Skip to content

docs: lead Quickstart with pytest path and expected output #7

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
AshSgDe29071999 wants to merge 2 commits into from
Closed

docs: lead Quickstart with pytest path and expected output #7

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3c8a1c6
docs: lead Quickstart with pytest path and expected output
AshSgDe29071999 Aug 10, 2026
f9fabf9
docs: show concrete expected output in Quickstart
AshSgDe29071999 Aug 10, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
37 changes: 33 additions & 4 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,21 +67,50 @@ sparse-only modes are exposed too, so the benchmark can compare all three.
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

# Run the real benchmark (downloads the embedding models on first run).
tenantq benchmark --embedder fastembed
# Fastest first check (no model downloads; used by CI):
pytest -q
```

Expected output (counts may grow as tests are added):

# Fully offline (deterministic hash embedder, no downloads) — used by CI/tests:
```
........ [100%]
```

```bash
# Fully offline benchmark (deterministic hash embedder, no downloads):
tenantq benchmark --embedder hash
```

Expected shape (absolute numbers vary by machine; modes and columns do not):

```
[ingest] 450 points in 0.42s (1071 pts/s)
### ingest throughput: 1071 pts/s

HNSW m=16 ef_construct=100 hnsw_ef=64 multitenant=True payload_m=16 docs=450 queries=30

| mode | recall@5 | recall@10 | index_recall@10 | p50 ms | p95 ms | p99 ms | qps |
| --- | --- | --- | --- | --- | --- | --- | --- |
| dense | 0.xxx | 0.xxx | 0.xxx | ... | ... | ... | ... |
| sparse | 0.xxx | 0.xxx | n/a | ... | ... | ... | ... |
| hybrid | 0.xxx | 0.xxx | n/a | ... | ... | ... | ... |
```

```bash
# Full embedder (downloads models on first run):
tenantq benchmark --embedder fastembed

# One-off tenant-scoped search against an ingested collection:
tenantq search "vector similarity ranking" --tenant acme --mode hybrid
tenantq search "neural networks" --tenant acme --mode hybrid --limit 5 --embedder hash
```

By default everything runs against an in-process Qdrant (`:memory:`), which
supports sparse vectors and Query API fusion — so the benchmark produces genuine
numbers with zero infrastructure. Point at a real server by setting `QDRANT_URL`
(see Docker below).


## Configuration

Everything is env-overridable (`Settings.from_env()`) or a CLI flag:
Expand Down
Loading