diff --git a/.github/workflows/build-reusable.yml b/.github/workflows/build-reusable.yml
index d0bfb16..6dfb43d 100644
--- a/.github/workflows/build-reusable.yml
+++ b/.github/workflows/build-reusable.yml
@@ -17,26 +17,17 @@ jobs:
- uses: actions/checkout@v4
with:
ref: ${{ inputs.ref }}
+ submodules: recursive
- - name: Setup Python
- uses: actions/setup-python@v5
+ - name: Install uv and Python
+ uses: astral-sh/setup-uv@v6
with:
python-version: '3.11'
- - name: Install uv
- uses: astral-sh/setup-uv@v4
-
- - name: Install ruff
+ - name: Run pre-commit with only lint group (no project deps)
run: |
- uv tool install ruff
+ uv run --only-group lint pre-commit run --all-files --show-diff-on-failure
- - name: Run ruff check
- run: |
- ruff check .
-
- - name: Run ruff format check
- run: |
- ruff format --check .
build:
needs: lint
@@ -103,14 +94,11 @@ jobs:
ref: ${{ inputs.ref }}
submodules: recursive
- - name: Setup Python
- uses: actions/setup-python@v5
+ - name: Install uv and Python
+ uses: astral-sh/setup-uv@v6
with:
python-version: ${{ matrix.python }}
- - name: Install uv
- uses: astral-sh/setup-uv@v6
-
- name: Install system dependencies (Ubuntu)
if: runner.os == 'Linux'
run: |
@@ -168,11 +156,24 @@ jobs:
- name: Install build dependencies
run: |
- uv pip install --system scikit-build-core numpy swig Cython pybind11
- if [[ "$RUNNER_OS" == "Linux" ]]; then
- uv pip install --system auditwheel
+ uv python install ${{ matrix.python }}
+ uv venv --python ${{ matrix.python }} .uv-build
+ if [[ "$RUNNER_OS" == "Windows" ]]; then
+ BUILD_PY=".uv-build\\Scripts\\python.exe"
else
- uv pip install --system delocate
+ BUILD_PY=".uv-build/bin/python"
+ fi
+ uv pip install --python "$BUILD_PY" scikit-build-core numpy swig Cython pybind11
+ if [[ "$RUNNER_OS" == "Linux" ]]; then
+ uv pip install --python "$BUILD_PY" auditwheel
+ else
+ uv pip install --python "$BUILD_PY" delocate
+ fi
+
+ if [[ "$RUNNER_OS" == "Windows" ]]; then
+ echo "$(pwd)\\.uv-build\\Scripts" >> $GITHUB_PATH
+ else
+ echo "$(pwd)/.uv-build/bin" >> $GITHUB_PATH
fi
- name: Set macOS environment variables
@@ -308,18 +309,66 @@ jobs:
- name: Install built packages for testing
run: |
- # Create a virtual environment with the correct Python version
+ # Create uv-managed virtual environment with the requested interpreter
+ uv python install ${{ matrix.python }}
uv venv --python ${{ matrix.python }}
source .venv/bin/activate || source .venv/Scripts/activate
- # Install packages using --find-links to prioritize local builds
- uv pip install --find-links packages/leann-core/dist --find-links packages/leann-backend-hnsw/dist --find-links packages/leann-backend-diskann/dist packages/leann-core/dist/*.whl || uv pip install --find-links packages/leann-core/dist packages/leann-core/dist/*.tar.gz
- uv pip install --find-links packages/leann-core/dist packages/leann-backend-hnsw/dist/*.whl
- uv pip install --find-links packages/leann-core/dist packages/leann-backend-diskann/dist/*.whl
- uv pip install packages/leann/dist/*.whl || uv pip install packages/leann/dist/*.tar.gz
+ if [[ "$RUNNER_OS" == "Windows" ]]; then
+ UV_PY=".venv\\Scripts\\python.exe"
+ else
+ UV_PY=".venv/bin/python"
+ fi
- # Install test dependencies using extras
- uv pip install -e ".[test]"
+ # Install test dependency group only (avoids reinstalling project package)
+ uv pip install --python "$UV_PY" --group test
+
+ # Install core wheel built in this job
+ CORE_WHL=$(find packages/leann-core/dist -maxdepth 1 -name "*.whl" -print -quit)
+ if [[ -n "$CORE_WHL" ]]; then
+ uv pip install --python "$UV_PY" "$CORE_WHL"
+ else
+ uv pip install --python "$UV_PY" packages/leann-core/dist/*.tar.gz
+ fi
+
+ PY_TAG=$($UV_PY -c "import sys; print(f'cp{sys.version_info[0]}{sys.version_info[1]}')")
+
+ if [[ "$RUNNER_OS" == "macOS" ]]; then
+ if [[ "${{ matrix.os }}" == "macos-13" ]]; then
+ export MACOSX_DEPLOYMENT_TARGET=13.3
+ elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
+ export MACOSX_DEPLOYMENT_TARGET=14.0
+ elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
+ export MACOSX_DEPLOYMENT_TARGET=15.0
+ fi
+ fi
+
+ HNSW_WHL=$(find packages/leann-backend-hnsw/dist -maxdepth 1 -name "*-${PY_TAG}-*.whl" -print -quit)
+ if [[ -z "$HNSW_WHL" ]]; then
+ HNSW_WHL=$(find packages/leann-backend-hnsw/dist -maxdepth 1 -name "*-py3-*.whl" -print -quit)
+ fi
+ if [[ -n "$HNSW_WHL" ]]; then
+ uv pip install --python "$UV_PY" "$HNSW_WHL"
+ else
+ uv pip install --python "$UV_PY" ./packages/leann-backend-hnsw
+ fi
+
+ DISKANN_WHL=$(find packages/leann-backend-diskann/dist -maxdepth 1 -name "*-${PY_TAG}-*.whl" -print -quit)
+ if [[ -z "$DISKANN_WHL" ]]; then
+ DISKANN_WHL=$(find packages/leann-backend-diskann/dist -maxdepth 1 -name "*-py3-*.whl" -print -quit)
+ fi
+ if [[ -n "$DISKANN_WHL" ]]; then
+ uv pip install --python "$UV_PY" "$DISKANN_WHL"
+ else
+ uv pip install --python "$UV_PY" ./packages/leann-backend-diskann
+ fi
+
+ LEANN_WHL=$(find packages/leann/dist -maxdepth 1 -name "*.whl" -print -quit)
+ if [[ -n "$LEANN_WHL" ]]; then
+ uv pip install --python "$UV_PY" "$LEANN_WHL"
+ else
+ uv pip install --python "$UV_PY" packages/leann/dist/*.tar.gz
+ fi
- name: Run tests with pytest
env:
diff --git a/.gitignore b/.gitignore
index 4e02b04..2dd0ad8 100755
--- a/.gitignore
+++ b/.gitignore
@@ -95,12 +95,6 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
batchtest.py
tests/__pytest_cache__/
tests/__pycache__/
-paru-bin/
-
-CLAUDE.md
-CLAUDE.local.md
-.claude/*.local.*
-.claude/local/*
benchmarks/data/
## multi vector
diff --git a/README.md b/README.md
index 8c63d89..0e7904e 100755
--- a/README.md
+++ b/README.md
@@ -774,9 +774,8 @@ results = searcher.search("banana‑crocodile", use_grep=True, top_k=1)
## Reproduce Our Results
```bash
-uv pip install -e ".[dev]" # Install dev dependencies
-python benchmarks/run_evaluation.py # Will auto-download evaluation data and run benchmarks
-python benchmarks/run_evaluation.py benchmarks/data/indices/rpj_wiki/rpj_wiki --num-queries 2000 # After downloading data, you can run the benchmark with our biggest index
+uv run benchmarks/run_evaluation.py # Will auto-download evaluation data and run benchmarks
+uv run benchmarks/run_evaluation.py benchmarks/data/indices/rpj_wiki/rpj_wiki --num-queries 2000 # After downloading data, you can run the benchmark with our biggest index
```
The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!
diff --git a/benchmarks/__init__.py b/benchmarks/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/benchmarks/bm25_diskann_baselines/README.md b/benchmarks/bm25_diskann_baselines/README.md
new file mode 100644
index 0000000..cd624ef
--- /dev/null
+++ b/benchmarks/bm25_diskann_baselines/README.md
@@ -0,0 +1,23 @@
+BM25 vs DiskANN Baselines
+
+```bash
+aws s3 sync s3://powerrag-diskann-rpj-wiki-20250824-224037-194d640c/bm25_rpj_wiki/index_en_only/ benchmarks/data/indices/bm25_index/
+aws s3 sync s3://powerrag-diskann-rpj-wiki-20250824-224037-194d640c/diskann_rpj_wiki/ benchmarks/data/indices/diskann_rpj_wiki/
+```
+
+- Dataset: `benchmarks/data/queries/nq_open.jsonl` (Natural Questions)
+- Machine-specific; results measured locally with the current repo.
+
+DiskANN (NQ queries, search-only)
+- Command: `uv run --script benchmarks/bm25_diskann_baselines/run_diskann.py`
+- Settings: `recompute_embeddings=False`, embeddings precomputed (excluded from timing), batching off, caching off (`cache_mechanism=2`, `num_nodes_to_cache=0`)
+- Result: avg 0.011093 s/query, QPS 90.15 (p50 0.010731 s, p95 0.015000 s)
+
+BM25
+- Command: `uv run --script benchmarks/bm25_diskann_baselines/run_bm25.py`
+- Settings: `k=10`, `k1=0.9`, `b=0.4`, queries=100
+- Result: avg 0.028589 s/query, QPS 34.97 (p50 0.026060 s, p90 0.043695 s, p95 0.053260 s, p99 0.055257 s)
+
+Notes
+- DiskANN measures search-only latency on real NQ queries (embeddings computed beforehand and excluded from timing).
+- Use `benchmarks/bm25_diskann_baselines/run_diskann.py` for DiskANN; `benchmarks/bm25_diskann_baselines/run_bm25.py` for BM25.
diff --git a/benchmarks/bm25_diskann_baselines/run_bm25.py b/benchmarks/bm25_diskann_baselines/run_bm25.py
new file mode 100644
index 0000000..f10ad93
--- /dev/null
+++ b/benchmarks/bm25_diskann_baselines/run_bm25.py
@@ -0,0 +1,183 @@
+# /// script
+# dependencies = [
+# "pyserini"
+# ]
+# ///
+# sudo pacman -S jdk21-openjdk
+# export JAVA_HOME=/usr/lib/jvm/java-21-openjdk
+# sudo archlinux-java status
+# sudo archlinux-java set java-21-openjdk
+# set -Ux JAVA_HOME /usr/lib/jvm/java-21-openjdk
+# fish_add_path --global $JAVA_HOME/bin
+# set -Ux LD_LIBRARY_PATH $JAVA_HOME/lib/server $LD_LIBRARY_PATH
+# which javac # Should be /usr/lib/jvm/java-21-openjdk/bin/javac
+
+import argparse
+import json
+import os
+import sys
+import time
+from statistics import mean
+
+
+def load_queries(path: str, limit: int | None) -> list[str]:
+ queries: list[str] = []
+ # Try JSONL with a 'query' or 'text' field; fallback to plain text (one query per line)
+ _, ext = os.path.splitext(path)
+ if ext.lower() in {".jsonl", ".json"}:
+ with open(path, encoding="utf-8") as f:
+ for line in f:
+ line = line.strip()
+ if not line:
+ continue
+ try:
+ obj = json.loads(line)
+ except json.JSONDecodeError:
+ # Not strict JSONL? treat the whole line as the query
+ queries.append(line)
+ continue
+ q = obj.get("query") or obj.get("text") or obj.get("question")
+ if q:
+ queries.append(str(q))
+ else:
+ with open(path, encoding="utf-8") as f:
+ for line in f:
+ s = line.strip()
+ if s:
+ queries.append(s)
+
+ if limit is not None and limit > 0:
+ queries = queries[:limit]
+ return queries
+
+
+def percentile(values: list[float], p: float) -> float:
+ if not values:
+ return 0.0
+ s = sorted(values)
+ k = (len(s) - 1) * (p / 100.0)
+ f = int(k)
+ c = min(f + 1, len(s) - 1)
+ if f == c:
+ return s[f]
+ return s[f] + (s[c] - s[f]) * (k - f)
+
+
+def main():
+ ap = argparse.ArgumentParser(description="Standalone BM25 latency benchmark (Pyserini)")
+ ap.add_argument(
+ "--bm25-index",
+ default="benchmarks/data/indices/bm25_index",
+ help="Path to Pyserini Lucene index directory",
+ )
+ ap.add_argument(
+ "--queries",
+ default="benchmarks/data/queries/nq_open.jsonl",
+ help="Path to queries file (JSONL with 'query'/'text' or plain txt one-per-line)",
+ )
+ ap.add_argument("--k", type=int, default=10, help="Top-k to retrieve (default: 10)")
+ ap.add_argument("--k1", type=float, default=0.9, help="BM25 k1 (default: 0.9)")
+ ap.add_argument("--b", type=float, default=0.4, help="BM25 b (default: 0.4)")
+ ap.add_argument("--limit", type=int, default=100, help="Max queries to run (default: 100)")
+ ap.add_argument(
+ "--warmup", type=int, default=5, help="Warmup queries not counted in latency (default: 5)"
+ )
+ ap.add_argument(
+ "--fetch-docs", action="store_true", help="Also fetch doc contents (slower; default: off)"
+ )
+ ap.add_argument("--report", type=str, default=None, help="Optional JSON report path")
+ args = ap.parse_args()
+
+ try:
+ from pyserini.search.lucene import LuceneSearcher
+ except Exception:
+ print("Pyserini not found. Install with: pip install pyserini", file=sys.stderr)
+ raise
+
+ if not os.path.isdir(args.bm25_index):
+ print(f"Index directory not found: {args.bm25_index}", file=sys.stderr)
+ sys.exit(1)
+
+ queries = load_queries(args.queries, args.limit)
+ if not queries:
+ print("No queries loaded.", file=sys.stderr)
+ sys.exit(1)
+
+ print(f"Loaded {len(queries)} queries from {args.queries}")
+ print(f"Opening BM25 index: {args.bm25_index}")
+ searcher = LuceneSearcher(args.bm25_index)
+ # Some builds of pyserini require explicit set_bm25; others ignore
+ try:
+ searcher.set_bm25(k1=args.k1, b=args.b)
+ except Exception:
+ pass
+
+ latencies: list[float] = []
+ total_searches = 0
+
+ # Warmup
+ for i in range(min(args.warmup, len(queries))):
+ _ = searcher.search(queries[i], k=args.k)
+
+ t0 = time.time()
+ for i, q in enumerate(queries):
+ t1 = time.time()
+ hits = searcher.search(q, k=args.k)
+ t2 = time.time()
+ latencies.append(t2 - t1)
+ total_searches += 1
+
+ if args.fetch_docs:
+ # Optional doc fetch to include I/O time
+ for h in hits:
+ try:
+ _ = searcher.doc(h.docid)
+ except Exception:
+ pass
+
+ if (i + 1) % 50 == 0:
+ print(f"Processed {i + 1}/{len(queries)} queries")
+
+ t1 = time.time()
+ total_time = t1 - t0
+
+ if latencies:
+ avg = mean(latencies)
+ p50 = percentile(latencies, 50)
+ p90 = percentile(latencies, 90)
+ p95 = percentile(latencies, 95)
+ p99 = percentile(latencies, 99)
+ qps = total_searches / total_time if total_time > 0 else 0.0
+ else:
+ avg = p50 = p90 = p95 = p99 = qps = 0.0
+
+ print("BM25 Latency Report")
+ print(f" queries: {total_searches}")
+ print(f" k: {args.k}, k1: {args.k1}, b: {args.b}")
+ print(f" avg per query: {avg:.6f} s")
+ print(f" p50/p90/p95/p99: {p50:.6f}/{p90:.6f}/{p95:.6f}/{p99:.6f} s")
+ print(f" total time: {total_time:.3f} s, qps: {qps:.2f}")
+
+ if args.report:
+ payload = {
+ "queries": total_searches,
+ "k": args.k,
+ "k1": args.k1,
+ "b": args.b,
+ "avg_s": avg,
+ "p50_s": p50,
+ "p90_s": p90,
+ "p95_s": p95,
+ "p99_s": p99,
+ "total_time_s": total_time,
+ "qps": qps,
+ "index_dir": os.path.abspath(args.bm25_index),
+ "fetch_docs": bool(args.fetch_docs),
+ }
+ with open(args.report, "w", encoding="utf-8") as f:
+ json.dump(payload, f, indent=2)
+ print(f"Saved report to {args.report}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/bm25_diskann_baselines/run_diskann.py b/benchmarks/bm25_diskann_baselines/run_diskann.py
new file mode 100644
index 0000000..a4dd558
--- /dev/null
+++ b/benchmarks/bm25_diskann_baselines/run_diskann.py
@@ -0,0 +1,124 @@
+# /// script
+# dependencies = [
+# "leann-backend-diskann"
+# ]
+# ///
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+
+
+def load_queries(path: Path, limit: int | None) -> list[str]:
+ out: list[str] = []
+ with open(path, encoding="utf-8") as f:
+ for line in f:
+ obj = json.loads(line)
+ out.append(obj["query"])
+ if limit and len(out) >= limit:
+ break
+ return out
+
+
+def main() -> None:
+ ap = argparse.ArgumentParser(
+ description="DiskANN baseline on real NQ queries (search-only timing)"
+ )
+ ap.add_argument(
+ "--index-dir",
+ default="benchmarks/data/indices/diskann_rpj_wiki",
+ help="Directory containing DiskANN files",
+ )
+ ap.add_argument("--index-prefix", default="ann")
+ ap.add_argument("--queries-file", default="benchmarks/data/queries/nq_open.jsonl")
+ ap.add_argument("--num-queries", type=int, default=200)
+ ap.add_argument("--top-k", type=int, default=10)
+ ap.add_argument("--complexity", type=int, default=62)
+ ap.add_argument("--threads", type=int, default=1)
+ ap.add_argument("--beam-width", type=int, default=1)
+ ap.add_argument("--cache-mechanism", type=int, default=2)
+ ap.add_argument("--num-nodes-to-cache", type=int, default=0)
+ args = ap.parse_args()
+
+ index_dir = Path(args.index_dir).resolve()
+ if not index_dir.is_dir():
+ raise SystemExit(f"Index dir not found: {index_dir}")
+
+ qpath = Path(args.queries_file).resolve()
+ if not qpath.exists():
+ raise SystemExit(f"Queries file not found: {qpath}")
+
+ queries = load_queries(qpath, args.num_queries)
+ print(f"Loaded {len(queries)} queries from {qpath}")
+
+ # Compute embeddings once (exclude from timing)
+ from leann.api import compute_embeddings as _compute
+
+ embs = _compute(
+ queries,
+ model_name="facebook/contriever-msmarco",
+ mode="sentence-transformers",
+ use_server=False,
+ ).astype(np.float32)
+ if embs.ndim != 2:
+ raise SystemExit("Embedding compute failed or returned wrong shape")
+
+ # Build searcher
+ from leann_backend_diskann.diskann_backend import DiskannSearcher as _DiskannSearcher
+
+ index_prefix_path = str(index_dir / args.index_prefix)
+ searcher = _DiskannSearcher(
+ index_prefix_path,
+ num_threads=int(args.threads),
+ cache_mechanism=int(args.cache_mechanism),
+ num_nodes_to_cache=int(args.num_nodes_to_cache),
+ )
+
+ # Warmup (not timed)
+ _ = searcher.search(
+ embs[0:1],
+ top_k=args.top_k,
+ complexity=args.complexity,
+ beam_width=args.beam_width,
+ prune_ratio=0.0,
+ recompute_embeddings=False,
+ batch_recompute=False,
+ dedup_node_dis=False,
+ )
+
+ # Timed loop
+ times: list[float] = []
+ for i in range(embs.shape[0]):
+ t0 = time.time()
+ _ = searcher.search(
+ embs[i : i + 1],
+ top_k=args.top_k,
+ complexity=args.complexity,
+ beam_width=args.beam_width,
+ prune_ratio=0.0,
+ recompute_embeddings=False,
+ batch_recompute=False,
+ dedup_node_dis=False,
+ )
+ times.append(time.time() - t0)
+
+ times_sorted = sorted(times)
+ avg = float(sum(times) / len(times))
+ p50 = times_sorted[len(times) // 2]
+ p95 = times_sorted[max(0, int(len(times) * 0.95) - 1)]
+
+ print("\nDiskANN (NQ, search-only) Report")
+ print(f" queries: {len(times)}")
+ print(
+ f" k: {args.top_k}, complexity: {args.complexity}, beam_width: {args.beam_width}, threads: {args.threads}"
+ )
+ print(f" avg per query: {avg:.6f} s")
+ print(f" p50/p95: {p50:.6f}/{p95:.6f} s")
+ print(f" QPS: {1.0 / avg:.2f}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/enron_emails/README.md b/benchmarks/enron_emails/README.md
new file mode 100644
index 0000000..fdeae69
--- /dev/null
+++ b/benchmarks/enron_emails/README.md
@@ -0,0 +1,141 @@
+# Enron Emails Benchmark
+
+A comprehensive RAG benchmark for evaluating LEANN search and generation on the Enron email corpus. It mirrors the structure and CLI of the existing FinanceBench and LAION benches, using stage-based evaluation with Recall@3 and generation timing.
+
+- Dataset: Enron email CSV (e.g., Kaggle wcukierski/enron-email-dataset) for passages
+- Queries: corbt/enron_emails_sample_questions (filtered for realistic questions)
+- Metrics: Recall@3 vs FAISS Flat baseline + Generation evaluation with Qwen3-8B
+
+## Layout
+
+benchmarks/enron_emails/
+- setup_enron_emails.py: Prepare passages, build LEANN index, build FAISS baseline
+- evaluate_enron_emails.py: Evaluate retrieval recall (Stages 2-5) + generation with Qwen3-8B
+- data/: Generated passages, queries, embeddings-related files
+- baseline/: FAISS Flat baseline files
+- llm_utils.py: LLM utilities for Qwen3-8B generation (in parent directory)
+
+## Quickstart
+
+1) Prepare the data and index
+
+cd benchmarks/enron_emails
+python setup_enron_emails.py --data-dir data
+
+Notes:
+- If `--emails-csv` is omitted, the script attempts to download from Kaggle dataset `wcukierski/enron-email-dataset` using Kaggle API (requires `KAGGLE_USERNAME` and `KAGGLE_KEY`).
+ Alternatively, pass a local path to `--emails-csv`.
+
+Notes:
+- The script parses emails, chunks header/body into passages, builds a compact LEANN index, and then builds a FAISS Flat baseline from the same passages and embedding model.
+- Optionally, it will also create evaluation queries from HuggingFace dataset `corbt/enron_emails_sample_questions`.
+
+2) Run recall evaluation (Stage 2)
+
+python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 2
+
+3) Complexity sweep (Stage 3)
+
+python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 3 --target-recall 0.90 --max-queries 200
+
+Stage 3 uses binary search over complexity to find the minimal value achieving the target Recall@3 (assumes recall is non-decreasing with complexity). The search expands the upper bound as needed and snaps complexity to multiples of 8.
+
+4) Index comparison (Stage 4)
+
+python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 4 --complexity 88 --max-queries 100 --output results.json
+
+5) Generation evaluation (Stage 5)
+
+python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 5 --complexity 88 --llm-backend hf --model-name Qwen/Qwen3-8B
+
+6) Combined index + generation evaluation (Stages 4+5, recommended)
+
+python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 45 --complexity 88 --llm-backend hf
+
+Notes:
+- Minimal CLI: you can run from repo root with only `--index`, defaults match financebench/laion patterns:
+ - `--stage` defaults to `all` (runs 2, 3, 4, 5)
+ - `--baseline-dir` defaults to `baseline`
+ - `--queries` defaults to `data/evaluation_queries.jsonl` (or falls back to the index directory)
+ - `--llm-backend` defaults to `hf` (HuggingFace), can use `vllm`
+ - `--model-name` defaults to `Qwen/Qwen3-8B`
+- Fail-fast behavior: no silent fallbacks. If compact index cannot run with recompute, it errors out.
+- Stage 5 requires Stage 4 retrieval results. Use `--stage 45` to run both efficiently.
+
+Optional flags:
+- --queries data/evaluation_queries.jsonl (custom queries file)
+- --baseline-dir baseline (where FAISS baseline lives)
+- --complexity 88 (LEANN complexity parameter, optimal for 90% recall)
+- --llm-backend hf|vllm (LLM backend for generation)
+- --model-name Qwen/Qwen3-8B (LLM model for generation)
+- --max-queries 1000 (limit number of queries for evaluation)
+
+## Files Produced
+- data/enron_passages_preview.jsonl: Small preview of passages used (for inspection)
+- data/enron_index_hnsw.leann.*: LEANN index files
+- baseline/faiss_flat.index + baseline/metadata.pkl: FAISS baseline with passage IDs
+- data/evaluation_queries.jsonl: Query file (id + query; includes GT IDs for reference)
+
+## Notes
+- Evaluates both retrieval Recall@3 and generation timing with Qwen3-8B thinking model.
+- The emails CSV must contain a column named "message" (raw RFC822 email) and a column named "file" for source identifier. Message-ID headers are parsed as canonical message IDs when present.
+- Qwen3-8B requires special handling for thinking models with chat templates and tag processing.
+
+## Stages Summary
+
+- Stage 2 (Recall@3):
+ - Compares LEANN vs FAISS Flat baseline on Recall@3.
+ - Compact index runs with `recompute_embeddings=True`.
+
+- Stage 3 (Binary Search for Complexity):
+ - Builds a non-compact index (`_noncompact.leann`) and runs binary search with `recompute_embeddings=False` to find the minimal complexity achieving target Recall@3 (default 90%).
+
+- Stage 4 (Index Comparison):
+ - Reports .index-only sizes for compact vs non-compact.
+ - Measures timings on queries by default: non-compact (no recompute) vs compact (with recompute).
+ - Stores retrieval results for Stage 5 generation evaluation.
+ - Fails fast if compact recompute cannot run.
+ - If `--complexity` is not provided, the script tries to use the best complexity from Stage 3:
+ - First from the current run (when running `--stage all`), otherwise
+ - From `enron_stage3_results.json` saved next to the index during the last Stage 3 run.
+ - If neither exists, Stage 4 will error and ask you to run Stage 3 or pass `--complexity`.
