diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 75852f9..a79070c 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,6 +14,7 @@ jobs: - uses: astral-sh/setup-uv@v5 - run: uv sync - run: uv run ruff check . + - run: uv run mypy search/ test: runs-on: ubuntu-latest diff --git a/README.md b/README.md index 55d53d3..9fc3d08 100644 --- a/README.md +++ b/README.md @@ -14,19 +14,22 @@ Install dependencies: uv sync ``` -Run from the command line. On first run, the Wikipedia dataset (~20GB) will be downloaded from [Hugging Face](https://huggingface.co/datasets/wikimedia/wikipedia) and cached automatically: +Run the full-text search from the command line. On first run, the Wikipedia dataset (~20GB) will be downloaded from [Hugging Face](https://huggingface.co/datasets/wikimedia/wikipedia) and cached automatically: ```bash uv run python run.py -# loads of log output -index_documents took 1714.3159050941467 seconds -Index contains 6407814 documents -search took 0.3170650005340576 seconds -search took 4.130218982696533 seconds -search took 0.005632877349853516 seconds -search took 17.051696300506592 seconds ``` +Run the semantic (vector) search: + +```bash +uv run python run_semantic.py +``` + +On first run this builds a vector index by embedding all 6.4M documents. Embeddings are checkpointed to `data/checkpoints/` so you can resume if interrupted. The finished index is saved to `data/vector_index.*` and memory-mapped on subsequent runs. + +To skip the multi-hour encoding step, download the pre-computed embeddings from [Hugging Face](https://huggingface.co/datasets/bartdegoede/wikipedia-semantic-search), place the JSON and `.npy` files in `data/checkpoints/`, and run `uv run python run_semantic.py`. + If you'd like to download the dataset separately (e.g. before a demo): ```bash @@ -50,10 +53,11 @@ In [2]: index.search('python programming language', rank=True)[:5] ## Development -Lint with ruff: +Lint and type check: ```bash uv run ruff check . +uv run mypy search/ ``` Run tests: diff --git a/data/checkpoints/.keep b/data/checkpoints/.keep new file mode 100644 index 0000000..e69de29 diff --git a/load.py b/load.py index 70e11a2..173ea27 100644 --- a/load.py +++ b/load.py @@ -1,19 +1,30 @@ +from collections.abc import Generator + from datasets import load_dataset -from tqdm import tqdm from search.documents import Abstract -DATASET = "wikimedia/wikipedia" -DATASET_CONFIG = "20231101.en" +DATASET: str = "wikimedia/wikipedia" +DATASET_CONFIG: str = "20231101.en" + +def load_documents() -> tuple[int, Generator[Abstract, None, None]]: + """Load Wikipedia abstracts from HuggingFace. -def load_documents(): + Returns (total, iterator) so callers can create fixed-size structures + (like a memmap) without materializing all documents into memory. + The HF Dataset is Arrow-backed and memory-mapped, so iterating over it + doesn't load the full dataset into RAM. + """ ds = load_dataset(DATASET, DATASET_CONFIG, split="train") - for doc_id, row in enumerate(tqdm(ds, desc="Loading documents")): - title = row["title"] - url = row["url"] - # extract first paragraph as abstract - text = row["text"] - abstract = text.split("\n\n")[0] if text else "" - - yield Abstract(ID=doc_id, title=title, url=url, abstract=abstract) + + def _generate() -> Generator[Abstract, None, None]: + for doc_id, row in enumerate(ds): + title: str = row["title"] + url: str = row["url"] + # extract first paragraph as abstract + text: str = row["text"] + abstract = text.split("\n\n")[0] if text else "" + yield Abstract(ID=doc_id, title=title, url=url, abstract=abstract) + + return len(ds), _generate() diff --git a/pyproject.toml b/pyproject.toml index c907126..4c32259 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,19 +1,28 @@ [project] name = "python-searchengine" -version = "0.1.0" +version = "0.2.0" description = "Simple full-text search engine implementation in Python" readme = "README.md" requires-python = ">=3.10" dependencies = [ "datasets", "PyStemmer", + "numpy", + "sentence-transformers", ] [dependency-groups] dev = [ + "mypy>=1.19.1", "pytest", "ruff", ] +faiss = [ + "faiss-cpu", +] +openai = [ + "openai", +] [tool.ruff] line-length = 120 @@ -23,3 +32,6 @@ select = ["E", "F", "W", "I"] [tool.pytest.ini_options] testpaths = ["tests"] + +[tool.mypy] +ignore_missing_imports = true diff --git a/run.py b/run.py index 249b73a..45384fe 100644 --- a/run.py +++ b/run.py @@ -5,6 +5,8 @@ from search.timing import timing logging.basicConfig(level=logging.INFO) +# set httpx logging to WARNING to reduce noise from API calls +logging.getLogger("httpx").setLevel(logging.WARNING) @timing @@ -14,11 +16,11 @@ def index_documents(documents, index): return index -if __name__ == '__main__': +if __name__ == "__main__": index = index_documents(load_documents(), Index()) - print(f'Index contains {len(index.documents)} documents') + print(f"Index contains {len(index.documents)} documents") - index.search('London Beer Flood', search_type='AND') - index.search('London Beer Flood', search_type='OR') - index.search('London Beer Flood', search_type='AND', rank=True) - index.search('London Beer Flood', search_type='OR', rank=True) + index.search("London Beer Flood", search_type="AND") + index.search("London Beer Flood", search_type="OR") + index.search("London Beer Flood", search_type="AND", rank=True) + index.search("London Beer Flood", search_type="OR", rank=True) diff --git a/run_semantic.py b/run_semantic.py new file mode 100644 index 0000000..d3cca98 --- /dev/null +++ b/run_semantic.py @@ -0,0 +1,139 @@ +import itertools +import json +import logging +import tempfile +import time +from pathlib import Path + +import numpy as np + +from load import load_documents +from search.embeddings import embed_batch, embed_text, get_embedding_model +from search.timing import timing +from search.vector_index import VectorIndex + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) +# set httpx logging to WARNING to reduce noise from API calls +logging.getLogger("httpx").setLevel(logging.WARNING) + +BATCH_SIZE = 256 +CHECKPOINT_SIZE = 10_000 +INDEX_PATH = "data/vector_index" +CHECKPOINT_DIR = Path("data/checkpoints") + + +@timing +def build_vector_index(documents, total, model): + logger.info(f"Building index for {total} documents...") + CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True) + Path(INDEX_PATH).parent.mkdir(parents=True, exist_ok=True) + + num_chunks = (total + CHECKPOINT_SIZE - 1) // CHECKPOINT_SIZE + matrix = None + build_start = time.perf_counter() + + for chunk_num, i in enumerate(range(0, total, CHECKPOINT_SIZE), 1): + chunk_path = CHECKPOINT_DIR / f"chunk_{i}.npy" + docs_path = CHECKPOINT_DIR / f"chunk_{i}.json" + end = min(i + CHECKPOINT_SIZE, total) + chunk_size = end - i + + # Consume exactly one chunk from the generator. We only hold ~10k + # Abstract objects at a time instead of all 6.4M. + chunk_docs = list(itertools.islice(documents, chunk_size)) + + # Save doc metadata per-chunk so we never need all docs in memory. + # On resume these are read back from disk to assemble the final JSON. + # Write to a temp file then rename so a crash mid-write can't leave + # a corrupt file that blocks resume. + if not docs_path.exists(): + chunk_docs_data = { + str(i + j): {"ID": d.ID, "title": d.title, "abstract": d.abstract, "url": d.url} + for j, d in enumerate(chunk_docs) + } + with tempfile.NamedTemporaryFile("w", dir=CHECKPOINT_DIR, suffix=".json", delete=False) as f: + json.dump(chunk_docs_data, f) + tmp_path = Path(f.name) + tmp_path.rename(docs_path) + + t0 = time.perf_counter() + if chunk_path.exists(): + chunk_vectors = np.load(chunk_path) + elapsed = time.perf_counter() - t0 + logger.info(f" Chunk {chunk_num}/{num_chunks}: loaded checkpoint ({end}/{total} docs) in {elapsed:.1f}s") + else: + logger.info(f" Chunk {chunk_num}/{num_chunks}: embedding docs {i:,}–{end:,} of {total:,}") + texts = [d.fulltext for d in chunk_docs] + chunk_vectors = embed_batch(model, texts, batch_size=BATCH_SIZE, show_progress=True) + elapsed = time.perf_counter() - t0 + logger.info(f" Chunk {chunk_num}/{num_chunks}: embedded in {elapsed:.1f}s") + # Save raw embeddings via temp file + rename for crash safety + with tempfile.NamedTemporaryFile(dir=CHECKPOINT_DIR, suffix=".npy", delete=False) as f: + np.save(f, chunk_vectors) + tmp_path = Path(f.name) + tmp_path.rename(chunk_path) + + total_elapsed = time.perf_counter() - build_start + logger.info(f" Total elapsed: {total_elapsed:.1f}s") + + # We can only create the memmap once we know the embedding dimensions + # from the first chunk (e.g. 384 for all-MiniLM-L6-v2). + if matrix is None: + matrix = np.lib.format.open_memmap( + f"{INDEX_PATH}.npy", mode="w+", dtype=np.float16, + shape=(total, chunk_vectors.shape[1]), + ) + + # Normalize in float32 for numerical stability, then downcast to float16 + # to halve disk/memory usage. The precision loss is negligible for ranking. + norms = np.linalg.norm(chunk_vectors, axis=1, keepdims=True) + norms[norms == 0] = 1 + matrix[i:end] = (chunk_vectors / norms).astype(np.float16) + + if matrix is not None: + matrix.flush() + del matrix + + # Assemble final document metadata from per-chunk JSON files + all_docs_data = {} + for i in range(0, total, CHECKPOINT_SIZE): + with open(CHECKPOINT_DIR / f"chunk_{i}.json") as f: + all_docs_data.update(json.load(f)) + with open(f"{INDEX_PATH}.json", "w") as f: + json.dump(all_docs_data, f) + + # Load the finished index using memory-mapped I/O — the matrix stays on disk + # and the OS pages in data as needed during search. + index = VectorIndex() + index.load(INDEX_PATH) + return index + + +if __name__ == "__main__": + model = get_embedding_model() + + # try loading a saved index first + try: + index = VectorIndex() + index.load(INDEX_PATH) + logger.info(f"Loaded vector index with {len(index.documents)} documents from disk") + except FileNotFoundError: + logger.info("No saved index found, building from scratch...") + total, documents = load_documents() + index = build_vector_index(documents, total, model) + + logger.info(f"Index contains {len(index.documents)} documents") + + queries = [ + "London Beer Flood", + "alcoholic beverage disaster in England", + "python programming language", + "large constricting reptiles", + ] + for query in queries: + print(f'\n--- Query: "{query}" ---') + query_vector = embed_text(model, query) + results = index.search(query_vector, k=5) + for doc, score in results: + print(f" {score:.4f} | {doc.title}") diff --git a/search/embeddings.py b/search/embeddings.py new file mode 100644 index 0000000..3ba55a7 --- /dev/null +++ b/search/embeddings.py @@ -0,0 +1,21 @@ +import numpy as np +from sentence_transformers import SentenceTransformer + +DEFAULT_MODEL = "all-MiniLM-L6-v2" + + +def get_embedding_model(model_name=DEFAULT_MODEL): + """Load a sentence-transformers model.""" + return SentenceTransformer(model_name) + + +def embed_text(model, text): + """Embed a single text string. Returns a float32 numpy array.""" + return model.encode(text, convert_to_numpy=True).astype(np.float32) + + +def embed_batch(model, texts, batch_size=256, show_progress=False): + """Embed a list of texts in batches. Returns a (n, dims) float32 numpy array.""" + return model.encode( + texts, batch_size=batch_size, show_progress_bar=show_progress, convert_to_numpy=True + ).astype(np.float32) diff --git a/search/vector_index.py b/search/vector_index.py new file mode 100644 index 0000000..fcbfd64 --- /dev/null +++ b/search/vector_index.py @@ -0,0 +1,96 @@ +import json +from collections.abc import Iterable +from pathlib import Path + +import numpy as np +import numpy.typing as npt + +from .documents import Abstract +from .timing import timing + + +class VectorIndex: + def __init__(self, dimensions: int = 384): + self.dimensions = dimensions + self.documents: dict[int, Abstract] = {} + self._matrix: npt.NDArray[np.float32] | None = None + + def build( + self, documents: Iterable[Abstract], vectors: npt.NDArray[np.float32] + ) -> None: + """Store documents and their pre-computed embedding vectors.""" + for i, doc in enumerate(documents): + self.documents[i] = doc + + self._matrix = np.array(vectors, dtype=np.float32) + # normalize all vectors to unit length so dot