Init
This commit is contained in:
Mrna
10
.gitignore
vendored
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10
.gitignore
vendored
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# Python-generated files
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__pycache__/
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*.py[oc]
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build/
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dist/
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wheels/
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*.egg-info
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# Virtual environments
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.venv
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329
db.py
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329
db.py
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@@ -0,0 +1,329 @@
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import pickle
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from pathlib import Path
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from dataclasses import dataclass
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from typing import Final
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import numpy as np
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import pymupdf
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import ollama # TODO split to another file
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#
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# Types
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#
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type Vector = np.NDArray # np.NDArray[np.float32] ?
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type VectorBytes = bytes
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@dataclass(slots=True)
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class Record:
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document: Path
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page: int
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text: str
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chunk: int = 0 # Chunk number within the page (0-indexed)
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@dataclass(slots=True)
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class Database:
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"""
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"Vectors" hold the data for fast lookup of the vector,
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which can then be used to obtain record.
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TODO For faster nearest neighbour lookup we should use something else,
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e.g. kd-trees
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TODO The resulting db is huge (50 MB for 4 pdfs), contains a lot of duplicit uncompressed text.
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We should at least de-duplicate the document path.
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"""
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vectors: list[Vector]
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records: dict[VectorBytes, Record]
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#
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# Internal functions
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#
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def _find_nearest(vectors_db: list[Vector], query_vector: Vector, count: int = 10) -> list[tuple[float, int]]:
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"""
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Find the N nearest vectors to the query embedding.
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Args:
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vectors_db: List of vectors in the database
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query_vector: Query embedding vector
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n: Number of nearest neighbors to return
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Returns:
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List of (distance, index) tuples, sorted by distance (closest first)
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"""
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distances = [np.linalg.norm(x - query_vector) for x in vectors_db]
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# Get indices sorted by distance
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sorted_indices = np.argsort(distances)
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# Return top N results as (distance, index) tuples
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results = []
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for i in range(min(count, len(sorted_indices))):
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idx = sorted_indices[i]
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results.append((float(distances[idx]), int(idx)))
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return results
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def _embed(text: str) -> Vector:
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"""
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Generate embedding vector for given text.
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"""
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MODEL: Final[str] = "nomic-embed-text"
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return np.array(ollama.embeddings(model=MODEL, prompt=text)["embedding"])
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def _process_chunk(chunk_data: tuple) -> tuple[Vector, Record]:
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"""
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Process a single chunk: generate embedding and create record.
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Used for multithreading.
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Args:
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chunk_data: Tuple of (file_path, page_num, chunk_idx, chunk_text)
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Returns:
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Tuple of (vector, record)
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"""
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file_path, page_num, chunk_idx, chunk_text = chunk_data
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# Generate embedding vector for this chunk
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vector = _embed(chunk_text)
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# Create record for this chunk
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record = Record(
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document=file_path,
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page=page_num + 1, # 1-indexed for user-friendliness
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text=chunk_text,
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chunk=chunk_idx + 1 # 1-indexed for user-friendliness
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)
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return vector, record
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#
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# High-level (exported) functions
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#
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def create_dummy() -> Database:
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db_length: Final[int] = 10
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vectors = [np.array([i, 2*i, 3*i, 4*i]) for i in range(db_length)]
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records = {
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vector.tobytes(): Record(Path("dummy"), 1, "Lorem my ipsum", 1) for vector in vectors
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}
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return Database(vectors, records)
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def create_empty() -> Database:
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"""
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Creates a new empty database with no vectors or records.
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Returns:
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Empty Database object
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"""
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return Database(vectors=[], records={})
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def load(database_file: Path) -> Database:
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"""
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Loads a database from the given file.
