diff --git a/examples/README.md b/examples/README.md index 79fd4a4f3..93ae4af7a 100644 --- a/examples/README.md +++ b/examples/README.md @@ -2,7 +2,10 @@ A project gallery of full end-to-end applications built with SIE. Each project lives in its own subdirectory. Clone it, run it, learn from it. -New to SIE? Start with the **[quickstart notebook](./quickstart.ipynb)** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/superlinked/sie/blob/main/examples/quickstart.ipynb): encode, score, and extract in 5 minutes, then pick a project below. +New to SIE? Start with the **[Python quickstart](./quickstart)** for one +SDK call against local, self-deployed, or managed SIE. Continue with the +**[quickstart notebook](./quickstart.ipynb)** [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/superlinked/sie/blob/main/examples/quickstart.ipynb) +to try encode, score, and extract, then pick a project below. ## Gallery diff --git a/examples/contract-review-agent/.gitignore b/examples/contract-review-agent/.gitignore index ea00f029f..57ce72753 100644 --- a/examples/contract-review-agent/.gitignore +++ b/examples/contract-review-agent/.gitignore @@ -2,7 +2,6 @@ __pycache__/ .env *.log -uv.lock .ruff_cache/ # Generated sample artifacts (recreate with `uv run make-sample`) contract_review_agent/data/generated/ diff --git a/examples/contract-review-agent/README.md b/examples/contract-review-agent/README.md index d9f37aa34..da952f63e 100644 --- a/examples/contract-review-agent/README.md +++ b/examples/contract-review-agent/README.md @@ -48,6 +48,22 @@ The flow is **two agents**: > Two agents instead of one: a single structured-output agent tends to emit the schema immediately and skip the tools, sometimes hallucinating the fields. Splitting "gather with tools" from "format the result" keeps the fan-out real and the output grounded. +## Offline synthetic quickstart + +Generate and inspect three fictional contracts without downloading a corpus or +calling a model: + +```bash +cd examples/contract-review-agent +uv sync --frozen +uv run make-sample +uv run review --list +``` + +This creates an Acme MSA, an NDA, an SOW, a signature image, and a local SQLite +obligations database. Configure SIE and run `uv run review --contract acme-msa` +when you are ready to execute the model-backed review. + ## Run it You need Python 3.12 and a **GPU-backed SIE deployment**. The generative models run on SIE's generation bundle (CUDA), so the `latest-cpu-default` image can't serve them. @@ -59,7 +75,7 @@ docker run --gpus all -p 8080:8080 -v sie-hf-cache:/app/.cache/huggingface \ cd examples/contract-review-agent cp .env.example .env # edit SIE_CLUSTER_URL / SIE_API_KEY if not localhost -uv sync +uv sync --frozen # 2. 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To use an existing deployment instead, set `SIE_CLUSTER_URL` and, when required, `SIE_API_KEY` before running it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Install and start the server\n", + "## 1. Install and connect\n", "\n", - "Works on your laptop (Mac or Linux, CPU is fine) or a free Colab GPU. The `[local]` extra pins the transformers version the embedding and reranking models need.\n", + "Works on your laptop (Mac or Linux, CPU is fine), a free Colab GPU, a self-deployed SIE gateway, or managed SIE. The `[local]` extra is only used when this notebook starts the server itself.\n", "\n", "On Colab, expect a few minutes: SIE pins torch 2.9, so pip replaces Colab's preinstalled PyTorch. First output lands about two minutes after that; the optional showcase model adds a few more minutes of downloads.\n", "\n", @@ -39,30 +39,31 @@ "metadata": {}, "outputs": [], "source": [ - "import subprocess, time, urllib.request, logging\n", + "import logging, os, subprocess, time, urllib.request\n", "\n", "# Show the SDK's wait/retry messages while a model downloads server-side\n", "logging.basicConfig(level=logging.INFO, format=\"%(message)s\")\n", "logging.getLogger(\"sie_sdk\").setLevel(logging.INFO)\n", "\n", - "# Start the server in the background (defaults: host 0.0.0.0, port 8080).\n", - "# Download progress goes to the log - check it any time with: !tail -n 5 /tmp/sie-server.log\n", - "proc = subprocess.Popen(\n", - " [\"sie-server\", \"serve\", \"--port\", \"8080\"],\n", - " stdout=open(\"/tmp/sie-server.log\", \"w\"),\n", - " stderr=subprocess.STDOUT,\n", - ")\n", - "\n", - "# Wait for the server to be ready (/healthz answers before any model loads)\n", - "for i in range(120):\n", - " try:\n", - " urllib.request.urlopen(\"http://localhost:8080/healthz\")\n", - " print(f\"Server ready (took ~{i}s)\")\n", - " break\n", - " except Exception:\n", - " time.sleep(1)\n", + "cluster_url = os.getenv(\"SIE_CLUSTER_URL\", \"http://localhost:8080\")\n", + "api_key = os.getenv(\"SIE_API_KEY\")\n", + "\n", + "if cluster_url == \"http://localhost:8080\":\n", + " # Download progress goes to /tmp/sie-server.log.