+
+- Stage 5 (Generation Evaluation):
+ - Uses Qwen3-8B thinking model for RAG generation on retrieved documents from Stage 4.
+ - Supports HuggingFace (`hf`) and vLLM (`vllm`) backends.
+ - Measures generation timing separately from search timing.
+ - Requires Stage 4 results (no additional searching performed).
+
+## Example Results
+
+These are sample results obtained on Enron data using all-mpnet-base-v2 and Qwen3-8B.
+
+- Stage 3 (Binary Search):
+ - Minimal complexity achieving 90% Recall@3: 88
+ - Sampled points:
+ - C=8 → 59.9% Recall@3
+ - C=72 → 89.4% Recall@3
+ - C=88 → 90.2% Recall@3
+ - C=96 → 90.7% Recall@3
+ - C=112 → 91.1% Recall@3
+ - C=136 → 91.3% Recall@3
+ - C=256 → 92.0% Recall@3
+
+- Stage 4 (Index Sizes, .index only):
+ - Compact: ~2.2 MB
+ - Non-compact: ~82.0 MB
+ - Storage saving by compact: ~97.3%
+
+- Stage 4 (Search Timing, 988 queries, complexity=88):
+ - Non-compact (no recompute): ~0.0075 s avg per query
+ - Compact (with recompute): ~1.981 s avg per query
+ - Speed ratio (non-compact/compact): ~0.0038x
+
+- Stage 5 (RAG Generation, 988 queries, Qwen3-8B):
+ - Average generation time: ~22.302 s per query
+ - Total queries processed: 988
+ - LLM backend: HuggingFace transformers
+ - Model: Qwen/Qwen3-8B (thinking model with processing)
+
+Full JSON output is saved by the script (see `--output`), e.g.:
+`benchmarks/enron_emails/results_enron_stage45.json`.
diff --git a/benchmarks/enron_emails/data/.gitignore b/benchmarks/enron_emails/data/.gitignore
new file mode 100644
index 0000000..c4ffae9
--- /dev/null
+++ b/benchmarks/enron_emails/data/.gitignore
@@ -0,0 +1 @@
+downloads/
diff --git a/benchmarks/enron_emails/evaluate_enron_emails.py b/benchmarks/enron_emails/evaluate_enron_emails.py
new file mode 100644
index 0000000..c6e8518
--- /dev/null
+++ b/benchmarks/enron_emails/evaluate_enron_emails.py
@@ -0,0 +1,614 @@
+"""
+Enron Emails Benchmark Evaluation - Retrieval Recall@3 (Stages 2/3/4)
+Follows the style of FinanceBench/LAION: Stage 2 recall vs FAISS baseline,
+Stage 3 complexity sweep to target recall, Stage 4 index comparison.
+On errors, fail fast without fallbacks.
+"""
+
+import argparse
+import json
+import logging
+import os
+import pickle
+from pathlib import Path
+
+import numpy as np
+from leann import LeannBuilder, LeannSearcher
+from leann_backend_hnsw import faiss
+
+from ..llm_utils import generate_hf, generate_vllm, load_hf_model, load_vllm_model
+
+# Setup logging to reduce verbose output
+logging.basicConfig(level=logging.WARNING)
+logging.getLogger("leann.api").setLevel(logging.WARNING)
+logging.getLogger("leann_backend_hnsw").setLevel(logging.WARNING)
+
+
+class RecallEvaluator:
+ """Stage 2: Evaluate Recall@3 (LEANN vs FAISS)"""
+
+ def __init__(self, index_path: str, baseline_dir: str):
+ self.index_path = index_path
+ self.baseline_dir = baseline_dir
+ self.searcher = LeannSearcher(index_path)
+
+ baseline_index_path = os.path.join(baseline_dir, "faiss_flat.index")
+ metadata_path = os.path.join(baseline_dir, "metadata.pkl")
+
+ self.faiss_index = faiss.read_index(baseline_index_path)
+ with open(metadata_path, "rb") as f:
+ self.passage_ids = pickle.load(f)
+
+ print(f"📚 Loaded FAISS flat baseline with {self.faiss_index.ntotal} vectors")
+
+ # No fallbacks here; if embedding server is needed but fails, the caller will see the error.
+
+ def evaluate_recall_at_3(
+ self, queries: list[str], complexity: int = 64, recompute_embeddings: bool = True
+ ) -> float:
+ """Evaluate recall@3 using FAISS Flat as ground truth"""
+ from leann.api import compute_embeddings
+
+ recompute_str = "with recompute" if recompute_embeddings else "no recompute"
+ print(f"🔍 Evaluating recall@3 with complexity={complexity} ({recompute_str})...")
+
+ total_recall = 0.0
+ for i, query in enumerate(queries):
+ # Compute query embedding with the same model/mode as the index
+ q_emb = compute_embeddings(
+ [query],
+ self.searcher.embedding_model,
+ mode=self.searcher.embedding_mode,
+ use_server=False,
+ ).astype(np.float32)
+
+ # Search FAISS Flat ground truth
+ n = q_emb.shape[0]
+ k = 3
+ distances = np.zeros((n, k), dtype=np.float32)
+ labels = np.zeros((n, k), dtype=np.int64)
+ self.faiss_index.search(
+ n,
+ faiss.swig_ptr(q_emb),
+ k,
+ faiss.swig_ptr(distances),
+ faiss.swig_ptr(labels),
+ )
+
+ baseline_ids = {self.passage_ids[idx] for idx in labels[0]}
+
+ # Search with LEANN (may require embedding server depending on index configuration)
+ results = self.searcher.search(
+ query,
+ top_k=3,
+ complexity=complexity,
+ recompute_embeddings=recompute_embeddings,
+ )
+ test_ids = {r.id for r in results}
+
+ intersection = test_ids.intersection(baseline_ids)
+ recall = len(intersection) / 3.0
+ total_recall += recall
+
+ if i < 3:
+ print(f" Q{i + 1}: '{query[:60]}...' -> Recall@3: {recall:.3f}")
+ print(f" FAISS: {list(baseline_ids)}")
+ print(f" LEANN: {list(test_ids)}")
+ print(f" ∩: {list(intersection)}")
+
+ avg = total_recall / max(1, len(queries))
+ print(f"📊 Average Recall@3: {avg:.3f} ({avg * 100:.1f}%)")
+ return avg
+
+ def cleanup(self):
+ if hasattr(self, "searcher"):
+ self.searcher.cleanup()
+
+
+class EnronEvaluator:
+ def __init__(self, index_path: str):
+ self.index_path = index_path
+ self.searcher = LeannSearcher(index_path)
+
+ def load_queries(self, queries_file: str) -> list[str]:
+ queries: list[str] = []
+ with open(queries_file, encoding="utf-8") as f:
+ for line in f:
+ if not line.strip():
+ continue
+ data = json.loads(line)
+ if "query" in data:
+ queries.append(data["query"])
+ print(f"📊 Loaded {len(queries)} queries from {queries_file}")
+ return queries
+
+ def cleanup(self):
+ if self.searcher:
+ self.searcher.cleanup()
+
+ def analyze_index_sizes(self) -> dict:
+ """Analyze index sizes (.index only), similar to LAION bench."""
+
+ print("📏 Analyzing index sizes (.index only)...")
+ index_path = Path(self.index_path)
+ index_dir = index_path.parent
+ index_name = index_path.stem
+
+ sizes: dict[str, float] = {}
+ index_file = index_dir / f"{index_name}.index"
+ meta_file = index_dir / f"{index_path.name}.meta.json"
+ passages_file = index_dir / f"{index_path.name}.passages.jsonl"
+ passages_idx_file = index_dir / f"{index_path.name}.passages.idx"
+
+ sizes["index_only_mb"] = (
+ index_file.stat().st_size / (1024 * 1024) if index_file.exists() else 0.0
+ )
+ sizes["metadata_mb"] = (
+ meta_file.stat().st_size / (1024 * 1024) if meta_file.exists() else 0.0
+ )
+ sizes["passages_text_mb"] = (
+ passages_file.stat().st_size / (1024 * 1024) if passages_file.exists() else 0.0
+ )
+ sizes["passages_index_mb"] = (
+ passages_idx_file.stat().st_size / (1024 * 1024) if passages_idx_file.exists() else 0.0
+ )
+
+ print(f" 📁 .index size: {sizes['index_only_mb']:.1f} MB")
+ return sizes
+
+ def create_non_compact_index_for_comparison(self, non_compact_index_path: str) -> dict:
+ """Create a non-compact index for comparison using current passages and embeddings."""
+
+ current_index_path = Path(self.index_path)
+ current_index_dir = current_index_path.parent
+ current_index_name = current_index_path.name
+
+ # Read metadata to get passage source and embedding model
+ meta_path = current_index_dir / f"{current_index_name}.meta.json"
+ with open(meta_path, encoding="utf-8") as f:
+ meta = json.load(f)
+
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not Path(passage_file).is_absolute():
+ passage_file = current_index_dir / Path(passage_file).name
+
+ # Load all passages and ids
+ ids: list[str] = []
+ texts: list[str] = []
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ ids.append(str(data["id"]))
+ texts.append(data["text"])
+
+ # Compute embeddings using the same method as LEANN
+ from leann.api import compute_embeddings
+
+ embeddings = compute_embeddings(
+ texts,
+ meta["embedding_model"],
+ mode=meta.get("embedding_mode", "sentence-transformers"),
+ use_server=False,
+ ).astype(np.float32)
+
+ # Build non-compact index with same passages and embeddings
+ builder = LeannBuilder(
+ backend_name="hnsw",
+ embedding_model=meta["embedding_model"],
+ embedding_mode=meta.get("embedding_mode", "sentence-transformers"),
+ is_recompute=False,
+ is_compact=False,
+ **{
+ k: v
+ for k, v in meta.get("backend_kwargs", {}).items()
+ if k not in ["is_recompute", "is_compact"]
+ },
+ )
+
+ # Persist a pickle for build_index_from_embeddings
+ pkl_path = current_index_dir / f"{Path(non_compact_index_path).stem}_embeddings.pkl"
+ with open(pkl_path, "wb") as pf:
+ pickle.dump((ids, embeddings), pf)
+
+ print(
+ f"🔨 Building non-compact index at {non_compact_index_path} from precomputed embeddings..."
+ )
+ builder.build_index_from_embeddings(non_compact_index_path, str(pkl_path))
+
+ # Analyze the non-compact index size
+ temp_evaluator = EnronEvaluator(non_compact_index_path)
+ non_compact_sizes = temp_evaluator.analyze_index_sizes()
+ non_compact_sizes["index_type"] = "non_compact"
+
+ return non_compact_sizes
+
+ def compare_index_performance(
+ self, non_compact_path: str, compact_path: str, test_queries: list[str], complexity: int
+ ) -> dict:
+ """Compare search speed for non-compact vs compact indexes."""
+ import time
+
+ results: dict = {
+ "non_compact": {"search_times": []},
+ "compact": {"search_times": []},
+ "avg_search_times": {},
+ "speed_ratio": 0.0,
+ "retrieval_results": [], # Store retrieval results for Stage 5
+ }
+
+ print("⚡ Comparing search performance between indexes...")
+ # Non-compact (no recompute)
+ print(" 🔍 Testing non-compact index (no recompute)...")
+ non_compact_searcher = LeannSearcher(non_compact_path)
+ for q in test_queries:
+ t0 = time.time()
+ _ = non_compact_searcher.search(
+ q, top_k=3, complexity=complexity, recompute_embeddings=False
+ )
+ results["non_compact"]["search_times"].append(time.time() - t0)
+
+ # Compact (with recompute). Fail fast if it cannot run.
+ print(" 🔍 Testing compact index (with recompute)...")
+ compact_searcher = LeannSearcher(compact_path)
+ for q in test_queries:
+ t0 = time.time()
+ docs = compact_searcher.search(
+ q, top_k=3, complexity=complexity, recompute_embeddings=True
+ )
+ results["compact"]["search_times"].append(time.time() - t0)
+
+ # Store retrieval results for Stage 5
+ results["retrieval_results"].append(
+ {"query": q, "retrieved_docs": [{"id": doc.id, "text": doc.text} for doc in docs]}
+ )
+ compact_searcher.cleanup()
+
+ if results["non_compact"]["search_times"]:
+ results["avg_search_times"]["non_compact"] = sum(
+ results["non_compact"]["search_times"]
+ ) / len(results["non_compact"]["search_times"])
+ if results["compact"]["search_times"]:
+ results["avg_search_times"]["compact"] = sum(results["compact"]["search_times"]) / len(
+ results["compact"]["search_times"]
+ )
+ if results["avg_search_times"].get("compact", 0) > 0:
+ results["speed_ratio"] = (
+ results["avg_search_times"]["non_compact"] / results["avg_search_times"]["compact"]
+ )
+ else:
+ results["speed_ratio"] = 0.0
+
+ non_compact_searcher.cleanup()
+ return results
+
+ def evaluate_complexity(
+ self,
+ recall_eval: "RecallEvaluator",
+ queries: list[str],
+ target: float = 0.90,
+ c_min: int = 8,
+ c_max: int = 256,
+ max_iters: int = 10,
+ recompute: bool = False,
+ ) -> dict:
+ """Binary search minimal complexity achieving target recall (monotonic assumption)."""
+
+ def round_c(x: int) -> int:
+ # snap to multiple of 8 like other benches typically do
+ return max(1, int((x + 7) // 8) * 8)
+
+ metrics: list[dict] = []
+
+ lo = round_c(c_min)
+ hi = round_c(c_max)
+
+ print(
+ f"🧪 Binary search complexity in [{lo}, {hi}] for target Recall@3>={int(target * 100)}%..."
+ )
+
+ # Ensure upper bound can reach target; expand if needed (up to a cap)
+ r_lo = recall_eval.evaluate_recall_at_3(
+ queries, complexity=lo, recompute_embeddings=recompute
+ )
+ metrics.append({"complexity": lo, "recall_at_3": r_lo})
+ r_hi = recall_eval.evaluate_recall_at_3(
+ queries, complexity=hi, recompute_embeddings=recompute
+ )
+ metrics.append({"complexity": hi, "recall_at_3": r_hi})
+
+ cap = 1024
+ while r_hi < target and hi < cap:
+ lo = hi
+ r_lo = r_hi
+ hi = round_c(hi * 2)
+ r_hi = recall_eval.evaluate_recall_at_3(
+ queries, complexity=hi, recompute_embeddings=recompute
+ )
+ metrics.append({"complexity": hi, "recall_at_3": r_hi})
+
+ if r_hi < target:
+ print(f"⚠️ Max complexity {hi} did not reach target recall {target:.2f}.")
+ print("📈 Observations:")
+ for m in metrics:
+ print(f" C={m['complexity']:>4} -> Recall@3={m['recall_at_3'] * 100:.1f}%")
+ return {"metrics": metrics, "best_complexity": None, "target_recall": target}
+
+ # Binary search within [lo, hi]
+ best = hi
+ iters = 0
+ while lo < hi and iters < max_iters:
+ mid = round_c((lo + hi) // 2)
+ r_mid = recall_eval.evaluate_recall_at_3(
+ queries, complexity=mid, recompute_embeddings=recompute
+ )
+ metrics.append({"complexity": mid, "recall_at_3": r_mid})
+ if r_mid >= target:
+ best = mid
+ hi = mid
+ else:
+ lo = mid + 8 # move past mid, respecting multiple-of-8 step
+ iters += 1
+
+ print("📈 Binary search results (sampled points):")
+ # Print unique complexity entries ordered by complexity
+ for m in sorted(
+ {m["complexity"]: m for m in metrics}.values(), key=lambda x: x["complexity"]
+ ):
+ print(f" C={m['complexity']:>4} -> Recall@3={m['recall_at_3'] * 100:.1f}%")
+ print(f"✅ Minimal complexity achieving {int(target * 100)}% recall: {best}")
+ return {"metrics": metrics, "best_complexity": best, "target_recall": target}
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Enron Emails Benchmark Evaluation")
+ parser.add_argument("--index", required=True, help="Path to LEANN index")
+ parser.add_argument(
+ "--queries", default="data/evaluation_queries.jsonl", help="Path to evaluation queries"
+ )
+ parser.add_argument(
+ "--stage",
+ choices=["2", "3", "4", "5", "all", "45"],
+ default="all",
+ help="Which stage to run (2=recall, 3=complexity, 4=index comparison, 5=generation)",
+ )
+ parser.add_argument("--complexity", type=int, default=None, help="LEANN search complexity")
+ parser.add_argument("--baseline-dir", default="baseline", help="Baseline output directory")
+ parser.add_argument(
+ "--max-queries", type=int, help="Limit number of queries to evaluate", default=1000
+ )
+ parser.add_argument(
+ "--target-recall", type=float, default=0.90, help="Target Recall@3 for Stage 3"
+ )
+ parser.add_argument("--output", help="Save results to JSON file")
+ parser.add_argument("--llm-backend", choices=["hf", "vllm"], default="hf", help="LLM backend")
+ parser.add_argument("--model-name", default="Qwen/Qwen3-8B", help="Model name")
+
+ args = parser.parse_args()
+
+ # Resolve queries file: if default path not found, fall back to index's directory
+ if not os.path.exists(args.queries):
+ from pathlib import Path
+
+ idx_dir = Path(args.index).parent
+ fallback_q = idx_dir / "evaluation_queries.jsonl"
+ if fallback_q.exists():
+ args.queries = str(fallback_q)
+
+ baseline_index_path = os.path.join(args.baseline_dir, "faiss_flat.index")
+ if not os.path.exists(baseline_index_path):
+ print(f"❌ FAISS baseline not found at {baseline_index_path}")
+ print("💡 Please run setup_enron_emails.py first to build the baseline")
+ raise SystemExit(1)
+
+ results_out: dict = {}
+
+ if args.stage in ("2", "all"):
+ print("🚀 Starting Stage 2: Recall@3 evaluation")
+ evaluator = RecallEvaluator(args.index, args.baseline_dir)
+
+ enron_eval = EnronEvaluator(args.index)
+ queries = enron_eval.load_queries(args.queries)
+ queries = queries[:10]
+ print(f"🧪 Using first {len(queries)} queries")
+
+ complexity = args.complexity or 64
+ r = evaluator.evaluate_recall_at_3(queries, complexity)
+ results_out["stage2"] = {"complexity": complexity, "recall_at_3": r}
+ evaluator.cleanup()
+ enron_eval.cleanup()
+ print("✅ Stage 2 completed!\n")
+
+ if args.stage in ("3", "all"):
+ print("🚀 Starting Stage 3: Binary search for target recall (no recompute)")
+ enron_eval = EnronEvaluator(args.index)
+ queries = enron_eval.load_queries(args.queries)
+ queries = queries[: args.max_queries]
+ print(f"🧪 Using first {len(queries)} queries")
+
+ # Build non-compact index for fast binary search (recompute_embeddings=False)
+ from pathlib import Path
+
+ index_path = Path(args.index)
+ non_compact_index_path = str(index_path.parent / f"{index_path.stem}_noncompact.leann")
+ enron_eval.create_non_compact_index_for_comparison(non_compact_index_path)
+
+ # Use non-compact evaluator for binary search with recompute=False
+ evaluator_nc = RecallEvaluator(non_compact_index_path, args.baseline_dir)
+ sweep = enron_eval.evaluate_complexity(
+ evaluator_nc, queries, target=args.target_recall, recompute=False
+ )
+ results_out["stage3"] = sweep
+ # Persist default stage 3 results near the index for Stage 4 auto-pickup
+ from pathlib import Path
+
+ default_stage3_path = Path(args.index).parent / "enron_stage3_results.json"
+ with open(default_stage3_path, "w", encoding="utf-8") as f:
+ json.dump({"stage3": sweep}, f, indent=2)
+ print(f"📝 Saved Stage 3 summary to {default_stage3_path}")
+ evaluator_nc.cleanup()
+ enron_eval.cleanup()
+ print("✅ Stage 3 completed!\n")
+
+ if args.stage in ("4", "all", "45"):
+ print("🚀 Starting Stage 4: Index size + performance comparison")
+ evaluator = RecallEvaluator(args.index, args.baseline_dir)
+ enron_eval = EnronEvaluator(args.index)
+ queries = enron_eval.load_queries(args.queries)
+ test_q = queries[: min(args.max_queries, len(queries))]
+
+ current_sizes = enron_eval.analyze_index_sizes()
+ # Build non-compact index for comparison (no fallback)
+ from pathlib import Path
+
+ index_path = Path(args.index)
+ non_compact_path = str(index_path.parent / f"{index_path.stem}_noncompact.leann")
+ non_compact_sizes = enron_eval.create_non_compact_index_for_comparison(non_compact_path)
+ nc_eval = EnronEvaluator(non_compact_path)
+
+ if (
+ current_sizes.get("index_only_mb", 0) > 0
+ and non_compact_sizes.get("index_only_mb", 0) > 0
+ ):
+ storage_saving_percent = max(
+ 0.0,
+ 100.0 * (1.0 - current_sizes["index_only_mb"] / non_compact_sizes["index_only_mb"]),
+ )
+ else:
+ storage_saving_percent = 0.0
+
+ if args.complexity is None:
+ # Prefer in-session Stage 3 result
+ if "stage3" in results_out and results_out["stage3"].get("best_complexity") is not None:
+ complexity = results_out["stage3"]["best_complexity"]
+ print(f"📥 Using best complexity from Stage 3 in-session: {complexity}")
+ else:
+ # Try to load last saved Stage 3 result near index
+ default_stage3_path = Path(args.index).parent / "enron_stage3_results.json"
+ if default_stage3_path.exists():
+ with open(default_stage3_path, encoding="utf-8") as f:
+ prev = json.load(f)
+ complexity = prev.get("stage3", {}).get("best_complexity")
+ if complexity is None:
+ raise SystemExit(
+ "❌ Stage 4: No --complexity and no best_complexity found in saved Stage 3 results"
+ )
+ print(f"📥 Using best complexity from saved Stage 3: {complexity}")
+ else:
+ raise SystemExit(
+ "❌ Stage 4 requires --complexity if Stage 3 hasn't been run. Run stage 3 first or pass --complexity."
+ )
+ else:
+ complexity = args.complexity
+
+ comp = enron_eval.compare_index_performance(
+ non_compact_path, args.index, test_q, complexity=complexity
+ )
+ results_out["stage4"] = {
+ "current_index": current_sizes,
+ "non_compact_index": non_compact_sizes,
+ "storage_saving_percent": storage_saving_percent,
+ "performance_comparison": comp,
+ }
+ nc_eval.cleanup()
+ evaluator.cleanup()
+ enron_eval.cleanup()
+ print("✅ Stage 4 completed!\n")
+
+ if args.stage in ("5", "all"):
+ print("🚀 Starting Stage 5: Generation evaluation with Qwen3-8B")
+
+ # Check if Stage 4 results exist
+ if "stage4" not in results_out or "performance_comparison" not in results_out["stage4"]:
+ print("❌ Stage 5 requires Stage 4 retrieval results")
+ print("💡 Run Stage 4 first or use --stage all")
+ raise SystemExit(1)
+
+ retrieval_results = results_out["stage4"]["performance_comparison"]["retrieval_results"]
+ if not retrieval_results:
+ print("❌ No retrieval results found from Stage 4")
+ raise SystemExit(1)
+
+ print(f"📁 Using {len(retrieval_results)} retrieval results from Stage 4")
+
+ # Load LLM
+ try:
+ if args.llm_backend == "hf":
+ tokenizer, model = load_hf_model(args.model_name)
+
+ def llm_func(prompt):
+ return generate_hf(tokenizer, model, prompt)
+ else: # vllm
+ llm, sampling_params = load_vllm_model(args.model_name)
+
+ def llm_func(prompt):
+ return generate_vllm(llm, sampling_params, prompt)
+
+ # Run generation using stored retrieval results
+ import time
+
+ from llm_utils import create_prompt
+
+ generation_times = []
+ responses = []
+
+ print("🤖 Running generation on pre-retrieved results...")
+ for i, item in enumerate(retrieval_results):
+ query = item["query"]
+ retrieved_docs = item["retrieved_docs"]
+
+ # Prepare context from retrieved docs
+ context = "\n\n".join([doc["text"] for doc in retrieved_docs])
+ prompt = create_prompt(context, query, "emails")
+
+ # Time generation only
+ gen_start = time.time()
+ response = llm_func(prompt)
+ gen_time = time.time() - gen_start
+
+ generation_times.append(gen_time)
+ responses.append(response)
+
+ if i < 3:
+ print(f" Q{i + 1}: Gen={gen_time:.3f}s")
+
+ avg_gen_time = sum(generation_times) / len(generation_times)
+
+ print("\n📊 Generation Results:")
+ print(f" Total Queries: {len(retrieval_results)}")
+ print(f" Avg Generation Time: {avg_gen_time:.3f}s")
+ print(" (Search time from Stage 4)")
+
+ results_out["stage5"] = {
+ "total_queries": len(retrieval_results),
+ "avg_generation_time": avg_gen_time,
+ "generation_times": generation_times,
+ "responses": responses,
+ }
+
+ # Show sample results
+ print("\n📝 Sample Results:")
+ for i in range(min(3, len(retrieval_results))):
+ query = retrieval_results[i]["query"]
+ response = responses[i]
+ print(f" Q{i + 1}: {query[:60]}...")
+ print(f" A{i + 1}: {response[:100]}...")
+ print()
+
+ except Exception as e:
+ print(f"❌ Generation evaluation failed: {e}")
+ print("💡 Make sure transformers/vllm is installed and model is available")
+
+ print("✅ Stage 5 completed!\n")
+
+ if args.output and results_out:
+ with open(args.output, "w", encoding="utf-8") as f:
+ json.dump(results_out, f, indent=2)
+ print(f"📝 Saved results to {args.output}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/enron_emails/setup_enron_emails.py b/benchmarks/enron_emails/setup_enron_emails.py
new file mode 100644
index 0000000..ca88748
--- /dev/null
+++ b/benchmarks/enron_emails/setup_enron_emails.py
@@ -0,0 +1,359 @@
+"""
+Enron Emails Benchmark Setup Script
+Prepares passages from emails.csv, builds LEANN index, and FAISS Flat baseline
+"""
+
+import argparse
+import csv
+import json
+import os
+import re
+from collections.abc import Iterable
+from email import message_from_string
+from email.policy import default
+from pathlib import Path
+from typing import Optional
+
+from leann import LeannBuilder
+
+
+class EnronSetup:
+ def __init__(self, data_dir: str = "data"):
+ self.data_dir = Path(data_dir)
+ self.data_dir.mkdir(parents=True, exist_ok=True)
+
+ self.passages_preview = self.data_dir / "enron_passages_preview.jsonl"
+ self.index_path = self.data_dir / "enron_index_hnsw.leann"
+ self.queries_file = self.data_dir / "evaluation_queries.jsonl"
+ self.downloads_dir = self.data_dir / "downloads"
+ self.downloads_dir.mkdir(parents=True, exist_ok=True)
+
+ # ----------------------------
+ # Dataset acquisition
+ # ----------------------------
+ def ensure_emails_csv(self, emails_csv: Optional[str]) -> str:
+ """Return a path to emails.csv, downloading from Kaggle if needed."""
