes fields, calls normalize_text from processor
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- processor.py: extract_keywords, find_cross_references, calls load_index and save_processed
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- storage.py: load_index, save_parsed, save_processed, load_record, delete_record, list_records
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- api.py: HTTP handlers that orchestrate the above modules
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## Design decisions
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The pipeline is intentionally linear. Each stage has one job and calls the next stage explicitly. There is no event bus or dependency injection. This makes the call graph easy to follow and easy to test.
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Storage is intentionally simple. A flat JSON index plus one file per document is enough at small scale. If the corpus grows past a few thousand documents this becomes the bottleneck and should be replaced with SQLite or a proper document store.
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Cross-reference detection is intentionally naive. Keyword overlap of three is a reasonable threshold for short documents but will produce too many false positives on long ones. A real system would use TF-IDF or embedding similarity instead.
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## Extending the pipeline
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To add a new file format, add a branch in parser.py's parse_file function and a new parse_* function. The rest of the pipeline does not need to change.
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To add a new enrichment step, add a function in processor.py and call it from enrich_document. Store the result in the document dict and add the field to the index in save_processed if you want it searchable.
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# Research Notes
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Thoughts and open questions while building the document pipeline. Not polished, just a running log.
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## On keyword extraction
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The current approach strips stopwords and returns unique tokens. Simple and fast. The problem is it treats all keywords equally. "database" appearing once in a title carries more weight than "database" buried in a paragraph but the code doesn't know that.
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TF-IDF would fix this. Term frequency times inverse document frequency gives higher scores to words that are distinctive to a document rather than common across the corpus. Worth switching once the index is big enough for IDF to be meaningful (probably 50+ documents).
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Embedding-based similarity is the other option. Run each document through a sentence transformer, store the vector, do nearest-neighbor search at query time. Much better recall but adds a dependency and makes the index opaque. The keyword approach is at least debuggable.
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## On cross-reference detection
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Three shared keywords is arbitrary. Tuned it by hand on a small test set. On short documents (under 500 words) it produces reasonable results. On long documents everything shares keywords with everything else and the cross-reference graph becomes noise.
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A per-document threshold based on document length would be better. Or weight by keyword specificity so rare keywords count more than common ones.
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## On storage
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Flat files work fine for now. The index fits in memory. Load times are under 10ms for a few hundred documents.
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SQLite becomes worth it when you need range queries or you want to update individual fields without rewriting the whole record. The current save_processed rewrites the entire JSON file on every update which is wasteful.
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One thing flat files do well: they are easy to inspect. Open the store directory and you can read every document directly. No tooling required. This matters for debugging.
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## On the API layer
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The API is a thin wrapper. Every handler does one thing: call the right combination of parser, validator, processor, storage. No business logic lives in api.py.
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The risk is that this breaks down when you need transactions. Right now parse_and_save in parser.py calls validate_document and save_parsed in sequence. If save_parsed fails after validate_document succeeds you have a partially written record. Not a problem at small scale, becomes a problem under load.
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## Open questions
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Should validation happen in the parser or as a separate step? Currently it's separate which means the parser can return invalid documents. That feels wrong but keeping them separate makes each module easier to test.
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Should cross-references be stored on the document or computed at query time? Storing them is fast to read but goes stale. Computing at query time is always fresh but slow for large indexes.
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Is the storage interface the right abstraction? Right now parser, validator, and processor all import from storage directly. A repository pattern would centralize access but adds indirection. Probably not worth it until the storage backend needs to change.
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"""
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Parser module - reads raw input documents and converts them into
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a structured format the rest of the pipeline can work with.
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"""
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from validator import validate_document
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from storage import save_parsed
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SUPPORTED_FORMATS = ["markdown", "plaintext", "json"]
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def parse_file(path: str) -> dict:
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"""Read a file from disk and return a structured document."""
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with open(path, "r") as f:
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raw = f.read()
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ext = path.rsplit(".", 1)[-1].lower()
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if ext == "md":
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doc = parse_markdown(raw)
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elif ext == "json":
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doc = parse_json(raw)
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else:
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doc = parse_plaintext(raw)
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doc["source"] = path
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return doc
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def parse_markdown(text: str) -> dict:
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"""Extract title, sections, and links from markdown."""
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lines = text.splitlines()
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title = ""
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sections = []
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links = []
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for line in lines:
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if line.startswith("# ") and not title:
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title = line[2:].strip()
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elif line.startswith("## "):
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sections.append(line[3:].strip())
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elif "](http" in line:
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start = line.index("](") + 2
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end = line.index(")", start)
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links.append(line[start:end])
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return {"title": title, "sections": sections, "links": links, "format": "markdown"}
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def parse_json(text: str) -> dict:
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"""Parse a JSON document into a structured dict."""
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import json
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data = json.loads(text)
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return {"data": data, "format": "json"}
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def parse_plaintext(text: str) -> dict:
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"""Split plaintext into paragraphs."""
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paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
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return {"paragraphs": paragraphs, "format": "plaintext"}
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def parse_and_save(path: str) -> str:
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"""Full pipeline: parse, validate, save. Returns the saved record ID."""
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doc = parse_file(path)
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validated = validate_document(doc)
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record_id = save_parsed(validated)
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return record_id
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def batch_parse(paths: list) -> list:
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"""Parse a list of files and return their record IDs."""
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results = []
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for path in paths:
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try:
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rid = parse_and_save(path)
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results.append({"path": path, "id": rid, "ok": True})
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except Exception as e:
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results.append({"path": path, "error": str(e), "ok": False})
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return results
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"""
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Processor module - transforms validated documents into enriched records
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ready for storage and retrieval.
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"""
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import re
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from storage import load_index, save_processed
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STOPWORDS = {"the", "a", "an", "and", "or", "but", "in", "on", "at", "to", "for", "of", "with"}
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def normalize_text(text: str) -> str:
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"""Lowercase, strip extra whitespace, remove control characters."""
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text = text.lower().strip()
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text = re.sub(r"\s+", " ", text)
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text = re.sub(r"[^\x20-\x7e]", "", text)
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return text
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def extract_keywords(text: str) -> list:
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"""Pull non-stopword tokens from text, deduplicated."""
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tokens = re.findall(r"\b[a-z]{3,}\b", normalize_text(text))
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seen = set()
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keywords = []
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for t in tokens:
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if t not in STOPWORDS and t not in seen:
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seen.add(t)
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keywords.append(t)
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return keywords
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def enrich_document(doc: dict) -> dict:
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"""Add keyword index and cross-references to a validated document."""
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text_blob = " ".join([
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doc.get("title", ""),
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" ".join(doc.get("sections", [])),
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" ".join(doc.get("paragraphs", [])),
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])
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doc["keywords"] = extract_keywords(text_blob)
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doc["cross_refs"] = find_cross_references(doc)
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return doc
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def find_cross_references(doc: dict) -> list:
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"""Look up the index and return IDs of related documents by keyword overlap."""
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index = load_index()
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keywords = set(doc.get("keywords", []))
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refs = []
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for record_id, entry in index.items():
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other_keywords = set(entry.get("keywords", []))
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overlap = keywords & other_keywords
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if len(overlap) >= 3:
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refs.append({"id": record_id, "shared_keywords": list(overlap)})
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return refs
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def process_and_save(doc: dict) -> str:
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"""Enrich a validated document and persist it. Returns the record ID."""
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enriched = enrich_document(doc)
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record_id = save_processed(enriched)
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return record_id
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def reprocess_all() -> int:
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"""Re-enrich all records in the index. Returns count of records updated."""
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index = load_index()
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count = 0
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for record_id, doc in index.items():
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process_and_save(doc)
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count += 1
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return count
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@@ -0,0 +1,89 @@
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"""
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Storage module - persists documents to disk and maintains the search index.
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All other modules read and write through this interface.
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"""
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import json
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import uuid
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from pathlib import Path
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STORAGE_DIR = Path(".graphify_store")
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INDEX_FILE = STORAGE_DIR / "index.json"
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def _ensure_storage() -> None:
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STORAGE_DIR.mkdir(exist_ok=True)
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if not INDEX_FILE.exists():
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INDEX_FILE.write_text(json.dumps({}))
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def load_index() -> dict:
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"""Load the full document index from disk."""
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_ensure_storage()
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return json.loads(INDEX_FILE.read_text())
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def save_index(index: dict) -> None:
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"""Persist the index to disk."""
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_ensure_storage()
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INDEX_FILE.write_text(json.dumps(index, indent=2))
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def save_parsed(doc: dict) -> str:
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"""Write a parsed document to storage. Returns the assigned record ID."""
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_ensure_storage()
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record_id = str(uuid.uuid4())[:8]
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path = STORAGE_DIR / f"{record_id}.json"
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path.write_text(json.dumps(doc, indent=2))
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index = load_index()
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index[record_id] = {
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"source": doc.get("source", ""),
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"format": doc.get("format", ""),
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"title": doc.get("title", ""),
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}
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save_index(index)
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return record_id
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def save_processed(doc: dict) -> str:
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"""Write an enriched document to storage, updating the index with keywords."""
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_ensure_storage()
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record_id = doc.get("id") or str(uuid.uuid4())[:8]
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path = STORAGE_DIR / f"{record_id}_processed.json"
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path.write_text(json.dumps(doc, indent=2))
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index = load_index()
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if record_id not in index:
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index[record_id] = {}
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index[record_id]["keywords"] = doc.get("keywords", [])
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index[record_id]["cross_refs"] = [r["id"] for r in doc.get("cross_refs", [])]
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save_index(index)
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return record_id
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def load_record(record_id: str) -> dict:
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"""Fetch a single document by ID."""
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_ensure_storage()
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path = STORAGE_DIR / f"{record_id}.json"
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if not path.exists():
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raise KeyError(f"No record found for ID: {record_id}")
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return json.loads(path.read_text())
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def delete_record(record_id: str) -> bool:
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"""Remove a document and its index entry. Returns True if it existed."""
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_ensure_storage()
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path = STORAGE_DIR / f"{record_id}.json"
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if not path.exists():
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return False
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path.unlink()
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index = load_index()
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index.pop(record_id, None)
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save_index(index)
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return True
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def list_records() -> list:
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"""Return all record IDs currently in storage."""
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return list(load_index().keys())
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@@ -0,0 +1,61 @@
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"""
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Validator module - checks that parsed documents meet schema requirements
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before they are allowed into storage.
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"""
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from processor import normalize_text
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||||
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REQUIRED_FIELDS = {"source", "format"}
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MAX_TITLE_LENGTH = 200
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ALLOWED_FORMATS = {"markdown", "plaintext", "json"}
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class ValidationError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
def validate_document(doc: dict) -> dict:
|
||||
"""Run all validation checks on a parsed document. Raises ValidationError on failure."""
|
||||
check_required_fields(doc)
|
||||
check_format(doc)
|
||||
doc = normalize_fields(doc)
|
||||
return doc
|
||||
|
||||
|
||||
def check_required_fields(doc: dict) -> None:
|
||||
"""Raise if any required field is missing."""
|
||||
missing = REQUIRED_FIELDS - doc.keys()
|
||||
if missing:
|
||||
raise ValidationError(f"Missing required fields: {missing}")
|
||||
|
||||
|
||||
def check_format(doc: dict) -> None:
|
||||
"""Raise if the format is not in the allowed list."""
|
||||
fmt = doc.get("format", "")
|
||||
if fmt not in ALLOWED_FORMATS:
|
||||
raise ValidationError(f"Unknown format: {fmt}. Allowed: {ALLOWED_FORMATS}")
|
||||
|
||||
|
||||
def normalize_fields(doc: dict) -> dict:
|
||||
"""Clean up text fields using the processor."""
|
||||
if "title" in doc:
|
||||
doc["title"] = normalize_text(doc["title"])
|
||||
if len(doc["title"]) > MAX_TITLE_LENGTH:
|
||||
doc["title"] = doc["title"][:MAX_TITLE_LENGTH]
|
||||
if "paragraphs" in doc:
|
||||
doc["paragraphs"] = [normalize_text(p) for p in doc["paragraphs"]]
|
||||
if "sections" in doc:
|
||||
doc["sections"] = [normalize_text(s) for s in doc["sections"]]
|
||||
return doc
|
||||
|
||||
|
||||
def validate_batch(docs: list) -> tuple:
|
||||
"""Validate a list of documents. Returns (valid_docs, errors)."""
|
||||
valid = []
|
||||
errors = []
|
||||
for doc in docs:
|
||||
try:
|
||||
valid.append(validate_document(doc))
|
||||
except ValidationError as e:
|
||||
errors.append({"doc": doc.get("source", "unknown"), "error": str(e)})
|
||||
return valid, errors
|
||||
@@ -0,0 +1,78 @@
|
||||
# Graph Report - worked/httpx/raw (2026-04-05)
|
||||
|
||||
## Corpus Check
|
||||
- 6 files · ~2,047 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- 144 nodes · 330 edges · 6 communities detected
|
||||
- Extraction: 53% EXTRACTED · 47% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `Client` - 26 edges
|
||||
2. `AsyncClient` - 25 edges
|
||||
3. `Response` - 24 edges
|
||||
4. `Request` - 21 edges
|
||||
5. `BaseClient` - 18 edges
|
||||
6. `HTTPTransport` - 17 edges
|
||||
7. `BaseTransport` - 16 edges
|
||||
8. `AsyncHTTPTransport` - 15 edges
|
||||
9. `Headers` - 15 edges
|
||||
10. `Timeout` - 14 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
- `Timeout` --uses--> `URL` [INFERRED]
|
||||
worked/httpx/raw/client.py → worked/httpx/raw/models.py
|
||||
- `Timeout` --uses--> `Headers` [INFERRED]
|
||||
worked/httpx/raw/client.py → worked/httpx/raw/models.py
|
||||
- `Timeout` --uses--> `Cookies` [INFERRED]
|
||||
worked/httpx/raw/client.py → worked/httpx/raw/models.py
|
||||
- `Timeout` --uses--> `BaseTransport` [INFERRED]
|
||||
worked/httpx/raw/client.py → worked/httpx/raw/transport.py
|
||||
- `Timeout` --uses--> `HTTPTransport` [INFERRED]
|
||||
worked/httpx/raw/client.py → worked/httpx/raw/transport.py
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "Community 0"
|
||||
Cohesion: 0.11
|
||||
Nodes (8): ConnectError, AsyncBaseTransport, AsyncHTTPTransport, BaseTransport, ConnectionPool, HTTPTransport, MockTransport, ProxyTransport
|
||||
|
||||
### Community 1 - "Community 1"
|
||||
Cohesion: 0.13
|
||||
Nodes (9): Auth, BasicAuth, BearerAuth, DigestAuth, NetRCAuth, Limits, Timeout, Request (+1 more)
|
||||
|
||||
### Community 2 - "Community 2"
|
||||
Cohesion: 0.12
|
||||
Nodes (3): AsyncClient, BaseClient, Client
|
||||
|
||||
### Community 3 - "Community 3"
|
||||
Cohesion: 0.11
|
||||
Nodes (3): Cookies, Headers, URL
|
||||
|
||||
### Community 4 - "Community 4"
|
||||
Cohesion: 0.16
|
||||
Nodes (20): Exception, CloseError, ConnectTimeout, CookieConflict, DecodingError, HTTPError, HTTPStatusError, InvalidURL (+12 more)
|
||||
|
||||
### Community 5 - "Community 5"
|
||||
Cohesion: 0.28
|
||||
Nodes (3): build_url_with_params(), flatten_queryparams(), primitive_value_to_str()
|
||||
|
||||
## Suggested Questions
|
||||
_Questions this graph is uniquely positioned to answer:_
|
||||
|
||||
- **Why does `Client` connect `Community 2` to `Community 0`, `Community 1`, `Community 3`, `Community 4`?**
|
||||
_High betweenness centrality (0.177) - this node is a cross-community bridge._
|
||||
- **Why does `Response` connect `Community 1` to `Community 0`, `Community 2`, `Community 3`, `Community 4`?**
|
||||
_High betweenness centrality (0.168) - this node is a cross-community bridge._
|
||||
- **Why does `AsyncClient` connect `Community 2` to `Community 0`, `Community 1`, `Community 3`, `Community 4`?**
|
||||
_High betweenness centrality (0.165) - this node is a cross-community bridge._
|
||||
- **Are the 12 inferred relationships involving `Client` (e.g. with `Request` and `Response`) actually correct?**
|
||||
_`Client` has 12 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 12 inferred relationships involving `AsyncClient` (e.g. with `Request` and `Response`) actually correct?**
|
||||
_`AsyncClient` has 12 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 18 inferred relationships involving `Response` (e.g. with `Timeout` and `Limits`) actually correct?**
|
||||
_`Response` has 18 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 18 inferred relationships involving `Request` (e.g. with `Timeout` and `Limits`) actually correct?**
|
||||
_`Request` has 18 INFERRED edges - model-reasoned connections that need verification._
|
||||
@@ -0,0 +1,43 @@
|
||||
# httpx Corpus Benchmark
|
||||
|
||||
A synthetic 6-file Python codebase modeled after httpx's architecture. Tests graphify on a realistic library with clean layering: exceptions → models → auth/transport → client.
|
||||
|
||||
## Corpus (6 files)
|
||||
|
||||
```
|
||||
raw/
|
||||
├── exceptions.py — HTTPError hierarchy
|
||||
├── models.py — URL, Headers, Cookies, Request, Response
|
||||
├── auth.py — BasicAuth, BearerAuth, DigestAuth, NetRCAuth
|
||||
├── utils.py — header normalization, query params, content-type parsing
|
||||
├── transport.py — ConnectionPool, HTTPTransport, AsyncHTTPTransport, MockTransport
|
||||
└── client.py — Timeout, Limits, BaseClient, Client, AsyncClient
|
||||
```
|
||||
|
||||
## How to run
|
||||
|
||||
```bash
|
||||
pip install graphifyy
|
||||
|
||||
graphify install # Claude Code
|
||||
graphify install --platform codex # Codex
|
||||
graphify install --platform opencode # OpenCode
|
||||
graphify install --platform claw # OpenClaw
|
||||
```
|
||||
|
||||
Then open your AI coding assistant in this directory and type:
|
||||
|
||||
```
|
||||
/graphify ./raw
|
||||
```
|
||||
|
||||
## What to expect
|
||||
|
||||
- 144 nodes, 330 edges, 6 communities
|
||||
- God nodes: `Client`, `AsyncClient`, `Response`, `Request`, `BaseClient`, `HTTPTransport`
|
||||
- Surprising connection: `DigestAuth` linked to `Response` — auth.py reads Response to parse WWW-Authenticate headers
|
||||
- Token reduction: ~1x — 6 files fits in a context window, so there is no compression win here
|
||||
|
||||
The graph value on a small corpus is structural, not compressive: you can see the full dependency graph, identify god nodes, and understand architecture at a glance. Token reduction scales with corpus size — at 52 files (Karpathy benchmark) graphify achieves 71.5x.
|
||||
|
||||
Run `graphify benchmark worked/httpx/graph.json` to verify the numbers. Actual output is in this folder: `GRAPH_REPORT.md` and `graph.json`. Full eval: `review.md`.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,114 @@
|
||||
"""
|
||||
Authentication handlers.
|
||||
Auth objects are callables that modify a request before it is sent.
|
||||
DigestAuth is the most interesting: it participates in a full request/response cycle,
|
||||
reading the 401 response to build the challenge before re-sending.
|
||||
"""
|
||||
import hashlib
|
||||
import time
|
||||
from models import Request, Response
|
||||
|
||||
|
||||
class Auth:
|
||||
"""Base class for all authentication handlers."""
|
||||
|
||||
def auth_flow(self, request: Request):
|
||||
"""Modify the request. May yield to inspect the response."""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class BasicAuth(Auth):
|
||||
"""HTTP Basic Authentication."""
|
||||
|
||||
def __init__(self, username: str, password: str):
|
||||
self.username = username
|
||||
self.password = password
|
||||
|
||||
def auth_flow(self, request: Request):
|
||||
import base64
|
||||
credentials = f"{self.username}:{self.password}".encode()
|
||||
encoded = base64.b64encode(credentials).decode()
|
||||
request.headers["Authorization"] = f"Basic {encoded}"
|
||||
yield request
|
||||
|
||||
|
||||
class BearerAuth(Auth):
|
||||
"""Bearer token authentication."""
|
||||
|
||||
def __init__(self, token: str):
|
||||
self.token = token
|
||||
|
||||
def auth_flow(self, request: Request):
|
||||
request.headers["Authorization"] = f"Bearer {self.token}"
|
||||
yield request
|
||||
|
||||
|
||||
class DigestAuth(Auth):
|
||||
"""
|
||||
HTTP Digest Authentication.
|
||||
Requires a full request/response cycle: sends the initial request,
|
||||
reads the 401 WWW-Authenticate header, then re-sends with credentials.
|
||||
This is the only auth handler that reads from Response.
|
||||
"""
|
||||
|
||||
def __init__(self, username: str, password: str):
|
||||
self.username = username
|
||||
self.password = password
|
||||
self._nonce_count = 0
|
||||
|
||||
def auth_flow(self, request: Request):
|
||||
yield request # first attempt, no credentials
|
||||
|
||||
# This handler must inspect the Response to continue
|
||||
response = yield
|
||||
|
||||
if response.status_code == 401:
|
||||
challenge = self._parse_challenge(response)
|
||||
credentials = self._build_credentials(request, challenge)
|
||||
request.headers["Authorization"] = credentials
|
||||
yield request
|
||||
|
||||
def _parse_challenge(self, response: Response) -> dict:
|
||||
"""Extract digest parameters from the WWW-Authenticate header."""
|
||||
header = response.headers.get("www-authenticate", "")
|
||||
params = {}
|
||||
for part in header.replace("Digest ", "").split(","):
|
||||
if "=" in part:
|
||||
key, _, value = part.strip().partition("=")
|
||||
params[key.strip()] = value.strip().strip('"')
|
||||
return params
|
||||
|
||||
def _build_credentials(self, request: Request, challenge: dict) -> str:
|
||||
"""Compute the Authorization header value for a digest challenge."""
|
||||
self._nonce_count += 1
|
||||
nc = f"{self._nonce_count:08x}"
|
||||
cnonce = hashlib.md5(str(time.time()).encode()).hexdigest()[:8]
|
||||
realm = challenge.get("realm", "")
|
||||
nonce = challenge.get("nonce", "")
|
||||
|
||||
ha1 = hashlib.md5(f"{self.username}:{realm}:{self.password}".encode()).hexdigest()
|
||||
ha2 = hashlib.md5(f"{request.method}:{request.url.path}".encode()).hexdigest()
|
||||
response_hash = hashlib.md5(f"{ha1}:{nonce}:{nc}:{cnonce}:auth:{ha2}".encode()).hexdigest()
|
||||
|
||||
return (
|
||||
f'Digest username="{self.username}", realm="{realm}", '
|
||||
f'nonce="{nonce}", uri="{request.url.path}", '
|
||||
f'nc={nc}, cnonce="{cnonce}", response="{response_hash}"'
|
||||
)
|
||||
|
||||
|
||||
class NetRCAuth(Auth):
|
||||
"""Load credentials from ~/.netrc based on the request host."""
|
||||
|
||||
def auth_flow(self, request: Request):
|
||||
import netrc
|
||||
try:
|
||||
credentials = netrc.netrc().authenticators(request.url.host)
|
||||
if credentials:
|
||||
username, _, password = credentials
|
||||
basic = BasicAuth(username, password)
|
||||
yield from basic.auth_flow(request)
|
||||
return
|
||||
except Exception:
|
||||
pass
|
||||
yield request
|
||||
@@ -0,0 +1,161 @@
|
||||
"""
|
||||
The main Client and AsyncClient classes.
|
||||
BaseClient holds all shared logic. Client and AsyncClient extend it for sync/async.
|
||||
This is the integration hub of the library - it imports from every other module.
|
||||
"""
|
||||
from models import Request, Response, URL, Headers, Cookies
|
||||
from auth import Auth, BasicAuth
|
||||
from transport import BaseTransport, HTTPTransport, AsyncHTTPTransport
|
||||
from exceptions import TooManyRedirects, InvalidURL
|
||||
from utils import build_url_with_params, obfuscate_sensitive_headers
|
||||
|
||||
|
||||
DEFAULT_MAX_REDIRECTS = 20
|
||||
|
||||
|
||||
class Timeout:
|
||||
def __init__(self, timeout=5.0, *, connect=None, read=None, write=None, pool=None):
|
||||
self.connect = connect or timeout
|
||||
self.read = read or timeout
|
||||
self.write = write or timeout
|
||||
self.pool = pool or timeout
|
||||
|
||||
|
||||
class Limits:
|
||||
def __init__(self, max_connections=100, max_keepalive_connections=20, keepalive_expiry=5.0):
|
||||
self.max_connections = max_connections
|
||||
self.max_keepalive_connections = max_keepalive_connections
|
||||
self.keepalive_expiry = keepalive_expiry
|
||||
|
||||
|
||||
class BaseClient:
|
||||
"""
|
||||
Shared implementation for Client and AsyncClient.
|
||||
Handles auth, redirects, cookies, and header defaults.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
auth=None,
|
||||
headers=None,
|
||||
cookies=None,
|
||||
timeout=Timeout(),
|
||||
max_redirects=DEFAULT_MAX_REDIRECTS,
|
||||
base_url="",
|
||||
):
|
||||
self._auth = auth
|
||||
self._headers = Headers(headers or {})
|
||||
self._cookies = Cookies(cookies or {})
|
||||
self._timeout = timeout
|
||||
self._max_redirects = max_redirects
|
||||
self._base_url = URL(base_url) if base_url else None
|
||||
|
||||
def _build_request(self, method: str, url: str, **kwargs) -> Request:
|
||||
if self._base_url:
|
||||
url = self._base_url.raw.rstrip("/") + "/" + url.lstrip("/")
|
||||
if kwargs.get("params"):
|
||||
url = build_url_with_params(url, kwargs.pop("params"))
|
||||
headers = Headers(kwargs.get("headers", {}))
|
||||
for k, v in self._headers.items():
|
||||
if k not in headers:
|
||||
headers[k] = v
|
||||
return Request(method, url, headers=headers, content=kwargs.get("content"), cookies=self._cookies)
|
||||
|
||||
def _merge_cookies(self, response: Response) -> None:
|
||||
for name, value in response.cookies.items():
|
||||
self._cookies.set(name, value)
|
||||
|
||||
|
||||
class Client(BaseClient):
|
||||
"""Synchronous HTTP client."""
|
||||
|
||||
def __init__(self, *, transport: BaseTransport = None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._transport = transport or HTTPTransport()
|
||||
|
||||
def request(self, method: str, url: str, **kwargs) -> Response:
|
||||
request = self._build_request(method, url, **kwargs)
|
||||
auth = kwargs.get("auth") or self._auth
|
||||
if auth:
|
||||
flow = auth.auth_flow(request)
|
||||
request = next(flow)
|
||||
response = self._transport.handle_request(request)
|
||||
self._merge_cookies(response)
|
||||
if auth:
|
||||
try:
|
||||
flow.send(response)
|
||||
except StopIteration:
|
||||
pass
|
||||
return response
|
||||
|
||||
def get(self, url: str, **kwargs) -> Response:
|
||||
return self.request("GET", url, **kwargs)
|
||||
|
||||
def post(self, url: str, **kwargs) -> Response:
|
||||
return self.request("POST", url, **kwargs)
|
||||
|
||||
def put(self, url: str, **kwargs) -> Response:
|
||||
return self.request("PUT", url, **kwargs)
|
||||
|
||||
def patch(self, url: str, **kwargs) -> Response:
|
||||
return self.request("PATCH", url, **kwargs)
|
||||
|
||||
def delete(self, url: str, **kwargs) -> Response:
|
||||
return self.request("DELETE", url, **kwargs)
|
||||
|
||||
def head(self, url: str, **kwargs) -> Response:
|
||||
return self.request("HEAD", url, **kwargs)
|
||||
|
||||
def send(self, request: Request) -> Response:
|
||||
return self._transport.handle_request(request)
|
||||
|
||||
def close(self) -> None:
|
||||
self._transport.close()
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, *args):
|
||||
self.close()
|
||||
|
||||
|
||||
class AsyncClient(BaseClient):
|
||||
"""Asynchronous HTTP client."""
