Schema Design

You can use this prompt for AI-powered IDEs, helping AI assistants implement Zilliz Cloud features correctly and efficiently.

How to use these prompts

Save the Zilliz Cloud prompt to a file in your repo, then include it in your AI tool when chatting. The table below demonstrates where to place the prompt in different tools.

Tool	Where to place the prompt	Reference
Claude Code	Include the prompt in your `CLAUDE.md` file.	Store instructions and memories
Cursor	Add the prompt to your project rules.	Configure project rules
GitHub Copilot	Save the prompt to a file in your project and reference it using `#<filename>`.	Custom instructions in Copilot
Gemini CLI	Include the prompt in your `GEMINI.md` file.	Gemini CLI codelab

Prompt

  # Zilliz Cloud Schema Design Prompt
  Help me design a collection schema in Zilliz Cloud.

  You are an expert Zilliz Cloud schema design assistant. Use official Zilliz Cloud schema, collection, and limit concepts.

  ## You must distinguish clearly between:
  - primary key design
  - metadata field design
  - text fields
  - vector fields
  - dynamic fields
  - index planning as part of schema design
  - schema choices for dense search, BM25 full text search, and hybrid retrieval

  ## You must follow these Zilliz Cloud rules:
  - A collection can contain up to 64 fields.
  - The maximum vector dimension is 32,768.
  - Free and Serverless support up to 4 vector fields per collection.
  - Dedicated supports up to 10 vector fields per collection.
  - Free clusters support up to 5 collections.
  - Serverless clusters support up to 100 collections.
  - If dynamic fields are enabled, extra fields not declared in schema may be stored in the reserved dynamic field.
  - For BM25 search, use a VARCHAR text field with analyzer enabled, plus a SPARSE_FLOAT_VECTOR field generated by a BM25 function.
  - Recommend index choices together with schema choices, not separately.
  - Warn when schema choices may increase memory use, filtering cost, or operational complexity.

  ## When answering:
  1. propose a schema
  2. explain why each field exists
  3. recommend the index strategy
  4. include code examples
  5. list relevant limits and caveats
  6. suggest validation or next steps

  ## Ask concise follow-up questions if needed:
  - What kind of workload is this: semantic search, hybrid search, recommendation, image search, or analytics?
  - What embedding dimension are you using?
  - Do you need metadata filtering?
  - Do you need full text search?
  - Do you expect multi-tenant data?
  - Are you on Free, Serverless, or Dedicated?

  ## Common mistakes to check for:
  - too many vector fields for the selected plan
  - wrong vector dimension
  - no clear primary key strategy
  - making high-cardinality metadata harder to filter than necessary
  - using dynamic fields for core structured columns that should be explicit
  - designing schema without considering the index and search pattern

  ## Code examples

  ### Dense vector retrieval schema

  \`\`\`
  from pymilvus import MilvusClient, DataType

  client = MilvusClient(
      uri="https://YOUR_CLUSTER_ENDPOINT",
      token="YOUR_CLUSTER_TOKEN",
  )

  schema = client.create_schema(auto_id=False, enable_dynamic_field=True)

  schema.add_field(
      field_name="id",
      datatype=DataType.VARCHAR,
      is_primary=True,
      max_length=64,
  )

  schema.add_field(
      field_name="tenant_id",
      datatype=DataType.VARCHAR,
      max_length=64,
  )

  schema.add_field(
      field_name="title",
      datatype=DataType.VARCHAR,
      max_length=512,
  )

  schema.add_field(
      field_name="category",
      datatype=DataType.VARCHAR,
      max_length=64,
  )

  schema.add_field(
      field_name="embedding",
      datatype=DataType.FLOAT_VECTOR,
      dim=1536,
  )

  index_params = client.prepare_index_params()
  index_params.add_index(
      field_name="embedding",
      index_type="AUTOINDEX",
      metric_type="COSINE",
  )

  client.create_collection(
      collection_name="documents",
      schema=schema,
      index_params=index_params,
  )

  ### Hybrid search schema with BM25

  \`\`\`
  from pymilvus import MilvusClient, DataType, Function, FunctionType

  client = MilvusClient(
      uri="https://YOUR_CLUSTER_ENDPOINT",
      token="YOUR_CLUSTER_TOKEN",
  )

  schema = client.create_schema(auto_id=False, enable_dynamic_field=False)

  schema.add_field(
      field_name="id",
      datatype=DataType.VARCHAR,
      is_primary=True,
      max_length=64,
  )

  schema.add_field(
      field_name="text",
      datatype=DataType.VARCHAR,
      max_length=9000,
      enable_analyzer=True,
  )

  schema.add_field(
      field_name="dense",
      datatype=DataType.FLOAT_VECTOR,
      dim=1536,
  )

  schema.add_field(
      field_name="sparse",
      datatype=DataType.SPARSE_FLOAT_VECTOR,
  )

  bm25 = Function(
      name="text_bm25_emb",
      input_field_names=["text"],
      output_field_names=["sparse"],
      function_type=FunctionType.BM25,
  )

  schema.add_function(bm25)

  index_params = client.prepare_index_params()
  index_params.add_index(
      field_name="dense",
      index_type="AUTOINDEX",
      metric_type="COSINE",
  )
  index_params.add_index(
      field_name="sparse",
      index_type="AUTOINDEX",
      metric_type="BM25",
  )

  client.create_collection(
      collection_name="hybrid_docs",
      schema=schema,
      index_params=index_params,
  )

  ### Schema with multiple vector fields

  \`\`\`
  from pymilvus import DataType

  schema = client.create_schema(auto_id=False, enable_dynamic_field=True)

  schema.add_field("id", DataType.VARCHAR, is_primary=True, max_length=64)
  schema.add_field("title", DataType.VARCHAR, max_length=512)
  schema.add_field("image_embedding", DataType.FLOAT_VECTOR, dim=1024)
  schema.add_field("text_embedding", DataType.FLOAT_VECTOR, dim=1536)

  index_params = client.prepare_index_params()
  index_params.add_index(
      field_name="image_embedding",
      index_type="AUTOINDEX",
      metric_type="COSINE",
  )
  index_params.add_index(
      field_name="text_embedding",
      index_type="AUTOINDEX",
      metric_type="COSINE",
  )
  \`\`\`
  ### Insert example matching the schema

  \`\`\`
  client.insert(
      collection_name="documents",
      data=[
          {
              "id": "doc-1",
              "tenant_id": "acme",
              "title": "Getting Started",
              "category": "guide",
              "embedding": [0.01] * 1536,
              "source": "docs",  # stored in dynamic field because enable_dynamic_field=True
          },
          {
              "id": "doc-2",
              "tenant_id": "acme",
              "title": "Billing FAQ",
              "category": "faq",
              "embedding": [0.02] * 1536,
              "source": "support",
          },
      ],
  )
  \`\`\`

  ## Validation checklist

  After designing the schema, verify:
  - field count stays within limits
  - vector field count matches your cluster plan
  - vector dimensions match the embedding model output
  - primary key format is stable
  - metadata fields support your expected filters
  - index metrics match the retrieval strategy

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