Faster Update (#148)

* stash

* stash

* add std err in add and trace progress

* fix.

* docs

* style: format

* docs

* better figs

* better figs

* update results

* fotmat

---------

Co-authored-by: yichuan-w <[email protected]>

Author: andylizf

benchmarks/update/README.md

@@ -0,0 +1,143 @@
+# Update Benchmarks
+
+This directory hosts two benchmark suites that exercise LEANN’s HNSW “update +
+search” pipeline under different assumptions:
+
+1. **RNG recompute latency** – measure how random-neighbour pruning and cache
+   settings influence incremental `add()` latency when embeddings are fetched
+   over the ZMQ embedding server.
+2. **Update strategy comparison** – compare a fully sequential update pipeline
+   against an offline approach that keeps the graph static and fuses results.
+
+Both suites build a non-compact, `is_recompute=True` index so that new
+embeddings are pulled from the embedding server. Benchmark outputs are written
+under `.leann/bench/` by default and appended to CSV files for later plotting.
+
+## Benchmarks
+
+### 1. HNSW RNG Recompute Benchmark
+
+`bench_hnsw_rng_recompute.py` evaluates incremental update latency under four
+random-neighbour (RNG) configurations. Each scenario uses the same dataset but
+changes the forward / reverse RNG pruning flags and whether the embedding cache
+is enabled:
+
+| Scenario name                      | Forward RNG | Reverse RNG | ZMQ embedding cache |
+| ---------------------------------- | ----------- | ----------- | ------------------- |
+| `baseline`                         | Enabled     | Enabled     | Enabled             |
+| `no_cache_baseline`                | Enabled     | Enabled     | **Disabled**        |
+| `disable_forward_rng`              | **Disabled**| Enabled     | Enabled             |
+| `disable_forward_and_reverse_rng`  | **Disabled**| **Disabled**| Enabled             |
+
+For each scenario the script:
+1. (Re)builds a `is_recompute=True` index and writes it to `.leann/bench/`.
+2. Starts `leann_backend_hnsw.hnsw_embedding_server` for remote embeddings.
+3. Appends the requested updates using the scenario’s RNG flags.
+4. Records total time, latency per passage, ZMQ fetch counts, and stage-level
+   timings before appending a row to the CSV output.
+
+**Run:**
+```bash
+LEANN_HNSW_LOG_PATH=.leann/bench/hnsw_server.log \
+LEANN_LOG_LEVEL=INFO \
+uv run -m benchmarks.update.bench_hnsw_rng_recompute \
+  --runs 1 \
+  --index-path .leann/bench/test.leann \
+  --initial-files data/PrideandPrejudice.txt \
+  --update-files data/huawei_pangu.md \
+  --max-initial 300 \
+  --max-updates 1 \
+  --add-timeout 120
+```
+
+**Output:**
+- `benchmarks/update/bench_results.csv` – per-scenario timing statistics
+  (including ms/passage) for each run.
+- `.leann/bench/hnsw_server.log` – detailed ZMQ/server logs (path controlled by
+  `LEANN_HNSW_LOG_PATH`).
+  _The reference CSVs checked into this branch were generated on a workstation with an NVIDIA RTX 4090 GPU; throughput numbers will differ on other hardware._
+
+### 2. Sequential vs. Offline Update Benchmark
+
+`bench_update_vs_offline_search.py` compares two end-to-end strategies on the
+same dataset:
+
+- **Scenario A – Sequential Update**
+  - Start an embedding server.
+  - Sequentially call `index.add()`; each call fetches embeddings via ZMQ and
+    mutates the HNSW graph.
+  - After all inserts, run a search on the updated graph.
+  - Metrics recorded: update time (`add_total_s`), post-update search time
+    (`search_time_s`), combined total (`total_time_s`), and per-passage
+    latency.
+
+- **Scenario B – Offline Embedding + Concurrent Search**
+  - Stop Scenario A’s server and start a fresh embedding server.
+  - Spawn two threads: one generates embeddings for the new passages offline
+    (graph unchanged); the other computes the query embedding and searches the
+    existing graph.
+  - Merge offline similarities with the graph search results to emulate late
+    fusion, then report the merged top‑k preview.
+  - Metrics recorded: embedding time (`emb_time_s`), search time
+    (`search_time_s`), concurrent makespan (`makespan_s`), and scenario total.
+
+**Run (both scenarios):**
+```bash
+uv run -m benchmarks.update.bench_update_vs_offline_search \
+  --index-path .leann/bench/offline_vs_update.leann \
+  --max-initial 300 \
+  --num-updates 1
+```
+
+You can pass `--only A` or `--only B` to run a single scenario. The script will
+print timing summaries to stdout and append the results to CSV.
+
+**Output:**
+- `benchmarks/update/offline_vs_update.csv` – per-scenario timing statistics for
+  Scenario A and B.
+- Console output includes Scenario B’s merged top‑k preview for quick sanity
+  checks.
+  _The sample results committed here come from runs on an RTX 4090-equipped machine; expect variations if you benchmark on different GPUs._
+
+### 3. Visualisation
+
+`plot_bench_results.py` combines the RNG benchmark and the update strategy
+benchmark into a single two-panel plot.
+
+**Run:**
+```bash
+uv run -m benchmarks.update.plot_bench_results \
+  --csv benchmarks/update/bench_results.csv \
+  --csv-right benchmarks/update/offline_vs_update.csv \
+  --out benchmarks/update/bench_latency_from_csv.png
+```
+
+**Options:**
+- `--broken-y` – Enable a broken Y-axis (default: true when appropriate).
+- `--csv` – RNG benchmark results CSV (left panel).
+- `--csv-right` – Update strategy results CSV (right panel).
+- `--out` – Output image path (PNG/PDF supported).
+
+**Output:**
+- `benchmarks/update/bench_latency_from_csv.png` – visual comparison of the two
+  suites.
+- `benchmarks/update/bench_latency_from_csv.pdf` – PDF version, suitable for
+  slides/papers.
+
+## Parameters & Environment
+
+### Common CLI Flags
+- `--max-initial` – Number of initial passages used to seed the index.
+- `--max-updates` / `--num-updates` – Number of passages to treat as updates.
+- `--index-path` – Base path (without extension) where the LEANN index is stored.
+- `--runs` – Number of repetitions (RNG benchmark only).
+
+### Environment Variables
+- `LEANN_HNSW_LOG_PATH` – File to receive embedding-server logs (optional).
+- `LEANN_LOG_LEVEL` – Logging verbosity (DEBUG/INFO/WARNING/ERROR).
+- `CUDA_VISIBLE_DEVICES` – Set to empty string if you want to force CPU
+  execution of the embedding model.
+
+With these scripts you can easily replicate LEANN’s update benchmarks, compare
+multiple RNG strategies, and evaluate whether sequential updates or offline
+fusion better match your latency/accuracy trade-offs.

benchmarks/update/__init__.py

@@ -0,0 +1,16 @@
+"""Benchmarks for LEANN update workflows."""
+
+# Expose helper to locate repository root for other modules that need it.
+from pathlib import Path
+
+
+def find_repo_root() -> Path:
+    """Return the project root containing pyproject.toml."""
+    current = Path(__file__).resolve()
+    for parent in current.parents:
+        if (parent / "pyproject.toml").exists():
+            return parent
+    return current.parents[1]
+
+
+__all__ = ["find_repo_root"]