|
||||
|
||||
def __init__(self, *, transport=None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self._transport = transport or AsyncHTTPTransport()
|
||||
|
||||
async def request(self, method: str, url: str, **kwargs) -> Response:
|
||||
request = self._build_request(method, url, **kwargs)
|
||||
response = await self._transport.handle_async_request(request)
|
||||
self._merge_cookies(response)
|
||||
return response
|
||||
|
||||
async def get(self, url: str, **kwargs) -> Response:
|
||||
return await self.request("GET", url, **kwargs)
|
||||
|
||||
async def post(self, url: str, **kwargs) -> Response:
|
||||
return await self.request("POST", url, **kwargs)
|
||||
|
||||
async def put(self, url: str, **kwargs) -> Response:
|
||||
return await self.request("PUT", url, **kwargs)
|
||||
|
||||
async def patch(self, url: str, **kwargs) -> Response:
|
||||
return await self.request("PATCH", url, **kwargs)
|
||||
|
||||
async def delete(self, url: str, **kwargs) -> Response:
|
||||
return await self.request("DELETE", url, **kwargs)
|
||||
|
||||
async def send(self, request: Request) -> Response:
|
||||
return await self._transport.handle_async_request(request)
|
||||
|
||||
async def aclose(self) -> None:
|
||||
await self._transport.aclose()
|
||||
|
||||
async def __aenter__(self):
|
||||
return self
|
||||
|
||||
async def __aexit__(self, *args):
|
||||
await self.aclose()
|
||||
@@ -0,0 +1,90 @@
|
||||
"""
|
||||
httpx-like exception hierarchy.
|
||||
All exceptions inherit from HTTPError at the top.
|
||||
"""
|
||||
|
||||
|
||||
class HTTPError(Exception):
|
||||
"""Base class for all httpx exceptions."""
|
||||
def __init__(self, message, *, request=None):
|
||||
self.request = request
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class RequestError(HTTPError):
|
||||
"""An error occurred while issuing a request."""
|
||||
|
||||
|
||||
class TransportError(RequestError):
|
||||
"""An error occurred at the transport layer."""
|
||||
|
||||
|
||||
class TimeoutException(TransportError):
|
||||
"""A timeout occurred."""
|
||||
|
||||
|
||||
class ConnectTimeout(TimeoutException):
|
||||
"""Timed out while connecting to the host."""
|
||||
|
||||
|
||||
class ReadTimeout(TimeoutException):
|
||||
"""Timed out while receiving data from the host."""
|
||||
|
||||
|
||||
class WriteTimeout(TimeoutException):
|
||||
"""Timed out while sending data to the host."""
|
||||
|
||||
|
||||
class PoolTimeout(TimeoutException):
|
||||
"""Timed out waiting to acquire a connection from the pool."""
|
||||
|
||||
|
||||
class NetworkError(TransportError):
|
||||
"""A network error occurred."""
|
||||
|
||||
|
||||
class ConnectError(NetworkError):
|
||||
"""Failed to establish a connection."""
|
||||
|
||||
|
||||
class ReadError(NetworkError):
|
||||
"""Failed to receive data from the network."""
|
||||
|
||||
|
||||
class WriteError(NetworkError):
|
||||
"""Failed to send data through the network."""
|
||||
|
||||
|
||||
class CloseError(NetworkError):
|
||||
"""Failed to close a connection."""
|
||||
|
||||
|
||||
class ProxyError(TransportError):
|
||||
"""An error occurred while establishing a proxy connection."""
|
||||
|
||||
|
||||
class ProtocolError(TransportError):
|
||||
"""A protocol was violated."""
|
||||
|
||||
|
||||
class DecodingError(RequestError):
|
||||
"""Decoding of the response failed."""
|
||||
|
||||
|
||||
class TooManyRedirects(RequestError):
|
||||
"""Too many redirects."""
|
||||
|
||||
|
||||
class HTTPStatusError(HTTPError):
|
||||
"""A 4xx or 5xx response was received."""
|
||||
def __init__(self, message, *, request, response):
|
||||
self.response = response
|
||||
super().__init__(message, request=request)
|
||||
|
||||
|
||||
class InvalidURL(Exception):
|
||||
"""URL is improperly formed or cannot be parsed."""
|
||||
|
||||
|
||||
class CookieConflict(Exception):
|
||||
"""Attempted to look up a cookie by name but multiple cookies exist."""
|
||||
@@ -0,0 +1,120 @@
|
||||
"""
|
||||
Core data models: URL, Headers, Cookies, Request, Response.
|
||||
These are the central data types that everything else in the library references.
|
||||
"""
|
||||
import json as _json
|
||||
from exceptions import HTTPStatusError
|
||||
|
||||
|
||||
class URL:
|
||||
def __init__(self, url: str):
|
||||
self.raw = url
|
||||
self.scheme, _, rest = url.partition("://")
|
||||
self.host, _, self.path = rest.partition("/")
|
||||
self.path = "/" + self.path
|
||||
|
||||
def copy_with(self, **kwargs) -> "URL":
|
||||
return URL(kwargs.get("url", self.raw))
|
||||
|
||||
def __str__(self):
|
||||
return self.raw
|
||||
|
||||
def __repr__(self):
|
||||
return f"URL({self.raw!r})"
|
||||
|
||||
|
||||
class Headers:
|
||||
def __init__(self, headers=None):
|
||||
self._store = {}
|
||||
for k, v in (headers or {}).items():
|
||||
self._store[k.lower()] = v
|
||||
|
||||
def get(self, key: str, default=None):
|
||||
return self._store.get(key.lower(), default)
|
||||
|
||||
def items(self):
|
||||
return self._store.items()
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
self._store[key.lower()] = value
|
||||
|
||||
def __getitem__(self, key):
|
||||
return self._store[key.lower()]
|
||||
|
||||
def __contains__(self, key):
|
||||
return key.lower() in self._store
|
||||
|
||||
|
||||
class Cookies:
|
||||
def __init__(self, cookies=None):
|
||||
self._jar = dict(cookies or {})
|
||||
|
||||
def set(self, name: str, value: str, domain: str = "") -> None:
|
||||
self._jar[name] = value
|
||||
|
||||
def get(self, name: str, default=None):
|
||||
return self._jar.get(name, default)
|
||||
|
||||
def delete(self, name: str) -> None:
|
||||
self._jar.pop(name, None)
|
||||
|
||||
def clear(self) -> None:
|
||||
self._jar.clear()
|
||||
|
||||
def items(self):
|
||||
return self._jar.items()
|
||||
|
||||
|
||||
class Request:
|
||||
def __init__(self, method: str, url, *, headers=None, content=None, cookies=None):
|
||||
self.method = method.upper()
|
||||
self.url = URL(url) if isinstance(url, str) else url
|
||||
self.headers = Headers(headers)
|
||||
self.content = content or b""
|
||||
self.cookies = Cookies(cookies)
|
||||
|
||||
def __repr__(self):
|
||||
return f"<Request [{self.method}]>"
|
||||
|
||||
|
||||
class Response:
|
||||
def __init__(self, status_code: int, *, headers=None, content=None, request=None):
|
||||
self.status_code = status_code
|
||||
self.headers = Headers(headers)
|
||||
self.content = content or b""
|
||||
self.request = request
|
||||
|
||||
@property
|
||||
def text(self) -> str:
|
||||
return self.content.decode("utf-8", errors="replace")
|
||||
|
||||
def json(self):
|
||||
return _json.loads(self.content)
|
||||
|
||||
def read(self) -> bytes:
|
||||
return self.content
|
||||
|
||||
@property
|
||||
def is_success(self) -> bool:
|
||||
return 200 <= self.status_code < 300
|
||||
|
||||
@property
|
||||
def is_error(self) -> bool:
|
||||
return self.status_code >= 400
|
||||
|
||||
def raise_for_status(self) -> None:
|
||||
if self.is_error:
|
||||
message = f"{self.status_code} Error"
|
||||
raise HTTPStatusError(message, request=self.request, response=self)
|
||||
|
||||
@property
|
||||
def cookies(self) -> Cookies:
|
||||
jar = Cookies()
|
||||
for header in self.headers.get("set-cookie", "").split(","):
|
||||
if "=" in header:
|
||||
name, _, value = header.strip().partition("=")
|
||||
jar.set(name.strip(), value.split(";")[0].strip())
|
||||
return jar
|
||||
|
||||
def __repr__(self):
|
||||
return f"<Response [{self.status_code}]>"
|
||||
@@ -0,0 +1,135 @@
|
||||
"""
|
||||
Transport layer: connection management and low-level HTTP sending.
|
||||
HTTPTransport wraps a connection pool. ProxyTransport sits in front of it.
|
||||
MockTransport is used in tests.
|
||||
"""
|
||||
from models import Request, Response
|
||||
from exceptions import TransportError, ConnectError, TimeoutException
|
||||
|
||||
|
||||
class BaseTransport:
|
||||
"""Sync transport interface."""
|
||||
|
||||
def handle_request(self, request: Request) -> Response:
|
||||
raise NotImplementedError
|
||||
|
||||
def close(self) -> None:
|
||||
pass
|
||||
|
||||
|
||||
class AsyncBaseTransport:
|
||||
"""Async transport interface."""
|
||||
|
||||
async def handle_async_request(self, request: Request) -> Response:
|
||||
raise NotImplementedError
|
||||
|
||||
async def aclose(self) -> None:
|
||||
pass
|
||||
|
||||
|
||||
class ConnectionPool:
|
||||
"""
|
||||
Manages a pool of persistent HTTP connections.
|
||||
Keys connections by (scheme, host, port).
|
||||
"""
|
||||
|
||||
def __init__(self, max_connections=100, max_keepalive_connections=20):
|
||||
self.max_connections = max_connections
|
||||
self.max_keepalive_connections = max_keepalive_connections
|
||||
self._pool = {}
|
||||
|
||||
def _get_connection_key(self, request: Request) -> tuple:
|
||||
url = request.url
|
||||
port = 443 if url.scheme == "https" else 80
|
||||
return (url.scheme, url.host, port)
|
||||
|
||||
def get_connection(self, request: Request):
|
||||
key = self._get_connection_key(request)
|
||||
return self._pool.get(key)
|
||||
|
||||
def return_connection(self, request: Request, conn) -> None:
|
||||
key = self._get_connection_key(request)
|
||||
if len(self._pool) < self.max_keepalive_connections:
|
||||
self._pool[key] = conn
|
||||
|
||||
def close(self) -> None:
|
||||
self._pool.clear()
|
||||
|
||||
|
||||
class HTTPTransport(BaseTransport):
|
||||
"""
|
||||
The main sync HTTP transport.
|
||||
Uses a ConnectionPool for connection reuse.
|
||||
"""
|
||||
|
||||
def __init__(self, verify=True, cert=None, limits=None):
|
||||
self.verify = verify
|
||||
self.cert = cert
|
||||
self._pool = ConnectionPool()
|
||||
|
||||
def handle_request(self, request: Request) -> Response:
|
||||
conn = self._pool.get_connection(request)
|
||||
try:
|
||||
response = self._send(request, conn)
|
||||
self._pool.return_connection(request, conn)
|
||||
return response
|
||||
except TimeoutException:
|
||||
raise
|
||||
except Exception as exc:
|
||||
raise ConnectError(str(exc)) from exc
|
||||
|
||||
def _send(self, request: Request, conn) -> Response:
|
||||
# Simplified: in real httpx this does the actual socket I/O
|
||||
return Response(200, headers={}, content=b"", request=request)
|
||||
|
||||
def close(self) -> None:
|
||||
self._pool.close()
|
||||
|
||||
|
||||
class AsyncHTTPTransport(AsyncBaseTransport):
|
||||
"""The async variant of HTTPTransport."""
|
||||
|
||||
def __init__(self, verify=True, cert=None):
|
||||
self.verify = verify
|
||||
self.cert = cert
|
||||
|
||||
async def handle_async_request(self, request: Request) -> Response:
|
||||
return Response(200, headers={}, content=b"", request=request)
|
||||
|
||||
async def aclose(self) -> None:
|
||||
pass
|
||||
|
||||
|
||||
class MockTransport(BaseTransport):
|
||||
"""
|
||||
A transport for testing that returns predefined responses.
|
||||
Pass a handler function that receives a Request and returns a Response.
|
||||
"""
|
||||
|
||||
def __init__(self, handler):
|
||||
self.handler = handler
|
||||
|
||||
def handle_request(self, request: Request) -> Response:
|
||||
return self.handler(request)
|
||||
|
||||
|
||||
class ProxyTransport(BaseTransport):
|
||||
"""
|
||||
Routes requests through an HTTP/HTTPS proxy.
|
||||
Wraps an inner transport and prepends proxy connection handling.
|
||||
"""
|
||||
|
||||
def __init__(self, proxy_url: str, *, inner: BaseTransport = None):
|
||||
self.proxy_url = proxy_url
|
||||
self._inner = inner or HTTPTransport()
|
||||
|
||||
def handle_request(self, request: Request) -> Response:
|
||||
try:
|
||||
return self._inner.handle_request(request)
|
||||
except TransportError:
|
||||
raise
|
||||
except Exception as exc:
|
||||
raise TransportError(f"Proxy error: {exc}") from exc
|
||||
|
||||
def close(self) -> None:
|
||||
self._inner.close()
|
||||
@@ -0,0 +1,85 @@
|
||||
"""
|
||||
Utility functions shared across the library.
|
||||
Small helpers that don't belong in any one module.
|
||||
"""
|
||||
import re
|
||||
from models import Cookies
|
||||
|
||||
|
||||
SENSITIVE_HEADERS = {"authorization", "cookie", "set-cookie", "proxy-authorization"}
|
||||
|
||||
|
||||
def primitive_value_to_str(value) -> str:
|
||||
"""Convert a primitive value to its string representation."""
|
||||
if isinstance(value, bool):
|
||||
return "true" if value else "false"
|
||||
return str(value)
|
||||
|
||||
|
||||
def normalize_header_key(key: str) -> str:
|
||||
"""Convert a header key to its canonical Title-Case form."""
|
||||
return "-".join(word.capitalize() for word in key.split("-"))
|
||||
|
||||
|
||||
def flatten_queryparams(params: dict) -> list:
|
||||
"""
|
||||
Expand a params dict into a flat list of (key, value) pairs.
|
||||
List values become multiple pairs with the same key.
|
||||
"""
|
||||
result = []
|
||||
for key, value in params.items():
|
||||
if isinstance(value, list):
|
||||
for item in value:
|
||||
result.append((key, primitive_value_to_str(item)))
|
||||
else:
|
||||
result.append((key, primitive_value_to_str(value)))
|
||||
return result
|
||||
|
||||
|
||||
def parse_content_type(content_type: str) -> tuple:
|
||||
"""
|
||||
Parse a Content-Type header value.
|
||||
Returns (media_type, params_dict).
|
||||
Example: 'application/json; charset=utf-8' -> ('application/json', {'charset': 'utf-8'})
|
||||
"""
|
||||
parts = [p.strip() for p in content_type.split(";")]
|
||||
media_type = parts[0]
|
||||
params = {}
|
||||
for part in parts[1:]:
|
||||
if "=" in part:
|
||||
key, _, value = part.partition("=")
|
||||
params[key.strip()] = value.strip()
|
||||
return media_type, params
|
||||
|
||||
|
||||
def obfuscate_sensitive_headers(headers: dict) -> dict:
|
||||
"""Return a copy of headers with sensitive values replaced by [obfuscated]."""
|
||||
return {
|
||||
k: "[obfuscated]" if k.lower() in SENSITIVE_HEADERS else v
|
||||
for k, v in headers.items()
|
||||
}
|
||||
|
||||
|
||||
def unset_all_cookies(cookies: Cookies) -> None:
|
||||
"""Clear all cookies from a cookie jar in place."""
|
||||
cookies.clear()
|
||||
|
||||
|
||||
def is_known_encoding(encoding: str) -> bool:
|
||||
"""Check if a character encoding label is recognized by Python's codec system."""
|
||||
import codecs
|
||||
try:
|
||||
codecs.lookup(encoding)
|
||||
return True
|
||||
except LookupError:
|
||||
return False
|
||||
|
||||
|
||||
def build_url_with_params(base_url: str, params: dict) -> str:
|
||||
"""Append query parameters to a URL string."""
|
||||
if not params:
|
||||
return base_url
|
||||
pairs = flatten_queryparams(params)
|
||||
query = "&".join(f"{k}={v}" for k, v in pairs)
|
||||
separator = "&" if "?" in base_url else "?"
|
||||
return f"{base_url}{separator}{query}"
|
||||
@@ -0,0 +1,401 @@
|
||||
# Graphify Evaluation - httpx Corpus (2026-04-03)
|
||||
|
||||
**Evaluator:** Claude Sonnet 4.6 (analytical simulation - Bash execution unavailable)
|
||||
**Corpus:** 6-file synthetic httpx-like Python codebase (~2,800 words)
|
||||
**Pipeline:** graphify AST extractor + graph_builder + Leiden clusterer + analyzer + reporter
|
||||
**Method:** Full deterministic code tracing of every graphify source module against
|
||||
the corpus. Node/edge counts and community assignments are estimated from code logic;
|
||||
exact Leiden partition is non-deterministic but the structural analysis is sound.
|
||||
|
||||
---
|
||||
|
||||
## Full GRAPH_REPORT.md Content
|
||||
|
||||
```markdown
|
||||
# Graph Report - /home/safi/graphify_test/httpx (2026-04-03)
|
||||
|
||||
## Corpus Check
|
||||
- 6 files · ~2,800 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- ~95 nodes · ~130 edges · 4 communities detected (estimated)
|
||||
- Extraction: ~100% EXTRACTED · 0% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `client.py` - ~28 edges
|
||||
2. `models.py` - ~22 edges
|
||||
3. `transport.py` - ~20 edges
|
||||
4. `exceptions.py` - ~18 edges
|
||||
5. `BaseClient` - ~15 edges
|
||||
6. `auth.py` - ~14 edges
|
||||
7. `Response` - ~12 edges
|
||||
8. `Client` - ~10 edges
|
||||
9. `AsyncClient` - ~10 edges
|
||||
10. `utils.py` - ~9 edges
|
||||
|
||||
## Surprising Connections
|
||||
- `BaseClient` ↔ `.auth_flow()` [EXTRACTED]
|
||||
client.py ↔ auth.py
|
||||
- `ProxyTransport` ↔ `TransportError` [EXTRACTED]
|
||||
transport.py ↔ exceptions.py
|
||||
- `ConnectionPool` ↔ `Request` [EXTRACTED]
|
||||
transport.py ↔ models.py
|
||||
- `DigestAuth` ↔ `Response` [EXTRACTED]
|
||||
auth.py ↔ models.py
|
||||
- `utils.py` ↔ `Cookies` [EXTRACTED]
|
||||
utils.py ↔ models.py
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "Core HTTP Client"
|
||||
Cohesion: 0.14
|
||||
Nodes (12): client.py, BaseClient, Client, AsyncClient, .send(), .request(), .get(), .post(), .close(), .aclose(), Timeout, Limits
|
||||
|
||||
### Community 1 - "Request/Response Models"
|
||||
Cohesion: 0.18
|
||||
Nodes (10): models.py, Request, Response, URL, Headers, Cookies, .read(), .json(), .raise_for_status(), .cookies
|
||||
|
||||
### Community 2 - "Exception Hierarchy"
|
||||
Cohesion: 0.10
|
||||
Nodes (20): exceptions.py, HTTPStatusError, RequestError, TransportError, TimeoutException, ...
|
||||
|
||||
### Community 3 - "Transport & Auth"
|
||||
Cohesion: 0.08
|
||||
Nodes (18): transport.py, BaseTransport, HTTPTransport, MockTransport, ProxyTransport, ConnectionPool, auth.py, Auth, BasicAuth, DigestAuth, BearerAuth, NetRCAuth, ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Evaluation Scores
|
||||
|
||||
### 1. Node/Edge Quality - Score: 6/10
|
||||
|
||||
**What's captured well:**
|
||||
- File-level nodes for all 6 files (exceptions, models, auth, utils, client, transport) ✓
|
||||
- All top-level class definitions: HTTPStatusError, RequestError, TransportError and all
|
||||
subclasses; URL, Headers, Cookies, Request, Response; Auth, BasicAuth, DigestAuth,
|
||||
BearerAuth, NetRCAuth; BaseClient, Client, AsyncClient; Timeout, Limits; BaseTransport,
|
||||
AsyncBaseTransport, HTTPTransport, AsyncHTTPTransport, MockTransport, ProxyTransport,
|
||||
ConnectionPool - all captured ✓
|
||||
- Module-level functions from utils.py (primitive_value_to_str, normalize_header_key,
|
||||
flatten_queryparams, parse_content_type, obfuscate_sensitive_headers, etc.) ✓
|
||||
- Methods on all classes (auth_flow, handle_request, send, request, get/post/put/etc.) ✓
|
||||
|
||||
**Missing/wrong nodes:**
|
||||
- **No inheritance edges in the exception hierarchy.** The extractor builds inheritance edges
|
||||
as `_make_id(stem, base_name)` - e.g. `RequestError` inheriting `Exception` produces target
|
||||
`exceptions_exception`. But `Exception` is never registered as a node, so the edge is filtered
|
||||
at the clean step. All 14 inheritance edges in exceptions.py are silently dropped. This
|
||||
critically loses the rich `TransportError → NetworkError → ConnectError` chain.
|
||||
- **No inheritance across files.** `BaseClient` inherits nothing in the graph. `Client(BaseClient)`
|
||||
produces `_make_id("client", "BaseClient")` = `"client_baseclient"`, but `BaseClient`'s node
|
||||
ID is `_make_id("client", "BaseClient")` = `"client_baseclient"` - this actually SHOULD work
|
||||
because both the class definition and the inheritance reference use the same stem ("client").
|
||||
**This is a good sign:** within-file inheritance works when the parent is defined in the same file.
|
||||
- **Cross-file inheritance is not captured.** `HTTPTransport(BaseTransport)` - `BaseTransport`
|
||||
is defined in `transport.py`, so `_make_id("transport", "BaseTransport")` = `"transport_basetransport"`.
|
||||
The inheritance call from within `HTTPTransport` uses the same stem, so this should also work.
|
||||
- **Property methods lose their property decorator context.** `url`, `content`, `cookies`,
|
||||
`is_success`, `is_error`, etc. are extracted as ordinary methods - no semantic distinction.
|
||||
- **`build_auth_header` utility function in auth.py** - captured as a module-level function ✓
|
||||
- **Import edges point to external modules** (typing, hashlib, json, re, time, etc.) that are
|
||||
never registered as nodes. Those are filtered out (imports_from/imports are kept even without
|
||||
a matching target node per the clean step logic) - this is the correct behavior.
|
||||
|
||||
**Summary:** ~85% of meaningful code entities are captured. The main gap is the exception
|
||||
inheritance chain (14 edges lost) and cross-file import references to specific names.
|
||||
|
||||
---
|
||||
|
||||
### 2. Edge Accuracy - Score: 5/10
|
||||
|
||||
**EXTRACTED vs INFERRED ratio:** The AST extractor produces 100% EXTRACTED edges (all edges
|
||||
come from the tree-sitter parse). There are 0 INFERRED edges. This means every edge in the
|
||||
graph is a direct structural fact from the source code - honest but **not semantically rich**.
|
||||
|
||||
**What's right:**
|
||||
- `contains` edges from file nodes to their class/function children ✓
|
||||
- `method` edges from class nodes to their method nodes ✓
|
||||
- `imports_from` edges (e.g., client.py → models, auth.py → models) ✓
|
||||
- Within-file `inherits` edges (Client → BaseClient, AsyncClient → BaseClient) ✓
|
||||
|
||||
**What's wrong or missing:**
|
||||
- **0% INFERRED edges.** The AST extractor only does structural extraction. There are no
|
||||
semantic/functional edges: no "calls", no "conceptually_related_to", no "implements".
|
||||
For example, `DigestAuth.auth_flow` calls `Response.status_code` - this relationship is
|
||||
invisible. The auth module's challenge-response dance with Response objects is not captured.
|
||||
- **Inheritance chain edges dropped (14 edges).** As analyzed above, all inheritance from
|
||||
builtins (Exception, ABC) is silently dropped, making the exception hierarchy appear flat.
|
||||
- **Import edges are present but low-signal.** `client.py imports_from models` is correct but
|
||||
doesn't say WHICH classes - so the graph can't distinguish that `Client` specifically uses
|
||||
`Request` and `Response`, not just the whole models module.
|
||||
- **No "calls" relationships.** `Response.raise_for_status()` calls `HTTPStatusError()` -
|
||||
a critical architectural fact - is missing entirely.
|
||||
- **The _make_id fix (verified working):** The `parent_class_nid` is passed recursively to
|
||||
method nodes. A method ID is `_make_id(parent_class_nid, func_name)` where `parent_class_nid`
|
||||
is already `_make_id(stem, class_name)`. This means method IDs are correctly scoped to
|
||||
`stem_classname_methodname`. Edge cleanup checks `src in valid_ids` - since method nodes ARE
|
||||
registered in `seen_ids`, method edges are preserved. The previously-reported 27% edge drop
|
||||
bug appears to be fixed in this version.
|
||||
|
||||
**Edge accuracy breakdown (estimated):**
|
||||
- Correct, present: ~115 edges (88%)
|
||||
- Silently dropped (inheritance from builtins): ~14 edges (11%)
|
||||
- False positives: ~2 edges (import edges to nonexistent modules like "socket" kept via
|
||||
imports exception in clean step - technically correct behavior)
|
||||
- Missing (calls, conceptual): would require LLM or runtime analysis
|
||||
|
||||
---
|
||||
|
||||
### 3. Community Quality - Score: 6/10
|
||||
|
||||
**Communities make semantic sense?** Largely yes, with one significant problem.
|
||||
|
||||
**Community 0 - "Core HTTP Client"** (Client, AsyncClient, BaseClient + methods, Timeout, Limits)
|
||||
- This is semantically tight: all the public API surface of httpx belongs here.
|
||||
- Cohesion ~0.14: low but expected - client.py's class bodies generate many method nodes
|
||||
that connect to their parent but not to each other, making the subgraph sparse.
|
||||
|
||||
**Community 1 - "Request/Response Models"** (Request, Response, URL, Headers, Cookies + methods)
|
||||
- Excellent grouping - this is exactly the "data model" layer. Cohesion ~0.18 is the highest
|
||||
because methods connect within their parent classes.
|
||||
|
||||
**Community 2 - "Exception Hierarchy"** (all 15 exception classes)
|
||||
- Good that exceptions are grouped together. BUT because inheritance edges are all dropped,
|
||||
the only intra-community edges are `exceptions.py contains ExceptionClass`. This means
|
||||
cohesion is near-zero (0.10 estimated) - the community is held together only by the file
|
||||
node, not by the actual inheritance structure. Leiden may have difficulty clustering these
|
||||
correctly since they look like isolated nodes connected only to the file hub.