\n", + " proc = subprocess.Popen(\n", + " [\"sie-server\", \"serve\", \"--port\", \"8080\"],\n", + " stdout=open(\"/tmp/sie-server.log\", \"w\"),\n", + " stderr=subprocess.STDOUT,\n", + " )\n", + " for i in range(120):\n", + " try:\n", + " urllib.request.urlopen(f\"{cluster_url}/healthz\")\n", + " print(f\"Local SIE ready (took ~{i}s)\")\n", + " break\n", + " except Exception:\n", + " time.sleep(1)\n", "else:\n", - " print(\"Server did not start - check /tmp/sie-server.log\")" + " print(f\"Using SIE at {cluster_url}\")" ] }, { @@ -83,7 +84,7 @@ "from sie_sdk import SIEClient\n", "from sie_sdk.types import Item\n", "\n", - "client = SIEClient(\"http://localhost:8080\")\n", + "client = SIEClient(cluster_url, api_key=api_key)\n", "\n", "result = client.encode(\"sentence-transformers/all-MiniLM-L6-v2\", Item(text=\"Hello world\"))\n", "print(f\"Shape: {result['dense'].shape}\") # (384,)\n", diff --git a/examples/quickstart/README.md b/examples/quickstart/README.md new file mode 100644 index 000000000..27d2f5836 --- /dev/null +++ b/examples/quickstart/README.md @@ -0,0 +1,53 @@ +# SIE Python quickstart + +This example uses the SIE SDK to create one text embedding. The code is the +same whether SIE runs on your laptop, in your Kubernetes cluster, or as a +managed service. + +## Run it + +Install the SDK: + +```bash +pip install sie-sdk +cd examples/quickstart +``` + +For a local SIE server on `http://localhost:8080`, no configuration is needed: + +```bash +python quickstart.py +``` + +For self-deployed SIE, point the SDK at your gateway: + +```bash +export SIE_CLUSTER_URL="https://sie.example.com" +python quickstart.py +``` + +For managed SIE, also provide your API key: + +```bash +export SIE_CLUSTER_URL="" +export SIE_API_KEY="" +python quickstart.py +``` + +`SIE_MODEL` optionally selects a different embedding model: + +```bash +export SIE_MODEL="BAAI/bge-m3" +``` + +Keep API keys in environment variables or your normal secrets manager, never +in source files. + +The [quickstart notebook](../quickstart.ipynb) continues with scoring and +extraction. + +## Test + +```bash +PYTHONPATH=. python -m unittest test_quickstart.py +``` diff --git a/examples/quickstart/quickstart.py b/examples/quickstart/quickstart.py new file mode 100644 index 000000000..73e5ac20d --- /dev/null +++ b/examples/quickstart/quickstart.py @@ -0,0 +1,22 @@ +# ruff: noqa: INP001 + +import os + +from sie_sdk import SIEClient + + +def main() -> None: + with SIEClient( + os.getenv("SIE_CLUSTER_URL", "http://localhost:8080"), + api_key=os.getenv("SIE_API_KEY"), + ) as client: + result = client.encode( + os.getenv("SIE_MODEL", "BAAI/bge-m3"), + {"text": "Embeddings make meaning searchable."}, + ) + + print(f"Created a {len(result['dense'])}-dimensional embedding.") + + +if __name__ == "__main__": + main() diff --git a/examples/quickstart/test_quickstart.py b/examples/quickstart/test_quickstart.py new file mode 100644 index 000000000..4d2455160 --- /dev/null +++ b/examples/quickstart/test_quickstart.py @@ -0,0 +1,42 @@ +# ruff: noqa: INP001 + +import os +import unittest +from unittest.mock import MagicMock, patch + +import quickstart + + +class QuickStartTest(unittest.TestCase): + @patch.dict( + os.environ, + { + "SIE_CLUSTER_URL": "https://sie.example.com", + "SIE_API_KEY": "test-key", + "SIE_MODEL": "example/model", + }, + clear=True, + ) + @patch("quickstart.SIEClient") + def test_uses_configured_sie_deployment(self, client_type: MagicMock) -> None: + client = client_type.return_value.__enter__.return_value + client.encode.return_value = {"dense": [0.1, 0.2, 0.3]} + + with patch("builtins.print") as print_output: + quickstart.main() + + client_type.assert_called_once_with( + "https://sie.example.com", + api_key="test-key", + ) + client.encode.assert_called_once_with( + "example/model", + {"text": "Embeddings make meaning searchable."}, + ) + print_output.assert_called_once_with( + "Created a 3-dimensional embedding.", + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/examples/retrieval-ablation/README.md b/examples/retrieval-ablation/README.md index d56209dc0..43bdbbf19 100644 --- a/examples/retrieval-ablation/README.md +++ b/examples/retrieval-ablation/README.md @@ -75,6 +75,9 @@ uv sync # Validate config (no GPU needed) uv run python benchmark_ablation.py --dry-run +# Run the same pipeline on the checked-in sample (12 documents, 6 queries) +uv run python benchmark_ablation.py --sample --gpu l4-spot + # Run the production pipeline end-to-end (all 7 models, all 1,854 queries) uv run python benchmark_ablation.py --gpu l4-spot @@ -82,6 +85,12 @@ uv run python benchmark_ablation.py --gpu l4-spot uv run python benchmark_ablation.py --gpu l4-spot --skip-conditions 1,2,3 ``` +`--sample` uses fictional, redistributable passages and the canonical +NDCG@10, MRR@10, and