+ if emails_csv:
+ p = Path(emails_csv)
+ if not p.exists():
+ raise FileNotFoundError(f"emails.csv not found: {emails_csv}")
+ return str(p)
+
+ print(
+ "📥 Trying to download Enron emails.csv from Kaggle (wcukierski/enron-email-dataset)..."
+ )
+ try:
+ from kaggle.api.kaggle_api_extended import KaggleApi
+
+ api = KaggleApi()
+ api.authenticate()
+ api.dataset_download_files(
+ "wcukierski/enron-email-dataset", path=str(self.downloads_dir), unzip=True
+ )
+ candidate = self.downloads_dir / "emails.csv"
+ if candidate.exists():
+ print(f"✅ Downloaded emails.csv: {candidate}")
+ return str(candidate)
+ else:
+ raise FileNotFoundError(
+ f"emails.csv was not found in {self.downloads_dir} after Kaggle download"
+ )
+ except Exception as e:
+ print(
+ "❌ Could not download via Kaggle automatically. Provide --emails-csv or configure Kaggle API."
+ )
+ print(
+ " Set KAGGLE_USERNAME and KAGGLE_KEY env vars, or place emails.csv locally and pass --emails-csv."
+ )
+ raise e
+
+ # ----------------------------
+ # Data preparation
+ # ----------------------------
+ @staticmethod
+ def _extract_message_id(raw_email: str) -> str:
+ msg = message_from_string(raw_email, policy=default)
+ val = msg.get("Message-ID", "")
+ if val.startswith("<") and val.endswith(">"):
+ val = val[1:-1]
+ return val or ""
+
+ @staticmethod
+ def _split_header_body(raw_email: str) -> tuple[str, str]:
+ parts = raw_email.split("\n\n", 1)
+ if len(parts) == 2:
+ return parts[0].strip(), parts[1].strip()
+ # Heuristic fallback
+ first_lines = raw_email.splitlines()
+ if first_lines and ":" in first_lines[0]:
+ return raw_email.strip(), ""
+ return "", raw_email.strip()
+
+ @staticmethod
+ def _split_fixed_words(text: str, chunk_words: int, keep_last: bool) -> list[str]:
+ text = (text or "").strip()
+ if not text:
+ return []
+ if chunk_words <= 0:
+ return [text]
+ words = text.split()
+ if not words:
+ return []
+ limit = len(words)
+ if not keep_last:
+ limit = (len(words) // chunk_words) * chunk_words
+ if limit == 0:
+ return []
+ chunks = [" ".join(words[i : i + chunk_words]) for i in range(0, limit, chunk_words)]
+ return [c for c in (s.strip() for s in chunks) if c]
+
+ def _iter_passages_from_csv(
+ self,
+ emails_csv: Path,
+ chunk_words: int = 256,
+ keep_last_header: bool = True,
+ keep_last_body: bool = True,
+ max_emails: int | None = None,
+ ) -> Iterable[dict]:
+ with open(emails_csv, encoding="utf-8") as f:
+ reader = csv.DictReader(f)
+ count = 0
+ for i, row in enumerate(reader):
+ if max_emails is not None and count >= max_emails:
+ break
+
+ raw_message = row.get("message", "")
+ email_file_id = row.get("file", "")
+
+ if not raw_message.strip():
+ continue
+
+ message_id = self._extract_message_id(raw_message)
+ if not message_id:
+ # Fallback ID based on CSV position and file path
+ safe_file = re.sub(r"[^A-Za-z0-9_.-]", "_", email_file_id)
+ message_id = f"enron_{i}_{safe_file}"
+
+ header, body = self._split_header_body(raw_message)
+
+ # Header chunks
+ for chunk in self._split_fixed_words(header, chunk_words, keep_last_header):
+ yield {
+ "text": chunk,
+ "metadata": {
+ "message_id": message_id,
+ "is_header": True,
+ "email_file_id": email_file_id,
+ },
+ }
+
+ # Body chunks
+ for chunk in self._split_fixed_words(body, chunk_words, keep_last_body):
+ yield {
+ "text": chunk,
+ "metadata": {
+ "message_id": message_id,
+ "is_header": False,
+ "email_file_id": email_file_id,
+ },
+ }
+
+ count += 1
+
+ # ----------------------------
+ # Build LEANN index and FAISS baseline
+ # ----------------------------
+ def build_leann_index(
+ self,
+ emails_csv: Optional[str],
+ backend: str = "hnsw",
+ embedding_model: str = "sentence-transformers/all-mpnet-base-v2",
+ chunk_words: int = 256,
+ max_emails: int | None = None,
+ ) -> str:
+ emails_csv_path = self.ensure_emails_csv(emails_csv)
+ print(f"🏗️ Building LEANN index from {emails_csv_path}...")
+
+ builder = LeannBuilder(
+ backend_name=backend,
+ embedding_model=embedding_model,
+ embedding_mode="sentence-transformers",
+ graph_degree=32,
+ complexity=64,
+ is_recompute=True,
+ is_compact=True,
+ num_threads=4,
+ )
+
+ # Stream passages and add to builder
+ preview_written = 0
+ with open(self.passages_preview, "w", encoding="utf-8") as preview_out:
+ for p in self._iter_passages_from_csv(
+ Path(emails_csv_path), chunk_words=chunk_words, max_emails=max_emails
+ ):
+ builder.add_text(p["text"], metadata=p["metadata"])
+ if preview_written < 200:
+ preview_out.write(json.dumps({"text": p["text"][:200], **p["metadata"]}) + "\n")
+ preview_written += 1
+
+ print(f"🔨 Building index at {self.index_path}...")
+ builder.build_index(str(self.index_path))
+ print("✅ LEANN index built!")
+ return str(self.index_path)
+
+ def build_faiss_flat_baseline(self, index_path: str, output_dir: str = "baseline") -> str:
+ print("🔨 Building FAISS Flat baseline from LEANN passages...")
+
+ import pickle
+
+ import numpy as np
+ from leann.api import compute_embeddings
+ from leann_backend_hnsw import faiss
+
+ os.makedirs(output_dir, exist_ok=True)
+ baseline_path = os.path.join(output_dir, "faiss_flat.index")
+ metadata_path = os.path.join(output_dir, "metadata.pkl")
+
+ if os.path.exists(baseline_path) and os.path.exists(metadata_path):
+ print(f"✅ Baseline already exists at {baseline_path}")
+ return baseline_path
+
+ # Read meta for passage source and embedding model
+ meta_path = f"{index_path}.meta.json"
+ with open(meta_path, encoding="utf-8") as f:
+ meta = json.load(f)
+
+ embedding_model = meta["embedding_model"]
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ if not os.path.isabs(passage_file):
+ index_dir = os.path.dirname(index_path)
+ passage_file = os.path.join(index_dir, os.path.basename(passage_file))
+
+ # Load passages from builder output so IDs match LEANN
+ passages: list[str] = []
+ passage_ids: list[str] = []
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if not line.strip():
+ continue
+ data = json.loads(line)
+ passages.append(data["text"])
+ passage_ids.append(data["id"]) # builder-assigned ID
+
+ print(f"📄 Loaded {len(passages)} passages for baseline")
+ print(f"🤖 Embedding model: {embedding_model}")
+
+ embeddings = compute_embeddings(
+ passages,
+ embedding_model,
+ mode="sentence-transformers",
+ use_server=False,
+ )
+
+ # Build FAISS IndexFlatIP
+ dim = embeddings.shape[1]
+ index = faiss.IndexFlatIP(dim)
+ emb_f32 = embeddings.astype(np.float32)
+ index.add(emb_f32.shape[0], faiss.swig_ptr(emb_f32))
+
+ faiss.write_index(index, baseline_path)
+ with open(metadata_path, "wb") as pf:
+ pickle.dump(passage_ids, pf)
+
+ print(f"✅ FAISS baseline saved: {baseline_path}")
+ print(f"✅ Metadata saved: {metadata_path}")
+ print(f"📊 Total vectors: {index.ntotal}")
+ return baseline_path
+
+ # ----------------------------
+ # Queries (optional): prepare evaluation queries file
+ # ----------------------------
+ def prepare_queries(self, min_realism: float = 0.85) -> Path:
+ print(
+ "📝 Preparing evaluation queries from HuggingFace dataset corbt/enron_emails_sample_questions ..."
+ )
+ try:
+ from datasets import load_dataset
+
+ ds = load_dataset("corbt/enron_emails_sample_questions", split="train")
+ except Exception as e:
+ print(f"⚠️ Failed to load dataset: {e}")
+ return self.queries_file
+
+ kept = 0
+ with open(self.queries_file, "w", encoding="utf-8") as out:
+ for i, item in enumerate(ds):
+ how_realistic = float(item.get("how_realistic", 0.0))
+ if how_realistic < min_realism:
+ continue
+ qid = str(item.get("id", f"enron_q_{i}"))
+ query = item.get("question", "")
+ if not query:
+ continue
+ record = {
+ "id": qid,
+ "query": query,
+ # For reference only, not used in recall metric below
+ "gt_message_ids": item.get("message_ids", []),
+ }
+ out.write(json.dumps(record) + "\n")
+ kept += 1
+ print(f"✅ Wrote {kept} queries to {self.queries_file}")
+ return self.queries_file
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Setup Enron Emails Benchmark")
+ parser.add_argument(
+ "--emails-csv",
+ help="Path to emails.csv (Enron dataset). If omitted, attempt Kaggle download.",
+ )
+ parser.add_argument("--data-dir", default="data", help="Data directory")
+ parser.add_argument("--backend", choices=["hnsw", "diskann"], default="hnsw")
+ parser.add_argument(
+ "--embedding-model",
+ default="sentence-transformers/all-mpnet-base-v2",
+ help="Embedding model for LEANN",
+ )
+ parser.add_argument("--chunk-words", type=int, default=256, help="Fixed word chunk size")
+ parser.add_argument("--max-emails", type=int, help="Limit number of emails to process")
+ parser.add_argument("--skip-queries", action="store_true", help="Skip creating queries file")
+ parser.add_argument("--skip-build", action="store_true", help="Skip building LEANN index")
+
+ args = parser.parse_args()
+
+ setup = EnronSetup(args.data_dir)
+
+ # Build index
+ if not args.skip_build:
+ index_path = setup.build_leann_index(
+ emails_csv=args.emails_csv,
+ backend=args.backend,
+ embedding_model=args.embedding_model,
+ chunk_words=args.chunk_words,
+ max_emails=args.max_emails,
+ )
+
+ # Build FAISS baseline from the same passages & embeddings
+ setup.build_faiss_flat_baseline(index_path)
+ else:
+ print("⏭️ Skipping LEANN index build and baseline")
+
+ # Queries file (optional)
+ if not args.skip_queries:
+ setup.prepare_queries()
+ else:
+ print("⏭️ Skipping query preparation")
+
+ print("\n🎉 Enron Emails setup completed!")
+ print(f"📁 Data directory: {setup.data_dir.absolute()}")
+ print("Next steps:")
+ print(
+ "1) Evaluate recall: python evaluate_enron_emails.py --index data/enron_index_hnsw.leann --stage 2"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/financebench/README.md b/benchmarks/financebench/README.md
new file mode 100644
index 0000000..aefe9d6
--- /dev/null
+++ b/benchmarks/financebench/README.md
@@ -0,0 +1,115 @@
+# FinanceBench Benchmark for LEANN-RAG
+
+FinanceBench is a benchmark for evaluating retrieval-augmented generation (RAG) systems on financial document question-answering tasks.
+
+## Dataset
+
+- **Source**: [PatronusAI/financebench](https://huggingface.co/datasets/PatronusAI/financebench)
+- **Questions**: 150 financial Q&A examples
+- **Documents**: 368 PDF files (10-K, 10-Q, 8-K, earnings reports)
+- **Companies**: Major public companies (3M, Apple, Microsoft, Amazon, etc.)
+- **Paper**: [FinanceBench: A New Benchmark for Financial Question Answering](https://arxiv.org/abs/2311.11944)
+
+## Structure
+
+```
+benchmarks/financebench/
+├── setup_financebench.py # Downloads PDFs and builds index
+├── evaluate_financebench.py # Intelligent evaluation script
+├── data/
+│ ├── financebench_merged.jsonl # Q&A dataset
+│ ├── pdfs/ # Downloaded financial documents
+│ └── index/ # LEANN indexes
+│ └── financebench_full_hnsw.leann
+└── README.md
+```
+
+## Usage
+
+### 1. Setup (Download & Build Index)
+
+```bash
+cd benchmarks/financebench
+python setup_financebench.py
+```
+
+This will:
+- Download the 150 Q&A examples
+- Download all 368 PDF documents (parallel processing)
+- Build a LEANN index from 53K+ text chunks
+- Verify setup with test query
+
+### 2. Evaluation
+
+```bash
+# Basic retrieval evaluation
+python evaluate_financebench.py --index data/index/financebench_full_hnsw.leann
+
+
+# RAG generation evaluation with Qwen3-8B
+python evaluate_financebench.py --index data/index/financebench_full_hnsw.leann --stage 4 --complexity 64 --llm-backend hf --model-name Qwen/Qwen3-8B --output results_qwen3.json
+```
+
+## Evaluation Methods
+
+### Retrieval Evaluation
+Uses intelligent matching with three strategies:
+1. **Exact text overlap** - Direct substring matches
+2. **Number matching** - Key financial figures ($1,577, 1.2B, etc.)
+3. **Semantic similarity** - Word overlap with 20% threshold
+
+### QA Evaluation
+LLM-based answer evaluation using GPT-4o:
+- Handles numerical rounding and equivalent representations
+- Considers fractions, percentages, and decimal equivalents
+- Evaluates semantic meaning rather than exact text match
+
+## Benchmark Results
+
+### LEANN-RAG Performance (sentence-transformers/all-mpnet-base-v2)
+
+**Retrieval Metrics:**
+- **Question Coverage**: 100.0% (all questions retrieve relevant docs)
+- **Exact Match Rate**: 0.7% (substring overlap with evidence)
+- **Number Match Rate**: 120.7% (key financial figures matched)*
+- **Semantic Match Rate**: 4.7% (word overlap ≥20%)
+- **Average Search Time**: 0.097s
+
+**QA Metrics:**
+- **Accuracy**: 42.7% (LLM-evaluated answer correctness)
+- **Average QA Time**: 4.71s (end-to-end response time)
+
+**System Performance:**
+- **Index Size**: 53,985 chunks from 368 PDFs
+- **Build Time**: ~5-10 minutes with sentence-transformers/all-mpnet-base-v2
+
+*Note: Number match rate >100% indicates multiple retrieved documents contain the same financial figures, which is expected behavior for financial data appearing across multiple document sections.
+
+### LEANN-RAG Generation Performance (Qwen3-8B)
+
+- **Stage 4 (Index Comparison):**
+ - Compact Index: 5.0 MB
+ - Non-compact Index: 172.2 MB
+ - **Storage Saving**: 97.1%
+- **Search Performance**:
+ - Non-compact (no recompute): 0.009s avg per query
+ - Compact (with recompute): 2.203s avg per query
+ - Speed ratio: 0.004x
+
+**Generation Evaluation (20 queries, complexity=64):**
+- **Average Search Time**: 1.638s per query
+- **Average Generation Time**: 45.957s per query
+- **LLM Backend**: HuggingFace transformers
+- **Model**: Qwen/Qwen3-8B (thinking model with processing)
+- **Total Questions Processed**: 20
+
+## Options
+
+```bash
+# Use different backends
+python setup_financebench.py --backend diskann
+python evaluate_financebench.py --index data/index/financebench_full_diskann.leann
+
+# Use different embedding models
+python setup_financebench.py --embedding-model facebook/contriever
+```
diff --git a/benchmarks/financebench/evaluate_financebench.py b/benchmarks/financebench/evaluate_financebench.py
new file mode 100755
index 0000000..803d57e
--- /dev/null
+++ b/benchmarks/financebench/evaluate_financebench.py
@@ -0,0 +1,923 @@
+"""
+FinanceBench Evaluation Script - Modular Recall-based Evaluation
+"""
+
+import argparse
+import json
+import logging
+import os
+import pickle
+import time
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import openai
+from leann import LeannChat, LeannSearcher
+from leann_backend_hnsw import faiss
+
+from ..llm_utils import evaluate_rag, generate_hf, generate_vllm, load_hf_model, load_vllm_model
+
+# Setup logging to reduce verbose output
+logging.basicConfig(level=logging.WARNING)
+logging.getLogger("leann.api").setLevel(logging.WARNING)
+logging.getLogger("leann_backend_hnsw").setLevel(logging.WARNING)
+
+
+class RecallEvaluator:
+ """Stage 2: Evaluate Recall@3 (searcher vs baseline)"""
+
+ def __init__(self, index_path: str, baseline_dir: str):
+ self.index_path = index_path
+ self.baseline_dir = baseline_dir
+ self.searcher = LeannSearcher(index_path)
+
+ # Load FAISS flat baseline
+ baseline_index_path = os.path.join(baseline_dir, "faiss_flat.index")
+ metadata_path = os.path.join(baseline_dir, "metadata.pkl")
+
+ self.faiss_index = faiss.read_index(baseline_index_path)
+ with open(metadata_path, "rb") as f:
+ self.passage_ids = pickle.load(f)
+ print(f"📚 Loaded FAISS flat baseline with {self.faiss_index.ntotal} vectors")
+
+ def evaluate_recall_at_3(
+ self, queries: list[str], complexity: int = 64, recompute_embeddings: bool = True
+ ) -> float:
+ """Evaluate recall@3 for given queries at specified complexity"""
+ recompute_str = "with recompute" if recompute_embeddings else "no recompute"
+ print(f"🔍 Evaluating recall@3 with complexity={complexity} ({recompute_str})...")
+
+ total_recall = 0.0
+ num_queries = len(queries)
+
+ for i, query in enumerate(queries):
+ # Get ground truth: search with FAISS flat
+ from leann.api import compute_embeddings
+
+ query_embedding = compute_embeddings(
+ [query],
+ self.searcher.embedding_model,
+ mode=self.searcher.embedding_mode,
+ use_server=False,
+ ).astype(np.float32)
+
+ # Search FAISS flat for ground truth using LEANN's modified faiss API
+ n = query_embedding.shape[0] # Number of queries
+ k = 3 # Number of nearest neighbors
+ distances = np.zeros((n, k), dtype=np.float32)
+ labels = np.zeros((n, k), dtype=np.int64)
+
+ self.faiss_index.search(
+ n,
+ faiss.swig_ptr(query_embedding),
+ k,
+ faiss.swig_ptr(distances),
+ faiss.swig_ptr(labels),
+ )
+
+ # Extract the results
+ baseline_ids = {self.passage_ids[idx] for idx in labels[0]}
+
+ # Search with LEANN at specified complexity
+ test_results = self.searcher.search(
+ query,
+ top_k=3,
+ complexity=complexity,
+ recompute_embeddings=recompute_embeddings,
+ )
+ test_ids = {result.id for result in test_results}
+
+ # Calculate recall@3 = |intersection| / |ground_truth|
+ intersection = test_ids.intersection(baseline_ids)
+ recall = len(intersection) / 3.0 # Ground truth size is 3
+ total_recall += recall
+
+ if i < 3: # Show first few examples
+ print(f" Query {i + 1}: '{query[:50]}...' -> Recall@3: {recall:.3f}")
+ print(f" FAISS ground truth: {list(baseline_ids)}")
+ print(f" LEANN results (C={complexity}, {recompute_str}): {list(test_ids)}")
+ print(f" Intersection: {list(intersection)}")
+
+ avg_recall = total_recall / num_queries
+ print(f"📊 Average Recall@3: {avg_recall:.3f} ({avg_recall * 100:.1f}%)")
+ return avg_recall
+
+ def cleanup(self):
+ """Cleanup resources"""
+ if hasattr(self, "searcher"):
+ self.searcher.cleanup()
+
+
+class FinanceBenchEvaluator:
+ def __init__(self, index_path: str, openai_api_key: Optional[str] = None):
+ self.index_path = index_path
+ self.openai_client = openai.OpenAI(api_key=openai_api_key) if openai_api_key else None
+
+ self.searcher = LeannSearcher(index_path)
+ self.chat = LeannChat(index_path) if openai_api_key else None
+
+ def load_dataset(self, dataset_path: str = "data/financebench_merged.jsonl"):
+ """Load FinanceBench dataset"""
+ data = []
+ with open(dataset_path, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data.append(json.loads(line))
+
+ print(f"📊 Loaded {len(data)} FinanceBench examples")
+ return data
+
+ def analyze_index_sizes(self) -> dict:
+ """Analyze index sizes with and without embeddings"""
+
+ print("📏 Analyzing index sizes...")
+
+ # Get all index-related files
+ index_path = Path(self.index_path)
+ index_dir = index_path.parent
+ index_name = index_path.stem # Remove .leann extension
+
+ sizes = {}
+ total_with_embeddings = 0
+
+ # Core index files
+ index_file = index_dir / f"{index_name}.index"
+ meta_file = index_dir / f"{index_path.name}.meta.json" # Keep .leann for meta file
+ passages_file = index_dir / f"{index_path.name}.passages.jsonl" # Keep .leann for passages
+ passages_idx_file = index_dir / f"{index_path.name}.passages.idx" # Keep .leann for idx
+
+ for file_path, name in [
+ (index_file, "index"),
+ (meta_file, "metadata"),
+ (passages_file, "passages_text"),
+ (passages_idx_file, "passages_index"),
+ ]:
+ if file_path.exists():
+ size_mb = file_path.stat().st_size / (1024 * 1024)
+ sizes[name] = size_mb
+ total_with_embeddings += size_mb
+
+ else:
+ sizes[name] = 0
+
+ sizes["total_with_embeddings"] = total_with_embeddings
+ sizes["index_only_mb"] = sizes["index"] # Just the .index file for fair comparison
+
+ print(f" 📁 Total index size: {total_with_embeddings:.1f} MB")
+ print(f" 📁 Index file only: {sizes['index']:.1f} MB")
+
+ return sizes
+
+ def create_compact_index_for_comparison(self, compact_index_path: str) -> dict:
+ """Create a compact index for comparison purposes"""
+ print("🏗️ Building compact index from existing passages...")
+
+ # Load existing passages from current index
+
+ from leann import LeannBuilder
+
+ current_index_path = Path(self.index_path)
+ current_index_dir = current_index_path.parent
+ current_index_name = current_index_path.name
+
+ # Read metadata to get passage source
+ meta_path = current_index_dir / f"{current_index_name}.meta.json"
+ with open(meta_path) as f:
+ import json
+
+ meta = json.load(f)
+
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not Path(passage_file).is_absolute():
+ passage_file = current_index_dir / Path(passage_file).name
+
+ print(f"📄 Loading passages from {passage_file}...")
+
+ # Build compact index with same passages
+ builder = LeannBuilder(
+ backend_name="hnsw",
+ embedding_model=meta["embedding_model"],
+ embedding_mode=meta.get("embedding_mode", "sentence-transformers"),
+ is_recompute=True, # Enable recompute (no stored embeddings)
+ is_compact=True, # Enable compact storage
+ **meta.get("backend_kwargs", {}),
+ )
+
+ # Load all passages
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ builder.add_text(data["text"], metadata=data.get("metadata", {}))
+
+ print(f"🔨 Building compact index at {compact_index_path}...")
+ builder.build_index(compact_index_path)
+
+ # Analyze the compact index size
+ temp_evaluator = FinanceBenchEvaluator(compact_index_path)
+ compact_sizes = temp_evaluator.analyze_index_sizes()
+ compact_sizes["index_type"] = "compact"
+
+ return compact_sizes
+
+ def create_non_compact_index_for_comparison(self, non_compact_index_path: str) -> dict:
+ """Create a non-compact index for comparison purposes"""
+ print("🏗️ Building non-compact index from existing passages...")
+
+ # Load existing passages from current index
+
+ from leann import LeannBuilder
+
+ current_index_path = Path(self.index_path)
+ current_index_dir = current_index_path.parent
+ current_index_name = current_index_path.name
+
+ # Read metadata to get passage source
+ meta_path = current_index_dir / f"{current_index_name}.meta.json"
+ with open(meta_path) as f:
+ import json
+
+ meta = json.load(f)
+
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not Path(passage_file).is_absolute():
+ passage_file = current_index_dir / Path(passage_file).name
+
+ print(f"📄 Loading passages from {passage_file}...")
+
+ # Build non-compact index with same passages
+ builder = LeannBuilder(
+ backend_name="hnsw",
+ embedding_model=meta["embedding_model"],
+ embedding_mode=meta.get("embedding_mode", "sentence-transformers"),
+ is_recompute=False, # Disable recompute (store embeddings)
+ is_compact=False, # Disable compact storage
+ **{
+ k: v
+ for k, v in meta.get("backend_kwargs", {}).items()
+ if k not in ["is_recompute", "is_compact"]
+ },
+ )
+
+ # Load all passages
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ builder.add_text(data["text"], metadata=data.get("metadata", {}))
+
+ print(f"🔨 Building non-compact index at {non_compact_index_path}...")
+ builder.build_index(non_compact_index_path)
+
+ # Analyze the non-compact index size
+ temp_evaluator = FinanceBenchEvaluator(non_compact_index_path)
+ non_compact_sizes = temp_evaluator.analyze_index_sizes()
+ non_compact_sizes["index_type"] = "non_compact"
+
+ return non_compact_sizes
+
+ def compare_index_performance(
+ self, non_compact_path: str, compact_path: str, test_data: list, complexity: int
+ ) -> dict:
+ """Compare performance between non-compact and compact indexes"""
+ print("⚡ Comparing search performance between indexes...")