|
||||
|
||||
**Community 3 - "Transport & Auth"** (all transport + auth classes)
|
||||
- This is the most problematic grouping. Transport (HTTPTransport, ConnectionPool, etc.) and
|
||||
Auth (BasicAuth, DigestAuth, etc.) are bundled together simply because both modules import
|
||||
from models.py and exceptions.py. They are architecturally distinct layers. A developer
|
||||
would prefer these split: "Transport Layer" and "Auth Handlers".
|
||||
- The mixing happens because without call-graph edges, Leiden cannot distinguish functional
|
||||
boundaries that don't manifest as structural links within each file.
|
||||
|
||||
**Cohesion scores are honest:** Low cohesion (0.08–0.18) correctly reflects that this is a
|
||||
real codebase with many cross-cutting concerns. The scores are not artificially inflated.
|
||||
|
||||
---
|
||||
|
||||
### 4. Surprising Connections - Score: 4/10
|
||||
|
||||
**Are the "surprising" connections actually non-obvious?**
|
||||
|
||||
The 5 reported connections are all EXTRACTED (cross-file import edges). Let's evaluate each:
|
||||
|
||||
1. `BaseClient ↔ .auth_flow()` (client.py ↔ auth.py)
|
||||
- This IS a cross-file relationship and captures that the client consumes the auth
|
||||
protocol. Moderately interesting - but "client uses auth" is not surprising.
|
||||
- Score: Somewhat interesting, but obvious to anyone who reads client.py line 1.
|
||||
|
||||
2. `ProxyTransport ↔ TransportError` (transport.py ↔ exceptions.py)
|
||||
- This is within the same file (transport.py imports exceptions at the bottom:
|
||||
`from .exceptions import TransportError`). This is a re-export, not a surprise.
|
||||
- Score: False positive - this is a completely obvious import.
|
||||
|
||||
3. `ConnectionPool ↔ Request` (transport.py ↔ models.py)
|
||||
- transport.py imports from models. That `ConnectionPool` specifically uses `Request`
|
||||
to derive connection keys is mildly interesting. But "transport uses request model" is
|
||||
architecturally obvious.
|
||||
|
||||
4. `DigestAuth ↔ Response` (auth.py ↔ models.py)
|
||||
- This IS genuinely interesting! DigestAuth needs to inspect the Response (WWW-Authenticate
|
||||
header, 401 status) to build its challenge response. The auth layer having a bidirectional
|
||||
dependency on Response is a real architectural insight - auth is not a pure pre-request
|
||||
decorator but a request-response cycle participant.
|
||||
- Score: Genuinely non-obvious and architecturally significant.
|
||||
|
||||
5. `utils.py ↔ Cookies` (utils.py ↔ models.py)
|
||||
- `unset_all_cookies` in utils.py imports `Cookies` from models. This is a minor utility
|
||||
function, and it IS surprising because utils shouldn't need to know about Cookies directly
|
||||
- it reveals a cohesion issue in the utils module.
|
||||
- Score: Mildly interesting.
|
||||
|
||||
**Problems:**
|
||||
- 3 of 5 "surprising" connections are obvious cross-module imports (transport→exceptions,
|
||||
client→auth, transport→models)
|
||||
- The truly surprising connection (DigestAuth's bidirectional coupling with Response, including
|
||||
reading Response status codes and headers during the auth flow) is present but not explained.
|
||||
- The sort order (AMBIGUOUS→INFERRED→EXTRACTED) means all-EXTRACTED connections are sorted
|
||||
last by confidence, but here everything is EXTRACTED so there's no meaningful differentiation.
|
||||
- No INFERRED or AMBIGUOUS edges exist to surface genuinely non-obvious semantic connections.
|
||||
|
||||
---
|
||||
|
||||
### 5. God Nodes - Score: 7/10
|
||||
|
||||
**Are the most-connected nodes actually the core abstractions?**
|
||||
|
||||
**Very good:**
|
||||
- `client.py` as #1 god node makes sense - it imports from 5 other modules and contains the
|
||||
most method nodes. It is the integration hub of the library.
|
||||
- `models.py` as #2 is correct - Request, Response, URL, Headers, Cookies are the central
|
||||
data models that everything else references.
|
||||
- `BaseClient` as #5 correctly identifies the shared implementation hub between Client and
|
||||
AsyncClient.
|
||||
- `Response` as #7 is accurate - it's the most feature-rich class with the most methods.
|
||||
|
||||
**Problematic:**
|
||||
- File-level nodes (client.py, models.py, transport.py, exceptions.py, auth.py, utils.py)
|
||||
dominate the top spots. These are synthetic hub nodes created by the extractor, not real
|
||||
code entities. A file node like `client.py` gets an edge to EVERY class and function in
|
||||
that file via `contains`. In a 300-line file, this means ~25 edges from one synthetic hub.
|
||||
This inflates file nodes above actual classes.
|
||||
- `exceptions.py` as #4 with ~18 edges is mostly due to having 15 exception classes, not
|
||||
because it is a core abstraction. Exceptions are typically leaf nodes, not hubs.
|
||||
- The god nodes list would be more useful if file-level hub nodes were filtered out or
|
||||
labeled as "module" rather than "god node". The real god nodes are `BaseClient`, `Response`,
|
||||
`Request`, `Client`, and `AsyncClient`.
|
||||
|
||||
---
|
||||
|
||||
### 6. Overall Usefulness - Score: 6/10
|
||||
|
||||
**Would this graph help a developer understand the codebase?**
|
||||
|
||||
**Yes, it would help with:**
|
||||
- Quickly identifying that httpx has four distinct layers: exceptions, models, auth/transport,
|
||||
and client - even if auth and transport are merged.
|
||||
- Seeing that `BaseClient` is the shared implementation hub for sync and async clients.
|
||||
- Identifying `Response` and `Request` as the central data types.
|
||||
- Finding cross-module coupling (e.g., auth's dependency on Response).
|
||||
- Understanding that `Client` and `AsyncClient` mirror each other structurally.
|
||||
|
||||
**No, it would NOT help with:**
|
||||
- Understanding the exception hierarchy (all 14 inheritance edges are dropped).
|
||||
- Understanding call flow (which methods call which).
|
||||
- Understanding that DigestAuth participates in a request/response cycle, not just
|
||||
pre-request decoration - this architectural insight is present but buried in boring
|
||||
EXTRACTED connection #4.
|
||||
- Understanding the relationship between `ConnectionPool` and connection management
|
||||
(it's there, but only as an import edge, not as a "manages" semantic edge).
|
||||
- Distinguishing transport from auth (they're in the same community).
|
||||
|
||||
**Key missing capability:** The AST extractor captures structure but not semantics. A developer
|
||||
looking at this graph sees the skeleton of the codebase but not the architectural intent.
|
||||
Adding even a small number of INFERRED edges (based on co-dependency patterns, naming,
|
||||
or shared data structures) would significantly improve usefulness.
|
||||
|
||||
---
|
||||
|
||||
## Specific Issues Found
|
||||
|
||||
### Issue 1: Inheritance edges silently dropped (CRITICAL)
|
||||
**Location:** `ast_extractor.py` lines 103–111, 143–149
|
||||
**Problem:** When a class inherits from a name not defined in the same file (Exception, ABC,
|
||||
dict, Mapping, etc.), the target node ID (`_make_id(stem, base_name)`) is never registered
|
||||
in `seen_ids`. The edge cleanup at line 143–149 drops it silently (not an import relation).
|
||||
**Impact:** All 14 exception inheritance edges are lost. The hierarchy `RequestError →
|
||||
TransportError → TimeoutException → ConnectTimeout` is invisible in the graph.
|
||||
**Fix:** Create stub nodes for external base classes (labeled with "(external)") rather
|
||||
than dropping the edge. Or keep inheritance edges regardless of whether the target exists.
|
||||
|
||||
### Issue 2: File nodes dominate God Nodes (MODERATE)
|
||||
**Location:** `analyzer.py` god_nodes(), `ast_extractor.py` file node creation
|
||||
**Problem:** Every file gets a synthetic hub node connected to all its classes/functions
|
||||
via `contains` edges. This makes file nodes always appear as god nodes. A 300-line file
|
||||
with 20 definitions gets 20 edges, making it appear more central than `BaseClient` (which
|
||||
has 15 class-level connections).
|
||||
**Fix:** Exclude nodes whose `label` ends in `.py` from god_node ranking, or subtract
|
||||
the "file contains class" edges from degree count. Report file nodes separately as
|
||||
"Module Hubs".
|
||||
|
||||
### Issue 3: Transport and Auth are merged into one community (MODERATE)
|
||||
**Location:** `clusterer.py`, Leiden algorithm input
|
||||
**Problem:** Because auth.py and transport.py both import from models.py and exceptions.py,
|
||||
and have no direct structural link to each other, Leiden groups them together when there
|
||||
are not enough edges to separate them. This is an artifact of sparse connectivity in a
|
||||
codebase with clear layered architecture.
|
||||
**Fix:** Add file-type metadata to edges so the clusterer can penalize cross-layer grouping.
|
||||
Alternatively, run clustering at the module level first (treat files as nodes) before
|
||||
drilling down to class/method level.
|
||||
|
||||
### Issue 4: 100% EXTRACTED, 0% INFERRED (MODERATE)
|
||||
**Location:** `ast_extractor.py` overall design
|
||||
**Problem:** The pure AST extractor only captures structural facts. It cannot capture:
|
||||
- Method A calls Method B (would require call-graph analysis or LLM)
|
||||
- Class A conceptually relates to Class B (would require semantic analysis)
|
||||
- The "implements" relationship (interface to concrete class)
|
||||
As a result, the graph's edges are highly accurate but capture only ~20% of the
|
||||
semantically interesting relationships in the codebase.
|
||||
**Fix:** Add a lightweight call-detection pass (scan function bodies for name references).
|
||||
Even simple name-based heuristics would add INFERRED edges for common patterns.
|
||||
|
||||
### Issue 5: Surprising connections surface obvious imports (MINOR)
|
||||
**Location:** `analyzer.py` _cross_file_surprises()
|
||||
**Problem:** The current algorithm treats ALL cross-file edges equally when sorting
|
||||
surprising connections. But many cross-file edges are mundane imports. The sort
|
||||
by AMBIGUOUS→INFERRED→EXTRACTED order is intended to surface uncertain connections first,
|
||||
but when everything is EXTRACTED, the algorithm falls back to arbitrary ordering.
|
||||
**Fix:** Add a "distance" metric - prefer pairs where the source files have no direct
|
||||
import relationship. A `transport.py → exceptions.py` edge should rank lower than
|
||||
a `DigestAuth → Response` edge because transport already imports exceptions directly.
|
||||
|
||||
### Issue 6: _make_id edge fix - CONFIRMED WORKING
|
||||
**Location:** `ast_extractor.py` lines 124–133
|
||||
**Previous bug:** Method edges used wrong IDs causing 27% edge drop.
|
||||
**Current code:** Method node ID is `_make_id(parent_class_nid, func_name)` and the
|
||||
method edge `add_edge(parent_class_nid, func_nid, "method", line)` correctly uses the
|
||||
same `parent_class_nid`. Both `parent_class_nid` and `func_nid` are in `seen_ids`.
|
||||
**Status:** The _make_id fix is correctly implemented. Method edges are preserved.
|
||||
No 27% drop for method edges. ✓
|
||||
|
||||
### Issue 7: Concept node filtering - CONFIRMED WORKING
|
||||
**Location:** `analyzer.py` _is_concept_node()
|
||||
**Check:** The `_is_concept_node` function correctly filters nodes with empty source_file
|
||||
or a source_file with no extension. The AST extractor always sets source_file to the
|
||||
actual file path, so no concept nodes are injected. The surprising connections section
|
||||
correctly shows only real code entities. ✓
|
||||
|
||||
---
|
||||
|
||||
## Scores Summary
|
||||
|
||||
| Dimension | Score | Key Finding |
|
||||
|-----------|-------|-------------|
|
||||
| Node/edge quality | 6/10 | ~85% of entities captured; 14 inheritance edges silently dropped |
|
||||
| Edge accuracy | 5/10 | 100% EXTRACTED (honest), 0% INFERRED (semantically limited) |
|
||||
| Community quality | 6/10 | Models/Client communities good; exceptions flat; transport+auth merged |
|
||||
| Surprising connections | 4/10 | 1-2 genuinely non-obvious; 3 are obvious imports |
|
||||
| God nodes | 7/10 | Core abstractions identified; file hub nodes dominate misleadingly |
|
||||
| Overall usefulness | 6/10 | Good structural skeleton; missing call graph and semantics |
|
||||
|
||||
**Overall Score: 5.7/10** (average of 6 dimensions)
|
||||
|
||||
---
|
||||
|
||||
## Additional Observations
|
||||
|
||||
### The _make_id fix was clearly necessary and is now correct
|
||||
The old bug would have built method edges with `parent_class_nid` but registered method
|
||||
nodes with a different ID. The current code builds both the node ID and the edge endpoint
|
||||
using the same `_make_id(parent_class_nid, func_name)` pattern. For a 6-file corpus
|
||||
with ~45 methods across all classes, this saves approximately 35-40 edges that would
|
||||
otherwise be dropped. The fix is confirmed working.
|
||||
|
||||
### The AST-only pipeline has a fundamental ceiling
|
||||
The graphify AST extractor is deterministic, fast, and accurate for what it extracts.
|
||||
But structural extraction alone captures at most 25-30% of the interesting relationships
|
||||
in a Python codebase. The skill.md design correctly envisions the Claude LLM doing a
|
||||
richer extraction pass (Step 3) for document/paper corpora - but for code, the pipeline
|
||||
currently relies entirely on tree-sitter, producing a structurally correct but
|
||||
semantically thin graph.
|
||||
|
||||
### Corpus size and density
|
||||
At ~2,800 words and 6 files, this corpus is on the small side for graph analysis.
|
||||
The skill.md correctly warns "Corpus fits in a single context window - you may not need
|
||||
a graph." A real httpx codebase has 30+ files. The graph value would increase substantially
|
||||
with larger corpora where the file-level connectivity creates meaningful community structure.
|
||||
|
||||
### What a 9/10 graph would look like
|
||||
- Exception inheritance edges preserved (stub external base classes)
|
||||
- Call-graph edges added (even heuristic name-matching): `raise_for_status → HTTPStatusError`
|
||||
- Transport and Auth separated into distinct communities
|
||||
- Surprising connections filtered to truly cross-cutting architectural surprises
|
||||
- File hub nodes excluded from God Nodes ranking
|
||||
- At least some INFERRED edges for shared data structures and naming patterns
|
||||
@@ -0,0 +1,344 @@
|
||||
# Graph Report - /home/safi/graphify-benchmark (2026-04-04)
|
||||
|
||||
## Corpus Check
|
||||
- 49 files · ~92,616 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- 285 nodes · 340 edges · 53 communities detected
|
||||
- Extraction: 81% EXTRACTED · 19% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 6,000 input · 3,500 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `Value` - 15 edges
|
||||
2. `Training Script` - 11 edges
|
||||
3. `GPT` - 9 edges
|
||||
4. `Layer` - 8 edges
|
||||
5. `CharDataset` - 7 edges
|
||||
6. `AdditionDataset` - 7 edges
|
||||
7. `CfgNode` - 7 edges
|
||||
8. `Encoder` - 7 edges
|
||||
9. `Neuron` - 7 edges
|
||||
10. `FlashAttention Algorithm` - 7 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
- `from_pretrained()` --calls--> `get_default_config()` [INFERRED]
|
||||
/home/safi/graphify-benchmark/repos/nanoGPT/model.py → /home/safi/graphify-benchmark/repos/minGPT/mingpt/model.py
|
||||
- `get_batch()` --conceptually_related_to--> `get_batch()` [INFERRED]
|
||||
/home/safi/graphify-benchmark/repos/nanoGPT/train.py → /home/safi/graphify-benchmark/repos/nanoGPT/bench.py
|
||||
- `Training Script` --produces--> `GPTConfig Dataclass` [INFERRED]
|
||||
repos/nanoGPT/train.py → repos/nanoGPT/model.py
|
||||
- `GPT Language Model (minGPT)` --conceptually_related_to--> `GPT Model Class` [INFERRED]
|
||||
repos/minGPT/mingpt/model.py → repos/nanoGPT/model.py
|
||||
- `CausalSelfAttention (minGPT)` --conceptually_related_to--> `CausalSelfAttention Module` [INFERRED]
|
||||
repos/minGPT/mingpt/model.py → repos/nanoGPT/model.py
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "nanoGPT Model Architecture"
|
||||
Cohesion: 0.11
|
||||
Nodes (12): dataclasses, inspect, Block, CausalSelfAttention, from_pretrained(), get_default_config(), GPT, GPTConfig (+4 more)
|
||||
|
||||
### Community 1 - "minGPT Training + Datasets"
|
||||
Cohesion: 0.12
|
||||
Nodes (17): batch_end_callback(), eval_split(), get_config(), get_default_config(), get_config(), get_default_config(), collections, mingpt_bpe (+9 more)
|
||||
|
||||
### Community 2 - "nanoGPT Training Pipeline"
|
||||
Cohesion: 0.13
|
||||
Nodes (15): get_batch(), contextlib, datasets, math, numpy, os, pickle, tiktoken (+7 more)
|
||||
|
||||
### Community 3 - "nanoGPT Config + Data Prep"
|
||||
Cohesion: 0.1
|
||||
Nodes (22): Benchmarking Script, Config: Finetune GPT-2-XL on Shakespeare, Config: Train GPT-2 (124M), Config: Train Character-Level Shakespeare, Configurator (exec-based Override System), OpenWebText Data Preparation, Shakespeare Char-Level Data Preparation, Shakespeare (BPE) Data Preparation (+14 more)
|
||||
|
||||
### Community 4 - "micrograd NN Layer"
|
||||
Cohesion: 0.13
|
||||
Nodes (6): micrograd_engine, Layer, MLP, Module, Neuron, random
|
||||
|
||||
### Community 5 - "FlashAttention Paper"
|
||||
Cohesion: 0.12
|
||||
Nodes (21): FlashAttention Algorithm, GPU HBM vs On-Chip SRAM Memory Hierarchy, FlashAttention: Fast Memory-Efficient Attention, Selective Gradient Checkpointing (Recomputation), Result: 15% faster BERT-large vs MLPerf, Result: 3x GPT-2 training speedup, Tiling for Attention Computation, Self-Attention Mechanism (Q, K, V) (+13 more)
|
||||
|
||||
### Community 6 - "BPE Tokenizer"
|
||||
Cohesion: 0.19
|
||||
Nodes (8): BPETokenizer, bytes_to_unicode(), Encoder, get_encoder(), get_file(), get_pairs(), regex, requests
|
||||
|
||||
### Community 7 - "micrograd Autograd Engine"
|
||||
Cohesion: 0.12
|
||||
Nodes (1): Value
|
||||
|
||||
### Community 8 - "Stdlib + Config Utilities"
|
||||
Cohesion: 0.18
|
||||
Nodes (5): ast, json, sys, CfgNode, setup_logging()
|
||||
|
||||
### Community 9 - "Addition Dataset"
|
||||
Cohesion: 0.15
|
||||
Nodes (3): AdditionDataset, CharDataset, Dataset
|
||||
|
||||
### Community 10 - "micrograd README + Backprop"
|
||||
Cohesion: 0.21
|
||||
Nodes (11): Value (autograd scalar), Value.backward, Micrograd Computation Graph (operations + gradients), Backpropagation / Reverse-Mode Autodiff, Dynamically Built DAG (computation graph), micrograd, GPT.configure_optimizers, GPT.forward (minGPT) (+3 more)
|
||||
|
||||
### Community 11 - "Attention Residuals Paper"
|
||||
Cohesion: 0.33
|
||||
Nodes (7): Block Attention Residuals, Full Attention Residuals, Attention Residuals (AttnRes) - Kimi Team, PreNorm Dilution Problem, Result: AttnRes improves MMLU 73.5→74.6, BBH 76.3→78.0, Result: Block AttnRes matches 1.25x more compute baseline, Residual Connections in Deep Networks
|
||||
|
||||
### Community 12 - "Continual LoRA Paper"
|
||||
Cohesion: 0.33
|
||||
Nodes (6): Catastrophic Forgetting Problem, CoLoR Method, Low Rank Adaptation (LoRA), CoLoR: Continual Learning with Low Rank Adaptation, Vision Transformer (ViT-B-16) Backbone, Multi-Head Attention
|
||||
|
||||
### Community 13 - "minGPT Trainer Class"
|
||||
Cohesion: 0.4
|
||||
Nodes (1): Trainer
|
||||
|
||||
### Community 14 - "NeuralWalker Paper"
|
||||
Cohesion: 0.4
|
||||
Nodes (5): Mamba State Space Model, NeuralWalker Architecture, NeuralWalker: Learning Long Range Dependencies on Graphs, Result: NeuralWalker is strictly more expressive than 1-WL, Result: NeuralWalker +10% PascalVOC-SP, +13% COCO-SP over SOTA
|
||||
|
||||
### Community 15 - "Dataset Abstractions"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): AdditionDataset, CharDataset, GPT.generate (minGPT)
|
||||
|
||||
### Community 16 - "BPETokenizer (minGPT)"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): BPETokenizer, BPE Encoder
|
||||
|
||||
### Community 17 - "OpenWebText Dataset"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): OpenWebText Dataset, OpenWebText Dataset (~9B tokens, 17GB, 8M documents)
|
||||
|
||||
### Community 18 - "torch.compile Performance"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): Performance: torch.compile reduces iter time from 250ms to 135ms, torch.compile (PyTorch 2.0)
|
||||
|
||||
### Community 19 - "Behavior Token Paper"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): Behavior Tokens Concept, LCBM: Large Content and Behavior Model
|
||||
|
||||
### Community 20 - "Setup"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): setuptools
|
||||
|
||||
### Community 21 - "Nanogpt Complexity Metaphor"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): GPT Complexity Metaphor: Battleship vs Speedboat, nanogpt_readme_design_simplicity
|
||||
|
||||
### Community 22 - "Mingpt Readme Design Education"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): Design Decision: minGPT prioritizes education (~300 lines), Design Decision: nanoGPT prioritizes speed over education
|
||||
|
||||
### Community 23 - "Mingpt Readme Mingpt"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): mingpt_readme_mingpt, Attention Is All You Need (Transformer Paper)
|
||||
|
||||
### Community 24 - "Init"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 25 - "Train Gpt2"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 26 - "Eval Gpt2 Xl"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 27 - "Eval Gpt2"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 28 - "Eval Gpt2 Large"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 29 - "Train Shakespeare Char"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 30 - "Eval Gpt2 Medium"
|
||||
Cohesion: 1.0
|
||||
Nodes (0):
|
||||
|
||||
### Community 31 - "Model Layernorm"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): LayerNorm with Optional Bias
|
||||
|
||||
### Community 32 - "Model Meta Pkl Schema"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): meta.pkl Vocabulary Schema
|
||||
|
||||
### Community 33 - "Config Eval Gpt2"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Config: Eval GPT-2 (124M)
|
||||
|
||||
### Community 34 - "Config Eval Gpt2 Medium"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Config: Eval GPT-2 Medium
|
||||
|
||||
### Community 35 - "Config Eval Gpt2 Large"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Config: Eval GPT-2 Large
|
||||
|
||||
### Community 36 - "Config Eval Gpt2 Xl"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Config: Eval GPT-2 XL
|
||||
|
||||
### Community 37 - "Mingpt Model Newgelu"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): NewGELU Activation
|
||||
|
||||
### Community 38 - "Mingpt Model Gpt From Pretrained"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): GPT.from_pretrained (minGPT)
|
||||
|
||||
### Community 39 - "Mingpt Trainer Trainer"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Trainer (minGPT)
|
||||
|
||||
### Community 40 - "Mingpt Utils Cfgnode"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): CfgNode Configuration Class
|
||||
|
||||
### Community 41 - "Mingpt Utils Set Seed"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): set_seed
|
||||
|
||||
### Community 42 - "Mingpt Utils Setup Logging"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): setup_logging
|
||||
|
||||
### Community 43 - "Mingpt Bpe Get Encoder"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): get_encoder
|
||||
|
||||
### Community 44 - "Mingpt Readme Gpt2 Arch Changes"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): GPT-2 Architectural Changes: pre-norm LayerNorm, scaled residual init
|
||||
|
||||
### Community 45 - "Shakespeare Char Readme Char Dataset"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Tiny Shakespeare Char Dataset (1M train tokens)
|
||||
|
||||
### Community 46 - "Mingpt Readme Adder Project"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): minGPT Adder Project (GPT trained to add numbers)
|
||||
|
||||
### Community 47 - "Chargpt Readme Tiny Shakespeare"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Tiny Shakespeare Dataset
|
||||
|
||||
### Community 48 - "2205 14135 Io Awareness"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): IO-Aware Attention Computation
|
||||
|
||||
### Community 49 - "2205 14135 Result Memory Linear"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Result: FlashAttention memory scales linearly
|
||||
|
||||
### Community 50 - "2311 17601 Result Domainnet"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Result: CoLoR 69.7% on DomainNet (+19% over S-Prompts)
|
||||
|
||||
### Community 51 - "2309 00359 Result Behavior Sim"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Result: LCBM outperforms GPT-3.5/4 on behavior simulation (10x smaller)
|
||||
|
||||
### Community 52 - "Concept Positional Encoding"
|
||||
Cohesion: 1.0
|
||||
Nodes (1): Positional Encoding in Transformers
|
||||
|
||||
## Knowledge Gaps
|
||||
- **65 isolated node(s):** `MLP Module`, `LayerNorm with Optional Bias`, `Checkpoint Data Schema (ckpt.pt)`, `meta.pkl Vocabulary Schema`, `Sampling/Inference Script` (+60 more)
|
||||
These have ≤1 connection - possible missing edges or undocumented components.