product = cosine similarity + norms = np.linalg.norm(self._matrix, axis=1, keepdims=True) + norms[norms == 0] = 1 # avoid division by zero + self._matrix /= norms + + @timing + def search( + self, query_vector: npt.NDArray[np.float32], k: int = 10 + ) -> list[tuple[Abstract, float]]: + """Find the k documents most similar to the query vector.""" + if self._matrix is None: + raise ValueError("Index not built. Call build() first.") + # Keep the query in float32 for precision — numpy will upcast the matrix + # (which may be float16) automatically during matmul. + query = np.array(query_vector, dtype=np.float32) + norm = np.linalg.norm(query) + if norm > 0: + query = query / norm + + # Cosine similarity via dot product — works because all vectors are unit-normalized. + scores = self._matrix @ query + k = min(k, len(self.documents)) + # argpartition is O(n) vs O(n log n) for a full sort — we only need the top k. + top_k = np.argpartition(scores, -k)[-k:] + top_k = top_k[np.argsort(scores[top_k])[::-1]] + return [(self.documents[int(i)], float(scores[i])) for i in top_k] + + def save(self, path: str | Path) -> None: + """Save the vector index to disk. + + Creates two files: + - {path}.npy: the normalized embedding matrix + - {path}.json: document metadata + """ + if self._matrix is None: + raise ValueError("Index not built. Call build() first.") + path = Path(path) + np.save(f"{path}.npy", self._matrix) + + docs_data = { + str(i): { + "ID": doc.ID, + "title": doc.title, + "abstract": doc.abstract, + "url": doc.url, + } + for i, doc in self.documents.items() + } + with open(f"{path}.json", "w") as f: + json.dump(docs_data, f) + + def load(self, path: str | Path) -> None: + """Load a vector index from disk using memory-mapped I/O. + + The embedding matrix is memory-mapped (mmap_mode="r"), so it doesn't + need to fit in RAM. The OS will page in data from disk as needed + during search. This works transparently regardless of the matrix dtype + (float16 or float32). + """ + path = Path(path) + self._matrix = np.load(f"{path}.npy", mmap_mode="r") + + with open(f"{path}.json") as f: + docs_data = json.load(f) + + self.documents = { + int(i): Abstract( + ID=d["ID"], title=d["title"], abstract=d["abstract"], url=d["url"] + ) + for i, d in docs_data.items() + } diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py new file mode 100644 index 0000000..e3389a1 --- /dev/null +++ b/tests/test_embeddings.py @@ -0,0 +1,55 @@ +import numpy as np + +from search.embeddings import embed_batch, embed_text, get_embedding_model + + +def test_get_embedding_model(): + model = get_embedding_model() + assert model is not None + + +def test_embed_text_returns_numpy_array(): + model = get_embedding_model() + vector = embed_text(model, "hello world") + assert isinstance(vector, np.ndarray) + assert vector.dtype == np.float32 + assert vector.ndim == 1 + assert vector.shape[0] > 0 + + +def test_embed_text_different_texts_differ(): + model = get_embedding_model() + v1 = embed_text(model, "the cat sat on the mat") + v2 = embed_text(model, "quantum physics is fascinating") + # cosine similarity should be low for unrelated texts + similarity = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + assert similarity < 0.8 + + +def test_embed_text_similar_texts_similar(): + model = get_embedding_model() + v1 = embed_text(model, "the dog chased the cat") + v2 = embed_text(model, "a canine pursued a feline") + similarity = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)) + assert similarity > 0.4 + + +def test_embed_batch(): + model = get_embedding_model() + texts = ["hello world", "foo bar", "search engine"] + vectors = embed_batch(model, texts, batch_size=2) + assert isinstance(vectors, np.ndarray) + assert vectors.ndim == 2 + assert vectors.shape[0] == 3 + assert vectors.shape[1] > 0 + assert vectors.dtype == np.float32 + + +def test_embed_batch_consistent_with_single(): + """Batch and single embedding should produce the same vectors.""" + model = get_embedding_model() + text = "consistency check" + single = embed_text(model, text) + batch = embed_batch(model, [text]) + assert batch.shape == (1, single.shape[0]) + np.testing.assert_allclose(single, batch[0], atol=1e-5) diff --git a/tests/test_vector_index.py b/tests/test_vector_index.py new file mode 100644 index 0000000..02c2f36 --- /dev/null +++ b/tests/test_vector_index.py @@ -0,0 +1,106 @@ +import numpy as np + +from search.documents import Abstract +from search.vector_index import VectorIndex + + +def _make_abstract(id, title, abstract): + return Abstract(ID=id, title=title, abstract=abstract, url=f"https://example.com/{id}") + + +def _build_vector_index(): + """Build a small vector index with 4 documents and fake 4-dim embeddings.""" + docs = [ + _make_abstract(0, "London Beer Flood", "A flood of beer in London in 1814"), + _make_abstract(1, "Boston Molasses Flood", "A flood of molasses in Boston in 1919"), + _make_abstract(2, "Python programming", "Python is a programming language"), + _make_abstract(3, "Java programming", "Java is a programming language"), + ] + # fake embeddings: docs 0 and 1 are similar, docs 2 and 3 are similar + vectors = np.array([ + [1.0, 0.9, 0.0, 0.0], + [0.9, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.9], + [0.0, 0.0, 0.9, 1.0], + ], dtype=np.float32) + + index = VectorIndex(dimensions=4) + index.build(docs, vectors) + return index + + +class TestVectorIndex: + def test_build(self): + index = _build_vector_index() + assert len(index.documents) == 4 + + def test_search_returns_k_results(self): + index = _build_vector_index() + query = np.array([1.0, 0.8, 0.0, 0.0], dtype=np.float32) + results = index.search(query, k=2) + assert len(results) == 2 + + def test_search_returns_tuples(self): + index = _build_vector_index() + query = np.array([1.0, 0.8, 0.0, 0.0], dtype=np.float32) + results = index.search(query, k=2) + for doc, score in results: + assert isinstance(doc, Abstract) + assert isinstance(score, float) + + def test_search_similarity_ranking(self): + index = _build_vector_index() + # query similar to "flood" docs (dims 0-1) + query = np.array([1.0, 0.8, 0.0, 0.0], dtype=np.float32) + results = index.search(query, k=4) + # flood docs should rank higher than programming docs + ids = [doc.ID for doc, _ in results] + assert ids[0] in (0, 1) + assert ids[1] in (0, 1) + + def test_search_scores_descending(self): + index = _build_vector_index() + query = np.array([1.0, 0.8, 0.1, 0.0], dtype=np.float32) + results = index.search(query, k=4) + scores = [score for _, score in results] + assert scores == sorted(scores, reverse=True) + + def test_search_k_larger_than_index(self): + index = _build_vector_index() + query = np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32) + results = index.search(query, k=100) + assert len(results) == 4 + + +class TestVectorIndexPersistence: + def test_save_and_load(self, tmp_path): + index = _build_vector_index() + index.save(tmp_path / "test_index") + + loaded = VectorIndex(dimensions=4) + loaded.load(tmp_path / "test_index") + + assert len(loaded.documents) == 4 + assert loaded._matrix.shape == (4, 4) + + def test_loaded_index_search(self, tmp_path): + index = _build_vector_index() + index.save(tmp_path / "test_index") + + loaded = VectorIndex(dimensions=4) + loaded.load(tmp_path / "test_index") + + query = np.array([1.0, 0.8, 0.0, 0.0], dtype=np.float32) + results = loaded.search(query, k=2) + assert len(results) == 2 + ids = [doc.ID for doc, _ in results] + assert ids[0] in (0, 1) + + def test_loaded_index_is_memmap(self, tmp_path): + index = _build_vector_index() + index.save(tmp_path / "test_index") + + loaded = VectorIndex(dimensions=4) + loaded.load(tmp_path / "test_index") + + assert isinstance(loaded._matrix, np.memmap) diff --git a/uv.lock b/uv.lock index c80fb3d..c6edd18 100644 --- a/uv.lock +++ b/uv.lock @@ -5,9 +5,12 @@ resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'", - "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version == '3.11.*' and sys_platform == 'emscripten'", + "python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform != 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