Format document

db.py

@@ -6,15 +6,16 @@ from concurrent.futures import ThreadPoolExecutor, as_completed
 
 import numpy as np
 import pymupdf
-import ollama # TODO split to another file
+import ollama  # TODO split to another file
 
 #
 # Types
 #
 
-type Vector = np.NDArray # np.NDArray[np.float32] ?
+type Vector = np.NDArray  # np.NDArray[np.float32] ?
 type VectorBytes = bytes
 
+
 @dataclass(slots=True)
 class Record:
     document_index: int
@@ -22,12 +23,14 @@ class Record:
     text: str
     chunk: int = 0  # Chunk number within the page (0-indexed)
 
+
 @dataclass(slots=True)
 class QueryResult:
     record: Record
     distance: float
     document_name: str
 
+
 @dataclass(slots=True)
 class Database:
     """
@@ -36,41 +39,45 @@ class Database:
     TODO For faster nearest neighbour lookup we should use something else,
          e.g. kd-trees
     """
+
     vectors: list[Vector]
     records: dict[VectorBytes, Record]
     documents: list[Path]
-    
+
 
 #
 # Internal functions
 #
 
 
-def _find_nearest(vectors_db: list[Vector], query_vector: Vector, count: int = 10) -> list[tuple[float, int]]:
+def _find_nearest(
+    vectors_db: list[Vector], query_vector: Vector, count: int = 10
+) -> list[tuple[float, int]]:
     """
     Find the N nearest vectors to the query embedding.
-    
+
     Args:
         vectors_db: List of vectors in the database
         query_vector: Query embedding vector
         n: Number of nearest neighbors to return
-    
+
     Returns:
         List of (distance, index) tuples, sorted by distance (closest first)
     """
     distances = [np.linalg.norm(x - query_vector) for x in vectors_db]
-    
+
     # Get indices sorted by distance
     sorted_indices = np.argsort(distances)
-    
+
     # Return top N results as (distance, index) tuples
     results = []
     for i in range(min(count, len(sorted_indices))):
         idx = sorted_indices[i]
         results.append((float(distances[idx]), int(idx)))
-    
+
     return results
 
+
 def _embed(text: str) -> Vector:
     """
     Generate embedding vector for given text.
@@ -82,13 +89,15 @@ def _embed(text: str) -> Vector:
 def _vectorize_record(record: Record) -> tuple[Record, Vector]:
     return record, _embed(record.text)
 
+
 #
 # High-level (exported) functions
 #
 
+
 def create_dummy() -> Database:
     db_length: Final[int] = 10
-    vectors = [np.array([i, 2*i, 3*i, 4*i]) for i in range(db_length)]
+    vectors = [np.array([i, 2 * i, 3 * i, 4 * i]) for i in range(db_length)]
     records = {
         vector.tobytes(): Record(0, 1, "Lorem my ipsum", 1) for vector in vectors
     }
@@ -98,7 +107,7 @@ def create_dummy() -> Database:
 def create_empty() -> Database:
     """
     Creates a new empty database with no vectors or records.
-    
+
     Returns:
         Empty Database object
     """
@@ -108,105 +117,109 @@ def create_empty() -> Database:
 def load(database_file: Path) -> Database:
     """
     Loads a database from the given file.
-    
+
     Args:
         database_file: Path to the database file
-        
+
     Returns:
         Database object loaded from file
     """
     if not database_file.exists():
         raise FileNotFoundError(f"Database file not found: {database_file}")
-    
+
-    with open(database_file, 'rb') as f:
+    with open(database_file, "rb") as f:
         serializable_db = pickle.load(f)
-    
+
     # Reconstruct vectors from bytes
     vectors = []
-    vector_dtype = np.dtype(serializable_db.get('vector_dtype', 'float64'))
+    vector_dtype = np.dtype(serializable_db.get("vector_dtype", "float64"))
-    vector_shape = serializable_db.get('vector_shape', ())
+    vector_shape = serializable_db.get("vector_shape", ())
-    
+
-    for vector_bytes in serializable_db['vectors']:
+    for vector_bytes in serializable_db["vectors"]:
         vector = np.frombuffer(vector_bytes, dtype=vector_dtype).reshape(vector_shape)
         vectors.append(vector)
-    
-    # Records already use bytes as keys, so we can use them directly
-    records = serializable_db['records']
 