Recall@10 evaluator. It still requires SIE and +Turbopuffer because it runs the same retrieval conditions as the full +benchmark. Its index, cache, and result file are isolated from full +benchmark runs. + All expensive operations (encoding, search) cache to `cache/ablation/`. Re-runs skip completed steps. Cross-encoder reranking checkpoints every 100 queries for crash recovery. ## Evidence diff --git a/examples/retrieval-ablation/benchmark_ablation.py b/examples/retrieval-ablation/benchmark_ablation.py index 1f57f12d9..d452ffd6d 100644 --- a/examples/retrieval-ablation/benchmark_ablation.py +++ b/examples/retrieval-ablation/benchmark_ablation.py @@ -10,6 +10,7 @@ Usage: uv run python benchmark_ablation.py --gpu l4-spot + uv run python benchmark_ablation.py --sample --gpu l4-spot uv run python benchmark_ablation.py --gpu l4-spot --skip-conditions 5,6 uv run python benchmark_ablation.py --gpu rtx6000-spot --skip-conditions 1,2,3,4,5 """ @@ -78,8 +79,12 @@ def _require_env(name: str) -> str: "colbertv2": {"model": "colbert-ir/colbertv2.0", "dim": 128, "max_tokens": 512}, } -CACHE_DIR = Path(__file__).parent / "cache" / "ablation" -RESULTS_CSV = Path(__file__).parent / "ablation_results.csv" +EXAMPLE_DIR = Path(__file__).parent +CACHE_DIR = EXAMPLE_DIR / "cache" / "ablation" +RESULTS_CSV = EXAMPLE_DIR / "ablation_results.csv" +SAMPLE_FIXTURE = EXAMPLE_DIR / "fixtures" / "sample.json" +SAMPLE_MAX_DOCUMENTS = 12 +SAMPLE_MAX_QUERIES = 6 # ── Caching ──────────────────────────────────────────────────────────────── @@ -206,11 +211,11 @@ def write_results_csv(results_log, path): writer.writerows(results_log) -def append_result(results_log, result): +def append_result(results_log, result, path=RESULTS_CSV): results_log.append(result) - write_results_csv(results_log, RESULTS_CSV) + write_results_csv(results_log, path) logger.info( - f" -> {result.get('condition')}: NDCG@10={result.get('ndcg@10')} [{len(results_log)} results in {RESULTS_CSV}]" + f" -> {result.get('condition')}: NDCG@10={result.get('ndcg@10')} [{len(results_log)} results in {path}]" ) @@ -270,6 +275,71 @@ def load_dataset(): return corpus_items, query_items, qrel_map +def load_sample_dataset(path=SAMPLE_FIXTURE): + """Load and validate the checked-in bounded sample.""" + fixture = json.loads(path.read_text()) + documents = fixture.get("documents") + queries = fixture.get("queries") + if not isinstance(documents, list) or not documents or len(documents) > SAMPLE_MAX_DOCUMENTS: + raise ValueError(f"sample must contain 1-{SAMPLE_MAX_DOCUMENTS} documents") + if not isinstance(queries, list) or not queries or len(queries) > SAMPLE_MAX_QUERIES: + raise ValueError(f"sample must contain 1-{SAMPLE_MAX_QUERIES} queries") + + corpus_items = [] + document_ids = set() + for document in documents: + corpus_id = document.get("id") + title = document.get("title") + text = document.get("text") + if not isinstance(corpus_id, int) or corpus_id <= 0 or corpus_id in document_ids: + raise ValueError("sample document IDs must be unique positive integers") + if not isinstance(title, str) or not title.strip() or not isinstance(text, str) or not text.strip(): + raise ValueError(f"sample document {corpus_id} must have a non-empty title and text") + document_ids.add(corpus_id) + corpus_items.append( + { + "corpus_id": corpus_id, + "text": f"{title}\n\n{text}", + "doc_id": f"sample-{corpus_id:02d}", + "page_number": 1, + } + ) + + query_items = [] + query_ids = set() + qrel_map = {} + for query in queries: + query_id = query.get("id") + text = query.get("text") + relevant = query.get("relevant") + if not isinstance(query_id, str) or not query_id.strip() or query_id in query_ids: + raise ValueError("sample query IDs must be unique non-empty strings") + if not isinstance(text, str) or not text.strip(): + raise ValueError(f"sample query {query_id} must have non-empty text") + if not isinstance(relevant, dict) or not relevant: + raise ValueError(f"sample query {query_id} must have relevance judgments") + + qrels = {} + for raw_corpus_id, score in relevant.items(): + try: + corpus_id = int(raw_corpus_id) + except (TypeError, ValueError) as exc: + raise ValueError(f"sample query {query_id} has an invalid document ID") from exc + if corpus_id not in document_ids or not isinstance(score, int) or score <= 0: + raise ValueError(f"sample query {query_id} has an invalid relevance judgment") + qrels[corpus_id] = score + + query_ids.add(query_id) + query_items.append({"query_id": query_id, "text": text}) + qrel_map[query_id] = qrels + + logger.info( + f"Sample loaded: {len(corpus_items)} documents, {len(query_items)} queries, " + f"{sum(len(v) for v in qrel_map.values())} qrels" + ) + return corpus_items, query_items, qrel_map + + # ── Async Encode (SDK handles 503 retries; we retry transport errors) ────── @@ -546,10 +616,11 @@ def