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Args:
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database_file: Path to the database file
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Returns:
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Database object loaded from file
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"""
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if not database_file.exists():
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raise FileNotFoundError(f"Database file not found: {database_file}")
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with open(database_file, 'rb') as f:
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serializable_db = pickle.load(f)
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# Reconstruct vectors from bytes
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vectors = []
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vector_dtype = np.dtype(serializable_db.get('vector_dtype', 'float64'))
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vector_shape = serializable_db.get('vector_shape', ())
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for vector_bytes in serializable_db['vectors']:
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vector = np.frombuffer(vector_bytes, dtype=vector_dtype).reshape(vector_shape)
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vectors.append(vector)
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# Records already use bytes as keys, so we can use them directly
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records = serializable_db['records']
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return Database(vectors, records)
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def save(db: Database, database_file: Path) -> None:
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"""
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Saves the database to a file using pickle serialization.
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Args:
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db: The Database object to save
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database_file: Path where to save the database file
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"""
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# Ensure the directory exists
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database_file.parent.mkdir(parents=True, exist_ok=True)
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# Create a serializable version of the database
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# Records already use bytes as keys, so we can use them directly
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serializable_db = {
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'vectors': [vector.tobytes() for vector in db.vectors],
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'vector_dtype': str(db.vectors[0].dtype) if db.vectors else 'float64',
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'vector_shape': db.vectors[0].shape if db.vectors else (),
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'records': db.records # Already uses bytes as keys
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}
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# Save to file
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with open(database_file, 'wb') as f:
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pickle.dump(serializable_db, f)
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def query(db: Database | Path, text: str, record_count: int = 10) -> list[tuple[float, Record]]:
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"""
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Query the database and return the N nearest records.
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Args:
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db: Database object or path to database file
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text: Query text to search for
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record_count: Number of nearest neighbors to return (default: 10)
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Returns:
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List of (distance, Record) tuples, sorted by distance (closest first)
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"""
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if isinstance(db, Path):
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db = load(db)
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# Generate embedding for query text
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query_vector = _embed(text)
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# Find nearest vectors
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# NOTE We're using euclidean distance as a metric of similarity,
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# there are some alternatives (cos or dot product) which may be used.
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# See https://en.wikipedia.org/wiki/Embedding_(machine_learning)
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nearest_results = _find_nearest(db.vectors, query_vector, record_count)
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# Convert results to (distance, Record) tuples
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results = []
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for distance, vector_idx in nearest_results:
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# Get the vector at this index
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vector = db.vectors[vector_idx]
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vector_bytes = vector.tobytes()
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# Look up the corresponding record
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if vector_bytes in db.records:
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record = db.records[vector_bytes]
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results.append((distance, record))
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return results
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def add_document(db: Database | Path, file: Path, max_workers: int = 1) -> None:
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"""
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Adds a new document to the database. If path is given, do load, add, save.
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Loads PDF with PyMuPDF, splits by pages, and creates records and vectors.
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Uses multithreading for embedding generation.
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Args:
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db: Database object or path to database file
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file: Path to PDF file to add
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max_workers: Maximum number of threads for parallel processing
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"""
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save_to_file = False
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database_file_path = None
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if isinstance(db, Path):
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database_file_path = db
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db = load(db)
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save_to_file = True
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# Validate that the file exists and is a PDF
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if not file.exists():
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raise FileNotFoundError(f"File not found: {file}")
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if file.suffix.lower() != '.pdf':
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raise ValueError(f"File must be a PDF: {file}")
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print(f"Processing PDF: {file}")
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# Open PDF with PyMuPDF
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try:
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doc = pymupdf.open(file)
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print(f"PDF opened successfully: {len(doc)} pages")
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# Collect all chunks to process
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chunk_tasks = []
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# Process each page to collect chunks
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for page_num in range(len(doc)):
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page = doc[page_num]
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# Extract text from page
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text = page.get_text().strip()
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# Skip empty pages
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if not text:
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print(f" Page {page_num + 1}: Skipped (empty)")
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continue
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#print(f" Page {page_num + 1}: {len(text)} characters")
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# Split page text into chunks of 1024 characters
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chunk_size = 1024
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chunks = []
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# Simple chunking - split text into chunks of specified size
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for i in range(0, len(text), chunk_size):
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chunk = text[i:i + chunk_size]
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if chunk.strip(): # Only add non-empty chunks
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chunks.append(chunk.strip())
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#print(f" Split into {len(chunks)} chunks")
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# Add chunk tasks for parallel processing
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for chunk_idx, chunk_text in enumerate(chunks):
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chunk_tasks.append((file, page_num, chunk_idx, chunk_text))
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doc.close()
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# Process chunks in parallel
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print(f"Processing {len(chunk_tasks)} chunks with {max_workers} workers...")