-    documents = serializable_db['documents']
+    # Records already use bytes as keys, so we can use them directly
-    
+    records = serializable_db["records"]
+
+    documents = serializable_db["documents"]
+
     return Database(vectors, records, documents)
 
+
 def save(db: Database, database_file: Path) -> None:
     """
     Saves the database to a file using pickle serialization.
-    
+
     Args:
         db: The Database object to save
         database_file: Path where to save the database file
     """
     # Ensure the directory exists
     database_file.parent.mkdir(parents=True, exist_ok=True)
-    
+
     # Create a serializable version of the database
     # Records already use bytes as keys, so we can use them directly
     serializable_db = {
-        'vectors': [vector.tobytes() for vector in db.vectors],
+        "vectors": [vector.tobytes() for vector in db.vectors],
-        'vector_dtype': str(db.vectors[0].dtype) if db.vectors else 'float64',
+        "vector_dtype": str(db.vectors[0].dtype) if db.vectors else "float64",
-        'vector_shape': db.vectors[0].shape if db.vectors else (),
+        "vector_shape": db.vectors[0].shape if db.vectors else (),
-        'records': db.records,  # Already uses bytes as keys
+        "records": db.records,  # Already uses bytes as keys
-        'documents': db.documents,
+        "documents": db.documents,
     }
-    
+
     # Save to file
-    with open(database_file, 'wb') as f:
+    with open(database_file, "wb") as f:
         pickle.dump(serializable_db, f)
 
 
 def query(db: Database | Path, text: str, record_count: int = 10) -> list[QueryResult]:
     """
     Query the database and return the N nearest records.
-    
+
     Args:
         db: Database object or path to database file
         text: Query text to search for
         record_count: Number of nearest neighbors to return (default: 10)
-    
+
     Returns:
         List of (distance, Record) tuples, sorted by distance (closest first)
     """
     if isinstance(db, Path):
         db = load(db)
-    
+
     # Generate embedding for query text
     query_vector = _embed(text)
-    
+
     # Find nearest vectors
     # NOTE We're using euclidean distance as a metric of similarity,
     #      there are some alternatives (cos or dot product) which may be used.
     #      See https://en.wikipedia.org/wiki/Embedding_(machine_learning)
     nearest_results = _find_nearest(db.vectors, query_vector, record_count)
-    
+
     # Convert results to (distance, Record) tuples
     results: list[QueryResult] = []
     for distance, vector_idx in nearest_results:
         # Get the vector at this index
         vector = db.vectors[vector_idx]
         vector_bytes = vector.tobytes()
-        
+
         # Look up the corresponding record
         if vector_bytes in db.records:
             record = db.records[vector_bytes]
-            results.append(QueryResult(record, distance, db.documents[record.document_index].name))
+            results.append(
-    
+                QueryResult(record, distance, db.documents[record.document_index].name)
+            )
+
     return results
 
+
 def add_document(db: Database | Path, file: Path, max_workers: int = 4) -> None:
     """
     Adds a new document to the database. If path is given, do load, add, save.
     Loads PDF with PyMuPDF, splits by pages, and creates records and vectors.
     Uses multithreading for embedding generation.
-    
+
     Args:
         db: Database object or path to database file
         file: Path to PDF file to add
@@ -215,25 +228,25 @@ def add_document(db: Database | Path, file: Path, max_workers: int = 4) -> None:
     """
     save_to_file = False
     database_file_path = None
-    
+
     if isinstance(db, Path):
         database_file_path = db
         db = load(db)
         save_to_file = True
-    
+
     if not file.exists():
         raise FileNotFoundError(f"File not found: {file}")
-    
+
-    if file.suffix.lower() != '.pdf':
+    if file.suffix.lower() != ".pdf":
         raise ValueError(f"File must be a PDF: {file}")
-    
+
     print(f"Processing PDF: {file}")
-    
+
     document_index = len(db.documents)
     try:
         doc = pymupdf.open(file)
         print(f"PDF opened successfully: {len(doc)} pages")
-        
+
         records: list[Record] = []
         chunk_size = 1024
 
