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Data Types in CocoIndex

In CocoIndex, all data processed by the flow have a type determined when the flow is defined, before any actual data is processed at runtime.

This makes schema of data processed by CocoIndex clear, and easily determine the schema of your index.

Data Types​

As an engine written in Rust, designed to be used in different languages and data are always serializable, CocoIndex defines a type system independent of any specific programming language.

CocoIndex automatically infers data types of the output created by CocoIndex sources and functions. You don't need to spell out any data type explicitly when you define the flow. All you need to do is to make sure the data passed to functions and targets are compatible with them.

Each type in CocoIndex type system is mapped to one or multiple types in Python. When you define a custom function, you need to annotate the data types of arguments and return values.

  • When you pass a Python value to the engine (e.g. return values of a custom function), a specific type annotation is required. The type annotation needs to be specific in describing the target data type, as it provides the ground truth of the data type in the flow.

  • When you use a Python variable to bind to an engine value (e.g. arguments of a custom function), the engine already knows the specific data type, so we don't require a specific type annotation, e.g. type annotations can be omitted, or you can use Any at any level. When a specific type annotation is provided, it's still used as a guidance to construct the Python value with compatible type. Otherwise, we will bind to a default Python type.

Basic Types​

Primitive Types​

Primitive types are basic types that are not composed of other types. This is the list of all primitive types supported by CocoIndex:

CocoIndex TypePython TypesConvertible toExplanation
Bytesbytes
Strstr
Boolbool
Int64cocoindex.Int64, int, numpy.int64
Float32cocoindex.Float32, numpy.float32Float64
Float64cocoindex.Float64, float, numpy.float64
Rangecocoindex.Range
Uuiduuid.UUId
Datedatetime.date
Timedatetime.time
LocalDatetimecocoindex.LocalDateTimeOffsetDatetimewithout timezone
OffsetDatetimecocoindex.OffsetDateTime, datetime.datetimewith timezone
TimeDeltadatetime.timedelta

Notes:

  • For some CocoIndex types, we support multiple Python types. You can annotate with any of these Python types. The first one is the default type, i.e. CocoIndex will create a value with this type when a specific type annotation is not provided (e.g. for arguments of a custom function).

  • All Python types starting with cocoindex. are type aliases exported by CocoIndex. They're annotated types based on certain Python types:

    • cocoindex.Int64: int
    • cocoindex.Float64: float
    • cocoindex.Float32: float
    • cocoindex.Range: tuple[int, int], i.e. a start offset (inclusive) and an end offset (exclusive)
    • cocoindex.OffsetDateTime: datetime.datetime
    • cocoindex.LocalDateTime: datetime.datetime

    These aliases provide a non-ambiguous way to represent a specific type in CocoIndex, given their base Python types can represent a superset of possible values.

  • When we say a CocoIndex type is convertible to another type, it means Python types for the second type can be also used to bind to a value of the first type.

    • For example, Float32 is convertible to Float64, so you can bind a value of Float32 to a Python value of float or np.float64 types.
    • For LocalDatetime, when you use cocoindex.OffsetDateTime or datetime.datetime as the annotation to bind its value, the timezone will be set to UTC.

Json Type​

Json type can hold any data convertible to JSON by json package. In Python, it's represented by cocoindex.Json. It's useful to hold data without fixed schema known at flow definition time.

Vector Types​

A vector type is a collection of elements of the same basic type. Optionally, it can have a fixed dimension. Noted as Vector[Type] or Vector[Type, Dim], e.g. Vector[Float32] or Vector[Float32, 384].

It supports the following Python types:

  • cocoindex.Vector[T] or cocoindex.Vector[T, typing.Literal[Dim]], e.g. cocoindex.Vector[cocoindex.Float32] or cocoindex.Vector[cocoindex.Float32, typing.Literal[384]]
    • The underlying Python type is numpy.typing.NDArray[T] where T is a numpy numeric type (numpy.int64, numpy.float32 or numpy.float64) or array type (numpy.typing.NDArray[T]), or list[T] otherwise
  • numpy.typing.NDArray[T] where T is a numpy numeric type or array type
  • list[T]

Union Types​

A union type is a type that can represent values in one of multiple basic types. Noted as Type1 | Type2 | ..., e.g. Int64 | Float32 | Float64.

