Create Collection

Create a new Weaviate collection with a custom schema, optional vectorizer, and multi-tenancy support.

Usage

uv run scripts/create_collection.py CollectionName --properties '[...]' [--description "..."] [--vectorizer "..."] [--replication-factor N] [--multi-tenancy] [--auto-tenant-creation] [--json]

Parameters

Parameter	Flag	Required	Default	Description
`name`	—	Yes (positional)	—	Collection name (auto-capitalized per GraphQL convention)
`--properties`	`-p`	Yes	—	JSON array of property definitions
`--description`	`-d`	No	—	Collection description — strongly recommended. Weaviate agents (Query Agent, Personalization Agent) read this to understand what the collection contains and decide which collection to query
`--vectorizer`	`-v`	No	`text2vec_weaviate`	Vectorizer module to use
`--replication-factor`	`-r`	No	—	Replication factor (defers to server default when not set)
`--multi-tenancy`	`-m`	No	`false`	Enable multi-tenancy for data isolation
`--auto-tenant-creation`	`-a`	No	`false`	Auto-create tenants on insert (requires `--multi-tenancy`)
`--json`	—	No	`false`	Output in JSON format

Property Definition Format

{
  "name": "property_name",
  "data_type": "text",
  "description": "Optional description",
  "tokenization": "word",
  "index_filterable": true,
  "index_searchable": true,
  "index_range_filters": false,
  "nested_properties": []
}

name (required): Property name
data_type (required): One of the supported data types below
description (optional): Human-readable description — strongly recommended. The Query Agent reads property descriptions to understand your schema, choose the right collection, and construct accurate queries. Good descriptions include units, formats, and valid values (e.g., "Price in US dollars (USD)", "ISO two-character country code", "Date the paper was published on arXiv")
tokenization (optional): For text types — word, lowercase, whitespace, or field
index_filterable (optional): Enable roaring-bitmap filter index for where clauses. Default true for all types except blob, geoCoordinates, object, object[], phoneNumber
index_searchable (optional): Enable BM25/inverted index for keyword and hybrid search. Only applies to text and text[]. Default true
index_range_filters (optional): Enable range-comparison index (>, <, >=, <=, between) for int, int[], number, number[], date, date[]. Default false — set to true for any numeric or date field you plan to range-filter
nested_properties (optional): For object / object[] types — array of nested property definitions

Supported Data Types

text, text[], boolean, boolean[], int, int[], number, number[], date, date[], uuid, uuid[], geoCoordinates, phoneNumber, blob, object, object[]

Aliases: bool → boolean, bool[] → boolean[]

Supported Vectorizers

text2vec_weaviate, text2vec_openai, text2vec_cohere, text2vec_huggingface, text2vec_palm, text2vec_jinaai, text2vec_voyageai, text2vec_contextionary, text2vec_transformers, text2vec_gpt4all, text2vec_ollama, multi2vec_clip, multi2vec_bind, multi2vec_palm, img2vec_neural, ref2vec_centroid, none

Inferring Schema from Data Files

Before creating a collection, inspect a few rows from the source file to understand field names and value types. Use the commands below — they read only the first 3 objects and are safe on large files.

CSV:

python3 -c "
import csv, json
with open('data.csv') as f:
    rows = list(csv.DictReader(f))[:3]
print(json.dumps(rows, indent=2))
"

JSON:

python3 -c "
import json
print(json.dumps(json.load(open('data.json'))[:3], indent=2))
"

JSONL:

python3 -c "
import json
lines = []
with open('data.jsonl') as f:
    for line in f:
        if len(lines) >= 3: break
        if line.strip(): lines.append(json.loads(line))
print(json.dumps(lines, indent=2))
"

From the sample, map each field to a Weaviate data type:

Value looks like	data_type
`"hello"`, any text	`text`
`123`, `"123"`	`int`
`1.5`, `"1.5"`	`number`
`true`/`false`	`boolean`
`"2024-01-15"`, `"2024-01-15T10:30:00Z"`	`date`
UUID-shaped string	`uuid`
List of strings	`text[]`
List of numbers	`int[]` or `number[]`
Nested object	`object`

Important: id, _id, and _additional are reserved by Weaviate — never use them as property names. If they appear in your data, use --skip-fields or --mapping in import.py to handle them.

Examples

Basic collection:

uv run scripts/create_collection.py Article \
  --description "News articles with title and full body text." \
  --properties '[
    {"name": "title", "data_type": "text", "description": "Title of the article"},
    {"name": "body", "data_type": "text", "description": "Full text body of the article"}
  ]'

Collection with various data types, descriptions, and recommended index flags:

uv run scripts/create_collection.py Product \
  --description "E-commerce product catalog with pricing, brand, stock status, and tags." \
  --properties '[
    {"name": "name", "data_type": "text", "description": "Name or title of the product"},
    {"name": "sku", "data_type": "text", "index_searchable": false, "description": "Stock-keeping unit identifier"},
    {"name": "price", "data_type": "number", "index_range_filters": true, "description": "Product price in US dollars (USD)"},
    {"name": "created_at", "data_type": "date", "index_range_filters": true, "description": "Date the product was added to the catalog"},
    {"name": "in_stock", "data_type": "boolean", "description": "Whether the product is currently in stock"},
    {"name": "tags", "data_type": "text[]", "description": "List of descriptive tags for the product"}
  ]'

With explicit vectorizer:

uv run scripts/create_collection.py Article \
  --description "News articles with title and full body text." \
  --properties '[{"name": "title", "data_type": "text", "description": "Title of the article"}]' \
  --vectorizer "text2vec_openai"

With multi-tenancy:

uv run scripts/create_collection.py Workspace \
  --properties '[{"name": "content", "data_type": "text"}]' \
  --multi-tenancy --auto-tenant-creation

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