+
+ import time
+
+ from leann import LeannSearcher
+
+ # Test queries
+ test_queries = [item["question"] for item in test_data[:5]]
+
+ results = {
+ "non_compact": {"search_times": []},
+ "compact": {"search_times": []},
+ "avg_search_times": {},
+ "speed_ratio": 0.0,
+ }
+
+ # Test non-compact index (no recompute)
+ print(" 🔍 Testing non-compact index (no recompute)...")
+ non_compact_searcher = LeannSearcher(non_compact_path)
+
+ for query in test_queries:
+ start_time = time.time()
+ _ = non_compact_searcher.search(
+ query, top_k=3, complexity=complexity, recompute_embeddings=False
+ )
+ search_time = time.time() - start_time
+ results["non_compact"]["search_times"].append(search_time)
+
+ # Test compact index (with recompute)
+ print(" 🔍 Testing compact index (with recompute)...")
+ compact_searcher = LeannSearcher(compact_path)
+
+ for query in test_queries:
+ start_time = time.time()
+ _ = compact_searcher.search(
+ query, top_k=3, complexity=complexity, recompute_embeddings=True
+ )
+ search_time = time.time() - start_time
+ results["compact"]["search_times"].append(search_time)
+
+ # Calculate averages
+ results["avg_search_times"]["non_compact"] = sum(
+ results["non_compact"]["search_times"]
+ ) / len(results["non_compact"]["search_times"])
+ results["avg_search_times"]["compact"] = sum(results["compact"]["search_times"]) / len(
+ results["compact"]["search_times"]
+ )
+
+ # Performance ratio
+ if results["avg_search_times"]["compact"] > 0:
+ results["speed_ratio"] = (
+ results["avg_search_times"]["non_compact"] / results["avg_search_times"]["compact"]
+ )
+ else:
+ results["speed_ratio"] = float("inf")
+
+ print(
+ f" Non-compact (no recompute): {results['avg_search_times']['non_compact']:.3f}s avg"
+ )
+ print(f" Compact (with recompute): {results['avg_search_times']['compact']:.3f}s avg")
+ print(f" Speed ratio: {results['speed_ratio']:.2f}x")
+
+ # Cleanup
+ non_compact_searcher.cleanup()
+ compact_searcher.cleanup()
+
+ return results
+
+ def evaluate_timing_breakdown(
+ self, data: list[dict], max_samples: Optional[int] = None
+ ) -> dict:
+ """Evaluate timing breakdown and accuracy by hacking LeannChat.ask() for separated timing"""
+ if not self.chat or not self.openai_client:
+ print("⚠️ Skipping timing evaluation (no OpenAI API key provided)")
+ return {
+ "total_questions": 0,
+ "avg_search_time": 0.0,
+ "avg_generation_time": 0.0,
+ "avg_total_time": 0.0,
+ "accuracy": 0.0,
+ }
+
+ print("🔍🤖 Evaluating timing breakdown and accuracy (search + generation)...")
+
+ if max_samples:
+ data = data[:max_samples]
+ print(f"📝 Using first {max_samples} samples for timing evaluation")
+
+ search_times = []
+ generation_times = []
+ total_times = []
+ correct_answers = 0
+
+ for i, item in enumerate(data):
+ question = item["question"]
+ ground_truth = item["answer"]
+
+ try:
+ # Hack: Monkey-patch the ask method to capture internal timing
+ original_ask = self.chat.ask
+ captured_search_time = None
+ captured_generation_time = None
+
+ def patched_ask(*args, **kwargs):
+ nonlocal captured_search_time, captured_generation_time
+
+ # Time the search part
+ search_start = time.time()
+ results = self.chat.searcher.search(args[0], top_k=3, complexity=64)
+ captured_search_time = time.time() - search_start
+
+ # Time the generation part
+ context = "\n\n".join([r.text for r in results])
+ prompt = (
+ "Here is some retrieved context that might help answer your question:\n\n"
+ f"{context}\n\n"
+ f"Question: {args[0]}\n\n"
+ "Please provide the best answer you can based on this context and your knowledge."
+ )
+
+ generation_start = time.time()
+ answer = self.chat.llm.ask(prompt)
+ captured_generation_time = time.time() - generation_start
+
+ return answer
+
+ # Apply the patch
+ self.chat.ask = patched_ask
+
+ # Time the total QA
+ total_start = time.time()
+ generated_answer = self.chat.ask(question)
+ total_time = time.time() - total_start
+
+ # Restore original method
+ self.chat.ask = original_ask
+
+ # Store the timings
+ search_times.append(captured_search_time)
+ generation_times.append(captured_generation_time)
+ total_times.append(total_time)
+
+ # Check accuracy using LLM as judge
+ is_correct = self._check_answer_accuracy(generated_answer, ground_truth, question)
+ if is_correct:
+ correct_answers += 1
+
+ status = "✅" if is_correct else "❌"
+ print(
+ f"Question {i + 1}/{len(data)}: {status} Search={captured_search_time:.3f}s, Gen={captured_generation_time:.3f}s, Total={total_time:.3f}s"
+ )
+ print(f" GT: {ground_truth}")
+ print(f" Gen: {generated_answer[:100]}...")
+
+ except Exception as e:
+ print(f" ❌ Error: {e}")
+ search_times.append(0.0)
+ generation_times.append(0.0)
+ total_times.append(0.0)
+
+ accuracy = correct_answers / len(data) if data else 0.0
+
+ metrics = {
+ "total_questions": len(data),
+ "avg_search_time": sum(search_times) / len(search_times) if search_times else 0.0,
+ "avg_generation_time": sum(generation_times) / len(generation_times)
+ if generation_times
+ else 0.0,
+ "avg_total_time": sum(total_times) / len(total_times) if total_times else 0.0,
+ "accuracy": accuracy,
+ "correct_answers": correct_answers,
+ "search_times": search_times,
+ "generation_times": generation_times,
+ "total_times": total_times,
+ }
+
+ return metrics
+
+ def _check_answer_accuracy(
+ self, generated_answer: str, ground_truth: str, question: str
+ ) -> bool:
+ """Check if generated answer matches ground truth using LLM as judge"""
+ judge_prompt = f"""You are an expert judge evaluating financial question answering.
+
+Question: {question}
+
+Ground Truth Answer: {ground_truth}
+
+Generated Answer: {generated_answer}
+
+Task: Determine if the generated answer is factually correct compared to the ground truth. Focus on:
+1. Numerical accuracy (exact values, units, currency)
+2. Key financial concepts and terminology
+3. Overall factual correctness
+
+For financial data, small formatting differences are OK (e.g., "$1,577" vs "1577 million" vs "$1.577 billion"), but the core numerical value must match.
+
+Respond with exactly one word: "CORRECT" if the generated answer is factually accurate, or "INCORRECT" if it's wrong or significantly different."""
+
+ try:
+ judge_response = self.openai_client.chat.completions.create(
+ model="gpt-4o-mini",
+ messages=[{"role": "user", "content": judge_prompt}],
+ max_tokens=10,
+ temperature=0,
+ )
+ judgment = judge_response.choices[0].message.content.strip().upper()
+ return judgment == "CORRECT"
+ except Exception as e:
+ print(f" ⚠️ Judge error: {e}, falling back to string matching")
+ # Fallback to simple string matching
+ gen_clean = generated_answer.strip().lower().replace("$", "").replace(",", "")
+ gt_clean = ground_truth.strip().lower().replace("$", "").replace(",", "")
+ return gt_clean in gen_clean
+
+ def _print_results(self, timing_metrics: dict):
+ """Print evaluation results"""
+ print("\n🎯 EVALUATION RESULTS")
+ print("=" * 50)
+
+ # Index comparison analysis
+ if "current_index" in timing_metrics and "non_compact_index" in timing_metrics:
+ print("\n📏 Index Comparison Analysis:")
+ current = timing_metrics["current_index"]
+ non_compact = timing_metrics["non_compact_index"]
+
+ print(f" Compact index (current): {current.get('total_with_embeddings', 0):.1f} MB")
+ print(
+ f" Non-compact index (with embeddings): {non_compact.get('total_with_embeddings', 0):.1f} MB"
+ )
+ print(
+ f" Storage saving by compact: {timing_metrics.get('storage_saving_percent', 0):.1f}%"
+ )
+
+ print(" Component breakdown (non-compact):")
+ print(f" - Main index: {non_compact.get('index', 0):.1f} MB")
+ print(f" - Passages text: {non_compact.get('passages_text', 0):.1f} MB")
+ print(f" - Passages index: {non_compact.get('passages_index', 0):.1f} MB")
+ print(f" - Metadata: {non_compact.get('metadata', 0):.1f} MB")
+
+ # Performance comparison
+ if "performance_comparison" in timing_metrics:
+ perf = timing_metrics["performance_comparison"]
+ print("\n⚡ Performance Comparison:")
+ print(
+ f" Non-compact (no recompute): {perf.get('avg_search_times', {}).get('non_compact', 0):.3f}s avg"
+ )
+ print(
+ f" Compact (with recompute): {perf.get('avg_search_times', {}).get('compact', 0):.3f}s avg"
+ )
+ print(f" Speed ratio: {perf.get('speed_ratio', 0):.2f}x")
+
+ # Legacy single index analysis (fallback)
+ if "total_with_embeddings" in timing_metrics and "current_index" not in timing_metrics:
+ print("\n📏 Index Size Analysis:")
+ print(f" Total index size: {timing_metrics.get('total_with_embeddings', 0):.1f} MB")
+
+ print("\n📊 Accuracy:")
+ print(f" Accuracy: {timing_metrics.get('accuracy', 0) * 100:.1f}%")
+ print(
+ f" Correct Answers: {timing_metrics.get('correct_answers', 0)}/{timing_metrics.get('total_questions', 0)}"
+ )
+
+ print("\n📊 Timing Breakdown:")
+ print(f" Total Questions: {timing_metrics.get('total_questions', 0)}")
+ print(f" Avg Search Time: {timing_metrics.get('avg_search_time', 0):.3f}s")
+ print(f" Avg Generation Time: {timing_metrics.get('avg_generation_time', 0):.3f}s")
+ print(f" Avg Total Time: {timing_metrics.get('avg_total_time', 0):.3f}s")
+
+ if timing_metrics.get("avg_total_time", 0) > 0:
+ search_pct = (
+ timing_metrics.get("avg_search_time", 0)
+ / timing_metrics.get("avg_total_time", 1)
+ * 100
+ )
+ gen_pct = (
+ timing_metrics.get("avg_generation_time", 0)
+ / timing_metrics.get("avg_total_time", 1)
+ * 100
+ )
+ print("\n📈 Time Distribution:")
+ print(f" Search: {search_pct:.1f}%")
+ print(f" Generation: {gen_pct:.1f}%")
+
+ def cleanup(self):
+ """Cleanup resources"""
+ if self.searcher:
+ self.searcher.cleanup()
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Modular FinanceBench Evaluation")
+ parser.add_argument("--index", required=True, help="Path to LEANN index")
+ parser.add_argument("--dataset", default="data/financebench_merged.jsonl", help="Dataset path")
+ parser.add_argument(
+ "--stage",
+ choices=["2", "3", "4", "all"],
+ default="all",
+ help="Which stage to run (2=recall, 3=complexity, 4=generation)",
+ )
+ parser.add_argument("--complexity", type=int, default=None, help="Complexity for search")
+ parser.add_argument("--baseline-dir", default="baseline", help="Baseline output directory")
+ parser.add_argument("--openai-api-key", help="OpenAI API key for generation evaluation")
+ parser.add_argument("--output", help="Save results to JSON file")
+ parser.add_argument(
+ "--llm-backend", choices=["openai", "hf", "vllm"], default="openai", help="LLM backend"
+ )
+ parser.add_argument("--model-name", default="Qwen3-8B", help="Model name for HF/vLLM")
+
+ args = parser.parse_args()
+
+ try:
+ # Check if baseline exists
+ baseline_index_path = os.path.join(args.baseline_dir, "faiss_flat.index")
+ if not os.path.exists(baseline_index_path):
+ print(f"❌ FAISS baseline not found at {baseline_index_path}")
+ print("💡 Please run setup_financebench.py first to build the baseline")
+ exit(1)
+
+ if args.stage == "2" or args.stage == "all":
+ # Stage 2: Recall@3 evaluation
+ print("🚀 Starting Stage 2: Recall@3 evaluation")
+
+ evaluator = RecallEvaluator(args.index, args.baseline_dir)
+
+ # Load FinanceBench queries for testing
+ print("📖 Loading FinanceBench dataset...")
+ queries = []
+ with open(args.dataset, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ queries.append(data["question"])
+
+ # Test with more queries for robust measurement
+ test_queries = queries[:2000]
+ print(f"🧪 Testing with {len(test_queries)} queries")
+
+ # Test with complexity 64
+ complexity = 64
+ recall = evaluator.evaluate_recall_at_3(test_queries, complexity)
+ print(f"📈 Recall@3 at complexity {complexity}: {recall * 100:.1f}%")
+
+ evaluator.cleanup()
+ print("✅ Stage 2 completed!\n")
+
+ # Shared non-compact index path for Stage 3 and 4
+ non_compact_index_path = args.index.replace(".leann", "_noncompact.leann")
+ complexity = args.complexity
+
+ if args.stage == "3" or args.stage == "all":
+ # Stage 3: Binary search for 90% recall complexity (using non-compact index for speed)
+ print("🚀 Starting Stage 3: Binary search for 90% recall complexity")
+ print(
+ "💡 Creating non-compact index for fast binary search with recompute_embeddings=False"
+ )
+
+ # Create non-compact index for binary search (will be reused in Stage 4)
+ print("🏗️ Creating non-compact index for binary search...")
+ evaluator = FinanceBenchEvaluator(args.index)
+ evaluator.create_non_compact_index_for_comparison(non_compact_index_path)
+
+ # Use non-compact index for binary search
+ binary_search_evaluator = RecallEvaluator(non_compact_index_path, args.baseline_dir)
+
+ # Load queries for testing
+ print("📖 Loading FinanceBench dataset...")
+ queries = []
+ with open(args.dataset, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ queries.append(data["question"])
+
+ # Use more queries for robust measurement
+ test_queries = queries[:200]
+ print(f"🧪 Testing with {len(test_queries)} queries")
+
+ # Binary search for 90% recall complexity (without recompute for speed)
+ target_recall = 0.9
+ min_complexity, max_complexity = 1, 32
+
+ print(f"🔍 Binary search for {target_recall * 100}% recall complexity...")
+ print(f"Search range: {min_complexity} to {max_complexity}")
+
+ best_complexity = None
+ best_recall = 0.0
+
+ while min_complexity <= max_complexity:
+ mid_complexity = (min_complexity + max_complexity) // 2
+
+ print(
+ f"\n🧪 Testing complexity {mid_complexity} (no recompute, non-compact index)..."
+ )
+ # Use recompute_embeddings=False on non-compact index for fast binary search
+ recall = binary_search_evaluator.evaluate_recall_at_3(
+ test_queries, mid_complexity, recompute_embeddings=False
+ )
+
+ print(
+ f" Complexity {mid_complexity}: Recall@3 = {recall:.3f} ({recall * 100:.1f}%)"
+ )
+
+ if recall >= target_recall:
+ best_complexity = mid_complexity
+ best_recall = recall
+ max_complexity = mid_complexity - 1
+ print(" ✅ Target reached! Searching for lower complexity...")
+ else:
+ min_complexity = mid_complexity + 1
+ print(" ❌ Below target. Searching for higher complexity...")
+
+ if best_complexity is not None:
+ print("\n🎯 Optimal complexity found!")
+ print(f" Complexity: {best_complexity}")
+ print(f" Recall@3: {best_recall:.3f} ({best_recall * 100:.1f}%)")
+
+ # Test a few complexities around the optimal one for verification
+ print("\n🔬 Verification test around optimal complexity:")
+ verification_complexities = [
+ max(1, best_complexity - 2),
+ max(1, best_complexity - 1),
+ best_complexity,
+ best_complexity + 1,
+ best_complexity + 2,
+ ]
+
+ for complexity in verification_complexities:
+ if complexity <= 512: # reasonable upper bound
+ recall = binary_search_evaluator.evaluate_recall_at_3(
+ test_queries, complexity, recompute_embeddings=False
+ )
+ status = "✅" if recall >= target_recall else "❌"
+ print(f" {status} Complexity {complexity:3d}: {recall * 100:5.1f}%")
+
+ # Now test the optimal complexity with compact index and recompute for comparison
+ print(
+ f"\n🔄 Testing optimal complexity {best_complexity} on compact index WITH recompute..."
+ )
+ compact_evaluator = RecallEvaluator(args.index, args.baseline_dir)
+ recall_with_recompute = compact_evaluator.evaluate_recall_at_3(
+ test_queries[:10], best_complexity, recompute_embeddings=True
+ )
+ print(
+ f" ✅ Complexity {best_complexity} (compact index with recompute): {recall_with_recompute * 100:.1f}%"
+ )
+ complexity = best_complexity
+ print(
+ f" 📊 Recall difference: {abs(best_recall - recall_with_recompute) * 100:.2f}%"
+ )
+ compact_evaluator.cleanup()
+ else:
+ print(f"\n❌ Could not find complexity achieving {target_recall * 100}% recall")
+ print("All tested complexities were below target.")
+
+ # Cleanup evaluators (keep non-compact index for Stage 4)
+ binary_search_evaluator.cleanup()
+ evaluator.cleanup()
+
+ print("✅ Stage 3 completed! Non-compact index saved for Stage 4.\n")
+
+ if args.stage == "4" or args.stage == "all":
+ # Stage 4: Comprehensive evaluation with dual index comparison
+ print("🚀 Starting Stage 4: Comprehensive evaluation with dual index comparison")
+
+ # Use FinanceBench evaluator for QA evaluation
+ evaluator = FinanceBenchEvaluator(
+ args.index, args.openai_api_key if args.llm_backend == "openai" else None
+ )
+
+ print("📖 Loading FinanceBench dataset...")
+ data = evaluator.load_dataset(args.dataset)
+
+ # Step 1: Analyze current (compact) index
+ print("\n📏 Analyzing current index (compact, pruned)...")
+ compact_size_metrics = evaluator.analyze_index_sizes()
+ compact_size_metrics["index_type"] = "compact"
+
+ # Step 2: Use existing non-compact index or create if needed
+ from pathlib import Path
+
+ if Path(non_compact_index_path).exists():
+ print(
+ f"\n📁 Using existing non-compact index from Stage 3: {non_compact_index_path}"
+ )
+ temp_evaluator = FinanceBenchEvaluator(non_compact_index_path)
+ non_compact_size_metrics = temp_evaluator.analyze_index_sizes()
+ non_compact_size_metrics["index_type"] = "non_compact"
+ else:
+ print("\n🏗️ Creating non-compact index (with embeddings) for comparison...")
+ non_compact_size_metrics = evaluator.create_non_compact_index_for_comparison(
+ non_compact_index_path
+ )
+
+ # Step 3: Compare index sizes
+ print("\n📊 Index size comparison:")
+ print(
+ f" Compact index (current): {compact_size_metrics['total_with_embeddings']:.1f} MB"
+ )
+ print(
+ f" Non-compact index: {non_compact_size_metrics['total_with_embeddings']:.1f} MB"
+ )
+ print("\n📊 Index-only size comparison (.index file only):")
+ print(f" Compact index: {compact_size_metrics['index_only_mb']:.1f} MB")
+ print(f" Non-compact index: {non_compact_size_metrics['index_only_mb']:.1f} MB")
+ # Use index-only size for fair comparison (same as Enron emails)
+ storage_saving = (
+ (non_compact_size_metrics["index_only_mb"] - compact_size_metrics["index_only_mb"])
+ / non_compact_size_metrics["index_only_mb"]
+ * 100
+ )
+ print(f" Storage saving by compact: {storage_saving:.1f}%")
+
+ # Step 4: Performance comparison between the two indexes
+ if complexity is None:
+ raise ValueError("Complexity is required for performance comparison")
+
+ print("\n⚡ Performance comparison between indexes...")
+ performance_metrics = evaluator.compare_index_performance(
+ non_compact_index_path, args.index, data[:10], complexity=complexity
+ )
+
+ # Step 5: Generation evaluation
+ test_samples = 20
+ print(f"\n🧪 Testing with first {test_samples} samples for generation analysis")
+
+ if args.llm_backend == "openai" and args.openai_api_key:
+ print("🔍🤖 Running OpenAI-based generation evaluation...")
+ evaluation_start = time.time()
+ timing_metrics = evaluator.evaluate_timing_breakdown(data[:test_samples])
+ evaluation_time = time.time() - evaluation_start
+ else:
+ print(
+ f"🔍🤖 Running {args.llm_backend} generation evaluation with {args.model_name}..."
+ )
+ try:
+ # Load LLM
+ if args.llm_backend == "hf":
+ tokenizer, model = load_hf_model(args.model_name)
+
+ def llm_func(prompt):
+ return generate_hf(tokenizer, model, prompt)
+ else: # vllm
+ llm, sampling_params = load_vllm_model(args.model_name)
+
+ def llm_func(prompt):
+ return generate_vllm(llm, sampling_params, prompt)
+
+ # Simple generation evaluation
+ queries = [item["question"] for item in data[:test_samples]]
+ gen_results = evaluate_rag(
+ evaluator.searcher,
+ llm_func,
+ queries,
+ domain="finance",
+ complexity=complexity,
+ )
+
+ timing_metrics = {
+ "total_questions": len(queries),
+ "avg_search_time": gen_results["avg_search_time"],
+ "avg_generation_time": gen_results["avg_generation_time"],
+ "results": gen_results["results"],
+ }
+ evaluation_time = time.time()
+
+ except Exception as e:
+ print(f"❌ Generation evaluation failed: {e}")
+ timing_metrics = {
+ "total_questions": 0,
+ "avg_search_time": 0,
+ "avg_generation_time": 0,
+ }
+ evaluation_time = 0
+
+ # Combine all metrics
+ combined_metrics = {
+ **timing_metrics,
+ "total_evaluation_time": evaluation_time,
+ "current_index": compact_size_metrics,
+ "non_compact_index": non_compact_size_metrics,
+ "performance_comparison": performance_metrics,
+ "storage_saving_percent": storage_saving,
+ }
+
+ # Print results
+ print("\n📊 Generation Results:")
+ print(f" Total Questions: {timing_metrics.get('total_questions', 0)}")
+ print(f" Avg Search Time: {timing_metrics.get('avg_search_time', 0):.3f}s")
+ print(f" Avg Generation Time: {timing_metrics.get('avg_generation_time', 0):.3f}s")
+
+ # Save results if requested
+ if args.output:
+ print(f"\n💾 Saving results to {args.output}...")
+ with open(args.output, "w") as f:
+ json.dump(combined_metrics, f, indent=2, default=str)
+ print(f"✅ Results saved to {args.output}")
+
+ evaluator.cleanup()
+ print("✅ Stage 4 completed!\n")
+
+ if args.stage == "all":
+ print("🎉 All evaluation stages completed successfully!")
+ print("\n📋 Summary:")
+ print(" Stage 2: ✅ Recall@3 evaluation completed")
+ print(" Stage 3: ✅ Optimal complexity found")
+ print(" Stage 4: ✅ Generation accuracy & timing evaluation completed")
+ print("\n🔧 Recommended next steps:")
+ print(" - Use optimal complexity for best speed/accuracy balance")
+ print(" - Review accuracy and timing breakdown for performance optimization")
+ print(" - Run full evaluation on complete dataset if needed")
+
+ # Clean up non-compact index after all stages complete
+ print("\n🧹 Cleaning up temporary non-compact index...")
+ from pathlib import Path
+
+ if Path(non_compact_index_path).exists():
+ temp_index_dir = Path(non_compact_index_path).parent
+ temp_index_name = Path(non_compact_index_path).name
+ for temp_file in temp_index_dir.glob(f"{temp_index_name}*"):
+ temp_file.unlink()
+ print(f"✅ Cleaned up {non_compact_index_path}")
+ else:
+ print("📝 No temporary index to clean up")
+ except KeyboardInterrupt:
+ print("\n⚠️ Evaluation interrupted by user")
+ exit(1)
+ except Exception as e:
+ print(f"\n❌ Stage {args.stage} failed: {e}")
+ exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/financebench/setup_financebench.py b/benchmarks/financebench/setup_financebench.py
new file mode 100755
index 0000000..36bd0da
--- /dev/null
+++ b/benchmarks/financebench/setup_financebench.py
@@ -0,0 +1,462 @@
+#!/usr/bin/env python3
+"""
+FinanceBench Complete Setup Script
+Downloads all PDFs and builds full LEANN datastore
+"""
+
+import argparse
+import os
+import re
+import time
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+from threading import Lock
+
+import pymupdf
+import requests
+from leann import LeannBuilder, LeannSearcher
+from tqdm import tqdm
+
+
+class FinanceBenchSetup:
+ def __init__(self, data_dir: str = "data"):
+ self.base_dir = Path(__file__).parent # benchmarks/financebench/
+ self.data_dir = self.base_dir / data_dir
+ self.pdf_dir = self.data_dir / "pdfs"
+ self.dataset_file = self.data_dir / "financebench_merged.jsonl"
+ self.index_dir = self.data_dir / "index"
+ self.download_lock = Lock()
+
+ def download_dataset(self):
+ """Download the main FinanceBench dataset"""
+ print("📊 Downloading FinanceBench dataset...")
+ self.data_dir.mkdir(parents=True, exist_ok=True)
+
+ if self.dataset_file.exists():
+ print(f"✅ Dataset already exists: {self.dataset_file}")
+ return
+
+ url = "https://huggingface.co/datasets/PatronusAI/financebench/raw/main/financebench_merged.jsonl"
+ response = requests.get(url, stream=True)
+ response.raise_for_status()
+
+ with open(self.dataset_file, "wb") as f:
+ for chunk in response.iter_content(chunk_size=8192):
+ f.write(chunk)
+
+ print(f"✅ Dataset downloaded: {self.dataset_file}")
+
+ def get_pdf_list(self):
+ """Get list of all PDF files from GitHub"""
+ print("📋 Fetching PDF list from GitHub...")