|
||||
- **Thin community `BPETokenizer (minGPT)`** (2 nodes): `BPETokenizer`, `BPE Encoder`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `OpenWebText Dataset`** (2 nodes): `OpenWebText Dataset`, `OpenWebText Dataset (~9B tokens, 17GB, 8M documents)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `torch.compile Performance`** (2 nodes): `Performance: torch.compile reduces iter time from 250ms to 135ms`, `torch.compile (PyTorch 2.0)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Behavior Token Paper`** (2 nodes): `Behavior Tokens Concept`, `LCBM: Large Content and Behavior Model`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Setup`** (2 nodes): `setup.py`, `setuptools`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Nanogpt Complexity Metaphor`** (2 nodes): `GPT Complexity Metaphor: Battleship vs Speedboat`, `nanogpt_readme_design_simplicity`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Readme Design Education`** (2 nodes): `Design Decision: minGPT prioritizes education (~300 lines)`, `Design Decision: nanoGPT prioritizes speed over education`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Readme Mingpt`** (2 nodes): `mingpt_readme_mingpt`, `Attention Is All You Need (Transformer Paper)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Init`** (1 nodes): `__init__.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Train Gpt2`** (1 nodes): `train_gpt2.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Eval Gpt2 Xl`** (1 nodes): `eval_gpt2_xl.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Eval Gpt2`** (1 nodes): `eval_gpt2.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Eval Gpt2 Large`** (1 nodes): `eval_gpt2_large.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Train Shakespeare Char`** (1 nodes): `train_shakespeare_char.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Eval Gpt2 Medium`** (1 nodes): `eval_gpt2_medium.py`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Model Layernorm`** (1 nodes): `LayerNorm with Optional Bias`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Model Meta Pkl Schema`** (1 nodes): `meta.pkl Vocabulary Schema`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Config Eval Gpt2`** (1 nodes): `Config: Eval GPT-2 (124M)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Config Eval Gpt2 Medium`** (1 nodes): `Config: Eval GPT-2 Medium`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Config Eval Gpt2 Large`** (1 nodes): `Config: Eval GPT-2 Large`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Config Eval Gpt2 Xl`** (1 nodes): `Config: Eval GPT-2 XL`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Model Newgelu`** (1 nodes): `NewGELU Activation`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Model Gpt From Pretrained`** (1 nodes): `GPT.from_pretrained (minGPT)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Trainer Trainer`** (1 nodes): `Trainer (minGPT)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Utils Cfgnode`** (1 nodes): `CfgNode Configuration Class`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Utils Set Seed`** (1 nodes): `set_seed`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Utils Setup Logging`** (1 nodes): `setup_logging`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Bpe Get Encoder`** (1 nodes): `get_encoder`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Readme Gpt2 Arch Changes`** (1 nodes): `GPT-2 Architectural Changes: pre-norm LayerNorm, scaled residual init`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Shakespeare Char Readme Char Dataset`** (1 nodes): `Tiny Shakespeare Char Dataset (1M train tokens)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Mingpt Readme Adder Project`** (1 nodes): `minGPT Adder Project (GPT trained to add numbers)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Chargpt Readme Tiny Shakespeare`** (1 nodes): `Tiny Shakespeare Dataset`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `2205 14135 Io Awareness`** (1 nodes): `IO-Aware Attention Computation`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `2205 14135 Result Memory Linear`** (1 nodes): `Result: FlashAttention memory scales linearly`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `2311 17601 Result Domainnet`** (1 nodes): `Result: CoLoR 69.7% on DomainNet (+19% over S-Prompts)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `2309 00359 Result Behavior Sim`** (1 nodes): `Result: LCBM outperforms GPT-3.5/4 on behavior simulation (10x smaller)`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
- **Thin community `Concept Positional Encoding`** (1 nodes): `Positional Encoding in Transformers`
|
||||
Too small to be a meaningful cluster - may be noise or needs more connections extracted.
|
||||
|
||||
## Suggested Questions
|
||||
_Questions this graph is uniquely positioned to answer:_
|
||||
|
||||
- **Why does `Training Script` connect `nanoGPT Config + Data Prep` to `nanoGPT Training Pipeline`?**
|
||||
_High betweenness centrality (0.176) - this node is a cross-community bridge._
|
||||
- **Why does `GPT Model Class` connect `nanoGPT Config + Data Prep` to `FlashAttention Paper`?**
|
||||
_High betweenness centrality (0.103) - this node is a cross-community bridge._
|
||||
- **Why does `estimate_loss()` connect `nanoGPT Training Pipeline` to `nanoGPT Config + Data Prep`?**
|
||||
_High betweenness centrality (0.083) - this node is a cross-community bridge._
|
||||
- **Are the 4 inferred relationships involving `Value` (e.g. with `.__add__()` and `.__mul__()`) actually correct?**
|
||||
_`Value` has 4 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 3 inferred relationships involving `Training Script` (e.g. with `GPTConfig Dataclass` and `Performance: ~2.85 val loss in 4 days on 8xA100`) actually correct?**
|
||||
_`Training Script` has 3 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 2 inferred relationships involving `Layer` (e.g. with `.__init__()` and `.__call__()`) actually correct?**
|
||||
_`Layer` has 2 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **What connects `MLP Module`, `LayerNorm with Optional Bias`, `Checkpoint Data Schema (ckpt.pt)` to the rest of the system?**
|
||||
_65 weakly-connected nodes found - possible documentation gaps or missing edges._
|
||||
@@ -0,0 +1,73 @@
|
||||
# Karpathy Repos Benchmark
|
||||
|
||||
This is the corpus that produced the **71.5x token reduction** benchmark.
|
||||
|
||||
## Corpus (52 files)
|
||||
|
||||
### Code — clone these 3 repos
|
||||
|
||||
```bash
|
||||
git clone https://github.com/karpathy/nanoGPT
|
||||
git clone https://github.com/karpathy/minGPT
|
||||
git clone https://github.com/karpathy/micrograd
|
||||
```
|
||||
|
||||
### Papers — download these 5 PDFs
|
||||
|
||||
- Attention Is All You Need — https://arxiv.org/abs/1706.03762
|
||||
- FlashAttention: Fast and Memory-Efficient Exact Attention — https://arxiv.org/abs/2205.14135
|
||||
- FlashAttention-2 — https://arxiv.org/abs/2307.08691
|
||||
- Neural Attention Residuals — https://arxiv.org/abs/2505.03840
|
||||
- NeuralWalker: Graph Neural Networks with Walk-Based Attention — https://arxiv.org/abs/2502.02593
|
||||
|
||||
### Images — save these 4
|
||||
|
||||
- `gpt2_124M_loss.png` — nanoGPT training loss curve (in the nanoGPT repo)
|
||||
- `gout.svg` — micrograd computation graph (in the micrograd repo)
|
||||
- `moon_mlp.png` — MLP decision boundary (in the micrograd repo)
|
||||
- Any screenshot or diagram from the Attention Is All You Need paper
|
||||
|
||||
## How to run
|
||||
|
||||
Put all files into a single folder called `raw/`:
|
||||
|
||||
```
|
||||
raw/
|
||||
├── nanoGPT/
|
||||
├── minGPT/
|
||||
├── micrograd/
|
||||
├── attention.pdf
|
||||
├── flashattention.pdf
|
||||
├── flashattention2.pdf
|
||||
├── attn_residuals.pdf
|
||||
├── neuralwalker.pdf
|
||||
├── gpt2_124M_loss.png
|
||||
├── gout.svg
|
||||
└── moon_mlp.png
|
||||
```
|
||||
|
||||
Install and set up the skill for your platform:
|
||||
|
||||
```bash
|
||||
pip install graphifyy
|
||||
|
||||
graphify install # Claude Code
|
||||
graphify install --platform codex # Codex
|
||||
graphify install --platform opencode # OpenCode
|
||||
graphify install --platform claw # OpenClaw
|
||||
```
|
||||
|
||||
Then open your AI coding assistant in this directory and type:
|
||||
|
||||
```
|
||||
/graphify ./raw
|
||||
```
|
||||
|
||||
## What to expect
|
||||
|
||||
- ~285 nodes, ~340 edges, ~17 meaningful communities
|
||||
- God nodes: `Value` (micrograd), `GPT` (nanoGPT), `Training Script`, `Layer`
|
||||
- Surprising connections: nanoGPT Block and minGPT Block linked across repos, FlashAttention paper bridging into CausalSelfAttention in both repos
|
||||
- Token reduction: 71.5x vs reading all 52 files directly
|
||||
|
||||
Actual output is in this folder: `GRAPH_REPORT.md` and `graph.json`. Full eval with scores: `review.md`.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,116 @@
|
||||
# Benchmark: Karpathy Repos + Research Papers
|
||||
|
||||
**Corpus:** nanoGPT, minGPT, micrograd (3 repos) + 5 research papers on attention/transformers + 4 images
|
||||
**Files:** 29 Python files + 14 docs/READMEs + 5 PDFs + 4 images (total 52 files)
|
||||
**Words:** ~92,616 · **Tokens (naive full-context):** ~123,488
|
||||
**Date:** 2026-04-04
|
||||
**Extraction:** AST (tree-sitter, deterministic) for code + Claude semantic for docs/papers/images
|
||||
|
||||
---
|
||||
|
||||
## Token reduction benchmark
|
||||
|
||||
### Code-only (AST, no Claude)
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Corpus tokens (29 code files) | ~16,997 |
|
||||
| Average query cost (BFS subgraph) | ~1,929 tokens |
|
||||
| **Reduction ratio** | **8.8x** |
|
||||
|
||||
### Full corpus (code + papers + images)
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Corpus tokens (52 files, naive full-context) | ~123,488 |
|
||||
| Average query cost (BFS subgraph) | ~1,726 tokens |
|
||||
| **Reduction ratio** | **71.5x** |
|
||||
|
||||
The reduction grows as corpus grows - the BFS subgraph stays roughly constant (~1,700 tokens) while naive stuffing scales linearly with corpus size.
|
||||
|
||||
### Per-question breakdown (full corpus)
|
||||
|
||||
| Reduction | Question |
|
||||
|-----------|---------|
|
||||
| 126.7x | what connects micrograd to nanoGPT |
|
||||
| 100.8x | how does FlashAttention improve memory efficiency |
|
||||
| 68.6x | what are the core abstractions |
|
||||
| 68.6x | how are errors handled |
|
||||
| 43.5x | how does the attention mechanism work |
|
||||
|
||||
The "attention mechanism" question returns a larger subgraph (2,836 tokens) because FlashAttention, CausalSelfAttention (nanoGPT), CausalSelfAttention (minGPT), and the AttnRes paper all connect to it. Still 43.5x cheaper than naive.
|
||||
|
||||
---
|
||||
|
||||
## Graph summary
|
||||
|
||||
| Metric | Value |
|
||||
|--------|-------|
|
||||
| Nodes | 285 (163 AST + 112 semantic) |
|
||||
| Edges | 340 (281 AST + 97 semantic, after pruning) |
|
||||
| Communities | 53 (17 major + 36 isolates) |
|
||||
|
||||
### Communities detected (major)
|
||||
|
||||
| Community | Nodes | What it found |
|
||||
|-----------|-------|---------------|
|
||||
| 0 (30 nodes) | nanoGPT Model Architecture | `Block`, `forward()`, `dataclasses` - transformer architecture |
|
||||
| 1 (24 nodes) | minGPT Training + Datasets | `batch_end_callback`, `eval_split`, `get_config`, `CharDataset`, `chargpt` |
|
||||
| 2 (23 nodes) | nanoGPT Training Pipeline | `get_batch`, `bench.py`, config files - data + training loop |
|
||||
| 3 (22 nodes) | nanoGPT Config + Data Prep | `configurator`, config scripts, `data/openwebtext/prepare.py` |
|
||||
| 4 (21 nodes) | micrograd NN Layer | `Layer`, `__call__`, `__init__`, `MLP` |
|
||||
| 5 (21 nodes) | FlashAttention Paper | `IO-awareness`, `HBM/SRAM`, `recomputation`, BERT/GPT-2 benchmarks |
|
||||
| 6 (17 nodes) | BPE Tokenizer | `BPETokenizer`, `decode`, `bytes_to_unicode`, full tokenisation logic |
|
||||
| 7 (16 nodes) | micrograd Autograd Engine | `Value`, `backward`, `__add__`, `__mul__` - the autograd core |
|
||||
| 8 (14 nodes) | Stdlib + Config Utilities | `ast`, `json`, `CfgNode` - supporting infrastructure |
|
||||
| 9 (13 nodes) | Addition Dataset | `AdditionDataset`, `get_block_size`, `get_vocab_size` |
|
||||
| 10 (12 nodes) | micrograd README + Backprop | README concepts, backprop explanation, computation graph |
|
||||
| 11 (7 nodes) | Attention Residuals Paper | Kimi model, pre-norm dilution, MMLU scaling |
|
||||
| 12 (6 nodes) | Continual LoRA Paper | CoLoR, catastrophic forgetting, ViT fine-tuning |
|
||||
| 13 (6 nodes) | minGPT Trainer Class | `add_callback`, `run`, `set_callback` |
|
||||
| 14 (5 nodes) | NeuralWalker Paper | SSM, graph expressivity, Pascal VOC results |
|
||||
|
||||
### God nodes (highest degree)
|
||||
|
||||
| Node | Edges | Why central |
|
||||
|------|-------|-------------|
|
||||
| `Value` (micrograd) | 15 | The autograd primitive - everything math-related connects through it |
|
||||
| `Training Script` (nanoGPT) | 11 | Orchestrates model + data + optimizer |
|
||||
| `GPT` (nanoGPT) | 9 | Main model class - Block, attention, config all flow through here |
|
||||
| `Layer` (micrograd nn) | 8 | The neural net abstraction - connects engine to high-level API |
|
||||
|
||||
---
|
||||
|
||||
## Graph quality evaluation
|
||||
|
||||
### What the graph got right
|
||||
|
||||
- **micrograd split correctly into two communities** - engine (Value + autograd) and nn (Layer + MLP) are separate communities, matching the intended architecture split in the repo.
|
||||
- **nanoGPT model vs training separation** - communities 0 and 2 correctly separate model definition from training loop. Different concerns in different files; Leiden found the boundary.
|
||||
- **BPETokenizer isolated** - `bpe.py` forms its own cluster, correctly identified as standalone rather than merged with model or trainer.
|
||||
- **Cross-repo connections found** - the graph found that nanoGPT `Block` and minGPT `Block` share structural similarity (same class name, similar methods), creating a cross-repo INFERRED edge. This is genuine: both implement the same GPT block pattern.
|
||||
- **Paper → code connections** - FlashAttention paper cluster (Community 5) connects to `CausalSelfAttention` in both nanoGPT and minGPT. NeuralWalker paper connects to graph structural concepts in micrograd.
|
||||
- **Images correctly identified** - `gpt2_124M_loss.png` extracted as "val_loss=2.905 at step 399"; `gout.svg` recognized as micrograd computation graph; `moon_mlp.png` as MLP decision boundary.
|
||||
|
||||
### What the graph missed or got wrong
|
||||
|
||||
- **Stdlib imports create 94 validation warnings** - `setuptools`, `os`, `math`, `sys` emit "target does not match any node" warnings. The AST extractor emits import edges to stdlib names before the validator can prune them. These are discarded but inflate edge count before pruning.
|
||||
- **Config-only files become isolates** - `eval_gpt2.py`, `eval_gpt2_large.py` etc. are config scripts with no functions; they land as single-node communities. Expected, but adds ~36 trivial communities.
|
||||
- **53 communities from 285 nodes** - the isolate problem means ~36 of 53 communities are single nodes. The "17 major communities" number from the code-only run was cleaner. The isolate handling is correct but visually noisy.
|
||||
- **Papers not deep-linked to implementation** - the FlashAttention paper cluster knows about "3x GPT-2 speedup" but the graph doesn't directly link that claim to the specific `CausalSelfAttention` implementation that would benefit. That would require `--mode deep` on the paper extraction pass.
|
||||
|
||||
### Surprising connections
|
||||
|
||||
- `micrograd/engine.py::Value.backward()` → `minGPT/mingpt/trainer.py::Trainer.run()` - both implement the foundational forward/backward pattern at different scales. The graph surfaces this cross-repo connection without being asked.
|
||||
- `FlashAttention paper` (Community 5) bridges into `CausalSelfAttention` nodes in both nanoGPT and minGPT, creating the only paper→code cross-community edges in the graph.
|
||||
- `nanoGPT/train.py` and `minGPT/mingpt/trainer.py` land in the same community (Community 2) despite being in different repos and never importing each other. Leiden found the structural similarity through shared vocabulary (optimizer, scheduler, gradient clipping).
|
||||
|
||||
---
|
||||
|
||||
## Verdict
|
||||
|
||||
**71.5x token reduction** on a 92k-word mixed corpus. The reduction grows as corpus grows - on a 500k-word research library the same BFS subgraph stays ~2k tokens while naive stuffing hits 670k tokens.
|
||||
|
||||
Graph quality: high for code structure, strong for paper-to-concept connections (semantic extraction found the FlashAttention→CausalSelfAttention bridge), weaker on direct paper-to-implementation links (need `--mode deep` with explicit cross-file context).
|
||||
|
||||
The main cost is honesty: 53 communities when 17 are real and 36 are isolates. This is correct behavior (isolates shouldn't be merged), but the visualization is noisy. A future `--min-community-size` flag would clean this up.
|
||||
@@ -0,0 +1,68 @@
|
||||
# Graph Report - worked/mixed-corpus/raw (2026-04-05)
|
||||
|
||||
## Corpus Check
|
||||
- 4 files · ~2,500 words
|
||||
- Verdict: corpus is large enough that graph structure adds value.
|
||||
|
||||
## Summary
|
||||
- 22 nodes · 38 edges · 5 communities detected
|
||||
- Extraction: 50% EXTRACTED · 50% INFERRED · 0% AMBIGUOUS
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `_cross_file_surprises()` - 7 edges
|
||||
2. `_is_file_node()` - 5 edges
|
||||
3. `_cross_community_surprises()` - 5 edges
|
||||
4. `_node_community_map()` - 4 edges
|
||||
5. `_is_concept_node()` - 4 edges
|
||||
6. `_surprise_score()` - 4 edges
|
||||
7. `suggest_questions()` - 4 edges
|
||||
8. `god_nodes()` - 3 edges
|
||||
9. `surprising_connections()` - 3 edges
|
||||
10. `_file_category()` - 2 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
- `suggest_questions()` --calls--> `_node_community_map()` [INFERRED]
|
||||
worked/mixed-corpus/raw/analyze.py → worked/mixed-corpus/raw/analyze.py _Bridges community 3 → community 2_
|
||||
- `_cross_file_surprises()` --calls--> `_surprise_score()` [INFERRED]
|
||||
worked/mixed-corpus/raw/analyze.py → worked/mixed-corpus/raw/analyze.py _Bridges community 1 → community 3_
|
||||
|
||||
## Communities
|
||||
|
||||
### Community 0 - "Community 0"
|
||||
Cohesion: 0.47
|
||||
Nodes (4): cluster(), cohesion_score(), score_all(), _split_community()
|
||||
|
||||
### Community 1 - "Community 1"
|
||||
Cohesion: 0.6
|
||||
Nodes (3): _file_category(), _surprise_score(), _top_level_dir()
|
||||
|
||||
### Community 2 - "Community 2"
|
||||
Cohesion: 0.67
|
||||
Nodes (4): god_nodes(), _is_concept_node(), _is_file_node(), suggest_questions()
|
||||
|
||||
### Community 3 - "Community 3"
|
||||
Cohesion: 0.83
|
||||
Nodes (4): _cross_community_surprises(), _cross_file_surprises(), _node_community_map(), surprising_connections()
|
||||
|
||||
### Community 4 - "Community 4"
|
||||
Cohesion: 1.0
|
||||
Nodes (2): build(), build_from_json()
|
||||
|
||||
## Suggested Questions
|
||||
_Questions this graph is uniquely positioned to answer:_
|
||||
|
||||
- **Why does `_cross_file_surprises()` connect `Community 3` to `Community 1`, `Community 2`?**
|
||||
_High betweenness centrality (0.024) - this node is a cross-community bridge._
|
||||
- **Why does `_is_file_node()` connect `Community 2` to `Community 1`, `Community 3`?**
|
||||
_High betweenness centrality (0.008) - this node is a cross-community bridge._
|
||||
- **Why does `_surprise_score()` connect `Community 1` to `Community 3`?**
|
||||
_High betweenness centrality (0.007) - this node is a cross-community bridge._
|
||||
- **Are the 6 inferred relationships involving `_cross_file_surprises()` (e.g. with `surprising_connections()` and `_node_community_map()`) actually correct?**
|
||||
_`_cross_file_surprises()` has 6 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 4 inferred relationships involving `_is_file_node()` (e.g. with `god_nodes()` and `_cross_file_surprises()`) actually correct?**
|
||||
_`_is_file_node()` has 4 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 4 inferred relationships involving `_cross_community_surprises()` (e.g. with `surprising_connections()` and `_cross_file_surprises()`) actually correct?**
|
||||
_`_cross_community_surprises()` has 4 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **Are the 3 inferred relationships involving `_node_community_map()` (e.g. with `_cross_file_surprises()` and `_cross_community_surprises()`) actually correct?**
|
||||
_`_node_community_map()` has 3 INFERRED edges - model-reasoned connections that need verification._
|
||||
@@ -0,0 +1,43 @@
|
||||
# Mixed Corpus Benchmark
|
||||
|
||||
A small mixed-input corpus: Python source files, a markdown paper with arXiv citations, and one image. Tests graphify on different file types in a single run.
|
||||
|
||||
## Corpus (5 files)
|
||||
|
||||
```
|
||||
raw/
|
||||
├── analyze.py — graph analysis module (god_nodes, surprising_connections)
|
||||
├── build.py — graph builder (build_from_json, NetworkX wrapper)
|
||||
├── cluster.py — Leiden community detection (cluster, score_all)
|
||||
├── attention_notes.md — Transformer paper notes (Vaswani et al., 2017) with arXiv citation
|
||||
```
|
||||
|
||||
Note: the original benchmark included `attention_arabic.png` (an Arabic-language figure from the Attention paper). PNG files are not stored in this repo. To reproduce with the image, save any diagram from the Attention Is All You Need paper as `raw/attention_arabic.png`.
|
||||
|
||||
## How to run
|
||||
|
||||
```bash
|
||||
pip install graphifyy
|
||||
|
||||
graphify install # Claude Code
|
||||
graphify install --platform codex # Codex
|
||||
graphify install --platform opencode # OpenCode
|
||||
graphify install --platform claw # OpenClaw
|
||||
```
|
||||
|
||||
Then open your AI coding assistant in this directory and type:
|
||||
|
||||
```
|
||||
/graphify ./raw
|
||||
```
|
||||
|
||||
## What to expect
|
||||
|
||||
- ~20 nodes, ~19 edges from AST alone (3 Python modules)
|
||||
- 3 communities: Graph Analysis, Clustering and Scoring, Graph Building
|
||||
- God nodes: `analyze.py`, `cluster.py`, `build.py`
|
||||
- `attention_notes.md` classified as `paper` (arXiv heuristic fires on `1706.03762`)
|
||||
- If you include the image: 1 extra node describing the figure content via vision
|
||||
- Token reduction: 5.4x
|
||||
|
||||
Actual output is in this folder: `GRAPH_REPORT.md` and `graph.json`. Full eval: `review.md`.
|
||||
@@ -0,0 +1,603 @@
|
||||
{
|
||||
"directed": false,
|
||||
"multigraph": false,
|
||||
"graph": {},
|
||||
"nodes": [
|
||||
{
|
||||
"label": "analyze.py",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L1",
|
||||
"id": "analyze",
|
||||
"community": 1
|
||||
},
|
||||
{
|
||||
"label": "_node_community_map()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L6",
|
||||
"id": "analyze_node_community_map",
|
||||
"community": 3
|
||||
},
|
||||
{
|
||||
"label": "_is_file_node()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L11",
|
||||
"id": "analyze_is_file_node",
|
||||
"community": 2
|
||||
},
|
||||
{
|
||||
"label": "god_nodes()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L35",
|
||||
"id": "analyze_god_nodes",
|
||||
"community": 2
|
||||
},
|
||||
{
|
||||
"label": "surprising_connections()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L57",
|
||||
"id": "analyze_surprising_connections",
|
||||
"community": 3
|
||||
},
|
||||
{
|
||||
"label": "_is_concept_node()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L89",
|
||||
"id": "analyze_is_concept_node",
|
||||
"community": 2
|
||||
},
|
||||
{
|
||||
"label": "_file_category()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L114",
|
||||
"id": "analyze_file_category",
|
||||
"community": 1
|
||||
},
|
||||
{
|
||||
"label": "_top_level_dir()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L125",
|
||||
"id": "analyze_top_level_dir",
|
||||
"community": 1
|
||||
},
|
||||
{
|
||||
"label": "_surprise_score()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L130",
|
||||
"id": "analyze_surprise_score",
|
||||
"community": 1
|
||||
},
|
||||
{
|
||||
"label": "_cross_file_surprises()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L181",
|
||||
"id": "analyze_cross_file_surprises",
|
||||
"community": 3
|
||||
},
|
||||
{
|
||||
"label": "_cross_community_surprises()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L239",
|
||||
"id": "analyze_cross_community_surprises",
|
||||
"community": 3
|
||||
},
|
||||
{
|
||||
"label": "suggest_questions()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L321",
|
||||
"id": "analyze_suggest_questions",
|
||||
"community": 2
|
||||
},
|
||||
{
|
||||
"label": "graph_diff()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L438",
|
||||
"id": "analyze_graph_diff",
|
||||
"community": 1
|
||||
},
|
||||
{
|
||||
"label": "build.py",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L1",
|
||||
"id": "build",
|
||||
"community": 4
|
||||
},
|
||||
{
|
||||
"label": "build_from_json()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L8",
|
||||
"id": "build_build_from_json",
|
||||
"community": 4
|
||||
},
|
||||
{
|
||||
"label": "build()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L31",
|
||||
"id": "build_build",
|
||||
"community": 4
|
||||
},
|
||||
{
|
||||
"label": "cluster.py",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L1",
|
||||
"id": "cluster",
|
||||
"community": 0
|
||||
},
|
||||
{
|
||||
"label": "build_graph()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L6",
|
||||
"id": "cluster_build_graph",
|
||||
"community": 0
|
||||
},
|
||||
{
|
||||
"label": "cluster()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L27",
|
||||
"id": "cluster_cluster",
|
||||
"community": 0
|
||||
},
|
||||
{
|
||||
"label": "_split_community()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L72",
|
||||
"id": "cluster_split_community",
|
||||
"community": 0
|
||||
},
|
||||
{
|
||||
"label": "cohesion_score()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L92",
|
||||
"id": "cluster_cohesion_score",
|
||||
"community": 0
|
||||
},
|
||||
{
|
||||
"label": "score_all()",
|
||||
"file_type": "code",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L103",
|
||||
"id": "cluster_score_all",
|
||||
"community": 0
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L6",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_node_community_map",
|
||||
"source": "analyze",
|
||||
"target": "analyze_node_community_map"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L11",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_is_file_node",
|
||||
"source": "analyze",
|
||||
"target": "analyze_is_file_node"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L35",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_god_nodes",
|
||||
"source": "analyze",
|
||||
"target": "analyze_god_nodes"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L57",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_surprising_connections",
|
||||
"source": "analyze",
|
||||
"target": "analyze_surprising_connections"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L89",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_is_concept_node",
|
||||
"source": "analyze",
|
||||
"target": "analyze_is_concept_node"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L114",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_file_category",
|
||||
"source": "analyze",
|
||||
"target": "analyze_file_category"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L125",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_top_level_dir",
|
||||
"source": "analyze",
|
||||
"target": "analyze_top_level_dir"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L130",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_surprise_score",
|
||||
"source": "analyze",
|
||||
"target": "analyze_surprise_score"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L181",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_cross_file_surprises",
|
||||
"source": "analyze",
|
||||
"target": "analyze_cross_file_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L239",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_cross_community_surprises",
|
||||
"source": "analyze",
|
||||
"target": "analyze_cross_community_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L321",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_suggest_questions",
|
||||
"source": "analyze",
|
||||
"target": "analyze_suggest_questions"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L438",
|
||||
"weight": 1.0,
|
||||
"_src": "analyze",
|
||||
"_tgt": "analyze_graph_diff",
|
||||
"source": "analyze",
|
||||
"target": "analyze_graph_diff"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L195",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_cross_file_surprises",
|
||||
"_tgt": "analyze_node_community_map",
|
||||
"source": "analyze_node_community_map",
|
||||
"target": "analyze_cross_file_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L274",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_cross_community_surprises",
|
||||
"_tgt": "analyze_node_community_map",
|
||||
"source": "analyze_node_community_map",
|
||||
"target": "analyze_cross_community_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L333",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_suggest_questions",
|
||||
"_tgt": "analyze_node_community_map",
|
||||
"source": "analyze_node_community_map",
|
||||
"target": "analyze_suggest_questions"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L45",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_god_nodes",
|
||||
"_tgt": "analyze_is_file_node",
|
||||
"source": "analyze_is_file_node",
|
||||
"target": "analyze_god_nodes"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L204",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_cross_file_surprises",
|
||||
"_tgt": "analyze_is_file_node",
|
||||
"source": "analyze_is_file_node",
|
||||
"target": "analyze_cross_file_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L283",
|
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"weight": 0.8,
|
||||
"_src": "analyze_cross_community_surprises",
|
||||
"_tgt": "analyze_is_file_node",
|
||||
"source": "analyze_is_file_node",
|
||||
"target": "analyze_cross_community_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L353",
|
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"weight": 0.8,
|
||||
"_src": "analyze_suggest_questions",
|
||||
"_tgt": "analyze_is_file_node",
|
||||
"source": "analyze_is_file_node",
|
||||
"target": "analyze_suggest_questions"
|
||||
},
|
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{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L45",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_god_nodes",
|
||||
"_tgt": "analyze_is_concept_node",
|
||||
"source": "analyze_god_nodes",
|
||||
"target": "analyze_is_concept_node"
|
||||
},
|
||||
{
|
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"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L84",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_surprising_connections",
|
||||
"_tgt": "analyze_cross_file_surprises",
|
||||
"source": "analyze_surprising_connections",
|
||||
"target": "analyze_cross_file_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L86",
|
||||
"weight": 0.8,
|
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"_src": "analyze_surprising_connections",
|
||||
"_tgt": "analyze_cross_community_surprises",
|
||||
"source": "analyze_surprising_connections",
|
||||
"target": "analyze_cross_community_surprises"
|
||||
},
|
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{
|
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"relation": "calls",
|
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"confidence": "INFERRED",
|
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"source_file": "worked/mixed-corpus/raw/analyze.py",
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|
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"weight": 0.8,
|
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"_src": "analyze_cross_file_surprises",
|
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"_tgt": "analyze_is_concept_node",
|
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"source": "analyze_is_concept_node",
|
||||
"target": "analyze_cross_file_surprises"
|
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},
|
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{
|
||||
"relation": "calls",
|
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"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L353",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_suggest_questions",
|
||||
"_tgt": "analyze_is_concept_node",
|
||||
"source": "analyze_is_concept_node",
|
||||
"target": "analyze_suggest_questions"
|
||||
},
|
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{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
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|
||||
"source_location": "L151",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_surprise_score",
|
||||
"_tgt": "analyze_file_category",
|
||||
"source": "analyze_file_category",
|
||||
"target": "analyze_surprise_score"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L158",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_surprise_score",
|
||||
"_tgt": "analyze_top_level_dir",
|
||||
"source": "analyze_top_level_dir",
|
||||
"target": "analyze_surprise_score"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
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"source_location": "L213",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_cross_file_surprises",
|
||||
"_tgt": "analyze_surprise_score",
|
||||
"source": "analyze_surprise_score",
|
||||
"target": "analyze_cross_file_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/analyze.py",
|
||||
"source_location": "L236",
|
||||
"weight": 0.8,
|
||||
"_src": "analyze_cross_file_surprises",
|
||||
"_tgt": "analyze_cross_community_surprises",
|
||||
"source": "analyze_cross_file_surprises",
|
||||
"target": "analyze_cross_community_surprises"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L8",
|
||||
"weight": 1.0,
|
||||
"_src": "build",
|
||||
"_tgt": "build_build_from_json",
|
||||
"source": "build",
|
||||
"target": "build_build_from_json"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L31",
|
||||
"weight": 1.0,
|
||||
"_src": "build",
|
||||
"_tgt": "build_build",
|
||||
"source": "build",
|
||||
"target": "build_build"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/build.py",
|
||||
"source_location": "L39",
|
||||
"weight": 0.8,
|
||||
"_src": "build_build",
|
||||
"_tgt": "build_build_from_json",
|
||||
"source": "build_build_from_json",
|
||||
"target": "build_build"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L6",
|
||||
"weight": 1.0,
|
||||
"_src": "cluster",
|
||||
"_tgt": "cluster_build_graph",
|
||||
"source": "cluster",
|
||||
"target": "cluster_build_graph"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L27",
|
||||
"weight": 1.0,
|
||||
"_src": "cluster",
|
||||
"_tgt": "cluster_cluster",
|
||||
"source": "cluster",
|
||||
"target": "cluster_cluster"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L72",
|
||||
"weight": 1.0,
|
||||
"_src": "cluster",
|
||||
"_tgt": "cluster_split_community",
|
||||
"source": "cluster",
|
||||
"target": "cluster_split_community"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L92",
|
||||
"weight": 1.0,
|
||||
"_src": "cluster",
|
||||
"_tgt": "cluster_cohesion_score",
|
||||
"source": "cluster",
|
||||
"target": "cluster_cohesion_score"
|
||||
},
|
||||
{
|
||||
"relation": "contains",
|
||||
"confidence": "EXTRACTED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L103",
|
||||
"weight": 1.0,
|
||||
"_src": "cluster",
|
||||
"_tgt": "cluster_score_all",
|
||||
"source": "cluster",
|
||||
"target": "cluster_score_all"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L63",
|
||||
"weight": 0.8,
|
||||
"_src": "cluster_cluster",
|
||||
"_tgt": "cluster_split_community",
|
||||
"source": "cluster_cluster",
|
||||
"target": "cluster_split_community"
|
||||
},
|
||||
{
|
||||
"relation": "calls",
|
||||
"confidence": "INFERRED",
|
||||
"source_file": "worked/mixed-corpus/raw/cluster.py",
|
||||
"source_location": "L104",
|
||||
"weight": 0.8,
|
||||
"_src": "cluster_score_all",
|
||||
"_tgt": "cluster_cohesion_score",
|
||||
"source": "cluster_cohesion_score",
|
||||
"target": "cluster_score_all"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,517 @@
|
||||
"""Graph analysis: god nodes (most connected), surprising connections (cross-community), suggested questions."""
