seekdb documentation

The seekdb documentation provides a wide range of resources, including step-by-step getting started guides, examples of building AI applications with live demos, SDK and API code samples, comprehensive feature overviews, and detailed user manuals—all designed to help you quickly get up to speed and make the most of seekdb.

Get started

A minimalist API design that keeps you focused on building your AI

Use embedded seekdb

A lightweight and easy-to-use deployment mode recommended for both testing and production, delivering stable and efficient service.

Use server mode seekdb

Recommended deployment mode for testing and production environments. Lightweight and easy to use, ideal for stable and efficient service provision.

Embedded mode

1. Set up

import pyseekdb

client = pyseekdb.Client()

# create a knowledge base
collection = client.get_or_create_collection("product_database")

2. Insert

# Add product documents
collection.upsert(
    documents=[
        "Laptop Pro with 16GB RAM, 512GB SSD, and high-speed processor",
        "Gaming Laptop with 32GB RAM, 1TB SSD, and high-performance graphics",
        "Business Ultrabook with 8GB RAM, 256GB SSD, and long battery life",
        "Tablet with 6GB RAM, 128GB storage, and 10-inch display"
    ],
    metadatas=[
        {"category": "laptop", "ram": 16, "storage": 512, "price": 12000, "type": "professional"},
        {"category": "laptop", "ram": 32, "storage": 1000, "price": 25000, "type": "gaming"},
        {"category": "laptop", "ram": 8, "storage": 256, "price": 9000, "type": "business"},
        {"category": "tablet", "ram": 6, "storage": 128, "price": 6000, "type": "consumer"}
    ],
    ids=["1", "2", "3", "4"]
)

print("Product database built\n")

3. Query

# Hybrid search for high-performance laptops
print("Hybrid Search: High-performance laptops for professional work")
results = collection.query(
    query_texts=["powerful computer for professional work"],  # Vector search
    where={                                                                       # Relational filter
        "category": "laptop",
        "ram": {"$gte": 16}
    },
    where_document={"$contains": "RAM"},                            # Full-text search
    n_results=2
)

print("\nResults:")
for i, (doc, metadata) in enumerate(zip(results['documents'][0], results['metadatas'][0])):
    print(f"  {i+1}. {doc}")

Server mode

1. Set up

import pyseekdb

client = pyseekdb.Client(
        host = "127.0.0.1",          # server host
        port = 2881,                  # server port (default: 2881)
        )

# create a knowledge base
collection = client.get_or_create_collection("product_database")

2. Insert

# Add product documents
collection.upsert(
    documents=[
        "Laptop Pro with 16GB RAM, 512GB SSD, and high-speed processor",
        "Gaming Laptop with 32GB RAM, 1TB SSD, and high-performance graphics",
        "Business Ultrabook with 8GB RAM, 256GB SSD, and long battery life",
        "Tablet with 6GB RAM, 128GB storage, and 10-inch display"
    ],
    metadatas=[
        {"category": "laptop", "ram": 16, "storage": 512, "price": 12000, "type": "professional"},
        {"category": "laptop", "ram": 32, "storage": 1000, "price": 25000, "type": "gaming"},
        {"category": "laptop", "ram": 8, "storage": 256, "price": 9000, "type": "business"},
        {"category": "tablet", "ram": 6, "storage": 128, "price": 6000, "type": "consumer"}
    ],
    ids=["1", "2", "3", "4"]
)

print("Product database built\n")

3. Query

# Hybrid search for high-performance laptops
print("Hybrid Search: High-performance laptops for professional work")
results = collection.query(
    query_texts=["powerful computer for professional work"],  # Vector search
    where={                                                                       # Relational filter
        "category": "laptop",
        "ram": {"$gte": 16}
    },
    where_document={"$contains": "RAM"},                           # Full-text search
    n_results=2
)

print("\nResults:")
for i, (doc, metadata) in enumerate(zip(results['documents'][0], results['metadatas'][0])):
    print(f"  {i+1}. {doc}")

Start building

pyseekdb (Python SDK)

Overview and examples for using the seekdb Python SDK and API.

Integrations

See how seekdb connects with third-party platforms, with practical examples.

Tutorials

Step-by-step guides to using seekdb's AI features and building AI apps.