+
+ response = requests.get(
+ "https://api.github.com/repos/patronus-ai/financebench/contents/pdfs"
+ )
+ response.raise_for_status()
+ pdf_files = response.json()
+
+ print(f"Found {len(pdf_files)} PDF files")
+ return pdf_files
+
+ def download_single_pdf(self, pdf_info, position):
+ """Download a single PDF file"""
+ pdf_name = pdf_info["name"]
+ pdf_path = self.pdf_dir / pdf_name
+
+ # Skip if already downloaded
+ if pdf_path.exists() and pdf_path.stat().st_size > 0:
+ return f"✅ {pdf_name} (cached)"
+
+ try:
+ # Download PDF
+ response = requests.get(pdf_info["download_url"], timeout=60)
+ response.raise_for_status()
+
+ # Write to file
+ with self.download_lock:
+ with open(pdf_path, "wb") as f:
+ f.write(response.content)
+
+ return f"✅ {pdf_name} ({len(response.content) // 1024}KB)"
+
+ except Exception as e:
+ return f"❌ {pdf_name}: {e!s}"
+
+ def download_all_pdfs(self, max_workers: int = 5):
+ """Download all PDF files with parallel processing"""
+ self.pdf_dir.mkdir(parents=True, exist_ok=True)
+
+ pdf_files = self.get_pdf_list()
+
+ print(f"📥 Downloading {len(pdf_files)} PDFs with {max_workers} workers...")
+
+ with ThreadPoolExecutor(max_workers=max_workers) as executor:
+ # Submit all download tasks
+ future_to_pdf = {
+ executor.submit(self.download_single_pdf, pdf_info, i): pdf_info["name"]
+ for i, pdf_info in enumerate(pdf_files)
+ }
+
+ # Process completed downloads with progress bar
+ with tqdm(total=len(pdf_files), desc="Downloading PDFs") as pbar:
+ for future in as_completed(future_to_pdf):
+ result = future.result()
+ pbar.set_postfix_str(result.split()[-1] if "✅" in result else "Error")
+ pbar.update(1)
+
+ # Verify downloads
+ downloaded_pdfs = list(self.pdf_dir.glob("*.pdf"))
+ print(f"✅ Successfully downloaded {len(downloaded_pdfs)}/{len(pdf_files)} PDFs")
+
+ # Show any failures
+ missing_pdfs = []
+ for pdf_info in pdf_files:
+ pdf_path = self.pdf_dir / pdf_info["name"]
+ if not pdf_path.exists() or pdf_path.stat().st_size == 0:
+ missing_pdfs.append(pdf_info["name"])
+
+ if missing_pdfs:
+ print(f"⚠️ Failed to download {len(missing_pdfs)} PDFs:")
+ for pdf in missing_pdfs[:5]: # Show first 5
+ print(f" - {pdf}")
+ if len(missing_pdfs) > 5:
+ print(f" ... and {len(missing_pdfs) - 5} more")
+
+ def build_leann_index(
+ self,
+ backend: str = "hnsw",
+ embedding_model: str = "sentence-transformers/all-mpnet-base-v2",
+ ):
+ """Build LEANN index from all PDFs"""
+ print(f"🏗️ Building LEANN index with {backend} backend...")
+
+ # Check if we have PDFs
+ pdf_files = list(self.pdf_dir.glob("*.pdf"))
+ if not pdf_files:
+ raise RuntimeError("No PDF files found! Run download first.")
+
+ print(f"Found {len(pdf_files)} PDF files to process")
+
+ start_time = time.time()
+
+ # Initialize builder with standard compact configuration
+ builder = LeannBuilder(
+ backend_name=backend,
+ embedding_model=embedding_model,
+ embedding_mode="sentence-transformers",
+ graph_degree=32,
+ complexity=64,
+ is_recompute=True, # Enable recompute (no stored embeddings)
+ is_compact=True, # Enable compact storage (pruned)
+ num_threads=4,
+ )
+
+ # Process PDFs and extract text
+ total_chunks = 0
+ failed_pdfs = []
+
+ for pdf_path in tqdm(pdf_files, desc="Processing PDFs"):
+ try:
+ chunks = self.extract_pdf_text(pdf_path)
+ for chunk in chunks:
+ builder.add_text(chunk["text"], metadata=chunk["metadata"])
+ total_chunks += 1
+
+ except Exception as e:
+ print(f"❌ Failed to process {pdf_path.name}: {e}")
+ failed_pdfs.append(pdf_path.name)
+ continue
+
+ # Build index in index directory
+ self.index_dir.mkdir(parents=True, exist_ok=True)
+ index_path = self.index_dir / f"financebench_full_{backend}.leann"
+ print(f"🔨 Building index: {index_path}")
+ builder.build_index(str(index_path))
+
+ build_time = time.time() - start_time
+
+ print("✅ Index built successfully!")
+ print(f" 📁 Index path: {index_path}")
+ print(f" 📊 Total chunks: {total_chunks:,}")
+ print(f" 📄 Processed PDFs: {len(pdf_files) - len(failed_pdfs)}/{len(pdf_files)}")
+ print(f" ⏱️ Build time: {build_time:.1f}s")
+
+ if failed_pdfs:
+ print(f" ⚠️ Failed PDFs: {failed_pdfs}")
+
+ return str(index_path)
+
+ def build_faiss_flat_baseline(self, index_path: str, output_dir: str = "baseline"):
+ """Build FAISS flat baseline using the same embeddings as LEANN index"""
+ print("🔨 Building FAISS Flat baseline...")
+
+ import os
+ import pickle
+
+ import numpy as np
+ from leann.api import compute_embeddings
+ from leann_backend_hnsw import faiss
+
+ os.makedirs(output_dir, exist_ok=True)
+ baseline_path = os.path.join(output_dir, "faiss_flat.index")
+ metadata_path = os.path.join(output_dir, "metadata.pkl")
+
+ if os.path.exists(baseline_path) and os.path.exists(metadata_path):
+ print(f"✅ Baseline already exists at {baseline_path}")
+ return baseline_path
+
+ # Read metadata from the built index
+ meta_path = f"{index_path}.meta.json"
+ with open(meta_path) as f:
+ import json
+
+ meta = json.loads(f.read())
+
+ embedding_model = meta["embedding_model"]
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not os.path.isabs(passage_file):
+ index_dir = os.path.dirname(index_path)
+ passage_file = os.path.join(index_dir, os.path.basename(passage_file))
+
+ print(f"📊 Loading passages from {passage_file}...")
+ print(f"🤖 Using embedding model: {embedding_model}")
+
+ # Load all passages for baseline
+ passages = []
+ passage_ids = []
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ passages.append(data["text"])
+ passage_ids.append(data["id"])
+
+ print(f"📄 Loaded {len(passages)} passages")
+
+ # Compute embeddings using the same method as LEANN
+ print("🧮 Computing embeddings...")
+ embeddings = compute_embeddings(
+ passages,
+ embedding_model,
+ mode="sentence-transformers",
+ use_server=False,
+ )
+
+ print(f"📐 Embedding shape: {embeddings.shape}")
+
+ # Build FAISS flat index
+ print("🏗️ Building FAISS IndexFlatIP...")
+ dimension = embeddings.shape[1]
+ index = faiss.IndexFlatIP(dimension)
+
+ # Add embeddings to flat index
+ embeddings_f32 = embeddings.astype(np.float32)
+ index.add(embeddings_f32.shape[0], faiss.swig_ptr(embeddings_f32))
+
+ # Save index and metadata
+ faiss.write_index(index, baseline_path)
+ with open(metadata_path, "wb") as f:
+ pickle.dump(passage_ids, f)
+
+ print(f"✅ FAISS baseline saved to {baseline_path}")
+ print(f"✅ Metadata saved to {metadata_path}")
+ print(f"📊 Total vectors: {index.ntotal}")
+
+ return baseline_path
+
+ def extract_pdf_text(self, pdf_path: Path) -> list[dict]:
+ """Extract and chunk text from a PDF file"""
+ chunks = []
+ doc = pymupdf.open(pdf_path)
+
+ for page_num in range(len(doc)):
+ page = doc[page_num]
+ text = page.get_text() # type: ignore
+
+ if not text.strip():
+ continue
+
+ # Create metadata
+ metadata = {
+ "source_file": pdf_path.name,
+ "page_number": page_num + 1,
+ "document_type": "10K" if "10K" in pdf_path.name else "10Q",
+ "company": pdf_path.name.split("_")[0],
+ "doc_period": self.extract_year_from_filename(pdf_path.name),
+ }
+
+ # Use recursive character splitting like LangChain
+ if len(text.split()) > 500:
+ # Split by double newlines (paragraphs)
+ paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
+
+ current_chunk = ""
+ for para in paragraphs:
+ # If adding this paragraph would make chunk too long, save current chunk
+ if current_chunk and len((current_chunk + " " + para).split()) > 300:
+ if current_chunk.strip():
+ chunks.append(
+ {
+ "text": current_chunk.strip(),
+ "metadata": {
+ **metadata,
+ "chunk_id": f"page_{page_num + 1}_chunk_{len(chunks)}",
+ },
+ }
+ )
+ current_chunk = para
+ else:
+ current_chunk = (current_chunk + " " + para).strip()
+
+ # Add the last chunk
+ if current_chunk.strip():
+ chunks.append(
+ {
+ "text": current_chunk.strip(),
+ "metadata": {
+ **metadata,
+ "chunk_id": f"page_{page_num + 1}_chunk_{len(chunks)}",
+ },
+ }
+ )
+ else:
+ # Page is short enough, use as single chunk
+ chunks.append(
+ {
+ "text": text.strip(),
+ "metadata": {**metadata, "chunk_id": f"page_{page_num + 1}"},
+ }
+ )
+
+ doc.close()
+ return chunks
+
+ def extract_year_from_filename(self, filename: str) -> str:
+ """Extract year from PDF filename"""
+ # Try to find 4-digit year in filename
+
+ match = re.search(r"(\d{4})", filename)
+ return match.group(1) if match else "unknown"
+
+ def verify_setup(self, index_path: str):
+ """Verify the setup by testing a simple query"""
+ print("🧪 Verifying setup with test query...")
+
+ try:
+ searcher = LeannSearcher(index_path)
+
+ # Test query
+ test_query = "What is the capital expenditure for 3M in 2018?"
+ results = searcher.search(test_query, top_k=3)
+
+ print(f"✅ Test query successful! Found {len(results)} results:")
+ for i, result in enumerate(results, 1):
+ company = result.metadata.get("company", "Unknown")
+ year = result.metadata.get("doc_period", "Unknown")
+ page = result.metadata.get("page_number", "Unknown")
+ print(f" {i}. {company} {year} (page {page}) - Score: {result.score:.3f}")
+ print(f" {result.text[:100]}...")
+
+ searcher.cleanup()
+ print("✅ Setup verification completed successfully!")
+
+ except Exception as e:
+ print(f"❌ Setup verification failed: {e}")
+ raise
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Setup FinanceBench with full PDF datastore")
+ parser.add_argument("--data-dir", default="data", help="Data directory")
+ parser.add_argument(
+ "--backend", choices=["hnsw", "diskann"], default="hnsw", help="LEANN backend"
+ )
+ parser.add_argument(
+ "--embedding-model",
+ default="sentence-transformers/all-mpnet-base-v2",
+ help="Embedding model",
+ )
+ parser.add_argument("--max-workers", type=int, default=5, help="Parallel download workers")
+ parser.add_argument("--skip-download", action="store_true", help="Skip PDF download")
+ parser.add_argument("--skip-build", action="store_true", help="Skip index building")
+ parser.add_argument(
+ "--build-baseline-only",
+ action="store_true",
+ help="Only build FAISS baseline from existing index",
+ )
+
+ args = parser.parse_args()
+
+ print("🏦 FinanceBench Complete Setup")
+ print("=" * 50)
+
+ setup = FinanceBenchSetup(args.data_dir)
+
+ try:
+ if args.build_baseline_only:
+ # Only build baseline from existing index
+ index_path = setup.index_dir / f"financebench_full_{args.backend}"
+ index_file = f"{index_path}.index"
+ meta_file = f"{index_path}.leann.meta.json"
+
+ if not os.path.exists(index_file) or not os.path.exists(meta_file):
+ print("❌ Index files not found:")
+ print(f" Index: {index_file}")
+ print(f" Meta: {meta_file}")
+ print("💡 Run without --build-baseline-only to build the index first")
+ exit(1)
+
+ print(f"🔨 Building baseline from existing index: {index_path}")
+ baseline_path = setup.build_faiss_flat_baseline(str(index_path))
+ print(f"✅ Baseline built at {baseline_path}")
+ return
+
+ # Step 1: Download dataset
+ setup.download_dataset()
+
+ # Step 2: Download PDFs
+ if not args.skip_download:
+ setup.download_all_pdfs(max_workers=args.max_workers)
+ else:
+ print("⏭️ Skipping PDF download")
+
+ # Step 3: Build LEANN index
+ if not args.skip_build:
+ index_path = setup.build_leann_index(
+ backend=args.backend, embedding_model=args.embedding_model
+ )
+
+ # Step 4: Build FAISS flat baseline
+ print("\n🔨 Building FAISS flat baseline...")
+ baseline_path = setup.build_faiss_flat_baseline(index_path)
+ print(f"✅ Baseline built at {baseline_path}")
+
+ # Step 5: Verify setup
+ setup.verify_setup(index_path)
+ else:
+ print("⏭️ Skipping index building")
+
+ print("\n🎉 FinanceBench setup completed!")
+ print(f"📁 Data directory: {setup.data_dir.absolute()}")
+ print("\nNext steps:")
+ print(
+ "1. Run evaluation: python evaluate_financebench.py --index data/index/financebench_full_hnsw.leann"
+ )
+ print(
+ "2. Or test manually: python -c \"from leann import LeannSearcher; s = LeannSearcher('data/index/financebench_full_hnsw.leann'); print(s.search('3M capital expenditure 2018'))\""
+ )
+
+ except KeyboardInterrupt:
+ print("\n⚠️ Setup interrupted by user")
+ exit(1)
+ except Exception as e:
+ print(f"\n❌ Setup failed: {e}")
+ exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/financebench/verify_recall.py b/benchmarks/financebench/verify_recall.py
new file mode 100644
index 0000000..c4f77cb
--- /dev/null
+++ b/benchmarks/financebench/verify_recall.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.9"
+# dependencies = [
+# "faiss-cpu",
+# "numpy",
+# "sentence-transformers",
+# "torch",
+# "tqdm",
+# ]
+# ///
+
+"""
+Independent recall verification script using standard FAISS.
+Creates two indexes (HNSW and Flat) and compares recall@3 at different complexities.
+"""
+
+import json
+import time
+from pathlib import Path
+
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+
+
+def compute_embeddings_direct(chunks: list[str], model_name: str) -> np.ndarray:
+ """
+ Direct embedding computation using sentence-transformers.
+ Copied logic to avoid dependency issues.
+ """
+ print(f"Loading model: {model_name}")
+ model = SentenceTransformer(model_name)
+
+ print(f"Computing embeddings for {len(chunks)} chunks...")
+ embeddings = model.encode(
+ chunks,
+ show_progress_bar=True,
+ batch_size=32,
+ convert_to_numpy=True,
+ normalize_embeddings=False,
+ )
+
+ return embeddings.astype(np.float32)
+
+
+def load_financebench_queries(dataset_path: str, max_queries: int = 200) -> list[str]:
+ """Load FinanceBench queries from dataset"""
+ queries = []
+ with open(dataset_path, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ queries.append(data["question"])
+ if len(queries) >= max_queries:
+ break
+ return queries
+
+
+def load_passages_from_leann_index(index_path: str) -> tuple[list[str], list[str]]:
+ """Load passages from LEANN index structure"""
+ meta_path = f"{index_path}.meta.json"
+ with open(meta_path) as f:
+ meta = json.load(f)
+
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not Path(passage_file).is_absolute():
+ index_dir = Path(index_path).parent
+ passage_file = index_dir / Path(passage_file).name
+
+ print(f"Loading passages from {passage_file}")
+
+ passages = []
+ passage_ids = []
+ with open(passage_file, encoding="utf-8") as f:
+ for line in tqdm(f, desc="Loading passages"):
+ if line.strip():
+ data = json.loads(line)
+ passages.append(data["text"])
+ passage_ids.append(data["id"])
+
+ print(f"Loaded {len(passages)} passages")
+ return passages, passage_ids
+
+
+def build_faiss_indexes(embeddings: np.ndarray) -> tuple[faiss.Index, faiss.Index]:
+ """Build FAISS indexes: Flat (ground truth) and HNSW"""
+ dimension = embeddings.shape[1]
+
+ # Build Flat index (ground truth)
+ print("Building FAISS IndexFlatIP (ground truth)...")
+ flat_index = faiss.IndexFlatIP(dimension)
+ flat_index.add(embeddings)
+
+ # Build HNSW index
+ print("Building FAISS IndexHNSWFlat...")
+ M = 32 # Same as LEANN default
+ hnsw_index = faiss.IndexHNSWFlat(dimension, M, faiss.METRIC_INNER_PRODUCT)
+ hnsw_index.hnsw.efConstruction = 200 # Same as LEANN default
+ hnsw_index.add(embeddings)
+
+ print(f"Built indexes with {flat_index.ntotal} vectors, dimension {dimension}")
+ return flat_index, hnsw_index
+
+
+def evaluate_recall_at_k(
+ query_embeddings: np.ndarray,
+ flat_index: faiss.Index,
+ hnsw_index: faiss.Index,
+ passage_ids: list[str],
+ k: int = 3,
+ ef_search: int = 64,
+) -> float:
+ """Evaluate recall@k comparing HNSW vs Flat"""
+
+ # Set search parameters for HNSW
+ hnsw_index.hnsw.efSearch = ef_search
+
+ total_recall = 0.0
+ num_queries = query_embeddings.shape[0]
+
+ for i in range(num_queries):
+ query = query_embeddings[i : i + 1] # Keep 2D shape
+
+ # Get ground truth from Flat index (standard FAISS API)
+ flat_distances, flat_indices = flat_index.search(query, k)
+ ground_truth_ids = {passage_ids[idx] for idx in flat_indices[0]}
+
+ # Get results from HNSW index (standard FAISS API)
+ hnsw_distances, hnsw_indices = hnsw_index.search(query, k)
+ hnsw_ids = {passage_ids[idx] for idx in hnsw_indices[0]}
+
+ # Calculate recall
+ intersection = ground_truth_ids.intersection(hnsw_ids)
+ recall = len(intersection) / k
+ total_recall += recall
+
+ if i < 3: # Show first few examples
+ print(f" Query {i + 1}: Recall@{k} = {recall:.3f}")
+ print(f" Flat: {list(ground_truth_ids)}")
+ print(f" HNSW: {list(hnsw_ids)}")
+ print(f" Intersection: {list(intersection)}")
+
+ avg_recall = total_recall / num_queries
+ return avg_recall
+
+
+def main():
+ # Configuration
+ dataset_path = "data/financebench_merged.jsonl"
+ index_path = "data/index/financebench_full_hnsw.leann"
+ embedding_model = "sentence-transformers/all-mpnet-base-v2"
+
+ print("🔍 FAISS Recall Verification")
+ print("=" * 50)
+
+ # Check if files exist
+ if not Path(dataset_path).exists():
+ print(f"❌ Dataset not found: {dataset_path}")
+ return
+ if not Path(f"{index_path}.meta.json").exists():
+ print(f"❌ Index metadata not found: {index_path}.meta.json")
+ return
+
+ # Load data
+ print("📖 Loading FinanceBench queries...")
+ queries = load_financebench_queries(dataset_path, max_queries=50)
+ print(f"Loaded {len(queries)} queries")
+
+ print("📄 Loading passages from LEANN index...")
+ passages, passage_ids = load_passages_from_leann_index(index_path)
+
+ # Compute embeddings
+ print("🧮 Computing passage embeddings...")
+ passage_embeddings = compute_embeddings_direct(passages, embedding_model)
+
+ print("🧮 Computing query embeddings...")
+ query_embeddings = compute_embeddings_direct(queries, embedding_model)
+
+ # Build FAISS indexes
+ print("🏗️ Building FAISS indexes...")
+ flat_index, hnsw_index = build_faiss_indexes(passage_embeddings)
+
+ # Test different efSearch values (equivalent to LEANN complexity)
+ print("\n📊 Evaluating Recall@3 at different efSearch values...")
+ ef_search_values = [16, 32, 64, 128, 256]
+
+ for ef_search in ef_search_values:
+ print(f"\n🧪 Testing efSearch = {ef_search}")
+ start_time = time.time()
+
+ recall = evaluate_recall_at_k(
+ query_embeddings, flat_index, hnsw_index, passage_ids, k=3, ef_search=ef_search
+ )
+
+ elapsed = time.time() - start_time
+ print(
+ f"📈 efSearch {ef_search}: Recall@3 = {recall:.3f} ({recall * 100:.1f}%) in {elapsed:.2f}s"
+ )
+
+ print("\n✅ Verification completed!")
+ print("\n📋 Summary:")
+ print(" - Built independent FAISS Flat and HNSW indexes")
+ print(" - Compared recall@3 at different efSearch values")
+ print(" - Used same embedding model as LEANN")
+ print(" - This validates LEANN's recall measurements")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/laion/.gitignore b/benchmarks/laion/.gitignore
new file mode 100644
index 0000000..8fce603
--- /dev/null
+++ b/benchmarks/laion/.gitignore
@@ -0,0 +1 @@
+data/
diff --git a/benchmarks/laion/README.md b/benchmarks/laion/README.md
new file mode 100644
index 0000000..51dc721
--- /dev/null
+++ b/benchmarks/laion/README.md
@@ -0,0 +1,199 @@
+# LAION Multimodal Benchmark
+
+A multimodal benchmark for evaluating image retrieval and generation performance using LEANN with CLIP embeddings and Qwen2.5-VL for multimodal generation on LAION dataset subset.
+
+## Overview
+
+This benchmark evaluates:
+- **Image retrieval timing** using caption-based queries
+- **Recall@K performance** for image search
+- **Complexity analysis** across different search parameters
+- **Index size and storage efficiency**
+- **Multimodal generation** with Qwen2.5-VL for image understanding and description
+
+## Dataset Configuration
+
+- **Dataset**: LAION-400M subset (10,000 images)
+- **Embeddings**: Pre-computed CLIP ViT-B/32 (512 dimensions)
+- **Queries**: 200 random captions from the dataset
+- **Ground Truth**: Self-recall (query caption → original image)
+
+## Quick Start
+
+### 1. Setup the benchmark
+
+```bash
+cd benchmarks/laion
+python setup_laion.py --num-samples 10000 --num-queries 200
+```
+
+This will:
+- Create dummy LAION data (10K samples)
+- Generate CLIP embeddings (512-dim)
+- Build LEANN index with HNSW backend
+- Create 200 evaluation queries
+
+### 2. Run evaluation
+
+```bash
+# Run all evaluation stages
+python evaluate_laion.py --index data/laion_index.leann
+
+# Run specific stages
+python evaluate_laion.py --index data/laion_index.leann --stage 2 # Recall evaluation
+python evaluate_laion.py --index data/laion_index.leann --stage 3 # Complexity analysis
+python evaluate_laion.py --index data/laion_index.leann --stage 4 # Index comparison
+python evaluate_laion.py --index data/laion_index.leann --stage 5 # Multimodal generation
+
+# Multimodal generation with Qwen2.5-VL
+python evaluate_laion.py --index data/laion_index.leann --stage 5 --model-name Qwen/Qwen2.5-VL-7B-Instruct
+```
+
+### 3. Save results
+
+```bash
+python evaluate_laion.py --index data/laion_index.leann --output results.json
+```
+
+## Configuration Options
+
+### Setup Options
+```bash
+python setup_laion.py \
+ --num-samples 10000 \
+ --num-queries 200 \
+ --index-path data/laion_index.leann \
+ --backend hnsw
+```
+
+### Evaluation Options
+```bash
+python evaluate_laion.py \
+ --index data/laion_index.leann \
+ --queries data/evaluation_queries.jsonl \
+ --complexity 64 \
+ --top-k 3 \
+ --num-samples 100 \
+ --stage all
+```
+
+## Evaluation Stages
+
+### Stage 2: Recall Evaluation
+- Evaluates Recall@3 for multimodal retrieval
+- Compares LEANN vs FAISS baseline performance
+- Self-recall: query caption should retrieve original image
+
+### Stage 3: Complexity Analysis
+- Binary search for optimal complexity (90% recall target)
+- Tests performance across different complexity levels
+- Analyzes speed vs. accuracy tradeoffs
+
+### Stage 4: Index Comparison
+- Compares compact vs non-compact index sizes
+- Measures search performance differences
+- Reports storage efficiency and speed ratios
+
+### Stage 5: Multimodal Generation
+- Uses Qwen2.5-VL for image understanding and description
+- Retrieval-Augmented Generation (RAG) with multimodal context
+- Measures both search and generation timing
+
+## Output Metrics
+
+### Timing Metrics
+- Average/median/min/max search time
+- Standard deviation
+- Searches per second
+- Latency in milliseconds
+
+### Recall Metrics
+- Recall@3 percentage for image retrieval
+- Number of queries with ground truth
+
+### Index Metrics
+- Total index size (MB)
+- Component breakdown (index, passages, metadata)
+- Storage savings (compact vs non-compact)
+- Backend and embedding model info
+
+### Generation Metrics (Stage 5)
+- Average search time per query
+- Average generation time per query
+- Time distribution (search vs generation)
+- Sample multimodal responses
+- Model: Qwen2.5-VL performance
+
+## Benchmark Results
+
+### LEANN-RAG Performance (CLIP ViT-L/14 + Qwen2.5-VL)
+
+**Stage 3: Optimal Complexity Analysis**
+- **Optimal Complexity**: 85 (achieving 90% Recall@3)
+- **Binary Search Range**: 1-128
+- **Target Recall**: 90%
+- **Index Type**: Non-compact (for fast binary search)
+
+**Stage 5: Multimodal Generation Performance (Qwen2.5-VL)**
+- **Total Queries**: 20
+- **Average Search Time**: 1.200s per query
+- **Average Generation Time**: 6.558s per query
+- **Time Distribution**: Search 15.5%, Generation 84.5%
+- **LLM Backend**: HuggingFace transformers
+- **Model**: Qwen/Qwen2.5-VL-7B-Instruct
+- **Optimal Complexity**: 85
+
+**System Performance:**
+- **Index Size**: ~10,000 image embeddings from LAION subset
+- **Embedding Model**: CLIP ViT-L/14 (768 dimensions)
+- **Backend**: HNSW with cosine distance
+
+### Example Results
+
+```
+🎯 LAION MULTIMODAL BENCHMARK RESULTS
+============================================================
+
+📊 Multimodal Generation Results:
+ Total Queries: 20
+ Avg Search Time: 1.200s
+ Avg Generation Time: 6.558s
+ Time Distribution: Search 15.5%, Generation 84.5%
+ LLM Backend: HuggingFace transformers
+ Model: Qwen/Qwen2.5-VL-7B-Instruct
+
+⚙️ Optimal Complexity Analysis:
+ Target Recall: 90%
+ Optimal Complexity: 85
+ Binary Search Range: 1-128
+ Non-compact Index (fast search, no recompute)
+
+🚀 Performance Summary:
+ Multimodal RAG: 7.758s total per query
+ Search: 15.5% of total time
+ Generation: 84.5% of total time
+```
+
+## Directory Structure
+
+```
+benchmarks/laion/
+├── setup_laion.py # Setup script
+├── evaluate_laion.py # Evaluation script
+├── README.md # This file
+└── data/ # Generated data
+ ├── laion_images/ # Image files (placeholder)
+ ├── laion_metadata.jsonl # Image metadata
+ ├── laion_passages.jsonl # LEANN passages
+ ├── laion_embeddings.npy # CLIP embeddings
+ ├── evaluation_queries.jsonl # Evaluation queries
+ └── laion_index.leann/ # LEANN index files
+```
+
+## Notes
+
+- Current implementation uses dummy data for demonstration
+- For real LAION data, implement actual download logic in `setup_laion.py`
+- CLIP embeddings are randomly generated - replace with real CLIP model for production
+- Adjust `num_samples` and `num_queries` based on available resources
+- Consider using `--num-samples` during evaluation for faster testing
diff --git a/benchmarks/laion/evaluate_laion.py b/benchmarks/laion/evaluate_laion.py
new file mode 100644
index 0000000..dd30635
--- /dev/null
+++ b/benchmarks/laion/evaluate_laion.py
@@ -0,0 +1,725 @@
+"""
+LAION Multimodal Benchmark Evaluation Script - Modular Recall-based Evaluation
+"""
+
+import argparse
+import json
+import logging
+import os
+import pickle
+import time
+from pathlib import Path
+
+import numpy as np
+from leann import LeannSearcher
+from leann_backend_hnsw import faiss
+from sentence_transformers import SentenceTransformer
+
+from ..llm_utils import evaluate_multimodal_rag, load_qwen_vl_model
+
+# Setup logging to reduce verbose output
+logging.basicConfig(level=logging.WARNING)
+logging.getLogger("leann.api").setLevel(logging.WARNING)
+logging.getLogger("leann_backend_hnsw").setLevel(logging.WARNING)
+
+
+class RecallEvaluator:
+ """Stage 2: Evaluate Recall@3 (LEANN vs FAISS baseline for multimodal retrieval)"""
+
+ def __init__(self, index_path: str, baseline_dir: str):
+ self.index_path = index_path
+ self.baseline_dir = baseline_dir
+ self.searcher = LeannSearcher(index_path)
+
+ # Load FAISS flat baseline (image embeddings)
+ baseline_index_path = os.path.join(baseline_dir, "faiss_flat.index")
+ metadata_path = os.path.join(baseline_dir, "metadata.pkl")
+
+ self.faiss_index = faiss.read_index(baseline_index_path)
+ with open(metadata_path, "rb") as f:
+ self.image_ids = pickle.load(f)
+ print(f"📚 Loaded FAISS flat baseline with {self.faiss_index.ntotal} image vectors")
+
+ # Load sentence-transformers CLIP for text embedding (ViT-L/14)
+ self.st_clip = SentenceTransformer("clip-ViT-L-14")
+
+ def evaluate_recall_at_3(
+ self, captions: list[str], complexity: int = 64, recompute_embeddings: bool = True
+ ) -> float:
+ """Evaluate recall@3 for multimodal retrieval: caption queries -> image results"""
+ recompute_str = "with recompute" if recompute_embeddings else "no recompute"
+ print(f"🔍 Evaluating recall@3 with complexity={complexity} ({recompute_str})...")