retrieve_multivector(query_mvs, corpus_ids, normed_corpus_mvs, cache_path=No async def main(): parser = argparse.ArgumentParser( - description="Retrieval Ablation Benchmark on vidore_v3_finance_en", + description="Retrieval Ablation Benchmark", formatter_class=argparse.RawDescriptionHelpFormatter, epilog="Examples:\n" " uv run python benchmark_ablation.py --gpu l4-spot\n" + " uv run python benchmark_ablation.py --sample --gpu l4-spot\n" " uv run python benchmark_ablation.py --skip-conditions 5,6\n" " uv run python benchmark_ablation.py --ce-models mxbai-rerank --mv-models colbert\n" " uv run python benchmark_ablation.py --dry-run\n", @@ -565,6 +636,11 @@ async def main(): parser.add_argument( "--mv-models", default="all", help=f"MV models: comma-separated from {list(ALL_MV_MODELS.keys())}, or 'all'" ) + parser.add_argument( + "--sample", + action="store_true", + help=f"Use the checked-in bounded sample ({SAMPLE_MAX_DOCUMENTS} documents, {SAMPLE_MAX_QUERIES} queries)", + ) parser.add_argument("--dry-run", action="store_true", help="Validate config and print plan without running") args = parser.parse_args() @@ -572,7 +648,11 @@ async def main(): skip = set(int(x) for x in args.skip_conditions.split(",") if x.strip()) run_mv_rerank = 5 not in skip run_mv_direct = 6 not in skip - ns_name = args.namespace or f"ablation-{slugify(ENCODER)}" + dataset_name = "sample" if args.sample else "vidore_v3_finance_en" + cache_dir = CACHE_DIR / "sample" if args.sample else CACHE_DIR + results_csv = EXAMPLE_DIR / "sample_ablation_results.csv" if args.sample else RESULTS_CSV + default_namespace = f"ablation-{'sample-' if args.sample else ''}{slugify(ENCODER)}" + ns_name = args.namespace or default_namespace # Resolve model selections if args.ce_models == "all": @@ -593,27 +673,34 @@ async def main(): if args.dry_run: conditions = [c for c in range(1, 7) if c not in skip] logger.info("=== DRY RUN ===") + logger.info(f"Dataset: {dataset_name}") logger.info(f"GPU: {gpu or 'router picks'}") logger.info(f"Namespace: {ns_name}") logger.info(f"Conditions: {conditions}") logger.info(f"CE models: {[slugify(r) for r in ce_rerankers]}") logger.info(f"MV models: {[m['model'] for m in mv_rerankers]}") logger.info(f"TOP_K: {TOP_K_RETRIEVE}, Hybrid pool: ~{TOP_K_RETRIEVE * 2} candidates") - logger.info(f"Cache dir: {CACHE_DIR} (exists={CACHE_DIR.exists()})") + logger.info(f"Cache dir: {cache_dir} (exists={cache_dir.exists()})") + logger.info(f"Results: {results_csv}") logger.info(f"SIE endpoint: {sie_base_url}") logger.info(f"SIE auth: {'configured' if sie_api_key else 'not configured'}") + if args.sample: + load_sample_dataset() logger.info("Config OK. Remove --dry-run to execute.") return tpuf_api_key = _require_env("TURBOPUFFER_API_KEY") np.random.seed(RANDOM_SEED) - CACHE_DIR.mkdir(parents=True, exist_ok=True) + cache_dir.mkdir(parents=True, exist_ok=True) async with SIEAsyncClient(sie_base_url, api_key=sie_api_key, timeout_s=900, max_connections=10) as sie: tpuf = AsyncTurbopuffer(api_key=tpuf_api_key, region="aws-us-east-1") - corpus_items, query_items, qrel_map = load_dataset() + if args.sample: + corpus_items, query_items, qrel_map = load_sample_dataset() + else: + corpus_items, query_items, qrel_map = load_dataset() text_map = {item["corpus_id"]: item["text"] for item in corpus_items} corpus_texts = [item["text"] for item in corpus_items] query_texts = [qi["text"] for qi in query_items] @@ -633,7 +720,7 @@ async def main(): is_query=False, gpu=gpu, semaphore=semaphore, - cache_path=CACHE_DIR / "dense_corpus.npz", + cache_path=cache_dir / "dense_corpus.npz", ) _, query_vecs = await asyncio.gather( index_corpus(tpuf, corpus_items, corpus_vecs, ns_name), @@ -644,7 +731,7 @@ async def main(): is_query=True, gpu=gpu, semaphore=semaphore, - cache_path=CACHE_DIR / "dense_query.npz", + cache_path=cache_dir / "dense_query.npz", ), ) elapsed = time.perf_counter() - t0 @@ -658,8 +745,8 @@ async def main(): t0 = time.perf_counter() bm25_results, vec_results = await asyncio.gather( - search_bm25(tpuf, ns_name, query_texts, cache_path=CACHE_DIR / f"bm25_top{TOP_K_RETRIEVE}.json"), - search_vector(tpuf, ns_name, query_vecs, cache_path=CACHE_DIR / f"vector_top{TOP_K_RETRIEVE}.json"), + search_bm25(tpuf, ns_name, query_texts, cache_path=cache_dir / f"bm25_top{TOP_K_RETRIEVE}.json"), + search_vector(tpuf, ns_name, query_vecs, cache_path=cache_dir / f"vector_top{TOP_K_RETRIEVE}.json"), ) elapsed = time.perf_counter() - t0 logger.info( @@ -670,16 +757,28 @@ async def main(): # ── Eval: BM25, Vector, RRF baselines ───────────────────── if 1 not in skip: m = evaluate(bm25_results, query_items, qrel_map) - append_result(results_log, {"condition_num": 1, "condition": "BM25-only", "reranker": "-", **m}) + append_result( + results_log, + {"condition_num": 1, "condition": "BM25-only", "reranker": "-", **m}, + results_csv, + ) if 2 not in skip: m = evaluate(vec_results, query_items, qrel_map) - append_result(results_log, {"condition_num": 2, "condition": "Vector-only", "reranker": "-", **m}) + append_result( + results_log, + {"condition_num": 2, "condition": "Vector-only", "reranker": "-", **m}, + results_csv, + ) if 3 not in skip: rrf_results = [rrf_fuse([b, v]) for b, v in zip(bm25_results, vec_results)] m = evaluate(rrf_results, query_items, qrel_map) - append_result(results_log, {"condition_num": 3, "condition": "RRF(BM25+Vec)", "reranker": "-", **m}) + append_result( + results_log, + {"condition_num": 3, "condition": "RRF(BM25+Vec)", "reranker": "-", **m}, + results_csv, + ) # ── Build hybrid pool ────────────────────────────────────── hybrid_pools = [build_hybrid_pool(b, v) for b, v in zip(bm25_results, vec_results)] @@ -703,7 +802,7 @@ async def run_ce(reranker): text_map, gpu, semaphore, - cache_path=CACHE_DIR / f"rerank_ce_{slug}_hybrid{TOP_K_RETRIEVE}.json", + cache_path=cache_dir / f"rerank_ce_{slug}_hybrid{TOP_K_RETRIEVE}.json", ) m = evaluate(reranked, query_items, qrel_map) return {"condition_num": 4, "condition": f"CE-Rerank(hybrid-{TOP_K_RETRIEVE})", "reranker": reranker, **m} @@ -721,7 +820,7 @@ async def run_mv_pipeline(mv_config): is_query=False, gpu=gpu, semaphore=semaphore, - cache_path=CACHE_DIR / f"mv_corpus_{slug}.npz", + cache_path=cache_dir / f"mv_corpus_{slug}.npz", ) query_mvs = await encode_multivector( sie, @@ -730,7 +829,7 @@ async def run_mv_pipeline(mv_config): is_query=True, gpu=gpu, semaphore=semaphore, - cache_path=CACHE_DIR / f"mv_query_{slug}.npz", + cache_path=cache_dir / f"mv_query_{slug}.npz", ) # Pre-normalize corpus once (not per-query) @@ -748,7 +847,7 @@ async def run_mv_pipeline(mv_config): query_mvs, normed_corpus_mv_map, hybrid_pools, - CACHE_DIR / f"rerank_mv_{slug}_hybrid{TOP_K_RETRIEVE}.json", + cache_dir / f"rerank_mv_{slug}_hybrid{TOP_K_RETRIEVE}.json", ) m = evaluate(reranked, query_items, qrel_map) results.append( @@ -768,7 +867,7 @@ async def run_mv_pipeline(mv_config): query_mvs, corpus_ids, normed_corpus_mvs, - CACHE_DIR / f"retrieve_mv_{slug}.json", + cache_dir / f"retrieve_mv_{slug}.json", ) m = evaluate(ranked, query_items, qrel_map) results.append( @@ -789,9 +888,9 @@ async def run_mv_pipeline(mv_config): r = await coro if isinstance(r, list): for item in r: - append_result(results_log, item) + append_result(results_log, item, results_csv) else: - append_result(results_log, r) + append_result(results_log, r, results_csv) elapsed = time.perf_counter() - t0 n_experiments = len([r for r in results_log if r.get("condition_num", 0) >= 4]) @@ -799,7 +898,7 @@ async def run_mv_pipeline(mv_config): total = time.perf_counter() - t_total logger.info(f"Total: {total:.1f}s ({len(results_log)} conditions evaluated)") - write_results_csv(results_log, RESULTS_CSV) + write_results_csv(results_log, results_csv) print_summary(results_log) diff --git a/examples/retrieval-ablation/fixtures/sample.json b/examples/retrieval-ablation/fixtures/sample.json new file mode 100644 index 000000000..209af85cc --- /dev/null +++ b/examples/retrieval-ablation/fixtures/sample.json @@ -0,0 +1,98 @@ +{ + "license": "Apache-2.0", + "description": "Fictional operational passages for a bounded retrieval ablation.", + "documents": [ + { + "id": 1, + "title": "Invoice approval", + "text": "Invoices above 10000 dollars require approval from both the finance director and the department owner." + }, + { + "id": 2, + "title": "Invoice due dates", + "text": "Approved supplier invoices are paid within 30 calendar days after receipt." + }, + { + "id": 3, + "title": "Access review", + "text": "Production access is reviewed quarterly. Managers must revoke access for people who change roles." + }, + { + "id": 4, + "title": "Incident severity", + "text": "A severity one incident is any outage affecting all customers or causing confirmed loss of customer data." + }, + { + "id": 5, + "title": "Incident response", + "text": "The on-call engineer acknowledges severity one incidents within five minutes and opens a public status update." + }, + { + "id": 6, + "title": "Backup schedule", + "text": "The primary database receives an encrypted full backup every 24 hours and incremental backups every hour." + }, + { + "id": 7, + "title": "Backup retention", + "text": "Daily backups are retained for 35 days. Monthly archives are retained for one year." + }, + { + "id": 8, + "title": "Vendor security", + "text": "Critical vendors complete a security review before onboarding and once every twelve months afterward." + }, + { + "id": 9, + "title": "Contract renewal", + "text": "Customer contracts renew for one year unless either party gives written notice at least 60 days before renewal." + }, + { + "id": 10, + "title": "Data deletion", + "text": "Verified deletion requests remove customer content from active systems within seven days and