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Safi
44 lines
1.8 KiB
Markdown
44 lines
1.8 KiB
Markdown
# httpx Corpus Benchmark
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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.
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## Corpus (6 files)
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```
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raw/
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├── exceptions.py — HTTPError hierarchy
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├── models.py — URL, Headers, Cookies, Request, Response
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├── auth.py — BasicAuth, BearerAuth, DigestAuth, NetRCAuth
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├── utils.py — header normalization, query params, content-type parsing
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├── transport.py — ConnectionPool, HTTPTransport, AsyncHTTPTransport, MockTransport
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└── client.py — Timeout, Limits, BaseClient, Client, AsyncClient
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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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## What to expect
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- 144 nodes, 330 edges, 6 communities
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- God nodes: `Client`, `AsyncClient`, `Response`, `Request`, `BaseClient`, `HTTPTransport`
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- Surprising connection: `DigestAuth` linked to `Response` — auth.py reads Response to parse WWW-Authenticate headers
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- Token reduction: ~1x — 6 files fits in a context window, so there is no compression win here
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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.
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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`.
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