code: import argparse
  import os
  from typing import Any
  from PIL import Image
  
  import lancedb
  
  from schema import Myntra, get_schema_by_name
  
  
  def run_vector_search(
      database: str,
      table_name: str,
      schema: Any,
      search_query: Any,
      limit: int = 6,
      output_folder: str = "output",
  ) -> None:
      """
      This function performs a vector search on the specified database and table using the provided search query.
      The search can be performed on either text or image data. The function retrieves the top 'limit' number of results
      and saves the corresponding images in the 'output_folder' directory. The function assumes if the search query ends 
      with '.jpg' or '.png', it is an image search, otherwise it is a text search.
      Args:
          database (str): The path to the database.
          table_name (str): The name of the table.
          schema (Schema): The schema to use for converting search results to Pydantic models.
          search_query (Any): The search query, can be text or image.
          limit (int, optional): The maximum number of results to return. Defaults to 6.
          output_folder (str, optional): The folder to save the output images. Defaults to "output".
  
      Returns:
          None
  
      Usage:
      >>> run_vector_search(database="~/.lancedb", table_name="myntra", schema=Myntra, search_query="Black Kurta")
  
      """
  
      # Create the output folder if it does not exist
      if os.path.exists(output_folder):
          for file in os.listdir(output_folder):
              os.remove(os.path.join(output_folder, file))
      else:
          os.makedirs(output_folder)
  
      # Connect to the lancedb database
      db = lancedb.connect(database)
  
      # Open the table
      table = db.open_table(table_name)
  
      # Check if the search query is an image or text
      try:
          if search_query.endswith(".jpg") or search_query.endswith(".png"):
              search_query = Image.open(search_query)
          else:
              search_query = search_query
      except AttributeError as e:
          if str(e) == "'JpegImageFile' object has no attribute 'endswith'":
              print("Running via Streamlit, search query is already an array so skipping opening image using Pillow")
          else:
              raise
  
      # Perform the vector search and retrieve the results
      rs = table.search(search_query).limit(limit).to_pydantic(schema)
  
      # Save the images to the output folder
      for i in range(limit):
          image_path = os.path.join(output_folder, f"image_{i}.jpg")
          rs[i].image.save(image_path, "JPEG")
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description="Vector Search")
      parser.add_argument("--database", type=str, help="Path to the database")
      parser.add_argument("--table_name", type=str, help="Name of the table")
      parser.add_argument(
          "--schema", type=str, help="Schema of the table", default="Myntra"
      )
      parser.add_argument("--search_query", type=str, help="Search query")
      parser.add_argument(
          "--limit", type=int, default=6, help="Limit the number of results (default: 6)"
      )
      parser.add_argument(
          "--output_folder", type=str, default="output", help="Output folder path"
      )
  
      args = parser.parse_args()
  
      schema = get_schema_by_name(args.schema)
      if schema is None:
          raise ValueError(f"Unknown schema: {args.schema}")
  
      run_vector_search(
          args.database,
          args.table_name,
          schema,
          args.search_query,
          args.limit,
          args.output_folder,
      )
  
apis: lancedb.connect
extract_api: [((1422, 1451), 'os.path.exists', 'os.path.exists', (['output_folder'], {}), '(output_folder)\n', (1436, 1451), False, 'import os\n'), ((1650, 1675), 'lancedb.connect', 'lancedb.connect', (['database'], {}), '(database)\n', (1665, 1675), False, 'import lancedb\n'), ((2586, 2638), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Vector Search"""'}), "(description='Vector Search')\n", (2609, 2638), False, 'import argparse\n'), ((3248, 3279), 'schema.get_schema_by_name', 'get_schema_by_name', (['args.schema'], {}), '(args.schema)\n', (3266, 3279), False, 'from schema import Myntra, get_schema_by_name\n'), ((1473, 1498), 'os.listdir', 'os.listdir', (['output_folder'], {}), '(output_folder)\n', (1483, 1498), False, 'import os\n'), ((1575, 1601), 'os.makedirs', 'os.makedirs', (['output_folder'], {}), '(output_folder)\n', (1586, 1601), False, 'import os\n'), ((2453, 2498), 'os.path.join', 'os.path.join', (['output_folder', 'f"""image_{i}.jpg"""'], {}), "(output_folder, f'image_{i}.jpg')\n", (2465, 2498), False, 'import os\n'), ((1900, 1924), 'PIL.Image.open', 'Image.open', (['search_query'], {}), '(search_query)\n', (1910, 1924), False, 'from PIL import Image\n'), ((1522, 1555), 'os.path.join', 'os.path.join', (['output_folder', 'file'], {}), '(output_folder, file)\n', (1534, 1555), False, 'import os\n')]