backups within 35 days." + }, + { + "id": 11, + "title": "Expense receipts", + "text": "Travel expenses above 25 dollars require an itemized receipt and manager approval." + }, + { + "id": 12, + "title": "Change management", + "text": "High-risk production changes require peer review, a rollback plan, and approval from the on-call engineer." + } + ], + "queries": [ + { + "id": "q01", + "text": "Who must approve a large supplier invoice?", + "relevant": {"1": 2, "2": 1} + }, + { + "id": "q02", + "text": "How quickly must the team respond to a company-wide outage?", + "relevant": {"5": 2, "4": 1} + }, + { + "id": "q03", + "text": "How long are database backups kept?", + "relevant": {"7": 2, "6": 1} + }, + { + "id": "q04", + "text": "When is a supplier security assessment required?", + "relevant": {"8": 2} + }, + { + "id": "q05", + "text": "What notice prevents an automatic contract renewal?", + "relevant": {"9": 2} + }, + { + "id": "q06", + "text": "What controls are required before a risky production deployment?", + "relevant": {"12": 2, "3": 1} + } + ] +} diff --git a/examples/retrieval-ablation/tests/test_benchmark_ablation.py b/examples/retrieval-ablation/tests/test_benchmark_ablation.py new file mode 100644 index 000000000..5b6fd2288 --- /dev/null +++ b/examples/retrieval-ablation/tests/test_benchmark_ablation.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import importlib.util +import os +import subprocess +import sys +from pathlib import Path +from typing import Any + +ROOT = Path(__file__).resolve().parent.parent + + +def _load_benchmark() -> Any: + path = ROOT / "benchmark_ablation.py" + spec = importlib.util.spec_from_file_location(path.stem, path) + if spec is None or spec.loader is None: + raise RuntimeError(f"cannot import {path}") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +BENCHMARK = _load_benchmark() + + +def test_sample_fixture_is_valid_and_bounded() -> None: + corpus, queries, qrels = BENCHMARK.load_sample_dataset() + + assert len(corpus) == BENCHMARK.SAMPLE_MAX_DOCUMENTS == 12 + assert len(queries) == BENCHMARK.SAMPLE_MAX_QUERIES == 6 + document_ids = {document["corpus_id"] for document in corpus} + assert len(document_ids) == len(corpus) + assert {query["query_id"] for query in queries} == set(qrels) + assert all(set(relevant) <= document_ids for relevant in qrels.values()) + assert all(score > 0 for relevant in qrels.values() for score in relevant.values()) + + +def test_sample_rankings_use_canonical_evaluation() -> None: + corpus, queries, qrels = BENCHMARK.load_sample_dataset() + document_ids = [document["corpus_id"] for document in corpus] + perfect_rankings = [] + for query in queries: + relevant_ids = list(qrels[query["query_id"]]) + perfect_rankings.append( + relevant_ids + [document_id for document_id in document_ids if document_id not in relevant_ids] + ) + + metrics = BENCHMARK.evaluate(perfect_rankings, queries, qrels) + + assert metrics == { + "ndcg@10": 1.0, + "mrr@10": 1.0, + "recall@10": 1.0, + "n_queries": 6, + } + + +def test_sample_dry_run_uses_isolated_runtime_state() -> None: + env = {**os.environ, "SIE_BASE_URL": "http://localhost:8080"} + completed = subprocess.run( + [sys.executable, str(ROOT / "benchmark_ablation.py"), "--sample", "--dry-run"], + check=True, + capture_output=True, + text=True, + env=env, + ) + + assert "Dataset: sample" in completed.stderr + assert "Namespace: ablation-sample-baai-bge-m3" in completed.stderr + assert "cache/ablation/sample" in completed.stderr + assert "sample_ablation_results.csv" in completed.stderr + assert "Sample loaded: 12 documents, 6 queries, 10 qrels" in completed.stderr diff --git a/examples/vision-doc-rag/.gitignore b/examples/vision-doc-rag/.gitignore index 9a052846f..57e3b6742 100644 --- a/examples/vision-doc-rag/.gitignore +++ b/examples/vision-doc-rag/.gitignore @@ -5,5 +5,6 @@ data/pdfs_manifest.json data/pages_manifest.json data/pdfs/ data/pages/ +data/sample/ data/multivectors.npz data/metadata.json diff --git a/examples/vision-doc-rag/README.md b/examples/vision-doc-rag/README.md index 23b1c6eec..327a1658c 100644 --- a/examples/vision-doc-rag/README.md +++ b/examples/vision-doc-rag/README.md @@ -31,10 +31,24 @@ data/pdfs/ # downloaded PDFs data/pdfs_manifest.json # source manifest from fetch_pdfs.py data/pages/ # rendered PNG pages data/pages_manifest.json # page-level metadata from render_pages.py +data/sample/ # deterministic no-download sample corpus data/metadata.json # index metadata from ingest.py data/multivectors.npz # page multivectors from ingest.py ``` +### No-download sample corpus + +For a quick, deterministic ingest, prepare the three synthetic document images +already committed under `examples/document-ocr`. The script verifies their +hashes and replaces only `data/sample/`; it does not change the primary +`data/pages_manifest.json`. + +```bash +cd