seekdb 文档

seekdb 文档提供了丰富的各类内容，包括 step-by-step 的上手实践、AI 应用搭建示例和 Live Demos、SDK 和 API 代码示例、详细的功能介绍和使用文档等，来帮助您快速了解和使用 seekdb。

快速开始

极简的 API 设计，让您专注于构建 AI 应用。

使用嵌入式 seekdb

以轻量级库的形式嵌入您的应用，支持 pip 一键安装。适用于个人学习、快速原型开发，并可高效运行于多种端侧设备。

使用服务器模式 seekdb

推荐用于测试及生产环境的部署模式，轻量易用，适合稳定高效地提供服务。

嵌入式模式

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}")

服务器模式

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}")

开始构建

pyseekdb (Python SDK)

seekdb Python SDK 和 API 的详细介绍和使用示例

Integrations

seekdb 集成的三方生态和使用示例。

Tutorials

seekdb AI 能力使用实践和 AI 应用搭建实践。