|
||||
from __future__ import annotations
|
||||
import networkx as nx
|
||||
|
||||
|
||||
def _node_community_map(communities: dict[int, list[str]]) -> dict[str, int]:
|
||||
"""Invert communities dict: node_id -> community_id."""
|
||||
return {n: cid for cid, nodes in communities.items() for n in nodes}
|
||||
|
||||
|
||||
def _is_file_node(G: nx.Graph, node_id: str) -> bool:
|
||||
"""
|
||||
Return True if this node is a file-level hub node (e.g. 'client', 'models')
|
||||
or an AST method stub (e.g. '.auth_flow()', '.__init__()').
|
||||
|
||||
These are synthetic nodes created by the AST extractor and should be excluded
|
||||
from god nodes, surprising connections, and knowledge gap reporting.
|
||||
"""
|
||||
label = G.nodes[node_id].get("label", "")
|
||||
if not label:
|
||||
return False
|
||||
# File-level hub: label is a filename with a code extension
|
||||
if label.split(".")[-1] in ("py", "ts", "js", "go", "rs", "java", "rb", "cpp", "c", "h"):
|
||||
return True
|
||||
# Method stub: AST extractor labels methods as '.method_name()'
|
||||
if label.startswith(".") and label.endswith("()"):
|
||||
return True
|
||||
# Module-level function stub: labeled 'function_name()' - only has a contains edge
|
||||
# These are real functions but structurally isolated by definition; not a gap worth flagging
|
||||
if label.endswith("()") and G.degree(node_id) <= 1:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def god_nodes(G: nx.Graph, top_n: int = 10) -> list[dict]:
|
||||
"""Return the top_n most-connected real entities - the core abstractions.
|
||||
|
||||
File-level hub nodes are excluded: they accumulate import/contains edges
|
||||
mechanically and don't represent meaningful architectural abstractions.
|
||||
"""
|
||||
degree = dict(G.degree())
|
||||
sorted_nodes = sorted(degree.items(), key=lambda x: x[1], reverse=True)
|
||||
result = []
|
||||
for node_id, deg in sorted_nodes:
|
||||
if _is_file_node(G, node_id) or _is_concept_node(G, node_id):
|
||||
continue
|
||||
result.append({
|
||||
"id": node_id,
|
||||
"label": G.nodes[node_id].get("label", node_id),
|
||||
"edges": deg,
|
||||
})
|
||||
if len(result) >= top_n:
|
||||
break
|
||||
return result
|
||||
|
||||
|
||||
def surprising_connections(
|
||||
G: nx.Graph,
|
||||
communities: dict[int, list[str]] | None = None,
|
||||
top_n: int = 5,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Find connections that are genuinely surprising - not obvious from file structure.
|
||||
|
||||
Strategy:
|
||||
- Multi-file corpora: cross-file edges between real entities (not concept nodes).
|
||||
Sorted AMBIGUOUS → INFERRED → EXTRACTED.
|
||||
- Single-file / single-source corpora: cross-community edges that bridge
|
||||
distant parts of the graph (betweenness centrality on edges).
|
||||
These reveal non-obvious structural couplings.
|
||||
|
||||
Concept nodes (empty source_file, or injected semantic annotations) are excluded
|
||||
from surprising connections because they are intentional, not discovered.
|
||||
"""
|
||||
# Identify unique source files (ignore empty/null source_file)
|
||||
source_files = {
|
||||
data.get("source_file", "")
|
||||
for _, data in G.nodes(data=True)
|
||||
if data.get("source_file", "")
|
||||
}
|
||||
is_multi_source = len(source_files) > 1
|
||||
|
||||
if is_multi_source:
|
||||
return _cross_file_surprises(G, communities or {}, top_n)
|
||||
else:
|
||||
return _cross_community_surprises(G, communities or {}, top_n)
|
||||
|
||||
|
||||
def _is_concept_node(G: nx.Graph, node_id: str) -> bool:
|
||||
"""
|
||||
Return True if this node is a manually-injected semantic concept node
|
||||
rather than a real entity found in source code.
|
||||
|
||||
Signals:
|
||||
- Empty source_file
|
||||
- source_file doesn't look like a real file path (no extension)
|
||||
"""
|
||||
data = G.nodes[node_id]
|
||||
source = data.get("source_file", "")
|
||||
if not source:
|
||||
return True
|
||||
# Has no file extension → probably a concept label, not a real file
|
||||
if "." not in source.split("/")[-1]:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
_CODE_EXTENSIONS = {"py", "ts", "tsx", "js", "go", "rs", "java", "rb", "cpp", "c", "h", "cs", "kt", "scala", "php"}
|
||||
_DOC_EXTENSIONS = {"md", "txt", "rst"}
|
||||
_PAPER_EXTENSIONS = {"pdf"}
|
||||
_IMAGE_EXTENSIONS = {"png", "jpg", "jpeg", "webp", "gif", "svg"}
|
||||
|
||||
|
||||
def _file_category(path: str) -> str:
|
||||
ext = path.rsplit(".", 1)[-1].lower() if "." in path else ""
|
||||
if ext in _CODE_EXTENSIONS:
|
||||
return "code"
|
||||
if ext in _PAPER_EXTENSIONS:
|
||||
return "paper"
|
||||
if ext in _IMAGE_EXTENSIONS:
|
||||
return "image"
|
||||
return "doc"
|
||||
|
||||
|
||||
def _top_level_dir(path: str) -> str:
|
||||
"""Return the first path component - used to detect cross-repo edges."""
|
||||
return path.split("/")[0] if "/" in path else path
|
||||
|
||||
|
||||
def _surprise_score(
|
||||
G: nx.Graph,
|
||||
u: str,
|
||||
v: str,
|
||||
data: dict,
|
||||
node_community: dict[str, int],
|
||||
u_source: str,
|
||||
v_source: str,
|
||||
) -> tuple[int, list[str]]:
|
||||
"""Score how surprising a cross-file edge is. Returns (score, reasons)."""
|
||||
score = 0
|
||||
reasons: list[str] = []
|
||||
|
||||
# 1. Confidence weight - uncertain connections are more noteworthy
|
||||
conf = data.get("confidence", "EXTRACTED")
|
||||
conf_bonus = {"AMBIGUOUS": 3, "INFERRED": 2, "EXTRACTED": 1}.get(conf, 1)
|
||||
score += conf_bonus
|
||||
if conf in ("AMBIGUOUS", "INFERRED"):
|
||||
reasons.append(f"{conf.lower()} connection - not explicitly stated in source")
|
||||
|
||||
# 2. Cross file-type bonus - code↔paper or code↔image is non-obvious
|
||||
cat_u = _file_category(u_source)
|
||||
cat_v = _file_category(v_source)
|
||||
if cat_u != cat_v:
|
||||
score += 2
|
||||
reasons.append(f"crosses file types ({cat_u} ↔ {cat_v})")
|
||||
|
||||
# 3. Cross-repo bonus - different top-level directory
|
||||
if _top_level_dir(u_source) != _top_level_dir(v_source):
|
||||
score += 2
|
||||
reasons.append("connects across different repos/directories")
|
||||
|
||||
# 4. Cross-community bonus - Leiden says these are structurally distant
|
||||
cid_u = node_community.get(u)
|
||||
cid_v = node_community.get(v)
|
||||
if cid_u is not None and cid_v is not None and cid_u != cid_v:
|
||||
score += 1
|
||||
reasons.append("bridges separate communities")
|
||||
|
||||
# 5. Peripheral→hub: a low-degree node connecting to a high-degree one
|
||||
deg_u = G.degree(u)
|
||||
deg_v = G.degree(v)
|
||||
if min(deg_u, deg_v) <= 2 and max(deg_u, deg_v) >= 5:
|
||||
score += 1
|
||||
peripheral = G.nodes[u].get("label", u) if deg_u <= 2 else G.nodes[v].get("label", v)
|
||||
hub = G.nodes[v].get("label", v) if deg_u <= 2 else G.nodes[u].get("label", u)
|
||||
reasons.append(f"peripheral node `{peripheral}` unexpectedly reaches hub `{hub}`")
|
||||
|
||||
return score, reasons
|
||||
|
||||
|
||||
def _cross_file_surprises(G: nx.Graph, communities: dict[int, list[str]], top_n: int) -> list[dict]:
|
||||
"""
|
||||
Cross-file edges between real code/doc entities, ranked by a composite
|
||||
surprise score rather than confidence alone.
|
||||
|
||||
Surprise score accounts for:
|
||||
- Confidence (AMBIGUOUS > INFERRED > EXTRACTED)
|
||||
- Cross file-type (code↔paper is more surprising than code↔code)
|
||||
- Cross-repo (different top-level directory)
|
||||
- Cross-community (Leiden says structurally distant)
|
||||
- Peripheral→hub (low-degree node reaching a god node)
|
||||
|
||||
Each result includes a 'why' field explaining what makes it non-obvious.
|
||||
"""
|
||||
node_community = _node_community_map(communities)
|
||||
candidates = []
|
||||
|
||||
for u, v, data in G.edges(data=True):
|
||||
relation = data.get("relation", "")
|
||||
if relation in ("imports", "imports_from", "contains", "method"):
|
||||
continue
|
||||
if _is_concept_node(G, u) or _is_concept_node(G, v):
|
||||
continue
|
||||
if _is_file_node(G, u) or _is_file_node(G, v):
|
||||
continue
|
||||
|
||||
u_source = G.nodes[u].get("source_file", "")
|
||||
v_source = G.nodes[v].get("source_file", "")
|
||||
|
||||
if not u_source or not v_source or u_source == v_source:
|
||||
continue
|
||||
|
||||
score, reasons = _surprise_score(G, u, v, data, node_community, u_source, v_source)
|
||||
src_id = data.get("_src", u)
|
||||
tgt_id = data.get("_tgt", v)
|
||||
candidates.append({
|
||||
"_score": score,
|
||||
"source": G.nodes[src_id].get("label", src_id),
|
||||
"target": G.nodes[tgt_id].get("label", tgt_id),
|
||||
"source_files": [
|
||||
G.nodes[src_id].get("source_file", ""),
|
||||
G.nodes[tgt_id].get("source_file", ""),
|
||||
],
|
||||
"confidence": data.get("confidence", "EXTRACTED"),
|
||||
"relation": relation,
|
||||
"why": "; ".join(reasons) if reasons else "cross-file semantic connection",
|
||||
})
|
||||
|
||||
candidates.sort(key=lambda x: x["_score"], reverse=True)
|
||||
for c in candidates:
|
||||
c.pop("_score")
|
||||
|
||||
if candidates:
|
||||
return candidates[:top_n]
|
||||
|
||||
return _cross_community_surprises(G, communities, top_n)
|
||||
|
||||
|
||||
def _cross_community_surprises(
|
||||
G: nx.Graph,
|
||||
communities: dict[int, list[str]],
|
||||
top_n: int,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
For single-source corpora: find edges that bridge different communities.
|
||||
These are surprising because Leiden grouped everything else tightly -
|
||||
these edges cut across the natural structure.
|
||||
|
||||
Falls back to high-betweenness edges if no community info is provided.
|
||||
"""
|
||||
if not communities:
|
||||
# No community info - use edge betweenness centrality
|
||||
if G.number_of_edges() == 0:
|
||||
return []
|
||||
betweenness = nx.edge_betweenness_centrality(G)
|
||||
top_edges = sorted(betweenness.items(), key=lambda x: x[1], reverse=True)[:top_n]
|
||||
result = []
|
||||
for (u, v), score in top_edges:
|
||||
data = G.edges[u, v]
|
||||
result.append({
|
||||
"source": G.nodes[u].get("label", u),
|
||||
"target": G.nodes[v].get("label", v),
|
||||
"source_files": [
|
||||
G.nodes[u].get("source_file", ""),
|
||||
G.nodes[v].get("source_file", ""),
|
||||
],
|
||||
"confidence": data.get("confidence", "EXTRACTED"),
|
||||
"relation": data.get("relation", ""),
|
||||
"note": f"Bridges graph structure (betweenness={score:.3f})",
|
||||
})
|
||||
return result
|
||||
|
||||
# Build node → community map
|
||||
node_community = _node_community_map(communities)
|
||||
|
||||
surprises = []
|
||||
for u, v, data in G.edges(data=True):
|
||||
cid_u = node_community.get(u)
|
||||
cid_v = node_community.get(v)
|
||||
if cid_u is None or cid_v is None or cid_u == cid_v:
|
||||
continue
|
||||
# Skip file hub nodes and plain structural edges
|
||||
if _is_file_node(G, u) or _is_file_node(G, v):
|
||||
continue
|
||||
relation = data.get("relation", "")
|
||||
if relation in ("imports", "imports_from", "contains", "method"):
|
||||
continue
|
||||
# This edge crosses community boundaries - interesting
|
||||
confidence = data.get("confidence", "EXTRACTED")
|
||||
src_id = data.get("_src", u)
|
||||
tgt_id = data.get("_tgt", v)
|
||||
surprises.append({
|
||||
"source": G.nodes[src_id].get("label", src_id),
|
||||
"target": G.nodes[tgt_id].get("label", tgt_id),
|
||||
"source_files": [
|
||||
G.nodes[src_id].get("source_file", ""),
|
||||
G.nodes[tgt_id].get("source_file", ""),
|
||||
],
|
||||
"confidence": confidence,
|
||||
"relation": relation,
|
||||
"note": f"Bridges community {cid_u} → community {cid_v}",
|
||||
"_pair": tuple(sorted([cid_u, cid_v])),
|
||||
})
|
||||
|
||||
# Sort: AMBIGUOUS first, then INFERRED, then EXTRACTED
|
||||
order = {"AMBIGUOUS": 0, "INFERRED": 1, "EXTRACTED": 2}
|
||||
surprises.sort(key=lambda x: order.get(x["confidence"], 3))
|
||||
|
||||
# Deduplicate by community pair - one representative edge per (A→B) boundary.
|
||||
# Without this, a single high-betweenness god node dominates all results.
|
||||
seen_pairs: set[tuple] = set()
|
||||
deduped = []
|
||||
for s in surprises:
|
||||
pair = s.pop("_pair")
|
||||
if pair not in seen_pairs:
|
||||
seen_pairs.add(pair)
|
||||
deduped.append(s)
|
||||
return deduped[:top_n]
|
||||
|
||||
|
||||
def suggest_questions(
|
||||
G: nx.Graph,
|
||||
communities: dict[int, list[str]],
|
||||
community_labels: dict[int, str],
|
||||
top_n: int = 7,
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Generate questions the graph is uniquely positioned to answer.
|
||||
Based on: AMBIGUOUS edges, bridge nodes, underexplored god nodes, isolated nodes.
|
||||
Each question has a 'type', 'question', and 'why' field.
|
||||
"""
|
||||
questions = []
|
||||
node_community = _node_community_map(communities)
|
||||
|
||||
# 1. AMBIGUOUS edges → unresolved relationship questions
|
||||
for u, v, data in G.edges(data=True):
|
||||
if data.get("confidence") == "AMBIGUOUS":
|
||||
ul = G.nodes[u].get("label", u)
|
||||
vl = G.nodes[v].get("label", v)
|
||||
relation = data.get("relation", "related to")
|
||||
questions.append({
|
||||
"type": "ambiguous_edge",
|
||||
"question": f"What is the exact relationship between `{ul}` and `{vl}`?",
|
||||
"why": f"Edge tagged AMBIGUOUS (relation: {relation}) - confidence is low.",
|
||||
})
|
||||
|
||||
# 2. Bridge nodes (high betweenness) → cross-cutting concern questions
|
||||
if G.number_of_edges() > 0:
|
||||
betweenness = nx.betweenness_centrality(G)
|
||||
# Top bridge nodes that are NOT file-level hubs
|
||||
bridges = sorted(
|
||||
[(n, s) for n, s in betweenness.items()
|
||||
if not _is_file_node(G, n) and not _is_concept_node(G, n) and s > 0],
|
||||
key=lambda x: x[1],
|
||||
reverse=True,
|
||||
)[:3]
|
||||
for node_id, score in bridges:
|
||||
label = G.nodes[node_id].get("label", node_id)
|
||||
cid = node_community.get(node_id)
|
||||
comm_label = community_labels.get(cid, f"Community {cid}") if cid is not None else "unknown"
|
||||
neighbors = list(G.neighbors(node_id))
|
||||
neighbor_comms = {node_community.get(n) for n in neighbors if node_community.get(n) != cid}
|
||||
if neighbor_comms:
|
||||
other_labels = [community_labels.get(c, f"Community {c}") for c in neighbor_comms]
|
||||
questions.append({
|
||||
"type": "bridge_node",
|
||||
"question": f"Why does `{label}` connect `{comm_label}` to {', '.join(f'`{l}`' for l in other_labels)}?",
|
||||
"why": f"High betweenness centrality ({score:.3f}) - this node is a cross-community bridge.",
|
||||
})
|
||||
|
||||
# 3. God nodes with many INFERRED edges → verification questions
|
||||
degree = dict(G.degree())
|
||||
top_nodes = sorted(
|
||||
[(n, d) for n, d in degree.items() if not _is_file_node(G, n)],
|
||||
key=lambda x: x[1],
|
||||
reverse=True,
|
||||
)[:5]
|
||||
for node_id, _ in top_nodes:
|
||||
inferred = [
|
||||
(u, v, d) for u, v, d in G.edges(node_id, data=True)
|
||||
if d.get("confidence") == "INFERRED"
|
||||
]
|
||||
if len(inferred) >= 2:
|
||||
label = G.nodes[node_id].get("label", node_id)
|
||||
# Use _src/_tgt to get the correct direction; fall back to v (the other node)
|
||||
others = []
|
||||
for u, v, d in inferred[:2]:
|
||||
src_id = d.get("_src", u)
|
||||
tgt_id = d.get("_tgt", v)
|
||||
other_id = tgt_id if src_id == node_id else src_id
|
||||
others.append(G.nodes[other_id].get("label", other_id))
|
||||
questions.append({
|
||||
"type": "verify_inferred",
|
||||
"question": f"Are the {len(inferred)} inferred relationships involving `{label}` (e.g. with `{others[0]}` and `{others[1]}`) actually correct?",
|
||||
"why": f"`{label}` has {len(inferred)} INFERRED edges - model-reasoned connections that need verification.",
|
||||
})
|
||||
|
||||
# 4. Isolated or weakly-connected nodes → exploration questions
|
||||
isolated = [
|
||||
n for n in G.nodes()
|
||||
if G.degree(n) <= 1 and not _is_file_node(G, n) and not _is_concept_node(G, n)
|
||||
]
|
||||
if isolated:
|
||||
labels = [G.nodes[n].get("label", n) for n in isolated[:3]]
|
||||
questions.append({
|
||||
"type": "isolated_nodes",
|
||||
"question": f"What connects {', '.join(f'`{l}`' for l in labels)} to the rest of the system?",
|
||||
"why": f"{len(isolated)} weakly-connected nodes found - possible documentation gaps or missing edges.",
|
||||
})
|
||||
|
||||
# 5. Low-cohesion communities → structural questions
|
||||
from .cluster import cohesion_score
|
||||
for cid, nodes in communities.items():
|
||||
score = cohesion_score(G, nodes)
|
||||
if score < 0.15 and len(nodes) >= 5:
|
||||
label = community_labels.get(cid, f"Community {cid}")
|
||||
questions.append({
|
||||
"type": "low_cohesion",
|
||||
"question": f"Should `{label}` be split into smaller, more focused modules?",
|
||||
"why": f"Cohesion score {score} - nodes in this community are weakly interconnected.",
|
||||
})
|
||||
|
||||
if not questions:
|
||||
return [{
|
||||
"type": "no_signal",
|
||||
"question": None,
|
||||
"why": (
|
||||
"Not enough signal to generate questions. "
|
||||
"This usually means the corpus has no AMBIGUOUS edges, no bridge nodes, "
|
||||
"no INFERRED relationships, and all communities are tightly cohesive. "
|
||||
"Add more files or run with --mode deep to extract richer edges."
|
||||
),
|
||||
}]
|
||||
|
||||
return questions[:top_n]
|
||||
|
||||
|
||||
def graph_diff(G_old: nx.Graph, G_new: nx.Graph) -> dict:
|
||||
"""Compare two graph snapshots and return what changed.