@@ -243,13 +256,15 @@ def add_document(db: Database | Path, file: Path, max_workers: int = 4) -> None:
             if not text:
                 print(f"  Page {page_num + 1}: Skipped (empty)")
                 continue
-            
+
             # Simple chunking - split text into chunks of specified size
             for chunk_idx, i in enumerate(range(0, len(text), chunk_size)):
-                chunk = text[i:i + chunk_size]
+                chunk = text[i : i + chunk_size]
                 if chunk_stripped := chunk.strip():  # Only add non-empty chunks
                     # page_num + 1 for use friendliness
-                    records.append(Record(document_index, page_num + 1, chunk_stripped, chunk_idx))
+                    records.append(
+                        Record(document_index, page_num + 1, chunk_stripped, chunk_idx)
+                    )
         doc.close()
     except Exception as e:
         raise RuntimeError(f"Error processing PDF {file}: {e}")
@@ -261,34 +276,35 @@ def add_document(db: Database | Path, file: Path, max_workers: int = 4) -> None:
 
     # TODO measure with GIL disabled to check if multithreading actually helps
     with ThreadPoolExecutor(max_workers=max_workers) as pool:
-        futures = [ pool.submit(_vectorize_record, r) for r in records ]
+        futures = [pool.submit(_vectorize_record, r) for r in records]
         for f in as_completed(futures):
             record, vector = f.result()
             db.records[vector.tobytes()] = record
             db.vectors.append(vector)
 
     print(f"Successfully processed {file}: {len(records)} chunks")
-    
+
     # Save database if we loaded it from file
     if save_to_file and database_file_path:
         save(db, database_file_path)
         print(f"Database saved to {database_file_path}")
 
+
 def get_document_path(db: Database | Path, document_index: int) -> Path:
     """
     Get the file path of the document at the given index in the database.
-    
+
     Args:
         db: Database object or path to database file
         document_index: Index of the document to retrieve
-    
+
     Returns:
         Path to the document file
     """
     if isinstance(db, Path):
         db = load(db)
-    
+
     if document_index < 0 or document_index >= len(db.documents):
         raise IndexError(f"Document index out of range: {document_index}")
-    
+
-    return db.documents[document_index]
+    return db.documents[document_index]

main.py

@@ -9,56 +9,69 @@ import db
 
 DEFAULT_DB_PATH: Final[Path] = Path("db.pkl")
 