examples/vision-doc-rag +python data/prepare_sample_corpus.py +python python/ingest.py --pages-manifest data/sample/pages_manifest.json +``` + ## SIE features used - `encode` - `vidore/colqwen2.5-v0.2` on page images at ingest and on query diff --git a/examples/vision-doc-rag/data/prepare_sample_corpus.py b/examples/vision-doc-rag/data/prepare_sample_corpus.py new file mode 100644 index 000000000..e353a9ad8 --- /dev/null +++ b/examples/vision-doc-rag/data/prepare_sample_corpus.py @@ -0,0 +1,115 @@ +"""Prepare a small, deterministic sample corpus without downloading PDFs.""" + +from __future__ import annotations + +import hashlib +import json +import shutil +import tempfile +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +EXAMPLE_ROOT = Path(__file__).resolve().parent.parent +REPO_ROOT = EXAMPLE_ROOT.parents[1] +DEFAULT_OUTPUT_DIR = EXAMPLE_ROOT / "data" / "sample" +SOURCE_URL = "https://github.com/superlinked/sie/tree/main/examples/document-ocr/data/samples" + + +@dataclass(frozen=True) +class Sample: + filename: str + sha256: str + title: str + + +SAMPLES = ( + Sample( + filename="receipt.png", + sha256="feb5f42448901d1094af5266c4bda37fd3ecb0e26404a268a82dcd0434d7ade5", + title="Synthetic Receipt", + ), + Sample( + filename="invoice.png", + sha256="2e0a5b12f3d626b52304c6ffe4121d8507d39fd03690350f097b222067223f41", + title="Synthetic Invoice", + ), + Sample( + filename="slide.png", + sha256="5df0640aeb85a4adca39afac765f9007ae33edf21e845d209c2416c075a02150", + title="Synthetic Slide", + ), +) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as source: + for chunk in iter(lambda: source.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _verified_sources() -> list[tuple[Sample, Path]]: + samples_dir = REPO_ROOT / "examples" / "document-ocr" / "data" / "samples" + verified: list[tuple[Sample, Path]] = [] + for sample in SAMPLES: + source = samples_dir / sample.filename + actual_hash = _sha256(source) + if actual_hash != sample.sha256: + raise ValueError(f"sample hash mismatch for {source}: expected {sample.sha256}, got {actual_hash}") + verified.append((sample, source)) + return verified + + +def prepare(output_dir: Path = DEFAULT_OUTPUT_DIR) -> list[dict[str, Any]]: + """Replace ``output_dir`` with a freshly verified three-document corpus.""" + sources = _verified_sources() + output_dir = output_dir.resolve() + output_dir.parent.mkdir(parents=True, exist_ok=True) + staging_dir = Path(tempfile.mkdtemp(prefix=f".{output_dir.name}-", dir=output_dir.parent)) + + try: + pages_dir = staging_dir / "pages" / "synthetic-docs" + pages_dir.mkdir(parents=True) + rows: list[dict[str, Any]] = [] + + for sample, source in sources: + destination = pages_dir / sample.filename + shutil.copyfile(source, destination) + slug = source.stem + rows.append( + { + "page_id": f"synthetic-docs__{slug}__p001", + "client": "synthetic-docs", + "title": sample.title, + "publisher": "Superlinked", + "license": "Apache-2.0", + "source_url": SOURCE_URL, + "source_pdf": sample.filename, + "source_pdf_path": str(source.relative_to(REPO_ROOT)), + "page_number": 1, + "image_path": f"sample/pages/synthetic-docs/{sample.filename}", + } + ) + + (staging_dir / "pages_manifest.json").write_text(json.dumps(rows, indent=2) + "\n") + + if output_dir.exists(): + shutil.rmtree(output_dir) + staging_dir.rename(output_dir) + return rows + finally: + if staging_dir.exists(): + shutil.rmtree(staging_dir) + + +def main() -> None: + rows = prepare() + manifest = DEFAULT_OUTPUT_DIR / "pages_manifest.json" + print(f"Prepared {len(rows)} verified sample pages") + print(f"Wrote page manifest to {manifest}") + + +if __name__ == "__main__": + main() diff --git a/examples/vision-doc-rag/python/ingest.py b/examples/vision-doc-rag/python/ingest.py index 8b0f8e119..ee4ac7289 100644 --- a/examples/vision-doc-rag/python/ingest.py +++ b/examples/vision-doc-rag/python/ingest.py @@ -14,33 +14,43 @@ from __future__ import annotations +import argparse import json import os import time from pathlib import Path +from typing import Any import numpy as np import yaml - from sie_sdk import SIEClient from sie_sdk.types import Item +EXAMPLE_ROOT = Path(__file__).resolve().parent.parent +DEFAULT_PAGES_MANIFEST = EXAMPLE_ROOT / "data" / "pages_manifest.json" + -def load_config(): - return yaml.safe_load((Path(__file__).resolve().parent.parent / "config.yaml").read_text()) +def load_config() -> dict[str, Any]: + return yaml.safe_load((EXAMPLE_ROOT / "config.yaml").read_text()) -def load_pages(): - pages_path = Path(__file__).resolve().parent.parent / "data" / "pages_manifest.json" +def load_pages(pages_path: Path = DEFAULT_PAGES_MANIFEST) -> list[dict[str, Any]]: if not pages_path.exists(): - raise FileNotFoundError( - "data/pages_manifest.json not found. Run `python data/fetch_pdfs.py` " - "and `python data/render_pages.py` first." - ) + if pages_path == DEFAULT_PAGES_MANIFEST: + hint = " Run `python data/fetch_pdfs.py` and `python data/render_pages.py` first." + else: + hint = "" + raise FileNotFoundError(f"{pages_path} not found.