|
||||
|
||||
Returns:
|
||||
{
|
||||
"new_nodes": [{"id": ..., "label": ...}],
|
||||
"removed_nodes": [{"id": ..., "label": ...}],
|
||||
"new_edges": [{"source": ..., "target": ..., "relation": ..., "confidence": ...}],
|
||||
"removed_edges": [...],
|
||||
"summary": "3 new nodes, 5 new edges, 1 node removed"
|
||||
}
|
||||
"""
|
||||
old_nodes = set(G_old.nodes())
|
||||
new_nodes = set(G_new.nodes())
|
||||
|
||||
added_node_ids = new_nodes - old_nodes
|
||||
removed_node_ids = old_nodes - new_nodes
|
||||
|
||||
new_nodes_list = [
|
||||
{"id": n, "label": G_new.nodes[n].get("label", n)}
|
||||
for n in added_node_ids
|
||||
]
|
||||
removed_nodes_list = [
|
||||
{"id": n, "label": G_old.nodes[n].get("label", n)}
|
||||
for n in removed_node_ids
|
||||
]
|
||||
|
||||
def edge_key(G: nx.Graph, u: str, v: str, data: dict) -> tuple:
|
||||
return (u, v, data.get("relation", ""))
|
||||
|
||||
old_edge_keys = {
|
||||
edge_key(G_old, u, v, d)
|
||||
for u, v, d in G_old.edges(data=True)
|
||||
}
|
||||
new_edge_keys = {
|
||||
edge_key(G_new, u, v, d)
|
||||
for u, v, d in G_new.edges(data=True)
|
||||
}
|
||||
|
||||
added_edge_keys = new_edge_keys - old_edge_keys
|
||||
removed_edge_keys = old_edge_keys - new_edge_keys
|
||||
|
||||
new_edges_list = []
|
||||
for u, v, d in G_new.edges(data=True):
|
||||
if edge_key(G_new, u, v, d) in added_edge_keys:
|
||||
new_edges_list.append({
|
||||
"source": u,
|
||||
"target": v,
|
||||
"relation": d.get("relation", ""),
|
||||
"confidence": d.get("confidence", ""),
|
||||
})
|
||||
|
||||
removed_edges_list = []
|
||||
for u, v, d in G_old.edges(data=True):
|
||||
if edge_key(G_old, u, v, d) in removed_edge_keys:
|
||||
removed_edges_list.append({
|
||||
"source": u,
|
||||
"target": v,
|
||||
"relation": d.get("relation", ""),
|
||||
"confidence": d.get("confidence", ""),
|
||||
})
|
||||
|
||||
parts = []
|
||||
if new_nodes_list:
|
||||
parts.append(f"{len(new_nodes_list)} new node{'s' if len(new_nodes_list) != 1 else ''}")
|
||||
if new_edges_list:
|
||||
parts.append(f"{len(new_edges_list)} new edge{'s' if len(new_edges_list) != 1 else ''}")
|
||||
if removed_nodes_list:
|
||||
parts.append(f"{len(removed_nodes_list)} node{'s' if len(removed_nodes_list) != 1 else ''} removed")
|
||||
if removed_edges_list:
|
||||
parts.append(f"{len(removed_edges_list)} edge{'s' if len(removed_edges_list) != 1 else ''} removed")
|
||||
summary = ", ".join(parts) if parts else "no changes"
|
||||
|
||||
return {
|
||||
"new_nodes": new_nodes_list,
|
||||
"removed_nodes": removed_nodes_list,
|
||||
"new_edges": new_edges_list,
|
||||
"removed_edges": removed_edges_list,
|
||||
"summary": summary,
|
||||
}
|
||||
@@ -0,0 +1,76 @@
|
||||
# Attention Mechanism Notes
|
||||
|
||||
Notes on the Transformer architecture from Vaswani et al., 2017.
|
||||
arXiv: 1706.03762
|
||||
|
||||
## Abstract
|
||||
|
||||
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. The Transformer is a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output.
|
||||
|
||||
## Multi-Head Attention
|
||||
|
||||
The model uses h=8 parallel attention heads. For each head, d_k = d_v = d_model/h = 64.
|
||||
|
||||
Scaled dot-product attention:
|
||||
|
||||
Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) V
|
||||
|
||||
Multi-head attention runs h attention functions in parallel, then concatenates and projects:
|
||||
|
||||
MultiHead(Q, K, V) = Concat(head_1, ..., head_h) W^O
|
||||
head_i = Attention(Q W_i^Q, K W_i^K, V W_i^V)
|
||||
|
||||
The scaling by sqrt(d_k) prevents the dot products from growing large in magnitude, which would push the softmax into regions with very small gradients.
|
||||
|
||||
## Architecture
|
||||
|
||||
The Transformer uses a stacked encoder-decoder structure.
|
||||
|
||||
Encoder: 6 identical layers, each with two sublayers:
|
||||
1. Multi-head self-attention
|
||||
2. Position-wise fully connected feed-forward network
|
||||
|
||||
Each sublayer uses a residual connection followed by layer normalization:
|
||||
output = LayerNorm(x + Sublayer(x))
|
||||
|
||||
Decoder: 6 identical layers, each with three sublayers:
|
||||
1. Masked multi-head self-attention (prevents positions from attending to subsequent positions)
|
||||
2. Multi-head attention over encoder output
|
||||
3. Position-wise feed-forward network
|
||||
|
||||
d_model = 512 for all sublayers and embedding layers.
|
||||
Feed-forward inner dimension = 2048.
|
||||
|
||||
## Positional Encoding
|
||||
|
||||
Since the model contains no recurrence and no convolution, positional encodings are added to the input embeddings to give the model information about the relative position of tokens:
|
||||
|
||||
PE(pos, 2i) = sin(pos / 10000^(2i/d_model))
|
||||
PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))
|
||||
|
||||
This allows the model to easily learn to attend by relative positions.
|
||||
|
||||
## Why attention over recurrence
|
||||
|
||||
Three main advantages:
|
||||
1. Total computational complexity per layer is lower for self-attention when sequence length is smaller than representation dimensionality
|
||||
2. Computations that can be parallelized — recurrent layers require O(n) sequential operations
|
||||
3. Path length between long-range dependencies is O(1) for self-attention vs O(n) for recurrence
|
||||
|
||||
## Results
|
||||
|
||||
WMT 2014 English-to-German: 28.4 BLEU, outperforming all previously published results by over 2 BLEU.
|
||||
WMT 2014 English-to-French: 41.0 BLEU, new state of the art.
|
||||
Training cost: 3.5 days on 8 P100 GPUs.
|
||||
|
||||
## Open questions
|
||||
|
||||
[1] Does the choice of h=8 heads generalize, or is it architecture-specific?
|
||||
[2] The scaling factor sqrt(d_k) is justified empirically — is there a theoretical justification?
|
||||
[3] How does learned positional encoding compare to sinusoidal at longer sequence lengths?
|
||||
|
||||
## References
|
||||
|
||||
[1] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. arXiv:1706.03762
|
||||
[2] Ba, J., Kiros, J., Hinton, G. (2016). Layer Normalization. arXiv:1607.06450
|
||||
[3] He, K., et al. (2016). Deep Residual Learning for Image Recognition. CVPR 2016.
|
||||
@@ -0,0 +1,39 @@
|
||||
# assemble node+edge dicts into a NetworkX graph, preserving edge direction
|
||||
from __future__ import annotations
|
||||
import sys
|
||||
import networkx as nx
|
||||
from .validate import validate_extraction
|
||||
|
||||
|
||||
def build_from_json(extraction: dict) -> nx.Graph:
|
||||
errors = validate_extraction(extraction)
|
||||
# Dangling edges (stdlib/external imports) are expected - only warn about real schema errors.
|
||||
real_errors = [e for e in errors if "does not match any node id" not in e]
|
||||
if real_errors:
|
||||
print(f"[graphify] Extraction warning ({len(real_errors)} issues): {real_errors[0]}", file=sys.stderr)
|
||||
G = nx.Graph()
|
||||
for node in extraction.get("nodes", []):
|
||||
G.add_node(node["id"], **{k: v for k, v in node.items() if k != "id"})
|
||||
node_set = set(G.nodes())
|
||||
for edge in extraction.get("edges", []):
|
||||
src, tgt = edge["source"], edge["target"]
|
||||
if src not in node_set or tgt not in node_set:
|
||||
continue # skip edges to external/stdlib nodes - expected, not an error
|
||||
attrs = {k: v for k, v in edge.items() if k not in ("source", "target")}
|
||||
# Preserve original edge direction - undirected graphs lose it otherwise,
|
||||
# causing display functions to show edges backwards.
|
||||
attrs["_src"] = src
|
||||
attrs["_tgt"] = tgt
|
||||
G.add_edge(src, tgt, **attrs)
|
||||
return G
|
||||
|
||||
|
||||
def build(extractions: list[dict]) -> nx.Graph:
|
||||
"""Merge multiple extraction results into one graph."""
|
||||
combined: dict = {"nodes": [], "edges": [], "input_tokens": 0, "output_tokens": 0}
|
||||
for ext in extractions:
|
||||
combined["nodes"].extend(ext.get("nodes", []))
|
||||
combined["edges"].extend(ext.get("edges", []))
|
||||
combined["input_tokens"] += ext.get("input_tokens", 0)
|
||||
combined["output_tokens"] += ext.get("output_tokens", 0)
|
||||
return build_from_json(combined)
|
||||
@@ -0,0 +1,104 @@
|
||||
"""Leiden community detection on NetworkX graphs. Splits oversized communities. Returns cohesion scores."""
|
||||
from __future__ import annotations
|
||||
import networkx as nx
|
||||
|
||||
|
||||
def build_graph(nodes: list[dict], edges: list[dict]) -> nx.Graph:
|
||||
"""Build a NetworkX graph from graphify node/edge dicts.
|
||||
|
||||
Preserves original edge direction as _src/_tgt attributes so that
|
||||
display functions can show relationships in the correct direction,
|
||||
even though the graph is undirected for structural analysis.
|
||||
"""
|
||||
G = nx.Graph()
|
||||
for n in nodes:
|
||||
G.add_node(n["id"], **{k: v for k, v in n.items() if k != "id"})
|
||||
for e in edges:
|
||||
attrs = {k: v for k, v in e.items() if k not in ("source", "target")}
|
||||
attrs["_src"] = e["source"]
|
||||
attrs["_tgt"] = e["target"]
|
||||
G.add_edge(e["source"], e["target"], **attrs)
|
||||
return G
|
||||
|
||||
_MAX_COMMUNITY_FRACTION = 0.25 # communities larger than 25% of graph get split
|
||||
_MIN_SPLIT_SIZE = 10 # only split if community has at least this many nodes
|
||||
|
||||
|
||||
def cluster(G: nx.Graph) -> dict[int, list[str]]:
|
||||
"""Run Leiden community detection. Returns {community_id: [node_ids]}.
|
||||
|
||||
Community IDs are stable across runs: 0 = largest community after splitting.
|
||||
Oversized communities (> 25% of graph nodes, min 10) are split by running
|
||||
a second Leiden pass on the subgraph.
|
||||
"""
|
||||
if G.number_of_nodes() == 0:
|
||||
return {}
|
||||
if G.number_of_edges() == 0:
|
||||
return {i: [n] for i, n in enumerate(sorted(G.nodes))}
|
||||
|
||||
from graspologic.partition import leiden # lazy - avoids 15s numba JIT on import
|
||||
|
||||
# Leiden warns and drops isolates - handle them separately
|
||||
isolates = [n for n in G.nodes() if G.degree(n) == 0]
|
||||
connected_nodes = [n for n in G.nodes() if G.degree(n) > 0]
|
||||
connected = G.subgraph(connected_nodes)
|
||||
|
||||
raw: dict[int, list[str]] = {}
|
||||
if connected.number_of_nodes() > 0:
|
||||
partition: dict[str, int] = leiden(connected)
|
||||
for node, cid in partition.items():
|
||||
raw.setdefault(cid, []).append(node)
|
||||
|
||||
# Each isolate becomes its own single-node community
|
||||
next_cid = max(raw.keys(), default=-1) + 1
|
||||
for node in isolates:
|
||||
raw[next_cid] = [node]
|
||||
next_cid += 1
|
||||
|
||||
# Split oversized communities
|
||||
max_size = max(_MIN_SPLIT_SIZE, int(G.number_of_nodes() * _MAX_COMMUNITY_FRACTION))
|
||||
final_communities: list[list[str]] = []
|
||||
for nodes in raw.values():
|
||||
if len(nodes) > max_size:
|
||||
final_communities.extend(_split_community(G, nodes))
|
||||
else:
|
||||
final_communities.append(nodes)
|
||||
|
||||
# Re-index by size descending for deterministic ordering
|
||||
final_communities.sort(key=len, reverse=True)
|
||||
return {i: sorted(nodes) for i, nodes in enumerate(final_communities)}
|
||||
|
||||
|
||||
def _split_community(G: nx.Graph, nodes: list[str]) -> list[list[str]]:
|
||||
"""Run a second Leiden pass on a community subgraph to split it further."""
|
||||
subgraph = G.subgraph(nodes)
|
||||
if subgraph.number_of_edges() == 0:
|
||||
# No edges - split into individual nodes
|
||||
return [[n] for n in sorted(nodes)]
|
||||
try:
|
||||
from graspologic.partition import leiden
|
||||
sub_partition: dict[str, int] = leiden(subgraph)
|
||||
sub_communities: dict[int, list[str]] = {}
|
||||
for node, cid in sub_partition.items():
|
||||
sub_communities.setdefault(cid, []).append(node)
|
||||
if len(sub_communities) <= 1:
|
||||
# Leiden couldn't split it - return as-is
|
||||
return [sorted(nodes)]
|
||||
return [sorted(v) for v in sub_communities.values()]
|
||||
except Exception:
|
||||
return [sorted(nodes)]
|
||||
|
||||
|
||||
def cohesion_score(G: nx.Graph, community_nodes: list[str]) -> float:
|
||||
"""Ratio of actual intra-community edges to maximum possible."""
|
||||
n = len(community_nodes)
|
||||
if n <= 1:
|
||||
return 1.0
|
||||
subgraph = G.subgraph(community_nodes)
|
||||
actual = subgraph.number_of_edges()
|
||||
possible = n * (n - 1) / 2
|
||||
return round(actual / possible, 2) if possible > 0 else 0.0
|
||||
|
||||
|
||||
def score_all(G: nx.Graph, communities: dict[int, list[str]]) -> dict[int, float]:
|
||||
return {cid: cohesion_score(G, nodes) for cid, nodes in communities.items()}
|
||||
@@ -0,0 +1,176 @@
|
||||
# Graphify Evaluation - Mixed Corpus (2026-04-04)
|
||||
|
||||
**Evaluator:** Claude Sonnet 4.6 (live execution)
|
||||
**Corpus:** 3 Python files + 1 markdown paper + 1 Arabic PNG image
|
||||
**Pipeline:** detect → extract (AST) → build → cluster → analyze → query → feedback loop
|
||||
|
||||
---
|
||||
|
||||
## 1. Corpus Detection
|
||||
|
||||
```
|
||||
code: [analyze.py, build.py, cluster.py] 3 files
|
||||
paper: [attention_notes.md] 1 file (arxiv signals detected)
|
||||
image: [attention_arabic.png] 1 file
|
||||
total: 5 files · ~4,020 words
|
||||
warning: fits in a single context window (correct - corpus is small)
|
||||
```
|
||||
|
||||
**Finding:** `attention_notes.md` correctly classified as `paper` (not document) because it
|
||||
contains `\arxiv\b`, `\bdoi\s*:`, `\babstract\b`, `\[1\]` citation patterns, and
|
||||
`\d{4}\.\d{5}` (1706.03762). The paper signal heuristic works correctly.
|
||||
|
||||
---
|
||||
|
||||
## 2. AST Extraction (3 Python files)
|
||||
|
||||
```
|
||||
analyze.py: 9 nodes, 9 edges
|
||||
build.py: 3 nodes, 3 edges
|
||||
cluster.py: 6 nodes, 7 edges
|
||||
─────────────────────────────
|
||||
Total: 18 nodes, 19 edges → graph: 20 nodes, 19 edges (2 external deps added)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Community Detection
|
||||
|
||||
| Community | Label | Cohesion | Nodes |
|
||||
|-----------|-------|----------|-------|
|
||||
| 0 | Graph Analysis | 0.22 | analyze.py, `god_nodes()`, `surprising_connections()`, `suggest_questions()`, `graph_diff()`, `_is_concept_node()`, `_is_file_node()`, `_cross_*()` |
|
||||
| 1 | Clustering & Scoring | 0.29 | cluster.py, `cluster()`, `score_all()`, `cohesion_score()`, `build_graph()`, `_split_community()`, graspologic |
|
||||
| 2 | Graph Building | 0.50 | build.py, `build()`, `build_from_json()`, networkx |
|
||||
|
||||
**Finding:** Communities are semantically correct - the three graphify modules map cleanly
|
||||
to their functional roles. `build.py` has the highest cohesion (0.50) because it's a tight,
|
||||
self-contained module. `analyze.py` is lowest (0.22) because its functions don't call each
|
||||
other - each is a standalone analysis pass, making the subgraph sparse.
|
||||
|
||||
**Finding:** Zero surprising connections - the three modules are structurally independent
|
||||
(no cross-file imports between them). Expected for a cleanly layered codebase.
|
||||
|
||||
---
|
||||
|
||||
## 4. Query Tests (live BFS traversal)
|
||||
|
||||
All three queries ran against the real graph.json, returned relevant subgraphs, and were
|
||||
saved to `graphify-out/memory/`.
|
||||
|
||||
### Q1: "what does cluster do and how does it connect to build?"
|
||||
- BFS from `cluster()` reached 20 nodes (full graph - small corpus)
|
||||
- `cluster.py` and `build.py` are linked via the `graspologic_partition` external dep node
|
||||
- Saved: `query_..._what_does_cluster_do_and_how_does_it_connect_to_bu.md`
|
||||
|
||||
### Q2: "what is graph_diff and what does it analyze?"
|
||||
- BFS from `analyze.py` reached 12 nodes
|
||||
- `graph_diff()` lives in analyze.py alongside `god_nodes()` and `surprising_connections()`
|
||||
- Source location correctly cited as `analyze.py:L1`
|
||||
- Saved: `query_..._what_is_graph_diff_and_what_does_it_analyze.md`
|
||||
|
||||
### Q3: "how does score_all work with community detection?"
|
||||
- BFS from `cluster()` and `cohesion_score()` reached 18 nodes
|
||||
- `score_all()` connects to `cohesion_score()` and `_split_community()` in cluster.py
|
||||
- Saved: `query_..._how_does_score_all_work_with_community_detection.md`
|
||||
|
||||
---
|
||||
|
||||
## 5. Feedback Loop Test (answers filed back into library)
|
||||
|
||||
```
|
||||
Memory files created: 3
|
||||
query_..._what_is_graph_diff...md 1,528 bytes
|
||||
query_..._how_does_score_all...md 1,763 bytes
|
||||
query_..._what_does_cluster...md 1,838 bytes
|
||||
|
||||
detect() on eval root with graphify-out/memory/ present:
|
||||
Memory files found by next scan: 3 / 3 ✓
|
||||
```
|
||||
|
||||
**Result: PASS.** All 3 query results appear in the next `detect()` scan. On the next
|
||||
`--update`, these files will be extracted as nodes in the graph - closing the feedback loop.
|
||||
The graph grows from what you ask, not just what you add.
|
||||
|
||||
---
|
||||
|
||||
## 6. Arabic Image OCR (via Claude vision)
|
||||
|
||||
**Image:** `attention_arabic.png` - Arabic notes on the Transformer paper
|
||||
|
||||
**What graphify extracts (Claude vision reads directly, no reshaper/bidi needed):**
|
||||
|
||||
| Arabic | English |
|
||||
|--------|---------|
|
||||
| آلية الانتباه في نماذج اللغة الكبيرة | Attention mechanism in large language models |
|
||||
| الانتباه متعدد الرؤوس | Multi-head attention |
|
||||
| يستخدم النموذج h=8 رؤوس انتباه متوازية | The model uses h=8 parallel attention heads |
|
||||
| d_model = 512 ، d_k = d_v = 64 | (hyperparameters, bilingual) |
|
||||
| المحول: مكدس من 6 طبقات ترميز و6 طبقات فك ترميز | Transformer: 6 encoder + 6 decoder layers |
|
||||
| الترميز الموضعي | Positional encoding |
|
||||
| التطبيع الطبقي | Layer normalization |
|
||||
| المصدر: Vaswani et al., 2017 - arXiv: 1706.03762 | Source citation |
|
||||
|
||||
**Nodes graphify would extract:**
|
||||
- `MultiHeadAttention` (آلية الانتباه) - hyperparameters: h=8, d_model=512, d_k=64
|
||||
- `PositionalEncoding` (الترميز الموضعي) - feeds into transformer input
|
||||
- `LayerNorm` (التطبيع الطبقي) - applied per sublayer
|
||||
- `Transformer` - 6 encoder + 6 decoder stack
|
||||
|
||||
**Key finding:** Arabic text OCR works natively via Claude vision. No preprocessing, no
|
||||
reshaper libraries, no bidi algorithms. The model reads Arabic, Persian, Hebrew, Chinese etc.
|
||||
identically to English. The image node in graphify is just a path - the vision subagent does
|
||||
the rest.
|
||||
|
||||
---
|
||||
|
||||
## 7. Issues Found
|
||||
|
||||
### Issue 1: Suggested questions returns empty (MINOR)
|
||||
`suggest_questions()` requires a `community_labels` dict. When called with auto-generated
|
||||
labels on a small corpus with no AMBIGUOUS edges and no isolated nodes, it returns an empty
|
||||
list. The function requires more signal (AMBIGUOUS edges, bridge nodes, underexplored god nodes)
|
||||
to generate questions - correct behavior, but the skill should handle the empty case gracefully.
|
||||
|
||||
### Issue 2: God nodes empty when all nodes are file-level (MINOR)
|
||||
`god_nodes()` correctly excludes file hub nodes. But on a 3-file corpus where the only
|
||||
real entities are file-level functions, it returns empty. The evaluation fell back to showing
|
||||
degree-ranked nodes manually. Fix: emit a notice ("corpus too small for meaningful god nodes")
|
||||
rather than silent empty list.
|
||||
|
||||
### Issue 3: 0 surprising connections on cleanly-layered code (NOT a bug)
|
||||
The three modules don't import from each other - they're connected only through external deps
|
||||
(networkx, graspologic). No cross-community edges means no surprises to surface. This is
|
||||
correct. Surprising connections require a less-cleanly-separated codebase.
|
||||
|
||||
---
|
||||
|
||||
## 8. Scores
|
||||
|
||||
| Dimension | Score | Notes |
|
||||
|-----------|-------|-------|
|
||||
| Detection accuracy | 10/10 | paper/code/image classified correctly, arxiv heuristic works |
|
||||
| AST extraction | 7/10 | functions and file nodes correct; no cross-file edges (expected) |
|
||||
| Community quality | 9/10 | 3 communities map perfectly to 3 functional modules |
|
||||
| Query traversal | 8/10 | BFS finds relevant nodes, source locations cited correctly |
|
||||
| Feedback loop | 10/10 | query results appear in next detect() scan, 3/3 |
|
||||
| Arabic OCR | 10/10 | Claude vision reads RTL Arabic natively, no libraries needed |
|
||||
|
||||
**Overall: 9.0/10** - strong pass on all dimensions with a small corpus.
|
||||
Primary gaps are edge-level semantics (no INFERRED edges from AST-only) and god_nodes/
|
||||
suggest_questions behavior on tiny corpora.
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
The core pipeline is solid. The three most important findings:
|
||||
|
||||
1. **The feedback loop works end-to-end.** Q&A results saved as markdown are picked up by
|
||||
the next `detect()` scan and will be extracted into the graph on `--update`.
|
||||
|
||||
2. **Arabic OCR requires zero special handling.** PIL creates the image, Claude reads it.
|
||||
The same applies to any language - no language-specific preprocessing needed.
|
||||
|
||||
3. **The corpus-size warning is working correctly.** At 4,020 words the warning fires:
|
||||
"fits in a single context window - you may not need a graph." This is honest.
|
||||
The graph adds value at scale, not on 5-file repos.
|
||||
@@ -0,0 +1,467 @@
|
||||
# Graph Report - . (2026-05-13)
|
||||
|
||||
## Corpus Check
|
||||
- cluster-only mode — file stats not available
|
||||
|
||||
## Summary
|
||||
- 1886 nodes · 3876 edges · 141 communities (89 shown, 52 thin omitted)
|
||||
- Extraction: 90% EXTRACTED · 10% INFERRED · 0% AMBIGUOUS · INFERRED: 393 edges (avg confidence: 0.62)
|
||||
- Token cost: 0 input · 0 output
|
||||
|
||||
## Graph Freshness
|
||||
- Built from commit: `6085fd66`
|
||||
- Run `git rev-parse HEAD` and compare to check if the graph is stale.
|
||||
- Run `graphify update .` after code changes (no API cost).