+
+ total_recall = 0.0
+ num_queries = len(captions)
+
+ for i, caption in enumerate(captions):
+ # Get ground truth: search with FAISS flat using caption text embedding
+ # Generate CLIP text embedding for caption via sentence-transformers (normalized)
+ query_embedding = self.st_clip.encode(
+ [caption], convert_to_numpy=True, normalize_embeddings=True, show_progress_bar=False
+ ).astype(np.float32)
+
+ # Search FAISS flat for ground truth using LEANN's modified faiss API
+ n = query_embedding.shape[0] # Number of queries
+ k = 3 # Number of nearest neighbors
+ distances = np.zeros((n, k), dtype=np.float32)
+ labels = np.zeros((n, k), dtype=np.int64)
+
+ self.faiss_index.search(
+ n,
+ faiss.swig_ptr(query_embedding),
+ k,
+ faiss.swig_ptr(distances),
+ faiss.swig_ptr(labels),
+ )
+
+ # Extract the results (image IDs from FAISS)
+ baseline_ids = {self.image_ids[idx] for idx in labels[0]}
+
+ # Search with LEANN at specified complexity (using caption as text query)
+ test_results = self.searcher.search(
+ caption,
+ top_k=3,
+ complexity=complexity,
+ recompute_embeddings=recompute_embeddings,
+ )
+ test_ids = {result.id for result in test_results}
+
+ # Calculate recall@3 = |intersection| / |ground_truth|
+ intersection = test_ids.intersection(baseline_ids)
+ recall = len(intersection) / 3.0 # Ground truth size is 3
+ total_recall += recall
+
+ if i < 3: # Show first few examples
+ print(f" Query {i + 1}: '{caption[:50]}...' -> Recall@3: {recall:.3f}")
+ print(f" FAISS ground truth: {list(baseline_ids)}")
+ print(f" LEANN results (C={complexity}, {recompute_str}): {list(test_ids)}")
+ print(f" Intersection: {list(intersection)}")
+
+ avg_recall = total_recall / num_queries
+ print(f"📊 Average Recall@3: {avg_recall:.3f} ({avg_recall * 100:.1f}%)")
+ return avg_recall
+
+ def cleanup(self):
+ """Cleanup resources"""
+ if hasattr(self, "searcher"):
+ self.searcher.cleanup()
+
+
+class LAIONEvaluator:
+ def __init__(self, index_path: str):
+ self.index_path = index_path
+ self.searcher = LeannSearcher(index_path)
+
+ def load_queries(self, queries_file: str) -> list[str]:
+ """Load caption queries from evaluation file"""
+ captions = []
+ with open(queries_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ query_data = json.loads(line)
+ captions.append(query_data["query"])
+
+ print(f"📊 Loaded {len(captions)} caption queries")
+ return captions
+
+ def analyze_index_sizes(self) -> dict:
+ """Analyze index sizes, emphasizing .index only (exclude passages)."""
+ print("📏 Analyzing index sizes (.index only)...")
+
+ # Get all index-related files
+ index_path = Path(self.index_path)
+ index_dir = index_path.parent
+ index_name = index_path.stem # Remove .leann extension
+
+ sizes: dict[str, float] = {}
+
+ # Core index files
+ index_file = index_dir / f"{index_name}.index"
+ meta_file = index_dir / f"{index_path.name}.meta.json" # Keep .leann for meta file
+ passages_file = index_dir / f"{index_path.name}.passages.jsonl" # Keep .leann for passages
+ passages_idx_file = index_dir / f"{index_path.name}.passages.idx" # Keep .leann for idx
+
+ # Core index size (.index only)
+ index_mb = index_file.stat().st_size / (1024 * 1024) if index_file.exists() else 0.0
+ sizes["index_only_mb"] = index_mb
+
+ # Other files for reference (not counted in index_only_mb)
+ sizes["metadata_mb"] = (
+ meta_file.stat().st_size / (1024 * 1024) if meta_file.exists() else 0.0
+ )
+ sizes["passages_text_mb"] = (
+ passages_file.stat().st_size / (1024 * 1024) if passages_file.exists() else 0.0
+ )
+ sizes["passages_index_mb"] = (
+ passages_idx_file.stat().st_size / (1024 * 1024) if passages_idx_file.exists() else 0.0
+ )
+
+ print(f" 📁 .index size: {index_mb:.1f} MB")
+ if sizes["metadata_mb"]:
+ print(f" 🧾 metadata: {sizes['metadata_mb']:.3f} MB")
+ if sizes["passages_text_mb"] or sizes["passages_index_mb"]:
+ print(
+ f" (passages excluded) text: {sizes['passages_text_mb']:.1f} MB, idx: {sizes['passages_index_mb']:.1f} MB"
+ )
+
+ return sizes
+
+ def create_non_compact_index_for_comparison(self, non_compact_index_path: str) -> dict:
+ """Create a non-compact index for comparison purposes"""
+ print("🏗️ Building non-compact index from existing passages...")
+
+ # Load existing passages from current index
+ from leann import LeannBuilder
+
+ current_index_path = Path(self.index_path)
+ current_index_dir = current_index_path.parent
+ current_index_name = current_index_path.name
+
+ # Read metadata to get passage source
+ meta_path = current_index_dir / f"{current_index_name}.meta.json"
+ with open(meta_path) as f:
+ meta = json.load(f)
+
+ passage_source = meta["passage_sources"][0]
+ passage_file = passage_source["path"]
+
+ # Convert relative path to absolute
+ if not Path(passage_file).is_absolute():
+ passage_file = current_index_dir / Path(passage_file).name
+
+ print(f"📄 Loading passages from {passage_file}...")
+
+ # Load CLIP embeddings
+ embeddings_file = current_index_dir / "clip_image_embeddings.npy"
+ embeddings = np.load(embeddings_file)
+ print(f"📐 Loaded embeddings shape: {embeddings.shape}")
+
+ # Build non-compact index with same passages and embeddings
+ builder = LeannBuilder(
+ backend_name="hnsw",
+ # Use CLIP text encoder (ViT-L/14) to match image embeddings (768-dim)
+ embedding_model="clip-ViT-L-14",
+ embedding_mode="sentence-transformers",
+ is_recompute=False, # Disable recompute (store embeddings)
+ is_compact=False, # Disable compact storage
+ distance_metric="cosine",
+ **{
+ k: v
+ for k, v in meta.get("backend_kwargs", {}).items()
+ if k not in ["is_recompute", "is_compact", "distance_metric"]
+ },
+ )
+
+ # Prepare ids and add passages
+ ids: list[str] = []
+ with open(passage_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ data = json.loads(line)
+ ids.append(str(data["id"]))
+ # Ensure metadata contains the id used by the vector index
+ metadata = {**data.get("metadata", {}), "id": data["id"]}
+ builder.add_text(text=data["text"], metadata=metadata)
+
+ if len(ids) != embeddings.shape[0]:
+ raise ValueError(
+ f"IDs count ({len(ids)}) does not match embeddings ({embeddings.shape[0]})."
+ )
+
+ # Persist a pickle for build_index_from_embeddings
+ pkl_path = current_index_dir / "clip_image_embeddings.pkl"
+ with open(pkl_path, "wb") as pf:
+ pickle.dump((ids, embeddings.astype(np.float32)), pf)
+
+ print(
+ f"🔨 Building non-compact index at {non_compact_index_path} from precomputed embeddings..."
+ )
+ builder.build_index_from_embeddings(non_compact_index_path, str(pkl_path))
+
+ # Analyze the non-compact index size
+ temp_evaluator = LAIONEvaluator(non_compact_index_path)
+ non_compact_sizes = temp_evaluator.analyze_index_sizes()
+ non_compact_sizes["index_type"] = "non_compact"
+
+ return non_compact_sizes
+
+ def compare_index_performance(
+ self, non_compact_path: str, compact_path: str, test_captions: list, complexity: int
+ ) -> dict:
+ """Compare performance between non-compact and compact indexes"""
+ print("⚡ Comparing search performance between indexes...")
+
+ # Test queries
+ test_queries = test_captions[:5]
+
+ results = {
+ "non_compact": {"search_times": []},
+ "compact": {"search_times": []},
+ "avg_search_times": {},
+ "speed_ratio": 0.0,
+ }
+
+ # Test non-compact index (no recompute)
+ print(" 🔍 Testing non-compact index (no recompute)...")
+ non_compact_searcher = LeannSearcher(non_compact_path)
+
+ for caption in test_queries:
+ start_time = time.time()
+ _ = non_compact_searcher.search(
+ caption, top_k=3, complexity=complexity, recompute_embeddings=False
+ )
+ search_time = time.time() - start_time
+ results["non_compact"]["search_times"].append(search_time)
+
+ # Test compact index (with recompute)
+ print(" 🔍 Testing compact index (with recompute)...")
+ compact_searcher = LeannSearcher(compact_path)
+
+ for caption in test_queries:
+ start_time = time.time()
+ _ = compact_searcher.search(
+ caption, top_k=3, complexity=complexity, recompute_embeddings=True
+ )
+ search_time = time.time() - start_time
+ results["compact"]["search_times"].append(search_time)
+
+ # Calculate averages
+ results["avg_search_times"]["non_compact"] = sum(
+ results["non_compact"]["search_times"]
+ ) / len(results["non_compact"]["search_times"])
+ results["avg_search_times"]["compact"] = sum(results["compact"]["search_times"]) / len(
+ results["compact"]["search_times"]
+ )
+
+ # Performance ratio
+ if results["avg_search_times"]["compact"] > 0:
+ results["speed_ratio"] = (
+ results["avg_search_times"]["non_compact"] / results["avg_search_times"]["compact"]
+ )
+ else:
+ results["speed_ratio"] = float("inf")
+
+ print(
+ f" Non-compact (no recompute): {results['avg_search_times']['non_compact']:.3f}s avg"
+ )
+ print(f" Compact (with recompute): {results['avg_search_times']['compact']:.3f}s avg")
+ print(f" Speed ratio: {results['speed_ratio']:.2f}x")
+
+ # Cleanup
+ non_compact_searcher.cleanup()
+ compact_searcher.cleanup()
+
+ return results
+
+ def _print_results(self, timing_metrics: dict):
+ """Print evaluation results"""
+ print("\n🎯 LAION MULTIMODAL BENCHMARK RESULTS")
+ print("=" * 60)
+
+ # Index comparison analysis (prefer .index-only view if present)
+ if "current_index" in timing_metrics and "non_compact_index" in timing_metrics:
+ current = timing_metrics["current_index"]
+ non_compact = timing_metrics["non_compact_index"]
+
+ if "index_only_mb" in current and "index_only_mb" in non_compact:
+ print("\n📏 Index Comparison Analysis (.index only):")
+ print(f" Compact index (current): {current.get('index_only_mb', 0):.1f} MB")
+ print(f" Non-compact index: {non_compact.get('index_only_mb', 0):.1f} MB")
+ print(
+ f" Storage saving by compact: {timing_metrics.get('storage_saving_percent', 0):.1f}%"
+ )
+ # Show excluded components for reference if available
+ if any(
+ k in non_compact
+ for k in ("passages_text_mb", "passages_index_mb", "metadata_mb")
+ ):
+ print(" (passages excluded in totals, shown for reference):")
+ print(
+ f" - Passages text: {non_compact.get('passages_text_mb', 0):.1f} MB, "
+ f"Passages index: {non_compact.get('passages_index_mb', 0):.1f} MB, "
+ f"Metadata: {non_compact.get('metadata_mb', 0):.3f} MB"
+ )
+ else:
+ # Fallback to legacy totals if running with older metrics
+ print("\n📏 Index Comparison Analysis:")
+ print(
+ f" Compact index (current): {current.get('total_with_embeddings', 0):.1f} MB"
+ )
+ print(
+ f" Non-compact index (with embeddings): {non_compact.get('total_with_embeddings', 0):.1f} MB"
+ )
+ print(
+ f" Storage saving by compact: {timing_metrics.get('storage_saving_percent', 0):.1f}%"
+ )
+ print(" Component breakdown (non-compact):")
+ print(f" - Main index: {non_compact.get('index', 0):.1f} MB")
+ print(f" - Passages text: {non_compact.get('passages_text', 0):.1f} MB")
+ print(f" - Passages index: {non_compact.get('passages_index', 0):.1f} MB")
+ print(f" - Metadata: {non_compact.get('metadata', 0):.1f} MB")
+
+ # Performance comparison
+ if "performance_comparison" in timing_metrics:
+ perf = timing_metrics["performance_comparison"]
+ print("\n⚡ Performance Comparison:")
+ print(
+ f" Non-compact (no recompute): {perf.get('avg_search_times', {}).get('non_compact', 0):.3f}s avg"
+ )
+ print(
+ f" Compact (with recompute): {perf.get('avg_search_times', {}).get('compact', 0):.3f}s avg"
+ )
+ print(f" Speed ratio: {perf.get('speed_ratio', 0):.2f}x")
+
+ # Legacy single index analysis (fallback)
+ if "total_with_embeddings" in timing_metrics and "current_index" not in timing_metrics:
+ print("\n📏 Index Size Analysis:")
+ print(
+ f" Index with embeddings: {timing_metrics.get('total_with_embeddings', 0):.1f} MB"
+ )
+ print(
+ f" Estimated pruned index: {timing_metrics.get('total_without_embeddings', 0):.1f} MB"
+ )
+ print(f" Compression ratio: {timing_metrics.get('compression_ratio', 0):.2f}x")
+
+ def cleanup(self):
+ """Cleanup resources"""
+ if self.searcher:
+ self.searcher.cleanup()
+
+
+def main():
+ parser = argparse.ArgumentParser(description="LAION Multimodal Benchmark Evaluation")
+ parser.add_argument("--index", required=True, help="Path to LEANN index")
+ parser.add_argument(
+ "--queries", default="data/evaluation_queries.jsonl", help="Path to evaluation queries"
+ )
+ parser.add_argument(
+ "--stage",
+ choices=["2", "3", "4", "5", "all"],
+ default="all",
+ help="Which stage to run (2=recall, 3=complexity, 4=index comparison, 5=generation)",
+ )
+ parser.add_argument("--complexity", type=int, default=None, help="Complexity for search")
+ parser.add_argument("--baseline-dir", default="baseline", help="Baseline output directory")
+ parser.add_argument("--output", help="Save results to JSON file")
+ parser.add_argument(
+ "--llm-backend",
+ choices=["hf"],
+ default="hf",
+ help="LLM backend (Qwen2.5-VL only supports HF)",
+ )
+ parser.add_argument(
+ "--model-name", default="Qwen/Qwen2.5-VL-7B-Instruct", help="Multimodal model name"
+ )
+
+ args = parser.parse_args()
+
+ try:
+ # Check if baseline exists
+ baseline_index_path = os.path.join(args.baseline_dir, "faiss_flat.index")
+ if not os.path.exists(baseline_index_path):
+ print(f"❌ FAISS baseline not found at {baseline_index_path}")
+ print("💡 Please run setup_laion.py first to build the baseline")
+ exit(1)
+
+ if args.stage == "2" or args.stage == "all":
+ # Stage 2: Recall@3 evaluation
+ print("🚀 Starting Stage 2: Recall@3 evaluation for multimodal retrieval")
+
+ evaluator = RecallEvaluator(args.index, args.baseline_dir)
+
+ # Load caption queries for testing
+ laion_evaluator = LAIONEvaluator(args.index)
+ captions = laion_evaluator.load_queries(args.queries)
+
+ # Test with queries for robust measurement
+ test_captions = captions[:100] # Use subset for speed
+ print(f"🧪 Testing with {len(test_captions)} caption queries")
+
+ # Test with complexity 64
+ complexity = 64
+ recall = evaluator.evaluate_recall_at_3(test_captions, complexity)
+ print(f"📈 Recall@3 at complexity {complexity}: {recall * 100:.1f}%")
+
+ evaluator.cleanup()
+ print("✅ Stage 2 completed!\n")
+
+ # Shared non-compact index path for Stage 3 and 4
+ non_compact_index_path = args.index.replace(".leann", "_noncompact.leann")
+ complexity = args.complexity
+
+ if args.stage == "3" or args.stage == "all":
+ # Stage 3: Binary search for 90% recall complexity
+ print("🚀 Starting Stage 3: Binary search for 90% recall complexity")
+ print(
+ "💡 Creating non-compact index for fast binary search with recompute_embeddings=False"
+ )
+
+ # Create non-compact index for binary search
+ print("🏗️ Creating non-compact index for binary search...")
+ evaluator = LAIONEvaluator(args.index)
+ evaluator.create_non_compact_index_for_comparison(non_compact_index_path)
+
+ # Use non-compact index for binary search
+ binary_search_evaluator = RecallEvaluator(non_compact_index_path, args.baseline_dir)
+
+ # Load caption queries for testing
+ captions = evaluator.load_queries(args.queries)
+
+ # Use subset for robust measurement
+ test_captions = captions[:50] # Smaller subset for binary search speed
+ print(f"🧪 Testing with {len(test_captions)} caption queries")
+
+ # Binary search for 90% recall complexity
+ target_recall = 0.9
+ min_complexity, max_complexity = 1, 128
+
+ print(f"🔍 Binary search for {target_recall * 100}% recall complexity...")
+ print(f"Search range: {min_complexity} to {max_complexity}")
+
+ best_complexity = None
+ best_recall = 0.0
+
+ while min_complexity <= max_complexity:
+ mid_complexity = (min_complexity + max_complexity) // 2
+
+ print(
+ f"\n🧪 Testing complexity {mid_complexity} (no recompute, non-compact index)..."
+ )
+ # Use recompute_embeddings=False on non-compact index for fast binary search
+ recall = binary_search_evaluator.evaluate_recall_at_3(
+ test_captions, mid_complexity, recompute_embeddings=False
+ )
+
+ print(
+ f" Complexity {mid_complexity}: Recall@3 = {recall:.3f} ({recall * 100:.1f}%)"
+ )
+
+ if recall >= target_recall:
+ best_complexity = mid_complexity
+ best_recall = recall
+ max_complexity = mid_complexity - 1
+ print(" ✅ Target reached! Searching for lower complexity...")
+ else:
+ min_complexity = mid_complexity + 1
+ print(" ❌ Below target. Searching for higher complexity...")
+
+ if best_complexity is not None:
+ print("\n🎯 Optimal complexity found!")
+ print(f" Complexity: {best_complexity}")
+ print(f" Recall@3: {best_recall:.3f} ({best_recall * 100:.1f}%)")
+
+ # Test a few complexities around the optimal one for verification
+ print("\n🔬 Verification test around optimal complexity:")
+ verification_complexities = [
+ max(1, best_complexity - 2),
+ max(1, best_complexity - 1),
+ best_complexity,
+ best_complexity + 1,
+ best_complexity + 2,
+ ]
+
+ for complexity in verification_complexities:
+ if complexity <= 512: # reasonable upper bound
+ recall = binary_search_evaluator.evaluate_recall_at_3(
+ test_captions, complexity, recompute_embeddings=False
+ )
+ status = "✅" if recall >= target_recall else "❌"
+ print(f" {status} Complexity {complexity:3d}: {recall * 100:5.1f}%")
+
+ # Now test the optimal complexity with compact index and recompute for comparison
+ print(
+ f"\n🔄 Testing optimal complexity {best_complexity} on compact index WITH recompute..."
+ )
+ compact_evaluator = RecallEvaluator(args.index, args.baseline_dir)
+ recall_with_recompute = compact_evaluator.evaluate_recall_at_3(
+ test_captions[:10], best_complexity, recompute_embeddings=True
+ )
+ print(
+ f" ✅ Complexity {best_complexity} (compact index with recompute): {recall_with_recompute * 100:.1f}%"
+ )
+ complexity = best_complexity
+ print(
+ f" 📊 Recall difference: {abs(best_recall - recall_with_recompute) * 100:.2f}%"
+ )
+ compact_evaluator.cleanup()
+ else:
+ print(f"\n❌ Could not find complexity achieving {target_recall * 100}% recall")
+ print("All tested complexities were below target.")
+
+ # Cleanup evaluators (keep non-compact index for Stage 4)
+ binary_search_evaluator.cleanup()
+ evaluator.cleanup()
+
+ print("✅ Stage 3 completed! Non-compact index saved for Stage 4.\n")
+
+ if args.stage == "4" or args.stage == "all":
+ # Stage 4: Index comparison (without LLM generation)
+ print("🚀 Starting Stage 4: Index comparison analysis")
+
+ # Use LAION evaluator for index comparison
+ evaluator = LAIONEvaluator(args.index)
+
+ # Load caption queries
+ captions = evaluator.load_queries(args.queries)
+
+ # Step 1: Analyze current (compact) index
+ print("\n📏 Analyzing current index (compact, pruned)...")
+ compact_size_metrics = evaluator.analyze_index_sizes()
+ compact_size_metrics["index_type"] = "compact"
+
+ # Step 2: Use existing non-compact index or create if needed
+ if Path(non_compact_index_path).exists():
+ print(
+ f"\n📁 Using existing non-compact index from Stage 3: {non_compact_index_path}"
+ )
+ temp_evaluator = LAIONEvaluator(non_compact_index_path)
+ non_compact_size_metrics = temp_evaluator.analyze_index_sizes()
+ non_compact_size_metrics["index_type"] = "non_compact"
+ else:
+ print("\n🏗️ Creating non-compact index (with embeddings) for comparison...")
+ non_compact_size_metrics = evaluator.create_non_compact_index_for_comparison(
+ non_compact_index_path
+ )
+
+ # Step 3: Compare index sizes (.index only)
+ print("\n📊 Index size comparison (.index only):")
+ print(
+ f" Compact index (current): {compact_size_metrics.get('index_only_mb', 0):.1f} MB"
+ )
+ print(f" Non-compact index: {non_compact_size_metrics.get('index_only_mb', 0):.1f} MB")
+
+ storage_saving = 0.0
+ if non_compact_size_metrics.get("index_only_mb", 0) > 0:
+ storage_saving = (
+ (
+ non_compact_size_metrics.get("index_only_mb", 0)
+ - compact_size_metrics.get("index_only_mb", 0)
+ )
+ / non_compact_size_metrics.get("index_only_mb", 1)
+ * 100
+ )
+ print(f" Storage saving by compact: {storage_saving:.1f}%")
+
+ # Step 4: Performance comparison between the two indexes
+ if complexity is None:
+ raise ValueError("Complexity is required for index comparison")
+
+ print("\n⚡ Performance comparison between indexes...")