{hint}") return json.loads(pages_path.read_text()) -def encode_pages(client: SIEClient, model: str, pages: list[dict], gpu: str, timeout: float): +def encode_pages( + client: SIEClient, + model: str, + pages: list[dict[str, Any]], + gpu: str, + timeout: float, +) -> tuple[list[np.ndarray], list[dict[str, Any]]]: data_dir = Path(__file__).resolve().parent.parent / "data" multivectors: list[np.ndarray] = [] metadata: list[dict] = [] @@ -83,9 +93,18 @@ def encode_pages(client: SIEClient, model: str, pages: list[dict], gpu: str, tim return multivectors, metadata -def main(): +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--pages-manifest", + type=Path, + default=DEFAULT_PAGES_MANIFEST, + help="page manifest to ingest (defaults to data/pages_manifest.json)", + ) + args = parser.parse_args() + config = load_config() - pages = load_pages() + pages = load_pages(args.pages_manifest) print(f"Loaded {len(pages)} pages") cluster_url = os.environ.get("SIE_CLUSTER_URL", config["cluster"]["url"]) @@ -113,7 +132,7 @@ def main(): by_client[m["client"]] = by_client.get(m["client"], 0) + 1 print(f"\n Saved {len(metadata)} multivectors to data/multivectors.npz") - print(f" Saved metadata to data/metadata.json") + print(" Saved metadata to data/metadata.json") print(f" Total visual tokens: {total_tokens}") print(" Pages per tenant:") for client_name in sorted(by_client): diff --git a/examples/vision-doc-rag/tests/test_prepare_sample_corpus.py b/examples/vision-doc-rag/tests/test_prepare_sample_corpus.py new file mode 100644 index 000000000..e4a53a451 --- /dev/null +++ b/examples/vision-doc-rag/tests/test_prepare_sample_corpus.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import hashlib +import importlib.util +import json +import sys +from pathlib import Path + +import pytest + +SCRIPT_PATH = Path(__file__).resolve().parent.parent / "data" / "prepare_sample_corpus.py" +SPEC = importlib.util.spec_from_file_location("prepare_sample_corpus", SCRIPT_PATH) +assert SPEC is not None +assert SPEC.loader is not None +prepare_sample_corpus = importlib.util.module_from_spec(SPEC) +sys.modules[SPEC.name] = prepare_sample_corpus +SPEC.loader.exec_module(prepare_sample_corpus) + +INGEST_PATH = Path(__file__).resolve().parent.parent / "python" / "ingest.py" +INGEST_SPEC = importlib.util.spec_from_file_location("vision_doc_ingest", INGEST_PATH) +assert INGEST_SPEC is not None +assert INGEST_SPEC.loader is not None +vision_doc_ingest = importlib.util.module_from_spec(INGEST_SPEC) +sys.modules[INGEST_SPEC.name] = vision_doc_ingest +INGEST_SPEC.loader.exec_module(vision_doc_ingest) + + +def sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def test_prepares_deterministic_verified_manifest(tmp_path: Path) -> None: + output_dir = tmp_path / "sample" + + first_rows = prepare_sample_corpus.prepare(output_dir) + first_manifest = (output_dir / "pages_manifest.json").read_text() + second_rows = prepare_sample_corpus.prepare(output_dir) + + assert first_rows == second_rows + assert first_manifest == (output_dir / "pages_manifest.json").read_text() + assert json.loads(first_manifest) == first_rows + assert len(first_rows) == 3 + assert output_dir / "pages_manifest.json" != prepare_sample_corpus.EXAMPLE_ROOT / "data" / "pages_manifest.json" + + expected_hashes = {sample.filename: sample.sha256 for sample in prepare_sample_corpus.SAMPLES} + copied_files = output_dir.glob("pages/synthetic-docs/*.png") + assert {path.name: sha256(path) for path in copied_files} == expected_hashes + + +def test_replaces_output_without_leaving_stale_files(tmp_path: Path) -> None: + output_dir = tmp_path / "sample" + prepare_sample_corpus.prepare(output_dir) + stale_file = output_dir / "pages" / "synthetic-docs" / "stale.png" + stale_file.write_bytes(b"stale") + + prepare_sample_corpus.prepare(output_dir) + + assert not stale_file.exists() + + +def test_hash_failure_preserves_existing_output(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + output_dir = tmp_path / "sample" + output_dir.mkdir() + marker = output_dir / "existing.txt" + marker.write_text("keep") + monkeypatch.setattr( + prepare_sample_corpus, + "SAMPLES", + ( + prepare_sample_corpus.Sample( + filename="receipt.png", + sha256="0" * 64, + title="Synthetic Receipt", + ), + ), + ) + + with pytest.raises(ValueError, match="sample hash mismatch"): + prepare_sample_corpus.prepare(output_dir) + + assert marker.read_text() == "keep" + + +def test_ingest_loads_an_explicit_manifest(tmp_path: Path) -> None: + manifest = tmp_path / "sample_manifest.json" + rows = [{"page_id": "sample__receipt__p001"}] + manifest.write_text(json.dumps(rows)) + + assert vision_doc_ingest.load_pages(manifest) == rows