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# TODO measure with GIL disabled to check if multithreading actually helps
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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# Submit all chunk processing tasks
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future_to_chunk = {
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executor.submit(_process_chunk, chunk_data): chunk_data
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for chunk_data in chunk_tasks
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}
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# Collect results as they complete
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completed_chunks = 0
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for future in as_completed(future_to_chunk):
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try:
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vector, record = future.result()
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# Convert vector to bytes for use as dictionary key
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vector_bytes = vector.tobytes()
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# Add to database
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db.vectors.append(vector)
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db.records[vector_bytes] = record
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completed_chunks += 1
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# if completed_chunks % 10 == 0 or completed_chunks == len(chunk_tasks):
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# print(f" Completed {completed_chunks}/{len(chunk_tasks)} chunks")
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except Exception as e:
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chunk_data = future_to_chunk[future]
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print(f" Error processing chunk {chunk_data}: {e}")
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print(f"Successfully processed {file}: {len(chunk_tasks)} chunks")
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except Exception as e:
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raise RuntimeError(f"Error processing PDF {file}: {e}")
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# Save database if we loaded it from file
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if save_to_file and database_file_path:
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save(db, database_file_path)
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print(f"Database saved to {database_file_path}")
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217
main.py
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217
main.py
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import argparse
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import sys
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from pathlib import Path
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import tempfile
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import numpy as np
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from typing import Final
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import db
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DEFAULT_DB_PATH: Final[Path] = Path("db.pkl")
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def test_database():
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"""Test database save/load functionality by creating, saving, loading and comparing."""
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print("=== Database Test ===")
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# Create dummy database
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print("1. Creating dummy database...")
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original_db = db.create_dummy()
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print(f" Original DB: {len(original_db.vectors)} vectors, {len(original_db.records)} records")
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# Print some details about the original database
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print(" First vector shape:", original_db.vectors[0].shape if original_db.vectors else "No vectors")
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print(" Sample vector:", original_db.vectors[0][:4] if original_db.vectors else "No vectors")
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print(" Sample record keys (first 3):", list(original_db.records.keys())[:3])
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# Create temporary file for testing
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with tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) as tmp_file:
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test_file = Path(tmp_file.name)
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try:
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# Save database
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print(f"\n2. Saving database to {test_file}...")
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db.save(original_db, test_file)
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print(f" File size: {test_file.stat().st_size} bytes")
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# Load database
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print(f"\n3. Loading database from {test_file}...")
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loaded_db = db.load(test_file)
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print(f" Loaded DB: {len(loaded_db.vectors)} vectors, {len(loaded_db.records)} records")
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# Compare databases
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print("\n4. Comparing original vs loaded...")
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# Check vector count
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vectors_match = len(original_db.vectors) == len(loaded_db.vectors)
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print(f" Vector count match: {vectors_match}")
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# Check record count
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records_match = len(original_db.records) == len(loaded_db.records)
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print(f" Record count match: {records_match}")
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# Check vector equality
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vectors_equal = True
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if vectors_match and original_db.vectors:
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for i, (orig, loaded) in enumerate(zip(original_db.vectors, loaded_db.vectors)):
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if not np.array_equal(orig, loaded):
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vectors_equal = False
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print(f" Vector {i} mismatch!")
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break
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print(f" All vectors equal: {vectors_equal}")
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# Check record equality
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records_equal = True
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if records_match:
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for key in original_db.records:
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if key not in loaded_db.records:
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records_equal = False
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print(" Missing record key!")
