Struct DataPoint 

pub struct DataPoint {

    pub id: Uuid,
    pub created_at: i64,
    pub updated_at: i64,
    pub ontology_valid: bool,
    pub version: i32,
    pub topological_rank: Option<i32>,
    pub metadata: HashMap<String, Value>,
    pub data_type: String,
    pub belongs_to_set: Option<Vec<Value>>,
    pub source_pipeline: Option<String>,
    pub source_task: Option<String>,
    pub source_node_set: Option<String>,
    pub source_user: Option<String>,
    pub source_content_hash: Option<String>,
    pub feedback_weight: f64,
}

Base model for all storage-layer entities.

Provides:

• Unique identifier (UUID)
• Timestamps (created_at, updated_at) as milliseconds since epoch
• Ontology validation flag
• Version tracking (integer)
• Topological rank for graph traversal
• Flexible metadata storage
• Type discriminator
• Dataset membership
• Pipeline provenance fields
• Feedback weight

Fields

id: Uuid

Unique identifier

created_at: i64

Creation timestamp (milliseconds since epoch, matching Python)

updated_at: i64

Last update timestamp (milliseconds since epoch, matching Python)

ontology_valid: bool

Whether this entity has been validated against an ontology

version: i32

Version number (default 1, matching Python)

topological_rank: Option<i32>

Topological rank for graph traversal optimization

metadata: HashMap<String, Value>

Flexible metadata storage (e.g., index_fields, custom attributes)

data_type: String

Type discriminator (e.g., “Entity”, “EntityType”, “EdgeType”)

belongs_to_set: Option<Vec<Value>>

Dataset this data point belongs to (list of JSON values, matching Python)

source_pipeline: Option<String>

Pipeline that created this data point

source_task: Option<String>

Task that created this data point

source_node_set: Option<String>

Node set source

source_user: Option<String>

User that triggered creation

source_content_hash: Option<String>

Content hash of the raw Data artefact that produced this DataPoint. Propagates from upstream Data.content_hash through every task in the cognify pipeline, enabling content-addressed lineage queries.

feedback_weight: f64

Feedback weight (default 0.5, matching Python)

Implementations

impl DataPoint

pub fn new(data_type: impl Into<String>, dataset_id: Option<Uuid>) -> Self

Create a new DataPoint with default values.

Arguments

• data_type - Type discriminator (e.g., “Entity”, “EntityType”)
• dataset_id - Optional dataset UUID

pub fn with_metadata( data_type: impl Into<String>, dataset_id: Option<Uuid>, metadata: HashMap<String, Value>, ) -> Self

Create a DataPoint with specific metadata.

pub fn get_embeddable_data(&self) -> String

Get embeddable data as JSON string for vector indexing.

Returns a JSON representation of this DataPoint.

pub fn to_json(&self) -> Value

Convert to JSON value.

pub fn vector_metadata(&self) -> HashMap<String, Value>

Canonical vector-store payload keys for this DataPoint.

Mirrors Python’s DataPoint.model_dump() payload shape: every pydantic-equivalent field flows into the metadata map. Keys with None values are omitted (consistent with the skip_serializing_if = "Option::is_none" annotations on the struct).

Used by the cognify and memify pipelines when constructing VectorPoint payloads to keep the Rust shape byte-comparable to Python’s for the cross-SDK parity tests. Note: the data_type field carries #[serde(rename = "type")], so the resulting map uses the JSON key "type" (matching Python).

pub fn touch(&mut self)

Update the timestamp to current time.

pub fn set_ontology_valid(&mut self, valid: bool)

Set ontology validation status.

pub fn set_metadata(&mut self, key: impl Into<String>, value: Value)

Add or update metadata field.

pub fn get_metadata(&self, key: &str) -> Option<&Value>

Get metadata field.

Trait Implementations

impl Clone for DataPoint

fn clone(&self) -> DataPoint

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DataPoint

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for DataPoint

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for DataPoint

fn eq(&self, other: &DataPoint) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for DataPoint

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for DataPoint