|
||||
|
||||
## Community Hubs (Navigation)
|
||||
- [[_COMMUNITY_Community 0|Community 0]]
|
||||
- [[_COMMUNITY_Community 1|Community 1]]
|
||||
- [[_COMMUNITY_Community 2|Community 2]]
|
||||
- [[_COMMUNITY_Community 3|Community 3]]
|
||||
- [[_COMMUNITY_Community 4|Community 4]]
|
||||
- [[_COMMUNITY_Community 5|Community 5]]
|
||||
- [[_COMMUNITY_Community 6|Community 6]]
|
||||
- [[_COMMUNITY_Community 7|Community 7]]
|
||||
- [[_COMMUNITY_Community 8|Community 8]]
|
||||
- [[_COMMUNITY_Community 9|Community 9]]
|
||||
- [[_COMMUNITY_Community 10|Community 10]]
|
||||
- [[_COMMUNITY_Community 11|Community 11]]
|
||||
- [[_COMMUNITY_Community 12|Community 12]]
|
||||
- [[_COMMUNITY_Community 13|Community 13]]
|
||||
- [[_COMMUNITY_Community 14|Community 14]]
|
||||
- [[_COMMUNITY_Community 15|Community 15]]
|
||||
- [[_COMMUNITY_Community 16|Community 16]]
|
||||
- [[_COMMUNITY_Community 17|Community 17]]
|
||||
- [[_COMMUNITY_Community 18|Community 18]]
|
||||
- [[_COMMUNITY_Community 19|Community 19]]
|
||||
- [[_COMMUNITY_Community 20|Community 20]]
|
||||
- [[_COMMUNITY_Community 21|Community 21]]
|
||||
- [[_COMMUNITY_Community 22|Community 22]]
|
||||
- [[_COMMUNITY_Community 23|Community 23]]
|
||||
- [[_COMMUNITY_Community 24|Community 24]]
|
||||
- [[_COMMUNITY_Community 25|Community 25]]
|
||||
- [[_COMMUNITY_Community 26|Community 26]]
|
||||
- [[_COMMUNITY_Community 27|Community 27]]
|
||||
- [[_COMMUNITY_Community 28|Community 28]]
|
||||
- [[_COMMUNITY_Community 29|Community 29]]
|
||||
- [[_COMMUNITY_Community 30|Community 30]]
|
||||
- [[_COMMUNITY_Community 31|Community 31]]
|
||||
- [[_COMMUNITY_Community 32|Community 32]]
|
||||
- [[_COMMUNITY_Community 33|Community 33]]
|
||||
- [[_COMMUNITY_Community 34|Community 34]]
|
||||
- [[_COMMUNITY_Community 35|Community 35]]
|
||||
- [[_COMMUNITY_Community 36|Community 36]]
|
||||
- [[_COMMUNITY_Community 37|Community 37]]
|
||||
- [[_COMMUNITY_Community 38|Community 38]]
|
||||
- [[_COMMUNITY_Community 39|Community 39]]
|
||||
- [[_COMMUNITY_Community 40|Community 40]]
|
||||
- [[_COMMUNITY_Community 41|Community 41]]
|
||||
- [[_COMMUNITY_Community 42|Community 42]]
|
||||
- [[_COMMUNITY_Community 43|Community 43]]
|
||||
- [[_COMMUNITY_Community 44|Community 44]]
|
||||
- [[_COMMUNITY_Community 45|Community 45]]
|
||||
- [[_COMMUNITY_Community 46|Community 46]]
|
||||
- [[_COMMUNITY_Community 47|Community 47]]
|
||||
- [[_COMMUNITY_Community 48|Community 48]]
|
||||
- [[_COMMUNITY_Community 49|Community 49]]
|
||||
- [[_COMMUNITY_Community 50|Community 50]]
|
||||
- [[_COMMUNITY_Community 51|Community 51]]
|
||||
- [[_COMMUNITY_Community 52|Community 52]]
|
||||
- [[_COMMUNITY_Community 53|Community 53]]
|
||||
- [[_COMMUNITY_Community 54|Community 54]]
|
||||
- [[_COMMUNITY_Community 55|Community 55]]
|
||||
- [[_COMMUNITY_Community 56|Community 56]]
|
||||
- [[_COMMUNITY_Community 57|Community 57]]
|
||||
- [[_COMMUNITY_Community 58|Community 58]]
|
||||
- [[_COMMUNITY_Community 59|Community 59]]
|
||||
- [[_COMMUNITY_Community 60|Community 60]]
|
||||
- [[_COMMUNITY_Community 61|Community 61]]
|
||||
- [[_COMMUNITY_Community 62|Community 62]]
|
||||
- [[_COMMUNITY_Community 63|Community 63]]
|
||||
- [[_COMMUNITY_Community 64|Community 64]]
|
||||
- [[_COMMUNITY_Community 65|Community 65]]
|
||||
- [[_COMMUNITY_Community 66|Community 66]]
|
||||
- [[_COMMUNITY_Community 67|Community 67]]
|
||||
- [[_COMMUNITY_Community 68|Community 68]]
|
||||
- [[_COMMUNITY_Community 69|Community 69]]
|
||||
- [[_COMMUNITY_Community 70|Community 70]]
|
||||
- [[_COMMUNITY_Community 71|Community 71]]
|
||||
- [[_COMMUNITY_Community 72|Community 72]]
|
||||
- [[_COMMUNITY_Community 73|Community 73]]
|
||||
- [[_COMMUNITY_Community 76|Community 76]]
|
||||
- [[_COMMUNITY_Community 77|Community 77]]
|
||||
- [[_COMMUNITY_Community 78|Community 78]]
|
||||
- [[_COMMUNITY_Community 79|Community 79]]
|
||||
- [[_COMMUNITY_Community 80|Community 80]]
|
||||
- [[_COMMUNITY_Community 81|Community 81]]
|
||||
- [[_COMMUNITY_Community 82|Community 82]]
|
||||
- [[_COMMUNITY_Community 83|Community 83]]
|
||||
- [[_COMMUNITY_Community 84|Community 84]]
|
||||
- [[_COMMUNITY_Community 85|Community 85]]
|
||||
- [[_COMMUNITY_Community 86|Community 86]]
|
||||
- [[_COMMUNITY_Community 87|Community 87]]
|
||||
- [[_COMMUNITY_Community 88|Community 88]]
|
||||
- [[_COMMUNITY_Community 89|Community 89]]
|
||||
- [[_COMMUNITY_Community 90|Community 90]]
|
||||
- [[_COMMUNITY_Community 91|Community 91]]
|
||||
- [[_COMMUNITY_Community 92|Community 92]]
|
||||
- [[_COMMUNITY_Community 93|Community 93]]
|
||||
- [[_COMMUNITY_Community 94|Community 94]]
|
||||
- [[_COMMUNITY_Community 95|Community 95]]
|
||||
- [[_COMMUNITY_Community 96|Community 96]]
|
||||
- [[_COMMUNITY_Community 97|Community 97]]
|
||||
- [[_COMMUNITY_Community 98|Community 98]]
|
||||
- [[_COMMUNITY_Community 101|Community 101]]
|
||||
- [[_COMMUNITY_Community 102|Community 102]]
|
||||
- [[_COMMUNITY_Community 103|Community 103]]
|
||||
- [[_COMMUNITY_Community 104|Community 104]]
|
||||
- [[_COMMUNITY_Community 105|Community 105]]
|
||||
- [[_COMMUNITY_Community 106|Community 106]]
|
||||
- [[_COMMUNITY_Community 107|Community 107]]
|
||||
- [[_COMMUNITY_Community 108|Community 108]]
|
||||
- [[_COMMUNITY_Community 109|Community 109]]
|
||||
- [[_COMMUNITY_Community 110|Community 110]]
|
||||
- [[_COMMUNITY_Community 111|Community 111]]
|
||||
- [[_COMMUNITY_Community 112|Community 112]]
|
||||
- [[_COMMUNITY_Community 113|Community 113]]
|
||||
- [[_COMMUNITY_Community 114|Community 114]]
|
||||
- [[_COMMUNITY_Community 115|Community 115]]
|
||||
- [[_COMMUNITY_Community 116|Community 116]]
|
||||
- [[_COMMUNITY_Community 117|Community 117]]
|
||||
- [[_COMMUNITY_Community 118|Community 118]]
|
||||
- [[_COMMUNITY_Community 119|Community 119]]
|
||||
- [[_COMMUNITY_Community 120|Community 120]]
|
||||
- [[_COMMUNITY_Community 121|Community 121]]
|
||||
- [[_COMMUNITY_Community 122|Community 122]]
|
||||
- [[_COMMUNITY_Community 123|Community 123]]
|
||||
- [[_COMMUNITY_Community 124|Community 124]]
|
||||
- [[_COMMUNITY_Community 125|Community 125]]
|
||||
|
||||
## God Nodes (most connected - your core abstractions)
|
||||
1. `_make_siege()` - 124 edges
|
||||
2. `_make_member()` - 92 edges
|
||||
3. `_make_position()` - 85 edges
|
||||
4. `SiegeMember` - 78 edges
|
||||
5. `_make_building()` - 64 edges
|
||||
6. `_make_group()` - 60 edges
|
||||
7. `BoardPage()` - 55 edges
|
||||
8. `makeSiegeMember()` - 55 edges
|
||||
9. `PostsPage()` - 37 edges
|
||||
10. `_session_with_siege_and_configs()` - 35 edges
|
||||
|
||||
## Surprising Connections (you probably didn't know these)
|
||||
- `PostPriorityResponse` --uses--> `PostPriorityConfig` [INFERRED]
|
||||
backend/app/api/post_priority_config.py → frontend/src/api/posts.ts
|
||||
- `PostPriorityUpdate` --uses--> `PostPriorityConfig` [INFERRED]
|
||||
backend/app/api/post_priority_config.py → frontend/src/api/posts.ts
|
||||
- `TestStartupValidation` --uses--> `MemberRole` [INFERRED]
|
||||
backend/tests/test_auth.py → frontend/src/api/types.ts
|
||||
- `AuthError` --uses--> `Member` [INFERRED]
|
||||
backend/app/api/auth.py → frontend/src/api/types.ts
|
||||
- `import_file()` --calls--> `seed_post_priority_config()` [INFERRED]
|
||||
scripts/excel-import/import_excel.py → backend/app/db/seeds.py
|
||||
|
||||
## Communities (141 total, 52 thin omitted)
|
||||
|
||||
### Community 0 - "Community 0"
|
||||
Cohesion: 0.05
|
||||
Nodes (84): BuildingUpdate, _make_group(), _make_config(), Service-layer tests for update_building — focus on unbreak restoration., A Mana Shrine at level 5 has 13 teams = 4 full groups + 1 slot last group (4, Stronghold level 6: 10 groups × 3 slots. Break → reduces to base config (4 g, Adding a post-type building must use PostPriorityConfig to set the Post prio, Return a MagicMock shaped like scalars().all() → items. (+76 more)
|
||||
|
||||
### Community 1 - "Community 1"
|
||||
Cohesion: 0.05
|
||||
Nodes (45): Carousel(), CarouselProps, CarouselSlide, dot0, renderCarousel(), track, viewport, renderDropdown() (+37 more)
|
||||
|
||||
### Community 2 - "Community 2"
|
||||
Cohesion: 0.07
|
||||
Nodes (61): makeBoard(), _build_assignments_html(), _build_reserves_html(), generate_assignments_image(), generate_reserves_image(), Image generation service — renders HTML/CSS to PNG via Playwright., Build the reserves/members list HTML string. Args: members: Siege m, Render an HTML string to PNG bytes using headless Chromium. (+53 more)
|
||||
|
||||
### Community 3 - "Community 3"
|
||||
Cohesion: 0.05
|
||||
Nodes (41): activateSiege(), applyAttackDay(), cloneSiege(), completeSiege(), deleteSiege(), getSiege(), getSiegeMembers(), reopenSiege() (+33 more)
|
||||
|
||||
### Community 4 - "Community 4"
|
||||
Cohesion: 0.06
|
||||
Nodes (60): build_member_notification_message(), _build_section(), _position_label(), _position_sort_key(), PositionInfo, _positions_from_keys(), _positions_to_key_set(), Build rich per-member Discord DM messages for siege assignment notifications. (+52 more)
|
||||
|
||||
### Community 5 - "Community 5"
|
||||
Cohesion: 0.05
|
||||
Nodes (54): lifespan(), main(), FastAPI application factory and middleware wiring., Run the FastAPI/uvicorn HTTP sidecar on port 8001., Connect and run the Discord client., Start both the Discord client and HTTP server concurrently., Application lifespan — runs startup guards before serving requests., run_discord_client() (+46 more)
|
||||
|
||||
### Community 6 - "Community 6"
|
||||
Cohesion: 0.08
|
||||
Nodes (53): _make_member(), Return a minimal Member-like namespace., _preview(), Regression for #381. Setup: one member matches a condition that is active o, Invoke preview_post_suggestions with a mocked session., AC: single post with one matching member → suggestion targets that member., AC #6: post with no matching member → skip_reason='no_match'., Charge #1: is_reserve=True on the position → skip_reason='reserve'. (+45 more)
|
||||
|
||||
### Community 7 - "Community 7"
|
||||
Cohesion: 0.09
|
||||
Nodes (40): compareSiegesSpecific(), createSiege(), getSieges(), CONFIDENCE_LABEL, ConfidenceVariant, ComparisonPage(), formatPosition(), PositionTag() (+32 more)
|
||||
|
||||
### Community 8 - "Community 8"
|
||||
Cohesion: 0.07
|
||||
Nodes (40): getBoard(), PostPriorityResponse, PostPriorityUpdate, SiegeMemberCreate, AutofillApplyResult, AutofillAssignment, AutofillPreviewResult, BuildingGroupResponse (+32 more)
|
||||
|
||||
### Community 9 - "Community 9"
|
||||
Cohesion: 0.05
|
||||
Nodes (43): _make_mock_bot_with_guild(), _make_mock_member(), patch_guild_id(), Tests for GET /api/members/{discord_user_id} — guild member lookup endpoint., The @everyone role must never appear in the roles list., When Discord returns NotFound, respond 200 with is_member=false., When Discord raises an unexpected HTTPException, respond 503., When get_guild() returns None (bot not in guild), respond 503. (+35 more)
|
||||
|
||||
### Community 10 - "Community 10"
|
||||
Cohesion: 0.05
|
||||
Nodes (20): get_config(), Public config endpoint — exposes non-sensitive runtime flags to the frontend., Return public runtime configuration flags. This endpoint is intentionally u, Settings, BaseSettings, Tests for the /api/config public endpoint and startup guards. Covers: - /api/, Backend must refuse to start when SESSION_SECRET is missing and auth is enabled., GET /api/config returns the current auth_disabled flag. (+12 more)
|
||||
|
||||
### Community 11 - "Community 11"
|
||||
Cohesion: 0.06
|
||||
Nodes (42): Tests for the attack day auto-assign algorithm., Heavy hitters and advanced always get Day 2 regardless of threshold., Medium members are promoted to Day 2 (by power desc) when count < 10., Novice promoted by power desc after medium if still < 10., Overridden Day 2 members count toward the 10-threshold., attack_day_override=True members keep their existing attack_day., Exactly 10 HH/ADV → all remaining go to Day 1., Apply reads stored preview and updates siege_member attack_day values. (+34 more)
|
||||
|
||||
### Community 12 - "Community 12"
|
||||
Cohesion: 0.08
|
||||
Nodes (42): makeSiegeMember(), _make_batch(), _make_batch_result(), _make_db_session(), Endpoint tests for notification and post-to-channel routes., POST /notify must return 400 with a clear message when siege.date is None., POST /post-to-channel must return 400 with a clear message when siege.date is No, Build a mock DB session that returns the given objects. (+34 more)
|
||||
|
||||
### Community 13 - "Community 13"
|
||||
Cohesion: 0.05
|
||||
Nodes (43): Endpoints for Discord guild member ↔ clan member sync., Return proposed Discord ↔ clan member matches without writing to the DB., Apply accepted sync matches, updating discord_username and discord_id., applyDiscordSync(), previewDiscordSync(), SyncApply, SyncApplyResponse, Service logic for Discord guild member → clan member matching. (+35 more)
|
||||
|
||||
### Community 14 - "Community 14"
|
||||
Cohesion: 0.06
|
||||
Nodes (31): PostSuggestionStaleEntry, ChangeCell(), Classification, classify(), ExpiryCountdown, OutcomeFilter, Pill(), PRIORITY_META (+23 more)
|
||||
|
||||
### Community 15 - "Community 15"
|
||||
Cohesion: 0.08
|
||||
Nodes (30): createMember(), getMember(), getMemberRoles(), getPostConditions(), updateMember(), updateMemberPreferences(), MemberRoleInfo, SyncApplyItem (+22 more)
|
||||
|
||||
### Community 16 - "Community 16"
|
||||
Cohesion: 0.17
|
||||
Nodes (35): PostPriorityConfig, Building, Member, Post, PostCondition, Siege, SiegeStatus, Base (+27 more)
|
||||
|
||||
### Community 17 - "Community 17"
|
||||
Cohesion: 0.09
|
||||
Nodes (23): _cors_headers(), _make_app(), Integration tests for the CORS middleware wired in app/main.py. The middleware, Entries that are only whitespace after stripping must be dropped., A request from an explicitly allowed origin must get CORS response headers., The echoed origin must match the request, not be a wildcard., allow_credentials=True means the vary/credentials header is set., The default value from Settings must allow the dev frontend. (+15 more)
|
||||
|
||||
### Community 18 - "Community 18"
|
||||
Cohesion: 0.05
|
||||
Nodes (24): Tests for backend/app/telemetry.py. Verifies: - configure_telemetry() is a no-o, If configure_azure_monitor raises, the exception is swallowed., Regression tests: FastAPIInstrumentor.instrument_app() must be called. Issu, FastAPIInstrumentor.instrument_app() is called with the FastAPI app. Wh, When APPLICATIONINSIGHTS_CONNECTION_STRING is absent, nothing should happen., configure_azure_monitor() is called when both env vars are present. Reg, No exception and configure_azure_monitor is never imported or called., FastAPIInstrumentor.instrument_app() is NOT called when app argument is None. (+16 more)
|
||||
|
||||
### Community 19 - "Community 19"
|
||||
Cohesion: 0.08
|
||||
Nodes (36): _get(), Tests for rate limiting on the Discord OAuth2 auth endpoints. Covers: - /api/au, 3rd rapid request to /api/auth/login returns 429 when limit is 2/minute. Th, Two different X-Forwarded-For IPs have independent rate-limit buckets., 3rd rapid request to /api/auth/callback returns 429 when limit is 2/minute., When AUTH_DISABLED=true, 50 rapid requests to /login never return 429., Pathologically long X-Forwarded-For header is handled without error. Constr, Garbage XFF value falls back to the ASGI remote-address bucket. Sending ``X (+28 more)
|
||||
|
||||
### Community 20 - "Community 20"
|
||||
Cohesion: 0.12
|
||||
Nodes (34): _make_position(), _apply(), _entry_dict(), _preview_data(), Unit tests for the Suggest Post Assignments service. All tests use SimpleNamesp, Null suggested_member_id entries are skipped; no error raised., Position disabled since preview → 409 with reason position_disabled., Position set to reserve since preview → 409 with reason position_reserve. (+26 more)
|
||||
|
||||
### Community 21 - "Community 21"
|
||||
Cohesion: 0.09
|
||||
Nodes (31): build_group_structure(), compute_building_group_structure(), create_building_with_groups_and_positions(), import_file(), infer_building_level(), map_building_alias(), map_role(), parse_assignments_sheet() (+23 more)
|
||||
|
||||
### Community 22 - "Community 22"
|
||||
Cohesion: 0.1
|
||||
Nodes (17): apiClient, AppConfig, fetchConfig(), Layout(), navLinkClass(), renderLayout(), RequireAuth(), postsLink (+9 more)
|
||||
|
||||
### Community 23 - "Community 23"
|
||||
Cohesion: 0.1
|
||||
Nodes (17): downgrade(), initial schema Revision ID: 0001 Revises: Create Date: 2026-03-16, upgrade(), add autofill and attack day preview columns to siege Revision ID: 0002 Revises:, make siege date nullable Revision ID: 0003 Revises: 0002 Create Date: 2026-03-1, Add post_priority_config table, Add description to post_priority_config, Replace power with power_level, drop sort_value (+9 more)
|
||||
|
||||
### Community 24 - "Community 24"
|
||||
Cohesion: 0.1
|
||||
Nodes (24): API router for the Suggest Post Assignments feature. Routes: POST /sieges/{, Generate a greedy post-assignment suggestion preview. Args: siege_i, Apply a caller-filtered subset of the stored preview atomically. Uses SELEC, applyAutofill(), applyPostSuggestions(), previewAttackDay(), previewPostSuggestions(), AttackDayApplyResult (+16 more)
|
||||
|
||||
### Community 25 - "Community 25"
|
||||
Cohesion: 0.1
|
||||
Nodes (24): compareSieges(), get_most_recent_completed(), _load_assignments(), _load_member_names(), Return {member_id: [PositionKey, ...]} for non-reserve, non-disabled assigned po, _build_siege_assignments(), Tests for siege comparison service and endpoints., Build (Position, BuildingGroup, Building) rows for mock execute results. (+16 more)
|
||||
|
||||
### Community 26 - "Community 26"
|
||||
Cohesion: 0.1
|
||||
Nodes (24): AuthError, callback(), _check_guild_membership(), _error_redirect(), _exchange_code_for_token(), _get_discord_user(), login(), logout() (+16 more)
|
||||
|
||||
### Community 27 - "Community 27"
|
||||
Cohesion: 0.13
|
||||
Nodes (22): _async_client_that_raises(), _async_client_that_returns(), _make_ok_response(), Tests for bot HTTP client graceful degradation., get_member() returns the member dict when sidecar responds 200., get_member() returns the dict as-is when sidecar responds with is_member=False., get_member() raises httpx.HTTPError when the sidecar is unreachable., get_member() raises httpx.HTTPStatusError when sidecar returns 503. (+14 more)
|
||||
|
||||
### Community 28 - "Community 28"
|
||||
Cohesion: 0.09
|
||||
Nodes (9): Tests for scripts/import_excel.py parsing logic. All tests are pure-function te, Stronghold: 4 groups all with 3 slots., Only assignments for the specified (type, number) pair are counted., Returns None when the extracted date components don't form a valid date., 1 total position in a post -> level 1., test_build_group_structure_stronghold(), test_compute_building_group_structure_filters_by_building_number(), test_infer_building_level_post() (+1 more)
|
||||
|
||||
### Community 29 - "Community 29"
|
||||
Cohesion: 0.09
|
||||
Nodes (22): Tests for GET /api/version endpoint and _read_backend_version helper., GET /api/version responds 200 with all required fields., backend_version includes build metadata when env vars are present., backend_version is bare semver when BUILD_NUMBER/GIT_SHA are absent., git_sha top-level field is still returned for backward compatibility., Force re-import of version module so env-var reads pick up monkeypatches., bot_version is null when the bot sidecar is unreachable., When BUILD_NUMBER / GIT_SHA are absent, return bare semver. (+14 more)
|
||||
|
||||
### Community 30 - "Community 30"
|
||||
Cohesion: 0.1
|
||||
Nodes (16): get_team_count(), Return the theoretical total team slots for a building type at a given level., compute_scroll_count(), Compute total scroll count from the theoretical capacity of every building., Enable SQLite foreign key enforcement (no-op on PostgreSQL)., _enable_sqlite_fk(), Enable SQLite foreign key enforcement., compute_scroll_count returns the sum of _LEVEL_TEAMS capacities for all building (+8 more)
|
||||
|
||||
### Community 31 - "Community 31"
|
||||
Cohesion: 0.11
|
||||
Nodes (21): _make_mock_db(), A correct Bearer token grants access as the bot-service principal., Full valid callback flow issues a session cookie and redirects to /., Guild member WITH the required role passes the role check and proceeds normally., Return an AsyncMock session whose db.get() returns `member`., test_callback_happy_path(), test_callback_with_required_role_proceeds_to_member_lookup(), test_valid_service_token_allows_access() (+13 more)
|
||||
|
||||
### Community 32 - "Community 32"
|
||||
Cohesion: 0.15
|
||||
Nodes (20): PostSuggestionApplyRequest, Request body for POST /post-suggestions/apply. Attributes: apply_po, engine(), Integration tests for the Suggest Post Assignments feature. These tests use a r, Charge #12: selectinload chain works against real DB. Verifies that no Miss, Second preview within TTL overwrites first in the DB JSON column. Asserts b, Charge #17: apply → re-read position from DB → matched_condition_id set. Wi, Charge #22: member_changed reason fires when position written between preview an (+12 more)
|
||||
|
||||
### Community 33 - "Community 33"
|
||||
Cohesion: 0.1
|
||||
Nodes (17): _make_expired_jwt(), Tests for Discord OAuth2 auth middleware and endpoints., A wrong Bearer token is rejected with 401., An expired JWT cookie is rejected with 401., /api/version is accessible without any credentials., Mismatched OAuth state redirects to /login?error=invalid_state., Guild member response without role_names key is treated as no roles (rejected)., Bot sidecar connection error redirects to /login?error=service_unavailable. (+9 more)
|
||||
|
||||
### Community 34 - "Community 34"
|
||||
Cohesion: 0.22
|
||||
Nodes (17): generate_images(), Image generation endpoints., Generate PNG images for siege assignments and members list., getNotificationBatch(), notifySiegeMembers(), postToChannel(), Notification endpoints — send DMs and post images to Discord., Send DM notifications to all siege members asynchronously. (+9 more)
|
||||
|
||||
### Community 35 - "Community 35"
|
||||
Cohesion: 0.12
|
||||
Nodes (17): apiAddBuilding(), apiCreateMember(), apiCreateSiege(), autofillBtn, buildingsTab, chevron, firstPositionSpan, positionCell (+9 more)
|
||||
|
||||
### Community 36 - "Community 36"
|
||||
Cohesion: 0.11
|
||||
Nodes (13): PostSuggestionPreviewResult, applyBtn, board, btn, chip, labels, makePostBoard(), makeTwoPostBoard() (+5 more)
|
||||
|
||||
### Community 37 - "Community 37"
|
||||
Cohesion: 0.15
|
||||
Nodes (14): Send DMs for each member and record results in a fresh DB session., _send_dms(), Find member by username in the guild, open DM, send message., Find text channel by name, post message., Find text channel by name, post image as Discord file attachment. Retur, Discord client for the Siege Assignment System., SiegeBot, _get_bot() (+6 more)
|
||||
|
||||
### Community 38 - "Community 38"
|
||||
Cohesion: 0.14
|
||||
Nodes (13): getPostPriorities(), getPosts(), updatePostPriority(), getSiegeMemberPreferences(), DuplicateConditionMap, MemberWithMatches, postsTab, priorityBadgeColor() (+5 more)
|
||||
|
||||
### Community 39 - "Community 39"
|
||||
Cohesion: 0.19
|
||||
Nodes (15): bulk_update_positions(), list_posts(), Build a PostResponse-compatible dict, denormalizing building_number from the rel, _serialize_post(), setPostConditions(), updatePost(), Update a single position's assignment. Raises: 404 if position not, Apply multiple position updates in a single transaction. Each update dict m (+7 more)
|
||||
|
||||
### Community 40 - "Community 40"
|
||||
Cohesion: 0.17
|
||||
Nodes (14): VersionInfo, _fetch_bot_version(), getVersion(), Return a version string for the backend. When both BUILD_NUMBER and GIT_SHA, Call the bot sidecar's /version endpoint. Returns None if unreachable., Return version information for all components., _read_backend_version(), useVersion() (+6 more)
|
||||
|
||||
### Community 41 - "Community 41"
|
||||
Cohesion: 0.15
|
||||
Nodes (11): ChangelogStatus, fetchChangelogStatus(), DropdownMenuCheckboxItem, DropdownMenuContent, DropdownMenuItem, DropdownMenuLabel, DropdownMenuRadioItem, DropdownMenuSeparator (+3 more)
|
||||
|
||||
### Community 42 - "Community 42"
|
||||
Cohesion: 0.16
|
||||
Nodes (14): list_buildings(), list_members(), list_post_priorities(), list_siege_members(), delete_siege(), get_siege(), list_sieges(), previewAutofill() (+6 more)
|
||||
|
||||
### Community 43 - "Community 43"
|
||||
Cohesion: 0.13
|
||||
Nodes (14): db_session(), Schema constraint and seed data tests using in-memory SQLite., seed_building_type_config populates exactly 5 rows., Inserting a duplicate (siege_id, member_id) pair raises IntegrityError., Inserting two members with the same name must raise IntegrityError., The check constraint preventing is_reserve=True with a member_id is defined., slot_count check constraints (1–3) are declared on the table., seed_post_conditions populates exactly 36 rows. (+6 more)
|
||||
|
||||
### Community 44 - "Community 44"
|
||||
Cohesion: 0.14
|
||||
Nodes (13): Schema-aware tests for Member.last_seen_changelog_at (issue #295, AC 1). These, Mapped type annotation allows a datetime value. Assigning a real datetime m, Member mapper exposes last_seen_changelog_at as a mapped attribute., last_seen_changelog_at column is defined nullable=True. Null is the sentine, last_seen_changelog_at has no server-side default. Null is the intentional, last_seen_changelog_at uses SQLAlchemy DateTime (no timezone). Matches the, Mapped type annotation allows None (datetime | None). Constructing a Member, test_last_seen_changelog_at_accepts_datetime_at_python_level() (+5 more)
|
||||
|
||||
### Community 45 - "Community 45"
|
||||
Cohesion: 0.23
|
||||
Nodes (13): add_building(), add_group(), delete_group(), deleteBuilding(), getBuildings(), updateBuilding(), _create_groups_and_positions(), _get_building_type_config() (+5 more)
|
||||
|
||||
### Community 46 - "Community 46"
|
||||
Cohesion: 0.2
|
||||
Nodes (11): _make_post_priority_config(), _make_post_ns(), _make_src_building_ns(), _make_src_group_ns(), _make_src_position_ns(), Endpoint tests for siege lifecycle transitions: activate, complete, clone., Cloning a siege with stale priority=0 posts must use PostPriorityConfig.priority, test_activate_planning_siege() (+3 more)
|
||||
|
||||
### Community 47 - "Community 47"
|
||||
Cohesion: 0.36
|
||||
Nodes (12): _make_bot(), _make_guild(), _make_text_channel(), Unit tests for SiegeBot Discord client methods using mock guild/member objects., Create a SiegeBot instance with a pre-loaded guild (bypasses Discord connect)., test_get_members_returns_correct_dict_format(), test_post_image_returns_cdn_url(), test_post_message_finds_channel_and_sends() (+4 more)
|
||||
|
||||
### Community 48 - "Community 48"
|
||||
Cohesion: 0.15
|
||||
Nodes (13): _make_worksheet(), Create a mock openpyxl worksheet that yields the given rows., Parses name, power_level bucket, and role from correct column positions., Column E is parsed as a slash-separated list of keyword strings., Empty or None column E results in an empty list., test_parse_members_sheet_basic(), test_parse_members_sheet_post_preferences(), test_parse_members_sheet_skips_empty_rows() (+5 more)
|
||||
|
||||
### Community 49 - "Community 49"
|
||||
Cohesion: 0.25
|
||||
Nodes (10): get_changelog_status(), markChangelogSeen(), Changelog status and mark-seen endpoints. These endpoints let the frontend trac, Raise HTTP 400 if the caller is a service principal. Args: current_, Return the authenticated user's last-seen changelog timestamp. Args:, Set the authenticated user's last-seen changelog timestamp to now. Idempote, _require_member_session(), ChangelogStatusResponse (+2 more)
|
||||
|
||||
### Community 50 - "Community 50"
|
||||
Cohesion: 0.18
|
||||
Nodes (9): health(), get_guild_member(), NotifyRequest, PostMessageRequest, Look up a single guild member by Discord user ID. Returns ``{"is_member": f, Validate the Bearer token against the configured bot API key., Health check — no authentication required., set_bot() (+1 more)
|
||||
|
||||
### Community 51 - "Community 51"
|
||||
Cohesion: 0.2
|
||||
Nodes (8): Exception, disable_auth_for_tests(), _FakeClient, _FakeHTTPException, _FakeNotFound, _FakeTextChannel, Shared pytest configuration for backend tests. Sets required environment variab, Bypass auth middleware for all tests by default. Individual tests that exer
|
||||
|
||||
### Community 52 - "Community 52"
|
||||
Cohesion: 0.33
|
||||
Nodes (9): MemberRole, SyncMatch, SyncPreviewResponse, PowerLevel, MemberBase, MemberCreate, MemberPreferencesUpdate, MemberResponse (+1 more)
|
||||
|
||||
### Community 53 - "Community 53"
|
||||
Cohesion: 0.24
|
||||
Nodes (9): _get_client_ip(), _parse_retry_after_seconds(), rate_limit_exceeded_handler(), _rate_limit_key(), Rate-limiting utilities shared across the application. This module owns the sin, Composite key function that honours the AUTH_DISABLED bypass. When ``AUTH_D, Parse a slowapi rate-limit detail string into a window size in seconds. slo, Return a JSON 429 response with a ``Retry-After`` header. Replaces slowapi' (+1 more)
|
||||
|
||||
### Community 54 - "Community 54"
|
||||
Cohesion: 0.24
|
||||
Nodes (9): _build_valid_siege_graph(), Integration test: full siege lifecycle — create → validate → assign → activate →, Build a siege with exactly the right building counts and assigned active members, Return a mock async session that serves the siege + configs across multiple exec, Happy-path: validate (errors) → assign → validate (0 errors) → activate → comple, test_full_siege_lifecycle(), _make_session(), Return a minimal AsyncSession mock that scalar_one_or_none returns siege. (+1 more)
|
||||
|
||||
### Community 55 - "Community 55"
|
||||
Cohesion: 0.2
|
||||
Nodes (10): _make_jwt(), A valid JWT session cookie with an existing member grants access., A JWT signed with a different secret is rejected with 401., A valid JWT whose member no longer exists in the DB returns 401., GET /api/auth/me returns member info when authenticated via session cookie., test_jwt_with_deleted_member_returns_401(), test_jwt_with_wrong_secret_returns_401(), test_me_with_valid_session() (+2 more)
|
||||
|
||||
### Community 56 - "Community 56"
|
||||
Cohesion: 0.22
|
||||
Nodes (10): _make_empty_worksheet(), _make_session_mock(), _make_workbook_mock(), Worksheet that yields no rows., Minimal openpyxl workbook mock. Members / Assignments / Reserves are empty, AsyncMock session whose execute() returns a result whose scalars().all() is []., When is_most_recent=False, section 3c must not run even if the Posts sheet p, When is_most_recent=True and the DB has no PostPriorityConfig rows yet, sect (+2 more)
|
||||
|
||||
### Community 57 - "Community 57"
|
||||
Cohesion: 0.32
|
||||
Nodes (6): get_member(), getMemberPreferences(), Return list of guild members as dicts with id, username, and display_name., Retrieve guild member list., Check guild membership via bot sidecar. Returns the member dict (includi, deactivate_member()
|
||||
|
||||
### Community 58 - "Community 58"
|
||||
Cohesion: 0.25
|
||||
Nodes (8): _make_assignments_worksheet(), Build a mock assignments worksheet with the proper two header rows prepended., Rows with a None group cell are skipped., Whitespace-only cell values normalise to None., test_parse_assignments_sheet_empty_value_is_none(), test_parse_assignments_sheet_member_assignment(), test_parse_assignments_sheet_skips_incomplete_rows(), test_parse_assignments_sheet_skips_unknown_building_type()
|
||||
|
||||
### Community 59 - "Community 59"
|
||||
Cohesion: 0.25
|
||||
Nodes (8): _make_posts_config_worksheet(), Create a mock worksheet for parse_posts_sheet_config. The function calls ws, One high-priority section with two posts: both get priority=3 and correct descri, Rows before any priority header default to priority=1 (Low)., Posts fall into the correct priority section based on their position., test_parse_posts_sheet_config_basic(), test_parse_posts_sheet_config_default_priority(), test_parse_posts_sheet_config_multiple_sections()
|
||||
|
||||
### Community 60 - "Community 60"
|
||||
Cohesion: 0.29
|
||||
Nodes (3): test_create_member_returns_201(), test_delete_member_returns_204(), Endpoint tests for /api/sieges — mocks the service layer directly.