+
 def test_database():
     """Test database save/load functionality by creating, saving, loading and comparing."""
     print("=== Database Test ===")
-    
+
     # Create dummy database
     print("1. Creating dummy database...")
     original_db = db.create_dummy()
-    print(f"   Original DB: {len(original_db.vectors)} vectors, {len(original_db.records)} records")
+    print(
-    
+        f"   Original DB: {len(original_db.vectors)} vectors, {len(original_db.records)} records"
+    )
+
     # Print some details about the original database
-    print("   First vector shape:", original_db.vectors[0].shape if original_db.vectors else "No vectors")
+    print(
-    print("   Sample vector:", original_db.vectors[0][:4] if original_db.vectors else "No vectors")
+        "   First vector shape:",
+        original_db.vectors[0].shape if original_db.vectors else "No vectors",
+    )
+    print(
+        "   Sample vector:",
+        original_db.vectors[0][:4] if original_db.vectors else "No vectors",
+    )
     print("   Sample record keys (first 3):", list(original_db.records.keys())[:3])
-    
+
     # Create temporary file for testing
-    with tempfile.NamedTemporaryFile(suffix='.pkl', delete=False) as tmp_file:
+    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp_file:
         test_file = Path(tmp_file.name)
-    
+
     try:
         # Save database
         print(f"\n2. Saving database to {test_file}...")
         db.save(original_db, test_file)
         print(f"   File size: {test_file.stat().st_size} bytes")
-        
+
         # Load database
         print(f"\n3. Loading database from {test_file}...")
         loaded_db = db.load(test_file)
-        print(f"   Loaded DB: {len(loaded_db.vectors)} vectors, {len(loaded_db.records)} records")
+        print(
-        
+            f"   Loaded DB: {len(loaded_db.vectors)} vectors, {len(loaded_db.records)} records"
+        )
+
         # Compare databases
         print("\n4. Comparing original vs loaded...")
-        
+
         # Check vector count
         vectors_match = len(original_db.vectors) == len(loaded_db.vectors)
         print(f"   Vector count match: {vectors_match}")
-        
+
         # Check record count
         records_match = len(original_db.records) == len(loaded_db.records)
         print(f"   Record count match: {records_match}")
-        
+
         # Check vector equality
         vectors_equal = True
         if vectors_match and original_db.vectors:
-            for i, (orig, loaded) in enumerate(zip(original_db.vectors, loaded_db.vectors)):
+            for i, (orig, loaded) in enumerate(
+                zip(original_db.vectors, loaded_db.vectors)
+            ):
                 if not np.array_equal(orig, loaded):
                     vectors_equal = False
                     print(f"   Vector {i} mismatch!")
                     break
         print(f"   All vectors equal: {vectors_equal}")
-        
+
         # Check record equality
         records_equal = True
         if records_match:
@@ -72,21 +85,31 @@ def test_database():
                     print("   Record content mismatch!")
                     break
         print(f"   All records equal: {records_equal}")
-        
+
         # Test embedding functionality
         print("\n5. Testing embedding functionality (Ollama API server)...")
         try:
             test_embedding = db._embed("This is a test text for embedding.")
-            print(f"   Embedding test PASSED: Generated vector of shape {test_embedding.shape}")
+            print(
+                f"   Embedding test PASSED: Generated vector of shape {test_embedding.shape}"
+            )
             ollama_running = True
         except Exception as e:
-            print(f"   Embedding test FAILED: {e}\n   Did you start ollama docker image?")
+            print(
+                f"   Embedding test FAILED: {e}\n   Did you start ollama docker image?"
+            )
             ollama_running = False
 
         # Summary
-        all_good = vectors_match and records_match and vectors_equal and records_equal and ollama_running
+        all_good = (
+            vectors_match
+            and records_match
+            and vectors_equal
+            and records_equal
+            and ollama_running
+        )
         print(f"\n✅ Test {'PASSED' if all_good else 'FAILED'}")
-        
+
     finally:
         # Clean up temporary file
         if test_file.exists():
@@ -97,20 +120,20 @@ def test_database():
 def create_database(db_path: str):
     """Create a new empty database."""
     db_file = Path(db_path)
-    
+
     # Check if file already exists
     if db_file.exists():
         response = input(f"Database {db_file} already exists. Overwrite? (y/N): ")
-        if response.lower() != 'y':
+        if response.lower() != "y":
             print("Operation cancelled.")
             return
-    
+
     # Create empty database
     empty_db = db.create_empty()
-    
+
     # Save to file
     db.save(empty_db, db_file)
-    
+
     print(f"✅ Created empty database: {db_file}")
     print(f"   Vectors: {len(empty_db.vectors)}")
     print(f"   Records: {len(empty_db.records)}")
@@ -119,11 +142,11 @@ def create_database(db_path: str):
 def add_file(db_path: str, file_paths: list[str]):
     """Add one or more files to the semantic search database."""
     print(f"Adding {len(file_paths)} file(s) to database: {db_path}")
-    
+
     db_file = Path(db_path)
     successful_files = []
     failed_files = []
-    
+
     for i, file_path in enumerate(file_paths, 1):
         print(f"\n[{i}/{len(file_paths)}] Processing: {file_path}")
         try:
@@ -133,7 +156,7 @@ def add_file(db_path: str, file_paths: list[str]):
         except Exception as e:
             failed_files.append((file_path, str(e)))
             print(f"❌ Failed to add {file_path}: {e}")
-    
+
     # Summary
     print(f"\n{'='*60}")
     print("SUMMARY:")
@@ -141,44 +164,43 @@ def add_file(db_path: str, file_paths: list[str]):
     if successful_files:
         for file_path in successful_files:
             print(f"   - {Path(file_path).name}")
-    
+
     if failed_files:
         print(f"❌ Failed to add: {len(failed_files)} files")
         for file_path, error in failed_files:
             print(f"   - {Path(file_path).name}: {error}")
-    
+
     print(f"{'='*60}")
 