+ performance_metrics = evaluator.compare_index_performance(
+ non_compact_index_path, args.index, captions[:10], complexity=complexity
+ )
+
+ # Combine all metrics
+ combined_metrics = {
+ "current_index": compact_size_metrics,
+ "non_compact_index": non_compact_size_metrics,
+ "performance_comparison": performance_metrics,
+ "storage_saving_percent": storage_saving,
+ }
+
+ # Print comprehensive results
+ evaluator._print_results(combined_metrics)
+
+ # Save results if requested
+ if args.output:
+ print(f"\n💾 Saving results to {args.output}...")
+ with open(args.output, "w") as f:
+ json.dump(combined_metrics, f, indent=2, default=str)
+ print(f"✅ Results saved to {args.output}")
+
+ evaluator.cleanup()
+ print("✅ Stage 4 completed!\n")
+
+ if args.stage in ("5", "all"):
+ print("🚀 Starting Stage 5: Multimodal generation with Qwen2.5-VL")
+ evaluator = LAIONEvaluator(args.index)
+ captions = evaluator.load_queries(args.queries)
+ test_captions = captions[: min(20, len(captions))] # Use subset for generation
+
+ print(f"🧪 Testing multimodal generation with {len(test_captions)} queries")
+
+ # Load Qwen2.5-VL model
+ try:
+ print("Loading Qwen2.5-VL model...")
+ processor, model = load_qwen_vl_model(args.model_name)
+
+ # Run multimodal generation evaluation
+ complexity = args.complexity or 64
+ gen_results = evaluate_multimodal_rag(
+ evaluator.searcher,
+ test_captions,
+ processor=processor,
+ model=model,
+ complexity=complexity,
+ )
+
+ print("\n📊 Multimodal Generation Results:")
+ print(f" Total Queries: {len(test_captions)}")
+ print(f" Avg Search Time: {gen_results['avg_search_time']:.3f}s")
+ print(f" Avg Generation Time: {gen_results['avg_generation_time']:.3f}s")
+ total_time = gen_results["avg_search_time"] + gen_results["avg_generation_time"]
+ search_pct = (gen_results["avg_search_time"] / total_time) * 100
+ gen_pct = (gen_results["avg_generation_time"] / total_time) * 100
+ print(f" Time Distribution: Search {search_pct:.1f}%, Generation {gen_pct:.1f}%")
+ print(" LLM Backend: HuggingFace transformers")
+ print(f" Model: {args.model_name}")
+
+ # Show sample results
+ print("\n📝 Sample Multimodal Generations:")
+ for i, response in enumerate(gen_results["results"][:3]):
+ # Handle both string and dict formats for captions
+ if isinstance(test_captions[i], dict):
+ caption_text = test_captions[i].get("query", str(test_captions[i]))
+ else:
+ caption_text = str(test_captions[i])
+ print(f" Query {i + 1}: {caption_text[:60]}...")
+ print(f" Response {i + 1}: {response[:100]}...")
+ print()
+
+ except Exception as e:
+ print(f"❌ Multimodal generation evaluation failed: {e}")
+ print("💡 Make sure transformers and Qwen2.5-VL are installed")
+ import traceback
+
+ traceback.print_exc()
+
+ evaluator.cleanup()
+ print("✅ Stage 5 completed!\n")
+
+ if args.stage == "all":
+ print("🎉 All evaluation stages completed successfully!")
+ print("\n📋 Summary:")
+ print(" Stage 2: ✅ Multimodal Recall@3 evaluation completed")
+ print(" Stage 3: ✅ Optimal complexity found")
+ print(" Stage 4: ✅ Index comparison analysis completed")
+ print(" Stage 5: ✅ Multimodal generation evaluation completed")
+ print("\n🔧 Recommended next steps:")
+ print(" - Use optimal complexity for best speed/accuracy balance")
+ print(" - Review index comparison for storage vs performance tradeoffs")
+
+ # Clean up non-compact index after all stages complete
+ print("\n🧹 Cleaning up temporary non-compact index...")
+ if Path(non_compact_index_path).exists():
+ temp_index_dir = Path(non_compact_index_path).parent
+ temp_index_name = Path(non_compact_index_path).name
+ for temp_file in temp_index_dir.glob(f"{temp_index_name}*"):
+ temp_file.unlink()
+ print(f"✅ Cleaned up {non_compact_index_path}")
+ else:
+ print("📝 No temporary index to clean up")
+
+ except KeyboardInterrupt:
+ print("\n⚠️ Evaluation interrupted by user")
+ exit(1)
+ except Exception as e:
+ print(f"\n❌ Stage {args.stage} failed: {e}")
+ import traceback
+
+ traceback.print_exc()
+ exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/laion/setup_laion.py b/benchmarks/laion/setup_laion.py
new file mode 100644
index 0000000..849e7e6
--- /dev/null
+++ b/benchmarks/laion/setup_laion.py
@@ -0,0 +1,576 @@
+"""
+LAION Multimodal Benchmark Setup Script
+Downloads LAION subset and builds LEANN index with sentence embeddings
+"""
+
+import argparse
+import asyncio
+import io
+import json
+import os
+import pickle
+import time
+from pathlib import Path
+
+import aiohttp
+import numpy as np
+from datasets import load_dataset
+from leann import LeannBuilder
+from PIL import Image
+from sentence_transformers import SentenceTransformer
+from tqdm import tqdm
+
+
+class LAIONSetup:
+ def __init__(self, data_dir: str = "data"):
+ self.data_dir = Path(data_dir)
+ self.images_dir = self.data_dir / "laion_images"
+ self.metadata_file = self.data_dir / "laion_metadata.jsonl"
+
+ # Create directories
+ self.data_dir.mkdir(exist_ok=True)
+ self.images_dir.mkdir(exist_ok=True)
+
+ async def download_single_image(self, session, sample_data, semaphore, progress_bar):
+ """Download a single image asynchronously"""
+ async with semaphore: # Limit concurrent downloads
+ try:
+ image_url = sample_data["url"]
+ image_path = sample_data["image_path"]
+
+ # Skip if already exists
+ if os.path.exists(image_path):
+ progress_bar.update(1)
+ return sample_data
+
+ async with session.get(image_url, timeout=10) as response:
+ if response.status == 200:
+ content = await response.read()
+
+ # Verify it's a valid image
+ try:
+ img = Image.open(io.BytesIO(content))
+ img = img.convert("RGB")
+ img.save(image_path, "JPEG")
+ progress_bar.update(1)
+ return sample_data
+ except Exception:
+ progress_bar.update(1)
+ return None # Skip invalid images
+ else:
+ progress_bar.update(1)
+ return None
+
+ except Exception:
+ progress_bar.update(1)
+ return None
+
+ def download_laion_subset(self, num_samples: int = 1000):
+ """Download LAION subset from HuggingFace datasets with async parallel downloading"""
+ print(f"📥 Downloading LAION subset ({num_samples} samples)...")
+
+ # Load LAION-400M subset from HuggingFace
+ print("🤗 Loading from HuggingFace datasets...")
+ dataset = load_dataset("laion/laion400m", split="train", streaming=True)
+
+ # Collect sample metadata first (fast)
+ print("📋 Collecting sample metadata...")
+ candidates = []
+ for sample in dataset:
+ if len(candidates) >= num_samples * 3: # Get 3x more candidates in case some fail
+ break
+
+ image_url = sample.get("url", "")
+ caption = sample.get("caption", "")
+
+ if not image_url or not caption:
+ continue
+
+ image_filename = f"laion_{len(candidates):06d}.jpg"
+ image_path = self.images_dir / image_filename
+
+ candidate = {
+ "id": f"laion_{len(candidates):06d}",
+ "url": image_url,
+ "caption": caption,
+ "image_path": str(image_path),
+ "width": sample.get("original_width", 512),
+ "height": sample.get("original_height", 512),
+ "similarity": sample.get("similarity", 0.0),
+ }
+ candidates.append(candidate)
+
+ print(
+ f"📊 Collected {len(candidates)} candidates, downloading {num_samples} in parallel..."
+ )
+
+ # Download images in parallel
+ async def download_batch():
+ semaphore = asyncio.Semaphore(20) # Limit to 20 concurrent downloads
+ connector = aiohttp.TCPConnector(limit=100, limit_per_host=20)
+ timeout = aiohttp.ClientTimeout(total=30)
+
+ progress_bar = tqdm(total=len(candidates[: num_samples * 2]), desc="Downloading images")
+
+ async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
+ tasks = []
+ for candidate in candidates[: num_samples * 2]: # Try 2x more than needed
+ task = self.download_single_image(session, candidate, semaphore, progress_bar)
+ tasks.append(task)
+
+ # Wait for all downloads
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+ progress_bar.close()
+
+ # Filter successful downloads
+ successful = [r for r in results if r is not None and not isinstance(r, Exception)]
+ return successful[:num_samples]
+
+ # Run async download
+ loop = asyncio.new_event_loop()
+ asyncio.set_event_loop(loop)
+ try:
+ samples = loop.run_until_complete(download_batch())
+ finally:
+ loop.close()
+
+ # Save metadata
+ with open(self.metadata_file, "w", encoding="utf-8") as f:
+ for sample in samples:
+ f.write(json.dumps(sample) + "\n")
+
+ print(f"✅ Downloaded {len(samples)} real LAION samples with async parallel downloading")
+ return samples
+
+ def generate_clip_image_embeddings(self, samples: list[dict]):
+ """Generate CLIP image embeddings for downloaded images"""
+ print("🔍 Generating CLIP image embeddings...")
+
+ # Load sentence-transformers CLIP (ViT-L/14, 768-dim) for image embeddings
+ # This single model can encode both images and text.
+ model = SentenceTransformer("clip-ViT-L-14")
+
+ embeddings = []
+ valid_samples = []
+
+ for sample in tqdm(samples, desc="Processing images"):
+ try:
+ # Load image
+ image_path = sample["image_path"]
+ image = Image.open(image_path).convert("RGB")
+
+ # Encode image to 768-dim embedding via sentence-transformers (normalized)
+ vec = model.encode(
+ [image],
+ convert_to_numpy=True,
+ normalize_embeddings=True,
+ batch_size=1,
+ show_progress_bar=False,
+ )[0]
+ embeddings.append(vec.astype(np.float32))
+ valid_samples.append(sample)
+
+ except Exception as e:
+ print(f" ⚠️ Failed to process {sample['id']}: {e}")
+ # Skip invalid images
+
+ embeddings = np.array(embeddings, dtype=np.float32)
+
+ # Save embeddings
+ embeddings_file = self.data_dir / "clip_image_embeddings.npy"
+ np.save(embeddings_file, embeddings)
+ print(f"✅ Generated {len(embeddings)} image embeddings, shape: {embeddings.shape}")
+
+ return embeddings, valid_samples
+
+ def build_faiss_baseline(
+ self, embeddings: np.ndarray, samples: list[dict], output_dir: str = "baseline"
+ ):
+ """Build FAISS flat baseline using CLIP image embeddings"""
+ print("🔨 Building FAISS Flat baseline...")
+
+ from leann_backend_hnsw import faiss
+
+ os.makedirs(output_dir, exist_ok=True)
+ baseline_path = os.path.join(output_dir, "faiss_flat.index")
+ metadata_path = os.path.join(output_dir, "metadata.pkl")
+
+ if os.path.exists(baseline_path) and os.path.exists(metadata_path):
+ print(f"✅ Baseline already exists at {baseline_path}")
+ return baseline_path
+
+ # Extract image IDs (must be present)
+ if not samples or "id" not in samples[0]:
+ raise KeyError("samples missing 'id' field for FAISS baseline")
+ image_ids: list[str] = [str(sample["id"]) for sample in samples]
+
+ print(f"📐 Embedding shape: {embeddings.shape}")
+ print(f"📄 Processing {len(image_ids)} images")
+
+ # Build FAISS flat index
+ print("🏗️ Building FAISS IndexFlatIP...")
+ dimension = embeddings.shape[1]
+ index = faiss.IndexFlatIP(dimension)
+
+ # Add embeddings to flat index
+ embeddings_f32 = embeddings.astype(np.float32)
+ index.add(embeddings_f32.shape[0], faiss.swig_ptr(embeddings_f32))
+
+ # Save index and metadata
+ faiss.write_index(index, baseline_path)
+ with open(metadata_path, "wb") as f:
+ pickle.dump(image_ids, f)
+
+ print(f"✅ FAISS baseline saved to {baseline_path}")
+ print(f"✅ Metadata saved to {metadata_path}")
+ print(f"📊 Total vectors: {index.ntotal}")
+
+ return baseline_path
+
+ def create_leann_passages(self, samples: list[dict]):
+ """Create LEANN-compatible passages from LAION data"""
+ print("📝 Creating LEANN passages...")
+
+ passages_file = self.data_dir / "laion_passages.jsonl"
+
+ with open(passages_file, "w", encoding="utf-8") as f:
+ for i, sample in enumerate(samples):
+ passage = {
+ "id": sample["id"],
+ "text": sample["caption"], # Use caption as searchable text
+ "metadata": {
+ "image_url": sample["url"],
+ "image_path": sample.get("image_path", ""),
+ "width": sample["width"],
+ "height": sample["height"],
+ "similarity": sample["similarity"],
+ "image_index": i, # Index for embedding lookup
+ },
+ }
+ f.write(json.dumps(passage) + "\n")
+
+ print(f"✅ Created {len(samples)} passages")
+ return passages_file
+
+ def build_compact_index(
+ self, passages_file: Path, embeddings: np.ndarray, index_path: str, backend: str = "hnsw"
+ ):
+ """Build compact LEANN index with CLIP embeddings (recompute=True, compact=True)"""
+ print(f"🏗️ Building compact LEANN index with {backend} backend...")
+
+ start_time = time.time()
+
+ # Save CLIP embeddings (npy) and also a pickle with (ids, embeddings)
+ npy_path = self.data_dir / "clip_image_embeddings.npy"
+ np.save(npy_path, embeddings)
+ print(f"💾 Saved CLIP embeddings to {npy_path}")
+
+ # Prepare ids in the same order as passages_file (matches embeddings order)
+ ids: list[str] = []
+ with open(passages_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ rec = json.loads(line)
+ ids.append(str(rec["id"]))
+
+ if len(ids) != embeddings.shape[0]:
+ raise ValueError(
+ f"IDs count ({len(ids)}) does not match embeddings ({embeddings.shape[0]})."
+ )
+
+ pkl_path = self.data_dir / "clip_image_embeddings.pkl"
+ with open(pkl_path, "wb") as pf:
+ pickle.dump((ids, embeddings.astype(np.float32)), pf)
+ print(f"💾 Saved (ids, embeddings) pickle to {pkl_path}")
+
+ # Initialize builder - compact with recompute
+ # Note: For multimodal case, we need to handle embeddings differently
+ # Let's try using sentence-transformers mode but with custom embeddings
+ builder = LeannBuilder(
+ backend_name=backend,
+ # Use CLIP text encoder (ViT-L/14) to match image space (768-dim)
+ embedding_model="clip-ViT-L-14",
+ embedding_mode="sentence-transformers",
+ # HNSW params (or forwarded to chosen backend)
+ graph_degree=32,
+ complexity=64,
+ # Compact/pruned with recompute at query time
+ is_recompute=True,
+ is_compact=True,
+ distance_metric="cosine", # CLIP uses normalized vectors; cosine is appropriate
+ num_threads=4,
+ )
+
+ # Add passages (text + metadata)
+ print("📚 Adding passages...")
+ self._add_passages_with_embeddings(builder, passages_file, embeddings)
+
+ print(f"🔨 Building compact index at {index_path} from precomputed embeddings...")
+ builder.build_index_from_embeddings(index_path, str(pkl_path))
+
+ build_time = time.time() - start_time
+ print(f"✅ Compact index built in {build_time:.2f}s")
+
+ # Analyze index size
+ self._analyze_index_size(index_path)
+
+ return index_path
+
+ def build_non_compact_index(
+ self, passages_file: Path, embeddings: np.ndarray, index_path: str, backend: str = "hnsw"
+ ):
+ """Build non-compact LEANN index with CLIP embeddings (recompute=False, compact=False)"""
+ print(f"🏗️ Building non-compact LEANN index with {backend} backend...")
+
+ start_time = time.time()
+
+ # Ensure embeddings are saved (npy + pickle)
+ npy_path = self.data_dir / "clip_image_embeddings.npy"
+ if not npy_path.exists():
+ np.save(npy_path, embeddings)
+ print(f"💾 Saved CLIP embeddings to {npy_path}")
+ # Prepare ids in same order as passages_file
+ ids: list[str] = []
+ with open(passages_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ rec = json.loads(line)
+ ids.append(str(rec["id"]))
+ if len(ids) != embeddings.shape[0]:
+ raise ValueError(
+ f"IDs count ({len(ids)}) does not match embeddings ({embeddings.shape[0]})."
+ )
+ pkl_path = self.data_dir / "clip_image_embeddings.pkl"
+ if not pkl_path.exists():
+ with open(pkl_path, "wb") as pf:
+ pickle.dump((ids, embeddings.astype(np.float32)), pf)
+ print(f"💾 Saved (ids, embeddings) pickle to {pkl_path}")
+
+ # Initialize builder - non-compact without recompute
+ builder = LeannBuilder(
+ backend_name=backend,
+ embedding_model="clip-ViT-L-14",
+ embedding_mode="sentence-transformers",
+ graph_degree=32,
+ complexity=64,
+ is_recompute=False, # Store embeddings (no recompute needed)
+ is_compact=False, # Store full index (not pruned)
+ distance_metric="cosine",
+ num_threads=4,
+ )
+
+ # Add passages - embeddings will be loaded from file
+ print("📚 Adding passages...")
+ self._add_passages_with_embeddings(builder, passages_file, embeddings)
+
+ print(f"🔨 Building non-compact index at {index_path} from precomputed embeddings...")
+ builder.build_index_from_embeddings(index_path, str(pkl_path))
+
+ build_time = time.time() - start_time
+ print(f"✅ Non-compact index built in {build_time:.2f}s")
+
+ # Analyze index size
+ self._analyze_index_size(index_path)
+
+ return index_path
+
+ def _add_passages_with_embeddings(self, builder, passages_file: Path, embeddings: np.ndarray):
+ """Helper to add passages with pre-computed CLIP embeddings"""
+ with open(passages_file, encoding="utf-8") as f:
+ for line in tqdm(f, desc="Adding passages"):
+ if line.strip():
+ passage = json.loads(line)
+
+ # Add image metadata - LEANN will handle embeddings separately
+ # Note: We store image metadata and caption text for searchability
+ # Important: ensure passage ID in metadata matches vector ID
+ builder.add_text(
+ text=passage["text"], # Image caption for searchability
+ metadata={**passage["metadata"], "id": passage["id"]},
+ )
+
+ def _analyze_index_size(self, index_path: str):
+ """Analyze index file sizes"""
+ print("📏 Analyzing index sizes...")
+
+ index_path = Path(index_path)
+ index_dir = index_path.parent
+ index_name = index_path.name # e.g., laion_index.leann
+ index_prefix = index_path.stem # e.g., laion_index
+
+ files = [
+ (f"{index_prefix}.index", ".index", "core"),
+ (f"{index_name}.meta.json", ".meta.json", "core"),
+ (f"{index_name}.ids.txt", ".ids.txt", "core"),
+ (f"{index_name}.passages.jsonl", ".passages.jsonl", "passages"),
+ (f"{index_name}.passages.idx", ".passages.idx", "passages"),
+ ]
+
+ def _fmt_size(bytes_val: int) -> str:
+ if bytes_val < 1024:
+ return f"{bytes_val} B"
+ kb = bytes_val / 1024
+ if kb < 1024:
+ return f"{kb:.1f} KB"
+ mb = kb / 1024
+ if mb < 1024:
+ return f"{mb:.2f} MB"
+ gb = mb / 1024
+ return f"{gb:.2f} GB"
+
+ total_index_only_mb = 0.0
+ total_all_mb = 0.0
+ for filename, label, group in files:
+ file_path = index_dir / filename
+ if file_path.exists():
+ size_bytes = file_path.stat().st_size
+ print(f" {label}: {_fmt_size(size_bytes)}")
+ size_mb = size_bytes / (1024 * 1024)
+ total_all_mb += size_mb
+ if group == "core":
+ total_index_only_mb += size_mb
+ else:
+ print(f" {label}: (missing)")
+ print(f" Total (index only, exclude passages): {total_index_only_mb:.2f} MB")
+ print(f" Total (including passages): {total_all_mb:.2f} MB")
+
+ def create_evaluation_queries(self, samples: list[dict], num_queries: int = 200):
+ """Create evaluation queries from captions"""
+ print(f"📝 Creating {num_queries} evaluation queries...")
+
+ # Sample random captions as queries
+ import random
+
+ random.seed(42) # For reproducibility
+
+ query_samples = random.sample(samples, min(num_queries, len(samples)))
+
+ queries_file = self.data_dir / "evaluation_queries.jsonl"
+ with open(queries_file, "w", encoding="utf-8") as f:
+ for sample in query_samples:
+ query = {
+ "id": sample["id"],
+ "query": sample["caption"],
+ "ground_truth_id": sample["id"], # For potential recall evaluation
+ }
+ f.write(json.dumps(query) + "\n")
+
+ print(f"✅ Created {len(query_samples)} evaluation queries")
+ return queries_file
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Setup LAION Multimodal Benchmark")
+ parser.add_argument("--data-dir", default="data", help="Data directory")
+ parser.add_argument("--num-samples", type=int, default=1000, help="Number of LAION samples")
+ parser.add_argument("--num-queries", type=int, default=50, help="Number of evaluation queries")
+ parser.add_argument("--index-path", default="data/laion_index.leann", help="Output index path")
+ parser.add_argument(
+ "--backend", default="hnsw", choices=["hnsw", "diskann"], help="LEANN backend"
+ )
+ parser.add_argument("--skip-download", action="store_true", help="Skip LAION dataset download")
+ parser.add_argument("--skip-build", action="store_true", help="Skip index building")
+
+ args = parser.parse_args()
+
+ print("🚀 Setting up LAION Multimodal Benchmark")
+ print("=" * 50)
+
+ try:
+ # Initialize setup
+ setup = LAIONSetup(args.data_dir)
+
+ # Step 1: Download LAION subset
+ if not args.skip_download:
+ print("\n📦 Step 1: Download LAION subset")
+ samples = setup.download_laion_subset(args.num_samples)
+
+ # Step 2: Generate CLIP image embeddings
+ print("\n🔍 Step 2: Generate CLIP image embeddings")
+ embeddings, valid_samples = setup.generate_clip_image_embeddings(samples)
+
+ # Step 3: Create LEANN passages (image metadata with embeddings)
+ print("\n📝 Step 3: Create LEANN passages")
+ passages_file = setup.create_leann_passages(valid_samples)
+ else:
+ print("⏭️ Skipping LAION dataset download")
+ # Load existing data
+ passages_file = setup.data_dir / "laion_passages.jsonl"
+ embeddings_file = setup.data_dir / "clip_image_embeddings.npy"
+
+ if not passages_file.exists() or not embeddings_file.exists():
+ raise FileNotFoundError(
+ "Passages or embeddings file not found. Run without --skip-download first."
+ )
+
+ embeddings = np.load(embeddings_file)
+ print(f"📊 Loaded {len(embeddings)} embeddings from {embeddings_file}")
+
+ # Step 4: Build LEANN indexes (both compact and non-compact)
+ if not args.skip_build:
+ print("\n🏗️ Step 4: Build LEANN indexes with CLIP image embeddings")
+
+ # Build compact index (production mode - small, recompute required)
+ compact_index_path = args.index_path
+ print(f"Building compact index: {compact_index_path}")
+ setup.build_compact_index(passages_file, embeddings, compact_index_path, args.backend)
+
+ # Build non-compact index (comparison mode - large, fast search)
+ non_compact_index_path = args.index_path.replace(".leann", "_noncompact.leann")
+ print(f"Building non-compact index: {non_compact_index_path}")
+ setup.build_non_compact_index(
+ passages_file, embeddings, non_compact_index_path, args.backend
+ )
+
+ # Step 5: Build FAISS flat baseline
+ print("\n🔨 Step 5: Build FAISS flat baseline")
+ if not args.skip_download:
+ baseline_path = setup.build_faiss_baseline(embeddings, valid_samples)
+ else:
+ # Load valid_samples from passages file for FAISS baseline
+ valid_samples = []
+ with open(passages_file, encoding="utf-8") as f:
+ for line in f:
+ if line.strip():
+ passage = json.loads(line)
+ valid_samples.append({"id": passage["id"], "caption": passage["text"]})
+ baseline_path = setup.build_faiss_baseline(embeddings, valid_samples)
+
+ # Step 6: Create evaluation queries
+ print("\n📝 Step 6: Create evaluation queries")
+ queries_file = setup.create_evaluation_queries(valid_samples, args.num_queries)
+ else:
+ print("⏭️ Skipping index building")
+ baseline_path = "data/baseline/faiss_index.bin"
+ queries_file = setup.data_dir / "evaluation_queries.jsonl"
+
+ print("\n🎉 Setup completed successfully!")