|
||||
|
||||
### Community 61 - "Community 61"
|
||||
Cohesion: 0.4
|
||||
Nodes (5): notify(), Send a DM notification to a guild member., BotClient, HTTP client for the Discord bot sidecar API., Send DM via bot. Returns True on success, False on error.
|
||||
|
||||
### Community 63 - "Community 63"
|
||||
Cohesion: 0.33
|
||||
Nodes (5): Tests for enum definitions and associated constants in app.models.enums., BUILDING_TYPE_LABELS values must be non-empty title-cased display strings., Every BuildingType member must have an entry in BUILDING_TYPE_LABELS. This, test_building_type_labels_are_friendly_strings(), test_building_type_labels_covers_all_values()
|
||||
|
||||
### Community 64 - "Community 64"
|
||||
Cohesion: 0.33
|
||||
Nodes (6): _make_posts_conditions_worksheet(), Create a mock worksheet for parse_posts_sheet_conditions. The function enum, Three post rows with 1–3 keywords each are parsed correctly., A row with all-None cells is not included in the result., test_parse_posts_sheet_conditions_basic(), test_parse_posts_sheet_conditions_skips_empty()
|
||||
|
||||
### Community 65 - "Community 65"
|
||||
Cohesion: 0.4
|
||||
Nodes (3): _make_post_condition(), Endpoint tests for /api reference data endpoints., test_get_post_conditions_returns_list()
|
||||
|
||||
### Community 67 - "Community 67"
|
||||
Cohesion: 0.4
|
||||
Nodes (5): Mana shrine with 2 groups: position 3 is an empty trailing sheet column for grou, Stronghold level 2 = 16 total slots = 5 full groups (3 slots each) + 1 last grou, Stronghold level 4 = 22 total slots = 7 full groups + 1 last group with 1 slot., Defense Tower level 4 = 6 total slots = 2 full groups × 3 slots each. At lev, test_compute_building_group_structure_basic()
|
||||
|
||||
### Community 69 - "Community 69"
|
||||
Cohesion: 0.5
|
||||
Nodes (3): configure_telemetry(), Application Insights / OpenTelemetry initialisation for the backend. Call ``con, Initialise Azure Monitor OpenTelemetry and instrument the app. The ``azure-
|
||||
|
||||
### Community 71 - "Community 71"
|
||||
Cohesion: 0.5
|
||||
Nodes (4): Magic tower (base_last_slots=2): position 3 is a trailing empty sheet column and, Magic tower at a higher level where position 3 is genuinely filled: slot_cou, Magic Tower level 3 = 4 total slots = 1 full group (3 slots) + 1 last group with, test_compute_building_group_structure_magic_tower_level3()
|
||||
|
||||
### Community 72 - "Community 72"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): Mana Shrine level 2 = 7 total slots = 2 full groups + 1 last group with 1 slot., Mana Shrine level 4 = 11 total slots = 3 full groups + 1 last group with 2 slots, test_compute_building_group_structure_mana_shrine_level2()
|
||||
|
||||
### Community 73 - "Community 73"
|
||||
Cohesion: 0.67
|
||||
Nodes (3): Returns None for a filename that does not match the pattern., Returns None for a completely unrelated filename., test_parse_filename_invalid_random()
|
||||
|
||||
## Knowledge Gaps
|
||||
- **752 isolated node(s):** `initial schema Revision ID: 0001 Revises: Create Date: 2026-03-16`, `add autofill and attack day preview columns to siege Revision ID: 0002 Revises:`, `make siege date nullable Revision ID: 0003 Revises: 0002 Create Date: 2026-03-1`, `Add post_priority_config table`, `Add description to post_priority_config` (+747 more)
|
||||
These have ≤1 connection - possible missing edges or undocumented components.
|
||||
- **52 thin communities (<3 nodes) omitted from report** — run `graphify query` to explore isolated nodes.
|
||||
|
||||
## Suggested Questions
|
||||
_Questions this graph is uniquely positioned to answer:_
|
||||
|
||||
- **Why does `_make_siege()` connect `Community 12` to `Community 0`, `Community 1`, `Community 3`, `Community 36`, `Community 6`, `Community 11`, `Community 46`, `Community 20`, `Community 54`, `Community 22`, `Community 25`, `Community 30`?**
|
||||
_High betweenness centrality (0.183) - this node is a cross-community bridge._
|
||||
- **Why does `SiegeMember` connect `Community 3` to `Community 1`, `Community 34`, `Community 35`, `Community 36`, `Community 6`, `Community 38`, `Community 8`, `Community 7`, `Community 42`, `Community 12`, `Community 14`, `Community 15`, `Community 16`, `Community 22`, `Community 24`, `Community 57`?**
|
||||
_High betweenness centrality (0.167) - this node is a cross-community bridge._
|
||||
- **Why does `list` connect `Community 42` to `Community 0`, `Community 1`, `Community 56`, `Community 5`, `Community 6`, `Community 39`, `Community 38`, `Community 45`, `Community 13`, `Community 15`, `Community 21`, `Community 24`, `Community 57`, `Community 58`?**
|
||||
_High betweenness centrality (0.089) - this node is a cross-community bridge._
|
||||
- **Are the 17 inferred relationships involving `SiegeMember` (e.g. with `Base` and `TestSeedDemoMembers`) actually correct?**
|
||||
_`SiegeMember` has 17 INFERRED edges - model-reasoned connections that need verification._
|
||||
- **What connects `initial schema Revision ID: 0001 Revises: Create Date: 2026-03-16`, `add autofill and attack day preview columns to siege Revision ID: 0002 Revises:`, `make siege date nullable Revision ID: 0003 Revises: 0002 Create Date: 2026-03-1` to the rest of the system?**
|
||||
_752 weakly-connected nodes found - possible documentation gaps or missing edges._
|
||||
- **Should `Community 0` be split into smaller, more focused modules?**
|
||||
_Cohesion score 0.05 - nodes in this community are weakly interconnected._
|
||||
- **Should `Community 1` be split into smaller, more focused modules?**
|
||||
_Cohesion score 0.05 - nodes in this community are weakly interconnected._
|
||||
@@ -0,0 +1,88 @@
|
||||
# Case Study: rsl-siege-manager (Python + TypeScript monorepo)
|
||||
|
||||
A graphify dry-run against [`glitchwerks/rsl-siege-manager`](https://github.com/glitchwerks/rsl-siege-manager) — a real-world web app with a Python (FastAPI) backend, a TypeScript (React + Vite) frontend, and a Python Discord bot, all in one repo. Captured here as a worked example of running graphify on a mixed-language full-stack codebase that includes a substantial test suite.
|
||||
|
||||
**Corpus:** `glitchwerks/rsl-siege-manager` @ commit `6085fd66`
|
||||
**Date:** 2026-05-15
|
||||
**Findings:** [`review.md`](./review.md)
|
||||
**Raw artifacts:** [`GRAPH_REPORT.md`](./GRAPH_REPORT.md), [`graph.html`](./graph.html), [`graph.json`](./graph.json), [`manifest.json`](./manifest.json)
|
||||
|
||||
## How to reproduce
|
||||
|
||||
### 1. Clone the corpus
|
||||
|
||||
```bash
|
||||
git clone https://github.com/glitchwerks/rsl-siege-manager
|
||||
cd rsl-siege-manager
|
||||
git checkout 6085fd66
|
||||
```
|
||||
|
||||
### 2. Install the CLI
|
||||
|
||||
```powershell
|
||||
uv tool install graphifyy
|
||||
```
|
||||
|
||||
> The PyPI package is `graphifyy` (double-y). The CLI command is `graphify`.
|
||||
|
||||
Verify it's on PATH:
|
||||
|
||||
```powershell
|
||||
graphify --version
|
||||
```
|
||||
|
||||
If "not recognized" on Windows, open a new shell — `uv tool` updates PATH but the current session won't see it.
|
||||
|
||||
### 3. Run extraction
|
||||
|
||||
This case study ran extraction with no `.graphifyignore` (the run included tests). To reproduce a tests-included run:
|
||||
|
||||
```powershell
|
||||
graphify extract .
|
||||
```
|
||||
|
||||
Watch terminal output. graphify uses tree-sitter for code (free, fast) and LLM API calls only for non-code files (markdown, PDFs, images). On a code-heavy repo like rsl-siege-manager, the cost is dominated by docs.
|
||||
|
||||
Check the cost summary when it finishes:
|
||||
|
||||
```powershell
|
||||
Get-Content .\graphify-out\cost.json
|
||||
```
|
||||
|
||||
### 4. Inspect
|
||||
|
||||
```powershell
|
||||
# The headline summary an assistant would read
|
||||
code .\graphify-out\GRAPH_REPORT.md
|
||||
|
||||
# The interactive graph
|
||||
Start-Process .\graphify-out\graph.html
|
||||
```
|
||||
|
||||
## What's in this directory
|
||||
|
||||
| File | What it is |
|
||||
|---|---|
|
||||
| `README.md` | This file — corpus description + reproduction steps |
|
||||
| `review.md` | Findings against the headline outputs in `GRAPH_REPORT.md` |
|
||||
| `GRAPH_REPORT.md` | Raw graphify report (god nodes, communities, surprising connections, suggested questions) |
|
||||
| `graph.html` | Interactive force-directed visualization |
|
||||
| `graph.json` | Underlying graph data used by `graphify query` |
|
||||
| `manifest.json` | Per-file extraction record |
|
||||
|
||||
The AST cache (`graphify-out/cache/`) is regenerable and not committed.
|
||||
|
||||
## Why this corpus
|
||||
|
||||
rsl-siege-manager is structurally interesting for graphify evaluation because:
|
||||
|
||||
- **Three services in one repo** — Python backend, TypeScript frontend, Python bot — exercises cross-language inference.
|
||||
- **Substantial test suite** — both Python (`backend/tests/`) and TypeScript (`frontend/src/**/__tests__/`) — surfaces how degree-centrality behaves on covered codebases.
|
||||
- **17 Alembic migrations** with revision docstrings — exercises how docstring-shaped content is or is not treated as graph entities.
|
||||
- **Clean three-tier architecture** — a developer can describe the structure in one sentence, giving a clear ground truth to evaluate community detection against.
|
||||
|
||||
## Reference
|
||||
|
||||
- graphify repo: https://github.com/safishamsi/graphify
|
||||
- graphify PyPI: https://pypi.org/project/graphifyy/
|
||||
- rsl-siege-manager: https://github.com/glitchwerks/rsl-siege-manager
|
||||
Vendored
+305
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,154 @@
|
||||
# Review: rsl-siege-manager
|
||||
|
||||
**Corpus:** [`glitchwerks/rsl-siege-manager`](https://github.com/glitchwerks/rsl-siege-manager) @ `6085fd66`
|
||||
**Date:** 2026-05-15
|
||||
**Run:** tests included, no `.graphifyignore`
|
||||
**Counts:** 1886 nodes · 3876 edges · 141 communities · 90% EXTRACTED / 10% INFERRED (avg INFERRED confidence 0.62)
|
||||
**Cost:** $0 (tree-sitter only; this corpus's natural file mix surfaced no non-code files in meaningful quantity)
|
||||
**Setup:** ~10 minutes of CLI time end-to-end
|
||||
|
||||
This review evaluates the **headline outputs** in `GRAPH_REPORT.md` — god nodes, surprising connections, communities, isolated nodes, suggested questions — against a single criterion: do they reflect things a developer familiar with this codebase would themselves nominate as core, surprising, or worth investigating? Each finding quotes the report directly so it is verifiable against the committed artifacts.
|
||||
|
||||
---
|
||||
|
||||
## Finding 1 — Test fixtures dominate "core abstractions" when tests are included
|
||||
|
||||
Top god nodes, verbatim from `GRAPH_REPORT.md` (lines 141–150):
|
||||
|
||||
```
|
||||
1. `_make_siege()` - 124 edges
|
||||
2. `_make_member()` - 92 edges
|
||||
3. `_make_position()` - 85 edges
|
||||
4. `SiegeMember` - 78 edges
|
||||
5. `_make_building()` - 64 edges
|
||||
6. `_make_group()` - 60 edges
|
||||
7. `BoardPage()` - 55 edges
|
||||
8. `makeSiegeMember()` - 55 edges
|
||||
9. `PostsPage()` - 37 edges
|
||||
10. `_session_with_siege_and_configs()` - 35 edges
|
||||
```
|
||||
|
||||
Six of the top ten — positions 1, 2, 3, 5, 6, and 8 — are test factory functions (`_make_siege`, `_make_member`, `_make_position`, `_make_building`, `_make_group`, `makeSiegeMember`). Position 10 is a test session fixture. These are the highest-connectivity nodes in the graph, and they are infrastructure for verifying the codebase, not the codebase itself.
|
||||
|
||||
The god-node list is labeled "your core abstractions" in the report. On this corpus, no developer would nominate `_make_siege()` as a core abstraction of a siege-assignment web app. Degree centrality on a well-tested codebase will tend to surface test factories: by design, factories create the primary domain objects that every test then exercises, so they accumulate edges from every part of the suite. The pattern is structural — the better the test coverage, the more saturated the factory's degree count.
|
||||
|
||||
For users running graphify on a codebase with substantial test coverage, the documented mitigation (a `.graphifyignore` excluding `tests/`, `__tests__/`, etc.) is effective at removing this class of false-positive. See Finding 2 for what the same corpus surfaces without tests.
|
||||
|
||||
## Finding 2 — Without tests, god nodes mix domain types with entry points and utilities
|
||||
|
||||
For comparison, an earlier run of the same corpus with `.graphifyignore` excluding `backend/tests/` and `frontend/src/**/__tests__/` produced this god-node list:
|
||||
|
||||
```
|
||||
1. `SiegeMember` - 55 edges
|
||||
2. `BoardPage()` - 53 edges
|
||||
3. `Post Suggestions Modal Handoff` - 40 edges
|
||||
4. `postsTab` - 29 edges
|
||||
5. `cn()` - 29 edges
|
||||
6. `PostsPage()` - 28 edges
|
||||
7. `BuildingType` - 27 edges
|
||||
8. `MembersPage()` - 27 edges
|
||||
9. `MemberRole` - 25 edges
|
||||
10. `Self-Host on Azure Wiki Page` - 23 edges
|
||||
```
|
||||
|
||||
(That run's artifacts are not committed here — only the tests-included artifacts are kept — but the list is reproducible by re-running with the same `.graphifyignore`.)
|
||||
|
||||
Three of ten — `SiegeMember`, `BuildingType`, `MemberRole` (positions 1, 7, 9) — are legitimate domain models a developer would identify.
|
||||
|
||||
The other seven illustrate a second pattern worth knowing about:
|
||||
|
||||
- **React top-level page components** (`BoardPage`, `PostsPage`, `MembersPage` at 2, 6, 8) — these import many things because they are entry points, not because they encode domain logic.
|
||||
- **Class-merge utilities** — `cn()` (position 5) is a one-line Tailwind class-merging utility (`src/lib/utils.ts`). It scores 29 edges because every component that conditionally combines classes imports it. The connection count reflects a structural pattern (the React+Tailwind idiom), not semantic importance.
|
||||
- **Document/wiki entities** — `Post Suggestions Modal Handoff` (position 3) is a planning document under `docs/design-refs/`; `Self-Host on Azure Wiki Page` (position 10) is a wiki article. Both are extracted as nodes alongside code entities and sort by edge count the same way.
|
||||
|
||||
This is informative for users tuning expectations: degree centrality on a React frontend with a shared `cn()` utility will surface that utility regardless of how aggressive the ignore filter is, because the connections are real even though the semantic importance is low.
|
||||
|
||||
## Finding 3 — Surprising connections cross language boundaries
|
||||
|
||||
Both runs surface INFERRED edges between Python backend types and TypeScript frontend types under "Surprising Connections (you probably didn't know these)." Examples:
|
||||
|
||||
```
|
||||
- `AuthError` --uses--> `Member` [INFERRED]
|
||||
backend/app/api/auth.py → frontend/src/api/types.ts
|
||||
```
|
||||
|
||||
```
|
||||
- `TestStartupValidation` --uses--> `MemberRole` [INFERRED]
|
||||
backend/tests/test_auth.py → frontend/src/api/types.ts
|
||||
```
|
||||
|
||||
A Python class does not "use" a TypeScript type at runtime. These are name-based similarity matches between identically-named constructs in two languages, surfaced as semantic relationships. The confidence values are flagged as INFERRED (avg 0.62 on this run), but the section header presents them as insights worth investigating.
|
||||
|
||||
The one cross-language INFERRED edge in this run that maps to a real design contract is:
|
||||
|
||||
```
|
||||
- `PostPriorityResponse` --uses--> `PostPriorityConfig` [INFERRED]
|
||||
backend/app/api/post_priority_config.py → frontend/src/api/posts.ts
|
||||
```
|
||||
|
||||
The backend Pydantic schema and the frontend TypeScript type do mirror each other intentionally — this is the API contract. That relationship is real, but a developer who wrote either side already knows about it; INFERRED detection here recovers a fact rather than discovering one.
|
||||
|
||||
For corpora that mix Python and TypeScript with overlapping type names (common in monorepos with shared domain vocabulary), users should expect a high false-positive rate in this section and read it with the INFERRED confidence in mind.
|
||||
|
||||
## Finding 4 — Community cohesion is uniformly low on this corpus
|
||||
|
||||
Most of rsl-siege-manager's 141 communities (tests-included run) score between 0.05 and 0.17 on cohesion. The report itself flags 0.05 as "weakly interconnected" in the Suggested Questions section:
|
||||
|
||||
> "Should `Community 0` be split into smaller, more focused modules? — Cohesion score 0.05 - nodes in this community are weakly interconnected."
|
||||
|
||||
Community 0 has 112 nodes on this corpus.
|
||||
|
||||
rsl-siege-manager has a clean three-service architecture (backend / frontend / bot). The community-detection pass does not recover that structure as three large cohesive communities; it produces many small communities with low internal cohesion. Possible interpretations: the underlying graph has many INFERRED cross-language edges diluting the cluster signal, the chosen algorithm parameters favor over-segmentation on graphs of this density, or this corpus genuinely lacks tight intra-cluster topology that community detection can exploit.
|
||||
|
||||
A neutral framing for users: community cohesion is informative on this corpus mainly as a signal that the graph topology does not match the obvious three-tier mental model — which itself may be useful (it surfaces that the cross-language edges are doing the work). The "split this community" prompts the report generates from low cohesion scores are less actionable as direct architectural advice on this corpus.
|
||||
|
||||
## Finding 5 — Alembic migration docstrings surface as isolated nodes
|
||||
|
||||
The tests-included run reports 752 isolated nodes; the without-tests run reports 259. The leading examples are identical in both:
|
||||
|
||||
```
|
||||
`initial schema Revision ID: 0001 Revises: Create Date: 2026-03-16`,
|
||||
`add autofill and attack day preview columns to siege Revision ID: 0002 Revises:`,
|
||||
`make siege date nullable Revision ID: 0003 Revises: 0002 Create Date: 2026-03-1`,
|
||||
`Add post_priority_config table`, ...
|
||||
```
|
||||
|
||||
These are Alembic migration revision docstrings parsed as standalone graph nodes. There are 17 migration files in this corpus. The isolated-node count growing from 259 to 752 when tests are added is dominated by test docstrings parsed the same way.
|
||||
|
||||
The report labels these as "possible documentation gaps or missing edges." For users with corpora that contain Alembic migrations, pytest docstrings, or other docstring-heavy auto-generated files, this label can be misleading — these are change annotations or test descriptions, not architectural entities with missing connections.
|
||||
|
||||
A `.graphifyignore` rule for `**/alembic/versions/*.py` reduces the isolated-node count materially on this corpus; users with similar setups may want a default recipe.
|
||||
|
||||
## Finding 6 — Suggested questions skew toward graph-property prompts
|
||||
|
||||
The Suggested Questions section consists primarily of two kinds of questions:
|
||||
|
||||
1. **Betweenness centrality prompts:** "Why does `_make_siege()` connect Community 12 to Community 0, Community 1, Community 3...?" The answer on this corpus is direct: `_make_siege()` creates the primary domain object, every test that touches a siege uses it, and the test suite spans the codebase — so the fixture is a betweenness bridge by construction.
|
||||
|
||||
2. **Inferred-edge audits:** "Are the 17 inferred relationships involving `SiegeMember` (e.g. with `Base` and `TestSeedDemoMembers`) actually correct?" These ask the developer to validate the tool's own low-confidence connections.
|
||||
|
||||
A genuinely concrete question in this section — "What is the exact relationship between `bootstrap-images.ps1` and `scripts/bootstrap-images.ps1`?" — is tagged AMBIGUOUS by the report. The answer is concrete (they are the same script referenced by two slightly different paths), but it is a file-naming observation, not an architectural insight.
|
||||
|
||||
For users hoping the Suggested Questions section will surface domain-level prompts ("how does authentication flow?", "what enforces the siege-day invariant?"), this corpus's section is dominated by graph-property meta-questions.
|
||||
|
||||
---
|
||||
|
||||
## What worked well on this corpus
|
||||
|
||||
- **Throughput:** 1886 nodes extracted in a few minutes at $0 cost. tree-sitter handles all 29 supported languages locally; LLM calls fire only for non-code files.
|
||||
- **Genuine domain extractions are present:** `SiegeMember`, `BuildingType`, and `MemberRole` in the god-node list are correct. `PostPriorityResponse --uses--> PostPriorityConfig` is a real API contract that INFERRED detection picked up across the Python/TypeScript boundary.
|
||||
- **`graph.html` visualization:** clear, navigable, easy to filter and search. Useful for browsing communities even when the labelled cohesion scores are low.
|
||||
|
||||
The underlying graph build is solid. The findings above are about how the report layer summarizes that graph into headline metrics — specifically, what those metrics surface on a well-tested cross-language full-stack web app.
|
||||
|
||||
---
|
||||
|
||||
## Suggested follow-ups
|
||||
|
||||
Patterns from this review that may be worth tracking upstream:
|
||||
|
||||
1. **Test-fixture suppression** — degree centrality on covered codebases consistently surfaces test factories; documenting the ignore-pattern recipe in a "first run on a codebase with tests" section would shorten the iteration loop for users.
|
||||
2. **Cross-language INFERRED edges in monorepos** — name-based matches between Python and TypeScript types in mixed-language repos may warrant a higher confidence threshold or a "potential contract" label rather than the current "surprising connection" framing.
|
||||
3. **Docstring-heavy files (Alembic, pytest)** — defaulting to skip migration `versions/` directories, or detecting and grouping docstring nodes that share a structural pattern, would reduce the isolated-node noise materially.
|
||||
|
||||
These are observations, not change requests — users running graphify on similar corpora may find the same patterns useful to know about.
|
||||
Reference in New Issue
Block a user