 
 def query(db_path: str, query_text: str):
     """Query the semantic search database."""
     print(f"Querying: '{query_text}' in database: {db_path}")
-    
+
     try:
         results = db.query(Path(db_path), query_text)
-        
+
         if not results:
             print("No results found.")
             return
-        
+
         print(f"\nFound {len(results)} results:")
         print("=" * 60)
-        
+
         for i, res in enumerate(results, 1):
             print(f"\n{i}. Distance: {res.distance:.4f}")
             print(f"   Document: {res.document_name}")
             print(f"   Page: {res.record.page}, Chunk: {res.record.chunk}")
             # Replace all whitespace characters with regular spaces for cleaner display
-            clean_text = ' '.join(res.record.text[:200].split())
+            clean_text = " ".join(res.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 start_web_server(db_path: str, host: str = "127.0.0.1", port: int = 5000):
     """Start a web server for the semantic search tool."""
     try:
@@ -190,63 +212,67 @@ def start_web_server(db_path: str, host: str = "127.0.0.1", port: int = 5000):
     # Set template_folder to 'templates' directory
     app = Flask(__name__, template_folder="templates")
     db_file = Path(db_path)
-    
+
     # Check if database exists
     if not db_file.exists():
         print(f"❌ Database file not found: {db_file}")
         print("   Create a database first using: python main.py create")
         sys.exit(1)
-    
+
-    @app.route('/')
+    @app.route("/")
     def index():
         return render_template("index.html", results=None)
-    
+
-    @app.route('/file/<int:document_index>')
+    @app.route("/file/<int:document_index>")
     def serve_file(document_index):
         """Serve PDF files directly."""
         try:
             file_path = db.get_document_path(db_file, document_index)
             if not file_path.exists():
-                return jsonify({'error': 'File not found'}), 404
+                return jsonify({"error": "File not found"}), 404
             return send_file(file_path, as_attachment=False)
         except Exception as e:
-            return jsonify({'error': str(e)}), 500
+            return jsonify({"error": str(e)}), 500
-    
+
-    @app.route('/api/search', methods=['POST'])
+    @app.route("/api/search", methods=["POST"])
     def search():
         try:
             data = request.get_json()
-            if not data or 'query' not in data:
+            if not data or "query" not in data:
-                return jsonify({'error': 'Missing query parameter'}), 400
+                return jsonify({"error": "Missing query parameter"}), 400
-            
+
-            query_text = data['query'].strip()
+            query_text = data["query"].strip()
             if not query_text:
-                return jsonify({'error': 'Query cannot be empty'}), 400
+                return jsonify({"error": "Query cannot be empty"}), 400
-            
+
             # Perform the search
             results = db.query(db_file, query_text)
-            
+
             # Format results for JSON response
             formatted_results = []
             for res in results:
-                formatted_results.append({
+                formatted_results.append(
-                    'distance': float(res.distance),
+                    {
-                    'document_name': res.document_name,
+                        "distance": float(res.distance),
-                    'document_index': res.record.document_index,
+                        "document_name": res.document_name,
-                    'page': res.record.page,
+                        "document_index": res.record.document_index,
-                    'chunk': res.record.chunk,
+                        "page": res.record.page,
-                    'text': ' '.join(res.record.text[:300].split())  # Clean and truncate text
+                        "chunk": res.record.chunk,
-                })
