我做了client/server的这种架构，server上安装了seekdb没有问题。运行客户端（官网示例程序），有一个疑问，我看到客户端（windows）上下载了all-MiniLM-L6-v2这个模型，我能不能认为受到这个模型的能力影响，我只能chunk到256大小？
另外，既然我的seekdb是装在server（Linux）上，为什么embedding不能由server端来承担（能力强一些）
# Alternative: Server mode (connecting to remote SeekDB server)
client = pyseekdb.Client(
host="__my_server_ip__",
port=2881,
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}
]
)
运行程序能够明显看到客户端（windows运行）用了all-MiniLM-L6-v2，而且也有384维，感觉有点拉低了性能。