The Python type is T1 | T2 | ..., e.g. cocoindex.Int64 | cocoindex.Float32 | cocoindex.Float64, int | float (equivalent to cocoindex.Int64 | cocoindex.Float64)

Struct Types​

A Struct has a bunch of fields, each with a name and a type.

In Python, a Struct type is represented by either a dataclass or a NamedTuple, with all fields annotated with a specific type. Both options define a structured type with named fields, but they differ slightly:

  • Dataclass: A flexible class-based structure, mutable by default, defined using the @dataclass decorator.
  • NamedTuple: An immutable tuple-based structure, defined using typing.NamedTuple.

For example:

from dataclasses import dataclass
from typing import NamedTuple
import datetime

# Using dataclass
@dataclass
class Person:
    first_name: str
    last_name: str
    dob: datetime.date

# Using NamedTuple
class PersonTuple(NamedTuple):
    first_name: str
    last_name: str
    dob: datetime.date

Both Person and PersonTuple are valid Struct types in CocoIndex, with identical schemas (three fields: first_name (Str), last_name (Str), dob (Date)). Choose dataclass for mutable objects or when you need additional methods, and NamedTuple for immutable, lightweight structures.

Besides, for arguments of custom functions, CocoIndex also supports using dictionaries (dict[str, Any]) to represent a Struct type. It's the default Python type if you don't annotate the function argument with a specific type.

Table Types​

A Table type models a collection of rows, each with multiple columns. Each column of a table has a specific type.

We have two specific types of Table types: KTable and LTable.

KTable​

KTable is a Table type whose first column serves as the key. The row order of a KTable is not preserved. Type of the first column (key column) must be a key type.

In Python, a KTable type is represented by dict[K, V]. The K should be the type binding to a key type, and the V should be the type binding to a Struct type representing the value fields of each row. When the specific type annotation is not provided, the key type is bound to a tuple with its key parts when it's a Struct type, the value type is bound to dict[str, Any].

For example, you can use dict[str, Person] or dict[str, PersonTuple] to represent a KTable, with 4 columns: key (Str), first_name (Str), last_name (Str), dob (Date). It's bound to dict[str, dict[str, Any]] if you don't annotate the function argument with a specific type.

Note that if you want to use a Struct as the key, you need to ensure its value in Python is immutable. For dataclass, annotate it with @dataclass(frozen=True). For NamedTuple, immutability is built-in. For example:

@dataclass(frozen=True)
class PersonKey:
    id_kind: str
    id: str

class PersonKeyTuple(NamedTuple):
    id_kind: str
    id: str

Then you can use dict[PersonKey, Person] or dict[PersonKeyTuple, PersonTuple] to represent a KTable keyed by PersonKey or PersonKeyTuple. It's bound to dict[(str, str), dict[str, Any]] if you don't annotate the function argument with a specific type.

LTable​

LTable is a Table type whose row order is preserved. LTable has no key column.

In Python, a LTable type is represented by list[R], where R is the type binding to the Struct type representing the value fields of each row. For example, you can use list[Person] to represent a LTable with 3 columns: first_name (Str), last_name (Str), dob (Date). It's bound to list[dict[str, Any]] if you don't annotate the function argument with a specific type.

Key Types​

Currently, the following types are key types

  • Bytes
  • Str
  • Bool
  • Int64
  • Range
  • Uuid
  • Date
  • Struct with all fields being key types (using @dataclass(frozen=True) or NamedTuple)

None Values​

CocoIndex supports None values. A None value represents the absence of data or an unknown value, distinct from empty strings, zero numbers, or false boolean values.

Optional Type​

For any data (e.g. a field of a Struct, an argument or return value of a CocoIndex function), if it is optional, it means its value can be None. We use Optional[T] to indicate an optional type, e.g. Optional[Str], Optional[Person].

In Python, None is represented as None, so an optional type can be represented by T | None or typing.Optional[T].

None propagating on CocoIndex functions​

A function may specify whether each input argument is optional or not. Non-optional argument means the function needs a known value for the argument to work. However, it doesn't forbid the argument to be None at runtime. When a non-optional argument receives a None value, the function execution is skipped and the result is None.

For example, for SplitRecursively function, the text and chunk_size arguments are not optional. If the input value of either of them is None, the function will return None.