+                        "text": " ".join(
-            return jsonify({'results': formatted_results})
+                            res.record.text[:300].split()
-            
+                        ),  # Clean and truncate text
+                    }
+                )
+            return jsonify({"results": formatted_results})
+
         except Exception as e:
-            return jsonify({'error': str(e)}), 500
+            return jsonify({"error": str(e)}), 500
-    
+
     print("🚀 Starting web server...")
     print(f"   Database: {db_file}")
     print(f"   URL: http://{host}:{port}")
     print("   Press Ctrl+C to stop")
-    
+
     try:
         app.run(host=host, port=port, debug=False)
     except KeyboardInterrupt:
@@ -258,49 +284,71 @@ def start_web_server(db_path: str, host: str = "127.0.0.1", port: int = 5000):
 def main():
     parser = argparse.ArgumentParser(
         description="Semantic Search Tool",
-        formatter_class=argparse.RawDescriptionHelpFormatter
+        formatter_class=argparse.RawDescriptionHelpFormatter,
     )
-    
+
     # Create subparsers for different commands
-    subparsers = parser.add_subparsers(dest='command', help='Available 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 = subparsers.add_parser(
-    create_parser.add_argument('db_path', nargs='?', default=str(DEFAULT_DB_PATH), 
+        "create", aliases=["c"], help="Create a new empty database"
-                              help=f'Path to database file (default: {DEFAULT_DB_PATH})')
+    )
-    
+    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 = subparsers.add_parser(
-    add_parser.add_argument('db', help='Path to the database file (e.g., db.pkl)')
+        "add-file", aliases=["a"], help="Add one or more files to the search database"
-    add_parser.add_argument('file_paths', nargs='+', help='Path(s) to the PDF file(s) to add')
+    )
-    
+    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 = subparsers.add_parser(
-    query_parser.add_argument('db', help='Path to the database file (e.g., db.pkl)')
+        "query", aliases=["q"], help="Query the search database"
-    query_parser.add_argument('query_text', help='Text to search for')
+    )
-    
+    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")
+
     # Host command (web server)
-    host_parser = subparsers.add_parser('host', aliases=['h'], help='Start a web server for semantic search')
+    host_parser = subparsers.add_parser(
-    host_parser.add_argument('db', help='Path to the database file (e.g., db.pkl)')
+        "host", aliases=["h"], help="Start a web server for semantic search"
-    host_parser.add_argument('--host', default='127.0.0.1', help='Host address to bind to (default: 127.0.0.1)')
+    )
-    host_parser.add_argument('--port', type=int, default=5000, help='Port to listen on (default: 5000)')
+    host_parser.add_argument("db", help="Path to the database file (e.g., db.pkl)")
-    
+    host_parser.add_argument(
+        "--host",
+        default="127.0.0.1",
+        help="Host address to bind to (default: 127.0.0.1)",
+    )
+    host_parser.add_argument(
+        "--port", type=int, default=5000, help="Port to listen on (default: 5000)"
+    )
+
     # Test command
-    subparsers.add_parser('test', aliases=['t'], help='Test database save/load functionality')
+    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']:
+    if args.command in ["create", "c"]:
         create_database(args.db_path)
-    elif args.command in ['add-file', 'a']:
+    elif args.command in ["add-file", "a"]:
         add_file(args.db, args.file_paths)
-    elif args.command in ['query', 'q']:
+    elif args.command in ["query", "q"]:
         query(args.db, args.query_text)
-    elif args.command in ['host', 'h']:
+    elif args.command in ["host", "h"]:
         start_web_server(args.db, args.host, args.port)
-    elif args.command in ['test', 't']:
+    elif args.command in ["test", "t"]:
         test_database()
     else:
         parser.print_help()