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break
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if original_db.records[key] != loaded_db.records[key]:
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records_equal = False
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print(" Record content mismatch!")
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break
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print(f" All records equal: {records_equal}")
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# Summary
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all_good = vectors_match and records_match and vectors_equal and records_equal
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print(f"\n✅ Test {'PASSED' if all_good else 'FAILED'}: All data preserved through save/load cycle")
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finally:
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# Clean up temporary file
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if test_file.exists():
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test_file.unlink()
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print(f" Cleaned up temporary file: {test_file}")
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def create_database(db_path: str):
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"""Create a new empty database."""
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db_file = Path(db_path)
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# Check if file already exists
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if db_file.exists():
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response = input(f"Database {db_file} already exists. Overwrite? (y/N): ")
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if response.lower() != 'y':
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print("Operation cancelled.")
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return
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# Create empty database
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empty_db = db.create_empty()
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# Save to file
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db.save(empty_db, db_file)
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print(f"✅ Created empty database: {db_file}")
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print(f" Vectors: {len(empty_db.vectors)}")
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print(f" Records: {len(empty_db.records)}")
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def add_file(db_path: str, file_paths: list[str]):
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"""Add one or more files to the semantic search database."""
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print(f"Adding {len(file_paths)} file(s) to database: {db_path}")
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db_file = Path(db_path)
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successful_files = []
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failed_files = []
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for i, file_path in enumerate(file_paths, 1):
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print(f"\n[{i}/{len(file_paths)}] Processing: {file_path}")
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try:
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db.add_document(db_file, Path(file_path))
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successful_files.append(file_path)
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print(f"✅ Successfully added: {file_path}")
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except Exception as e:
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failed_files.append((file_path, str(e)))
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print(f"❌ Failed to add {file_path}: {e}")
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# Summary
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print(f"\n{'='*60}")
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print("SUMMARY:")
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print(f"✅ Successfully added: {len(successful_files)} files")
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if successful_files:
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for file_path in successful_files:
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print(f" - {Path(file_path).name}")
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if failed_files:
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print(f"❌ Failed to add: {len(failed_files)} files")
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for file_path, error in failed_files:
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print(f" - {Path(file_path).name}: {error}")
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print(f"{'='*60}")
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def query(db_path: str, query_text: str):
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"""Query the semantic search database."""
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print(f"Querying: '{query_text}' in database: {db_path}")
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try:
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results = db.query(Path(db_path), query_text)
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if not results:
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print("No results found.")
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return
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print(f"\nFound {len(results)} results:")
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print("=" * 60)
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for i, (distance, record) in enumerate(results, 1):
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print(f"\n{i}. Distance: {distance:.4f}")
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print(f" Document: {record.document.name}")
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print(f" Page: {record.page}, Chunk: {record.chunk}")
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||||
# Replace all whitespace characters with regular spaces for cleaner display
|
||||
clean_text = ' '.join(record.text[:200].split())
|
||||
print(f" Text preview: {clean_text}...")
