Mengimport Objek Data

import weaviate

def main():
  client = weaviate.connect_to_local()
  if not client.is_connected():
    print("Connection error. Exiting.")
    return

  article_obj = {
    "title": "Introduction to Embeddings",
    "content": "Embeddings convert data into numerical vectors for AI tasks."
  }

  try:
    data_id = client.data_object.create(
      properties=article_obj,
      collection="Article" # Must match your schema
    )
    print(f"Article object created with ID: {data_id}")
  except Exception as e:
    print(f"Error creating object: {e}")
  finally:
    client.close()

if __name__ == "__main__":
  main()

import weaviate

def main():
  client = weaviate.connect_to_local()
  if not client.is_connected():
    print("Connection error. Exiting.")
    return

  article_obj = {
    "title": "Advanced RAG Techniques",
    "content": "RAG systems can be enhanced with query transformations."
  }

  # Example custom vector (dimensions must match your collection config)
  custom_vector = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]

  try:
    data_id = client.data_object.create(
      properties=article_obj,
      collection="Article",
      vector=custom_vector # Provide your pre-generated vector
    )
    print(f"Article object created with ID: {data_id}")
  except Exception as e:
    print(f"Error creating object: {e}")
  finally:
    client.close()

if __name__ == "__main__":
  main()

import weaviate

def main():
  client = weaviate.connect_to_local()
  if not client.is_connected():
    print("Connection error. Exiting.")
    return

  articles_to_import = [
    {"title": "Vector DBs Explained", "content": "Store and search vectors."},
    {"title": "Pinecone vs. Weaviate", "content": "Comparing vector databases."},
    {"title": "Introduction to pgvector", "content": "Vectors in PostgreSQL."}
  ]

  try:
    with client.batch as batch:
      for article in articles_to_import:
        batch.add_object(
          properties=article,
          collection="Article"
        )
    print(f"Successfully added {len(articles_to_import)} objects in batch.")
  except Exception as e:
    print(f"Error during batch import: {e}")
  finally:
    client.close()

if __name__ == "__main__":
  main()

import weaviate

def main():
  client = weaviate.connect_to_local()
  if not client.is_connected():
    print("Connection error. Exiting.")
    return

  data_with_vectors = [
    {
      "properties": {"title": "AI in Healthcare", "content": "LLMs for medical research."},
      "vector": [0.11, 0.22, 0.33, 0.44, 0.55, 0.66, 0.77, 0.88]
    },
    {
      "properties": {"title": "Future of Robotics", "content": "Automation and ML."},
      "vector": [0.88, 0.77, 0.66, 0.55, 0.44, 0.33, 0.22, 0.11]
    }
  ]

  try:
    with client.batch as batch:
      for item in data_with_vectors:
        batch.add_object(
          properties=item["properties"],
          collection="Article",
          vector=item["vector"]
        )
    print(f"Successfully added {len(data_with_vectors)} objects with vectors in batch.")
  except Exception as e:
    print(f"Error during batch import with vectors: {e}")
  finally:
    client.close()

if __name__ == "__main__":
  main()