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Simple Example

This example demonstrates the basic operations of Embedding Functions in embedded mode of seekdb to help you understand how to use Embedding Functions.

  1. Connect to seekdb.
  2. Create a collection with Embedding Functions.
  3. Add data using documents (vectors will be automatically generated).
  4. Query using texts (vectors will be automatically generated).
  5. Print the query results.

Prerequisites​

This example uses seekdb in embedded mode. Before using this example, make sure that you have deployed seekdb in server mode.

For information about how to deploy seekdb in embedded mode, see Embedded Mode.

Example​

import pyseekdb

# ==================== Step 1: Create Client Connection ====================
# You can use embedded mode, server mode, or OceanBase mode

# Embedded mode (local SeekDB)
client = pyseekdb.Client()
# Alternative: Server mode (connecting to remote SeekDB server)
# client = pyseekdb.Client(
#     host="127.0.0.1",
#     port=2881,
#     database="test",
#     user="root",
#     password=""
# )

# Alternative: Remote server mode (OceanBase Server)
# client = pyseekdb.Client(
#     host="127.0.0.1",
#     port=2881,
#     tenant="test",  # OceanBase default tenant
#     database="test",
#     user="root",
#     password=""
# )

# ==================== Step 2: Create a Collection with Embedding Function ====================
# A collection is like a table that stores documents with vector embeddings
collection_name = "my_simple_collection"

# Create collection with default embedding function
# The embedding function will automatically convert documents to embeddings
collection = client.create_collection(
    name=collection_name,
)

print(f"Created collection '{collection_name}' with dimension: {collection.dimension}")
print(f"Embedding function: {collection.embedding_function}")

# ==================== Step 3: Add Data to Collection ====================
# With embedding function, you can add documents directly without providing embeddings
# The embedding function will automatically generate embeddings from documents

documents = [
    "Machine learning is a subset of artificial intelligence",
    "Python is a popular programming language",
    "Vector databases enable semantic search",
    "Neural networks are inspired by the human brain",
    "Natural language processing helps computers understand text"
]

ids = ["id1", "id2", "id3", "id4", "id5"]

# Add data with documents only - embeddings will be auto-generated by embedding function
collection.add(
    ids=ids,
    documents=documents,  # embeddings will be automatically generated
    metadatas=[
        {"category": "AI", "index": 0},
        {"category": "Programming", "index": 1},
        {"category": "Database", "index": 2},
        {"category": "AI", "index": 3},
        {"category": "NLP", "index": 4}
    ]
)

print(f"\nAdded {len(documents)} documents to collection")
print("Note: Embeddings were automatically generated from documents using the embedding function")

# ==================== Step 4: Query the Collection ====================
# With embedding function, you can query using text directly
# The embedding function will automatically convert query text to query vector

# Query using text - query vector will be auto-generated by embedding function
query_text = "artificial intelligence and machine learning"

results = collection.query(
    query_texts=query_text,  # Query text - will be embedded automatically
    n_results=3  # Return top 3 most similar documents
)

print(f"\nQuery: '{query_text}'")
print(f"Query results: {len(results['ids'][0])} items found")

# ==================== Step 5: Print Query Results ====================
for i in range(len(results['ids'][0])):
    print(f"\nResult {i+1}:")
    print(f"  ID: {results['ids'][0][i]}")
    print(f"  Distance: {results['distances'][0][i]:.4f}")
    if results.get('documents'):
        print(f"  Document: {results['documents'][0][i]}")
    if results.get('metadatas'):
        print(f"  Metadata: {results['metadatas'][0][i]}")

# ==================== Step 6: Cleanup ====================
# Delete the collection
client.delete_collection(collection_name)
print(f"\nDeleted collection '{collection_name}'")

References​

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