+ print("📊 Summary:")
+ if not args.skip_download:
+ print(f" Downloaded samples: {len(samples)}")
+ print(f" Valid samples with embeddings: {len(valid_samples)}")
+ else:
+ print(f" Loaded {len(embeddings)} embeddings")
+
+ if not args.skip_build:
+ print(f" Compact index: {compact_index_path}")
+ print(f" Non-compact index: {non_compact_index_path}")
+ print(f" FAISS baseline: {baseline_path}")
+ print(f" Queries: {queries_file}")
+
+ print("\n🔧 Next steps:")
+ print(f" Run evaluation: python evaluate_laion.py --index {compact_index_path}")
+ print(f" Or compare with: python evaluate_laion.py --index {non_compact_index_path}")
+ else:
+ print(" Skipped building indexes")
+
+ except KeyboardInterrupt:
+ print("\n⚠️ Setup interrupted by user")
+ exit(1)
+ except Exception as e:
+ print(f"\n❌ Setup failed: {e}")
+ exit(1)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/benchmarks/llm_utils.py b/benchmarks/llm_utils.py
new file mode 100644
index 0000000..9a8217c
--- /dev/null
+++ b/benchmarks/llm_utils.py
@@ -0,0 +1,301 @@
+"""
+LLM utils for RAG benchmarks with Qwen3-8B and Qwen2.5-VL (multimodal)
+"""
+
+import time
+
+try:
+ import torch
+ from transformers import AutoModelForCausalLM, AutoTokenizer
+
+ HF_AVAILABLE = True
+except ImportError:
+ HF_AVAILABLE = False
+
+try:
+ from vllm import LLM, SamplingParams
+
+ VLLM_AVAILABLE = True
+except ImportError:
+ VLLM_AVAILABLE = False
+
+
+def is_qwen3_model(model_name):
+ """Check if model is Qwen3"""
+ return "Qwen3" in model_name or "qwen3" in model_name.lower()
+
+
+def is_qwen_vl_model(model_name):
+ """Check if model is Qwen2.5-VL"""
+ return "Qwen2.5-VL" in model_name or "qwen2.5-vl" in model_name.lower()
+
+
+def apply_qwen3_chat_template(tokenizer, prompt):
+ """Apply Qwen3 chat template with thinking enabled"""
+ messages = [{"role": "user", "content": prompt}]
+ return tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True,
+ enable_thinking=True,
+ )
+
+
+def extract_thinking_answer(response):
+ """Extract final answer from Qwen3 thinking model response"""
+ if "" in response and "" in response:
+ try:
+ think_end = response.index("") + len("")
+ final_answer = response[think_end:].strip()
+ return final_answer
+ except (ValueError, IndexError):
+ pass
+
+ return response.strip()
+
+
+def load_hf_model(model_name="Qwen/Qwen3-8B"):
+ """Load HuggingFace model"""
+ if not HF_AVAILABLE:
+ raise ImportError("transformers not available")
+
+ print(f"Loading HF: {model_name}")
+ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+ model = AutoModelForCausalLM.from_pretrained(
+ model_name,
+ torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+ device_map="auto",
+ trust_remote_code=True,
+ )
+ return tokenizer, model
+
+
+def load_vllm_model(model_name="Qwen/Qwen3-8B"):
+ """Load vLLM model"""
+ if not VLLM_AVAILABLE:
+ raise ImportError("vllm not available")
+
+ print(f"Loading vLLM: {model_name}")
+ llm = LLM(model=model_name, trust_remote_code=True)
+
+ # Qwen3 specific config
+ if is_qwen3_model(model_name):
+ stop_tokens = ["<|im_end|>", "<|end_of_text|>"]
+ max_tokens = 2048
+ else:
+ stop_tokens = None
+ max_tokens = 1024
+
+ sampling_params = SamplingParams(temperature=0.7, max_tokens=max_tokens, stop=stop_tokens)
+ return llm, sampling_params
+
+
+def generate_hf(tokenizer, model, prompt, max_tokens=None):
+ """Generate with HF - supports Qwen3 thinking models"""
+ model_name = getattr(model, "name_or_path", "unknown")
+ is_qwen3 = is_qwen3_model(model_name)
+
+ # Apply chat template for Qwen3
+ if is_qwen3:
+ prompt = apply_qwen3_chat_template(tokenizer, prompt)
+ max_tokens = max_tokens or 2048
+ else:
+ max_tokens = max_tokens or 1024
+
+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+ with torch.no_grad():
+ outputs = model.generate(
+ **inputs,
+ max_new_tokens=max_tokens,
+ temperature=0.7,
+ do_sample=True,
+ pad_token_id=tokenizer.eos_token_id,
+ )
+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+ response = response[len(prompt) :].strip()
+
+ # Extract final answer for thinking models
+ if is_qwen3:
+ return extract_thinking_answer(response)
+ return response
+
+
+def generate_vllm(llm, sampling_params, prompt):
+ """Generate with vLLM - supports Qwen3 thinking models"""
+ outputs = llm.generate([prompt], sampling_params)
+ response = outputs[0].outputs[0].text.strip()
+
+ # Extract final answer for Qwen3 thinking models
+ model_name = str(llm.llm_engine.model_config.model)
+ if is_qwen3_model(model_name):
+ return extract_thinking_answer(response)
+ return response
+
+
+def create_prompt(context, query, domain="default"):
+ """Create RAG prompt"""
+ if domain == "emails":
+ return f"Email content:\n{context}\n\nQuestion: {query}\n\nAnswer:"
+ elif domain == "finance":
+ return f"Financial content:\n{context}\n\nQuestion: {query}\n\nAnswer:"
+ elif domain == "multimodal":
+ return f"Image context:\n{context}\n\nQuestion: {query}\n\nAnswer:"
+ else:
+ return f"Context: {context}\n\nQuestion: {query}\n\nAnswer:"
+
+
+def evaluate_rag(searcher, llm_func, queries, domain="default", top_k=3, complexity=64):
+ """Simple RAG evaluation with timing"""
+ search_times = []
+ gen_times = []
+ results = []
+
+ for i, query in enumerate(queries):
+ # Search
+ start = time.time()
+ docs = searcher.search(query, top_k=top_k, complexity=complexity)
+ search_time = time.time() - start
+
+ # Generate
+ context = "\n\n".join([doc.text for doc in docs])
+ prompt = create_prompt(context, query, domain)
+
+ start = time.time()
+ response = llm_func(prompt)
+ gen_time = time.time() - start
+
+ search_times.append(search_time)
+ gen_times.append(gen_time)
+ results.append(response)
+
+ if i < 3:
+ print(f"Q{i + 1}: Search={search_time:.3f}s, Gen={gen_time:.3f}s")
+
+ return {
+ "avg_search_time": sum(search_times) / len(search_times),
+ "avg_generation_time": sum(gen_times) / len(gen_times),
+ "results": results,
+ }
+
+
+def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
+ """Load Qwen2.5-VL multimodal model"""
+ if not HF_AVAILABLE:
+ raise ImportError("transformers not available")
+
+ print(f"Loading Qwen2.5-VL: {model_name}")
+
+ try:
+ from transformers import AutoModelForVision2Seq, AutoProcessor
+
+ processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+ model = AutoModelForVision2Seq.from_pretrained(
+ model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+ )
+
+ return processor, model
+
+ except Exception as e:
+ print(f"Failed to load with AutoModelForVision2Seq, trying specific class: {e}")
+
+ # Fallback to specific class
+ try:
+ from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+
+ processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+ model = Qwen2VLForConditionalGeneration.from_pretrained(
+ model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+ )
+
+ return processor, model
+
+ except Exception as e2:
+ raise ImportError(f"Failed to load Qwen2.5-VL model: {e2}")
+
+
+def generate_qwen_vl(processor, model, prompt, image_path=None, max_tokens=512):
+ """Generate with Qwen2.5-VL multimodal model"""
+ from PIL import Image
+
+ # Prepare inputs
+ if image_path:
+ image = Image.open(image_path)
+ inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
+ else:
+ inputs = processor(text=prompt, return_tensors="pt").to(model.device)
+
+ # Generate
+ with torch.no_grad():
+ generated_ids = model.generate(
+ **inputs, max_new_tokens=max_tokens, do_sample=False, temperature=0.1
+ )
+
+ # Decode response
+ generated_ids = generated_ids[:, inputs["input_ids"].shape[1] :]
+ response = processor.decode(generated_ids[0], skip_special_tokens=True)
+
+ return response
+
+
+def create_multimodal_prompt(context, query, image_descriptions, task_type="images"):
+ """Create prompt for multimodal RAG"""
+ if task_type == "images":
+ return f"""Based on the retrieved images and their descriptions, answer the following question.
+
+Retrieved Image Descriptions:
+{context}
+
+Question: {query}
+
+Provide a detailed answer based on the visual content described above."""
+
+ return f"Context: {context}\nQuestion: {query}\nAnswer:"
+
+
+def evaluate_multimodal_rag(searcher, queries, processor=None, model=None, complexity=64):
+ """Evaluate multimodal RAG with Qwen2.5-VL"""
+ search_times = []
+ gen_times = []
+ results = []
+
+ for i, query_item in enumerate(queries):
+ # Handle both string and dict formats for queries
+ if isinstance(query_item, dict):
+ query = query_item.get("query", "")
+ image_path = query_item.get("image_path") # Optional reference image
+ else:
+ query = str(query_item)
+ image_path = None
+
+ # Search
+ start_time = time.time()
+ search_results = searcher.search(query, top_k=3, complexity=complexity)
+ search_time = time.time() - start_time
+ search_times.append(search_time)
+
+ # Prepare context from search results
+ context_parts = []
+ for result in search_results:
+ context_parts.append(f"- {result.text}")
+ context = "\n".join(context_parts)
+
+ # Generate with multimodal model
+ start_time = time.time()
+ if processor and model:
+ prompt = create_multimodal_prompt(context, query, context_parts)
+ response = generate_qwen_vl(processor, model, prompt, image_path)
+ else:
+ response = f"Context: {context}"
+ gen_time = time.time() - start_time
+
+ gen_times.append(gen_time)
+ results.append(response)
+
+ if i < 3:
+ print(f"Q{i + 1}: Search={search_time:.3f}s, Gen={gen_time:.3f}s")
+
+ return {
+ "avg_search_time": sum(search_times) / len(search_times),
+ "avg_generation_time": sum(gen_times) / len(gen_times),
+ "results": results,
+ }
diff --git a/benchmarks/run_evaluation.py b/benchmarks/run_evaluation.py
index ab4e169..6c34f15 100644
--- a/benchmarks/run_evaluation.py
+++ b/benchmarks/run_evaluation.py
@@ -53,7 +53,7 @@ def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
print(
"Error: huggingface_hub is not installed. Please install it to download the data:"
)
- print("uv pip install -e '.[dev]'")
+ print("uv sync --only-group dev")
sys.exit(1)
except Exception as e:
print(f"An error occurred during data download: {e}")
diff --git a/docs/CONTRIBUTING.md b/docs/CONTRIBUTING.md
index 4a37e26..1431c7b 100644
--- a/docs/CONTRIBUTING.md
+++ b/docs/CONTRIBUTING.md
@@ -53,9 +53,9 @@ We use pre-commit hooks to ensure code quality and consistency. This runs automa
### Setup Pre-commit
-1. **Install pre-commit** (already included when you run `uv sync`):
+1. **Install pre-commit tools**:
```bash
- uv pip install pre-commit
+ uv sync lint
```
2. **Install the git hooks**:
@@ -65,7 +65,7 @@ We use pre-commit hooks to ensure code quality and consistency. This runs automa
3. **Run pre-commit manually** (optional):
```bash
- pre-commit run --all-files
+ uv run pre-commit run --all-files
```
### Pre-commit Checks
@@ -85,6 +85,9 @@ Our pre-commit configuration includes:
### Running Tests
```bash
+# Install test tools only (no project runtime)
+uv sync --group test
+
# Run all tests
uv run pytest
diff --git a/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py b/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py
index 96fb9ee..65aa9db 100644
--- a/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py
+++ b/packages/leann-backend-diskann/leann_backend_diskann/diskann_backend.py
@@ -343,7 +343,8 @@ class DiskannSearcher(BaseSearcher):
"full_index_prefix": full_index_prefix,
"num_threads": self.num_threads,
"num_nodes_to_cache": kwargs.get("num_nodes_to_cache", 0),
- "cache_mechanism": 1,
+ # 1 -> initialize cache using sample_data; 2 -> ready cache without init; others disable cache
+ "cache_mechanism": kwargs.get("cache_mechanism", 1),
"pq_prefix": "",
"partition_prefix": partition_prefix,
}
diff --git a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
index cabfa0d..6a831a5 100644
--- a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
+++ b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py
@@ -90,6 +90,15 @@ class HNSWBuilder(LeannBackendBuilderInterface):
index_file = index_dir / f"{index_prefix}.index"
faiss.write_index(index, str(index_file))
+ # Persist ID map so searcher can map FAISS integer labels back to passage IDs
+ try:
+ idmap_file = index_dir / f"{index_prefix}.ids.txt"
+ with open(idmap_file, "w", encoding="utf-8") as f:
+ for id_str in ids:
+ f.write(str(id_str) + "\n")
+ except Exception as e:
+ logger.warning(f"Failed to write ID map: {e}")
+
if self.is_compact:
self._convert_to_csr(index_file)
elif self.is_recompute:
@@ -152,6 +161,16 @@ class HNSWSearcher(BaseSearcher):
self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
+ # Load ID map if available
+ self._id_map: list[str] = []
+ try:
+ idmap_file = self.index_dir / f"{self.index_path.stem}.ids.txt"
+ if idmap_file.exists():
+ with open(idmap_file, encoding="utf-8") as f:
+ self._id_map = [line.rstrip("\n") for line in f]
+ except Exception as e:
+ logger.warning(f"Failed to load ID map: {e}")
+
def search(
self,
query: np.ndarray,
@@ -250,6 +269,19 @@ class HNSWSearcher(BaseSearcher):
)
search_time = time.time() - search_time
logger.info(f" Search time in HNSWSearcher.search() backend: {search_time} seconds")
- string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels]
+ if self._id_map:
+
+ def map_label(x: int) -> str:
+ if 0 <= x < len(self._id_map):
+ return self._id_map[x]
+ return str(x)
+
+ string_labels = [
+ [map_label(int(label)) for label in batch_labels] for batch_labels in labels
+ ]
+ else:
+ string_labels = [
+ [str(int_label) for int_label in batch_labels] for batch_labels in labels
+ ]
return {"labels": string_labels, "distances": distances}
diff --git a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py
index b6a0eb0..882acbf 100644
--- a/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py
+++ b/packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_embedding_server.py
@@ -114,6 +114,35 @@ def create_hnsw_embedding_server(
embedding_dim = 0
logger.info(f"Loaded PassageManager with {len(passages)} passages from metadata")
+ # Attempt to load ID map (maps FAISS integer labels -> passage IDs)
+ id_map: list[str] = []
+ try:
+ meta_path = Path(passages_file)
+ base = meta_path.name
+ if base.endswith(".meta.json"):
+ base = base[: -len(".meta.json")] # e.g., laion_index.leann
+ if base.endswith(".leann"):
+ base = base[: -len(".leann")] # e.g., laion_index
+ idmap_file = meta_path.parent / f"{base}.ids.txt"
+ if idmap_file.exists():
+ with open(idmap_file, encoding="utf-8") as f:
+ id_map = [line.rstrip("\n") for line in f]
+ logger.info(f"Loaded ID map with {len(id_map)} entries from {idmap_file}")
+ else:
+ logger.warning(f"ID map file not found at {idmap_file}; will use raw labels")
+ except Exception as e:
+ logger.warning(f"Failed to load ID map: {e}")
+
+ def _map_node_id(nid) -> str:
+ try:
+ if id_map is not None and len(id_map) > 0 and isinstance(nid, (int, np.integer)):
+ idx = int(nid)
+ if 0 <= idx < len(id_map):
+ return id_map[idx]
+ except Exception:
+ pass
+ return str(nid)
+
# (legacy ZMQ thread removed; using shutdown-capable server only)
def zmq_server_thread_with_shutdown(shutdown_event):
@@ -195,13 +224,14 @@ def create_hnsw_embedding_server(
found_indices: list[int] = []
for idx, nid in enumerate(node_ids):
try:
- passage_data = passages.get_passage(str(nid))
+ passage_id = _map_node_id(nid)
+ passage_data = passages.get_passage(passage_id)
txt = passage_data.get("text", "")
if isinstance(txt, str) and len(txt) > 0:
texts.append(txt)
found_indices.append(idx)
else:
- logger.error(f"Empty text for passage ID {nid}")
+ logger.error(f"Empty text for passage ID {passage_id}")
except KeyError:
logger.error(f"Passage ID {nid} not found")
except Exception as e:
@@ -268,13 +298,14 @@ def create_hnsw_embedding_server(
found_indices: list[int] = []
for idx, nid in enumerate(node_ids):
try:
- passage_data = passages.get_passage(str(nid))
+ passage_id = _map_node_id(nid)
+ passage_data = passages.get_passage(passage_id)
txt = passage_data.get("text", "")
if isinstance(txt, str) and len(txt) > 0:
texts.append(txt)
found_indices.append(idx)
else:
- logger.error(f"Empty text for passage ID {nid}")
+ logger.error(f"Empty text for passage ID {passage_id}")
except KeyError:
logger.error(f"Passage with ID {nid} not found")
except Exception as e:
diff --git a/packages/leann-core/src/leann/api.py b/packages/leann-core/src/leann/api.py
index dfa4685..1c8ab55 100644
--- a/packages/leann-core/src/leann/api.py
+++ b/packages/leann-core/src/leann/api.py
@@ -454,6 +454,17 @@ class LeannBuilder:
provider_options=self.embedding_options,
)
string_ids = [chunk["id"] for chunk in self.chunks]
+ # Persist ID map alongside index so backends that return integer labels can remap to passage IDs
+ try:
+ idmap_file = (
+ index_dir
+ / f"{index_name[: -len('.leann')] if index_name.endswith('.leann') else index_name}.ids.txt"
+ )
+ with open(idmap_file, "w", encoding="utf-8") as f:
+ for sid in string_ids:
+ f.write(str(sid) + "\n")
+ except Exception:
+ pass
current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}
builder_instance = self.backend_factory.builder(**current_backend_kwargs)
builder_instance.build(embeddings, string_ids, index_path, **current_backend_kwargs)
@@ -573,6 +584,17 @@ class LeannBuilder:
# Build the vector index using precomputed embeddings
string_ids = [str(id_val) for id_val in ids]
+ # Persist ID map (order == embeddings order)
+ try:
+ idmap_file = (
+ index_dir
+ / f"{index_name[: -len('.leann')] if index_name.endswith('.leann') else index_name}.ids.txt"
+ )
+ with open(idmap_file, "w", encoding="utf-8") as f:
+ for sid in string_ids:
+ f.write(str(sid) + "\n")
+ except Exception:
+ pass
current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}
builder_instance = self.backend_factory.builder(**current_backend_kwargs)
builder_instance.build(embeddings, string_ids, index_path)
diff --git a/pyproject.toml b/pyproject.toml
index 37d9ac6..81b2304 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,27 +53,10 @@ dependencies = [
"tree-sitter-java>=0.20.0",
"tree-sitter-c-sharp>=0.20.0",
"tree-sitter-typescript>=0.20.0",
+ "torchvision>=0.23.0",
]
[project.optional-dependencies]
-dev = [
- "pytest>=7.0",
- "pytest-cov>=4.0",
- "pytest-xdist>=3.0", # For parallel test execution
- "black>=23.0",
- "ruff==0.12.7", # Fixed version to ensure consistent formatting across all environments
- "matplotlib",
- "huggingface-hub>=0.20.0",
- "pre-commit>=3.5.0",
-]
-
-test = [
- "pytest>=7.0",
- "pytest-timeout>=2.0",
- "llama-index-core>=0.12.0",
- "python-dotenv>=1.0.0",
-]
-
diskann = [
"leann-backend-diskann",
]
@@ -101,6 +84,28 @@ leann-backend-diskann = { path = "packages/leann-backend-diskann", editable = tr
leann-backend-hnsw = { path = "packages/leann-backend-hnsw", editable = true }
astchunk = { path = "packages/astchunk-leann", editable = true }
+[dependency-groups]
+# Minimal lint toolchain for CI and local hooks
+lint = [
+ "pre-commit>=3.5.0",
+ "ruff==0.12.7", # Fixed version to ensure consistent formatting across all environments
+]
+
+# Test toolchain (no heavy project runtime deps)
+test = [
+ "pytest>=7.0",
+ "pytest-cov>=4.0",
+ "pytest-xdist>=3.0",
+ "pytest-timeout>=2.0",
+ "python-dotenv>=1.0.0",
+]
+
+# dependencies by apps/ should list here
+dev = [
+ "matplotlib",
+ "huggingface-hub>=0.20.0",
+]
+
[tool.ruff]
target-version = "py39"
line-length = 100
diff --git a/scripts/hf_upload.py b/scripts/hf_upload.py
new file mode 100644
index 0000000..48ef14c
--- /dev/null
+++ b/scripts/hf_upload.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+"""
+Upload local evaluation data to Hugging Face Hub, excluding diskann_rpj_wiki.
+
+Defaults:
+- repo_id: LEANN-RAG/leann-rag-evaluation-data (dataset)
+- folder_path: benchmarks/data
+- ignore_patterns: diskann_rpj_wiki/** and .cache/**
+
+Requires authentication via `huggingface-cli login` or HF_TOKEN env var.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+
+try:
+ from huggingface_hub import HfApi
+except Exception as e:
+ raise SystemExit(
+ "huggingface_hub is required. Install with: pip install huggingface_hub hf_transfer"
+ ) from e
+
+
+def _enable_transfer_accel_if_available() -> None:
+ """Best-effort enabling of accelerated transfers across hub versions.
+
+ Tries the public util if present; otherwise, falls back to env flag when
+ hf_transfer is installed. Silently no-ops if unavailable.
+ """
+ try:
+ # Newer huggingface_hub exposes this under utils
+ from huggingface_hub.utils import hf_hub_enable_hf_transfer # type: ignore
+
+ hf_hub_enable_hf_transfer()
+ return
+ except Exception:
+ pass
+
+ try:
+ # If hf_transfer is installed, set env flag recognized by the hub
+ import hf_transfer # noqa: F401
+
+ os.environ.setdefault("HF_HUB_ENABLE_HF_TRANSFER", "1")
+ except Exception:
+ # Acceleration not available; proceed without it
+ pass
+
+
+def parse_args() -> argparse.Namespace:
+ p = argparse.ArgumentParser(description="Upload local data to HF, excluding diskann_rpj_wiki")
+ p.add_argument(
+ "--repo-id",
+ default="LEANN-RAG/leann-rag-evaluation-data",
+ help="Target dataset repo id (namespace/name)",
+ )
+ p.add_argument(
+ "--folder-path",
+ default="benchmarks/data",
+ help="Local folder to upload (default: benchmarks/data)",
+ )
+ p.add_argument(
+ "--ignore",
+ default=["diskann_rpj_wiki/**", ".cache/**"],
+ nargs="+",
+ help="Glob patterns to ignore (space-separated)",
+ )
+ p.add_argument(
+ "--allow",
+ default=["**"],
+ nargs="+",
+ help="Glob patterns to allow (space-separated). Defaults to everything.",
+ )
+ p.add_argument(
+ "--message",
+ default="sync local data (exclude diskann_rpj_wiki)",
+ help="Commit message",
+ )
+ p.add_argument(
+ "--no-transfer-accel",
+ action="store_true",
+ help="Disable hf_transfer accelerated uploads",
+ )
+ return p.parse_args()
+
+
+def main() -> None:
+ args = parse_args()
+
+ if not args.no_transfer_accel:
+ _enable_transfer_accel_if_available()
+
+ if not os.path.isdir(args.folder_path):
+ raise SystemExit(f"Folder not found: {args.folder_path}")
+
+ print("Uploading to Hugging Face Hub:")
+ print(f" repo_id: {args.repo_id}")
+ print(" repo_type: dataset")
+ print(f" folder_path: {args.folder_path}")
+ print(f" allow_patterns: {args.allow}")
+ print(f" ignore_patterns:{args.ignore}")
+
+ api = HfApi()
+
+ # Perform upload. This skips unchanged files by content hash.
+ api.upload_folder(
+ repo_id=args.repo_id,
+ repo_type="dataset",
+ folder_path=args.folder_path,
+ path_in_repo=".",
+ allow_patterns=args.allow,
+ ignore_patterns=args.ignore,
+ commit_message=args.message,
+ )
+
+ print("Upload completed (unchanged files were skipped by the Hub).")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/tests/README.md b/tests/README.md
index 4d56d10..d3b3ec9 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -40,8 +40,8 @@ Tests DiskANN graph partitioning functionality:
### Install test dependencies:
```bash
-# Using extras
-uv pip install -e ".[test]"
+# Using uv dependency groups (tools only)
+uv sync --only-group test
```
### Run all tests:
diff --git a/uv.lock b/uv.lock
index b499dfe..0cbc29a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,22 +10,22 @@ resolution-markers = [
[[package]]
name = "accelerate"
-version = "1.10.0"
+version = "1.10.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "huggingface-hub" },
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
- { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+ { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
{ name = "packaging" },
{ name = "psutil" },
{ name = "pyyaml" },
{ name = "safetensors" },
{ name = "torch" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/66/be171836d86dc5b8698b3a9bf4b9eb10cb53369729939f88bf650167588b/accelerate-1.10.0.tar.gz", hash = "sha256:8270568fda9036b5cccdc09703fef47872abccd56eb5f6d53b54ea5fb7581496", size = 392261 }
+sdist = { url = "https://files.pythonhosted.org/packages/b1/72/ff3961c19ee395c3d30ac630ee77bfb0e1b46b87edc504d4f83bb4a89705/accelerate-1.10.1.tar.gz", hash = "sha256:3dea89e433420e4bfac0369cae7e36dcd6a56adfcfd38cdda145c6225eab5df8", size = 392446 }
wheels = [
- { url = "https://files.pythonhosted.org/packages/30/dd/0107f0aa179869ee9f47ef5a2686abd5e022fdc82af901d535e52fe91ce1/accelerate-1.10.0-py3-none-any.whl", hash = "sha256:260a72b560e100e839b517a331ec85ed495b3889d12886e79d1913071993c5a3", size = 374718 },
+ { url = "https://files.pythonhosted.org/packages/5f/a0/d9ef19f780f319c21ee90ecfef4431cbeeca95bec7f14071785c17b6029b/accelerate-1.10.1-py3-none-any.whl", hash = "sha256:3621cff60b9a27ce798857ece05e2b9f56fcc71631cfb31ccf71f0359c311f11", size = 374909 },
]
[[package]]
@@ -176,7 +176,7 @@ wheels = [
[[package]]
name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
@@ -184,9 +184,9 @@ dependencies = [
{ name = "sniffio" },
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252 }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094 }
wheels = [
- { url = "https://files.pythonhosted.org/packages/6f/12/e5e0282d673bb9746bacfb6e2dba8719989d3660cdb2ea79aee9a9651afb/anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1", size = 107213 },
+ { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097 },
]
[[package]]
@@ -205,13 +205,14 @@ source = { editable = "packages/astchunk-leann" }
dependencies = [
{ name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
- { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+ { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
{ name = "pyrsistent" },
{ name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
{ name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
{ name = "tree-sitter-c-sharp" },
{ name = "tree-sitter-java" },
- { name = "tree-sitter-python" },
+ { name = "tree-sitter-python", version = "0.23.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+ { name = "tree-sitter-python", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
{ name = "tree-sitter-typescript" },
]
@@ -286,54 +287,15 @@ wheels = [
[[package]]
name = "beautifulsoup4"
-version = "4.13.4"
+version = "4.13.5"
source = { registry = "https://pypi.org/simple" }
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