|
||||
if i < len(results):
|
||||
print("-" * 40)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error querying database: {e}")
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Semantic Search Tool",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter
|
||||
)
|
||||
|
||||
# Create subparsers for different commands
|
||||
subparsers = parser.add_subparsers(dest='command', help='Available commands')
|
||||
|
||||
# Create command
|
||||
create_parser = subparsers.add_parser('create', aliases=['c'], help='Create a new empty database')
|
||||
create_parser.add_argument('db_path', nargs='?', default=str(DEFAULT_DB_PATH),
|
||||
help=f'Path to database file (default: {DEFAULT_DB_PATH})')
|
||||
|
||||
# Add file command
|
||||
add_parser = subparsers.add_parser('add-file', aliases=['a'], help='Add one or more files to the search database')
|
||||
add_parser.add_argument('db', help='Path to the database file (e.g., db.pkl)')
|
||||
add_parser.add_argument('file_paths', nargs='+', help='Path(s) to the PDF file(s) to add')
|
||||
|
||||
# Query command
|
||||
query_parser = subparsers.add_parser('query', aliases=['q'], help='Query the search database')
|
||||
query_parser.add_argument('db', help='Path to the database file (e.g., db.pkl)')
|
||||
query_parser.add_argument('query_text', help='Text to search for')
|
||||
|
||||
# Test command
|
||||
subparsers.add_parser('test', aliases=['t'], help='Test database save/load functionality')
|
||||
|
||||
# Parse arguments
|
||||
args = parser.parse_args()
|
||||
|
||||
# Handle commands
|
||||
if args.command in ['create', 'c']:
|
||||
create_database(args.db_path)
|
||||
elif args.command in ['add-file', 'a']:
|
||||
add_file(args.db, args.file_paths)
|
||||
elif args.command in ['query', 'q']:
|
||||
query(args.db, args.query_text)
|
||||
elif args.command in ['test', 't']:
|
||||
test_database()
|
||||
else:
|
||||
parser.print_help()
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
11
pyproject.toml
Normal file
11
pyproject.toml
Normal file
@@ -0,0 +1,11 @@
|
||||
[project]
|
||||
name = "semantic-doc-search"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
dependencies = [
|
||||
"numpy>=2.3.4",
|
||||
"ollama>=0.6.0",
|
||||
"pymupdf>=1.26.5",
|
||||
]
|
||||
345
uv.lock
generated
Normal file
345
uv.lock
generated
Normal file
@@ -0,0 +1,345 @@
|
||||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.11"
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
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sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.11.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "sniffio" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
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wheels = [
|
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{ url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2025.10.5"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/4c/5b/b6ce21586237c77ce67d01dc5507039d444b630dd76611bbca2d8e5dcd91/certifi-2025.10.5.tar.gz", hash = "sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43", size = 164519, upload-time = "2025-10-05T04:12:15.808Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/e4/37/af0d2ef3967ac0d6113837b44a4f0bfe1328c2b9763bd5b1744520e5cfed/certifi-2025.10.5-py3-none-any.whl", hash = "sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de", size = 163286, upload-time = "2025-10-05T04:12:14.03Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
version = "0.16.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/01/ee/02a2c011bdab74c6fb3c75474d40b3052059d95df7e73351460c8588d963/h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1", size = 101250, upload-time = "2025-04-24T03:35:25.427Z" }
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||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/04/4b/29cac41a4d98d144bf5f6d33995617b185d14b22401f75ca86f384e87ff1/h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86", size = 37515, upload-time = "2025-04-24T03:35:24.344Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpcore"
|
||||
version = "1.0.9"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "certifi" },
|
||||
{ name = "h11" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/06/94/82699a10bca87a5556c9c59b5963f2d039dbd239f25bc2a63907a05a14cb/httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8", size = 85484, upload-time = "2025-04-24T22:06:22.219Z" }
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wheels = [
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|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "httpx"
|
||||
version = "0.28.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
{ name = "certifi" },
|
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{ name = "httpcore" },
|
||||
{ name = "idna" },
|
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]
|
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sdist = { url = "https://files.pythonhosted.org/packages/b1/df/48c586a5fe32a0f01324ee087459e112ebb7224f646c0b5023f5e79e9956/httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc", size = 141406, upload-time = "2024-12-06T15:37:23.222Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/2a/39/e50c7c3a983047577ee07d2a9e53faf5a69493943ec3f6a384bdc792deb2/httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad", size = 73517, upload-time = "2024-12-06T15:37:21.509Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "idna"
|
||||
version = "3.11"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582, upload-time = "2025-10-12T14:55:20.501Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" },
|
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|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "2.3.4"
|
||||
source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/b5/f4/098d2270d52b41f1bd7db9fc288aaa0400cb48c2a3e2af6fa365d9720947/numpy-2.3.4.tar.gz", hash = "sha256:a7d018bfedb375a8d979ac758b120ba846a7fe764911a64465fd87b8729f4a6a", size = 20582187, upload-time = "2025-10-15T16:18:11.77Z" }
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wheels = [
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Reference in New Issue
Block a user