trustgraph/docs/tech-specs/STRUCTURED_DATA_SCHEMAS.md

Structured Data Pulsar Schema Changes

Overview

Based on the STRUCTURED_DATA.md specification, this document proposes the necessary Pulsar schema additions and modifications to support structured data capabilities in TrustGraph.

Required Schema Changes

1. Core Schema Enhancements

Enhanced Field Definition

The existing Field class in core/primitives.py needs additional properties:

class Field(Record):
    name = String()
    type = String()  # int, string, long, bool, float, double, timestamp
    size = Integer()
    primary = Boolean()
    description = String()
    # NEW FIELDS:
    required = Boolean()  # Whether field is required
    enum_values = Array(String())  # For enum type fields
    indexed = Boolean()  # Whether field should be indexed

2. New Knowledge Schemas

2.1 Structured Data Submission

New file: knowledge/structured.py

from pulsar.schema import Record, String, Bytes, Map
from ..core.metadata import Metadata

class StructuredDataSubmission(Record):
    metadata = Metadata()
    format = String()  # "json", "csv", "xml"
    schema_name = String()  # Reference to schema in config
    data = Bytes()  # Raw data to ingest
    options = Map(String())  # Format-specific options

3. New Service Schemas

3.1 NLP to Structured Query Service

New file: services/nlp_query.py

from pulsar.schema import Record, String, Array, Map, Integer, Double
from ..core.primitives import Error

class NLPToStructuredQueryRequest(Record):
    natural_language_query = String()
    max_results = Integer()
    context_hints = Map(String())  # Optional context for query generation

class NLPToStructuredQueryResponse(Record):
    error = Error()
    graphql_query = String()  # Generated GraphQL query
    variables = Map(String())  # GraphQL variables if any
    detected_schemas = Array(String())  # Which schemas the query targets
    confidence = Double()

3.2 Structured Query Service

New file: services/structured_query.py

from pulsar.schema import Record, String, Map, Array
from ..core.primitives import Error

class StructuredQueryRequest(Record):
    query = String()  # GraphQL query
    variables = Map(String())  # GraphQL variables
    operation_name = String()  # Optional operation name for multi-operation documents

class StructuredQueryResponse(Record):
    error = Error()
    data = String()  # JSON-encoded GraphQL response data
    errors = Array(String())  # GraphQL errors if any

2.2 Object Extraction Output

New file: knowledge/object.py

from pulsar.schema import Record, String, Map, Double
from ..core.metadata import Metadata

class ExtractedObject(Record):
    metadata = Metadata()
    schema_name = String()  # Which schema this object belongs to
    values = Map(String())  # Field name -> value
    confidence = Double()
    source_span = String()  # Text span where object was found

4. Enhanced Knowledge Schemas

4.1 Object Embeddings Enhancement

Update knowledge/embeddings.py to support structured object embeddings better:

class StructuredObjectEmbedding(Record):
    metadata = Metadata()
    vectors = Array(Array(Double()))
    schema_name = String()
    object_id = String()  # Primary key value
    field_embeddings = Map(Array(Double()))  # Per-field embeddings

Integration Points

Flow Integration

The schemas will be used by new flow modules:

• trustgraph-flow/trustgraph/decoding/structured - Uses StructuredDataSubmission
• trustgraph-flow/trustgraph/query/nlp_query/cassandra - Uses NLP query schemas
• trustgraph-flow/trustgraph/query/objects/cassandra - Uses structured query schemas
• trustgraph-flow/trustgraph/extract/object/row/ - Consumes Chunk, produces ExtractedObject
• trustgraph-flow/trustgraph/storage/objects/cassandra - Uses Rows schema
• trustgraph-flow/trustgraph/embeddings/object_embeddings/qdrant - Uses object embedding schemas

Implementation Notes

1. Schema Versioning: Consider adding a version field to RowSchema for future migration support
2. Type System: The Field.type should support all Cassandra native types
3. Batch Operations: Most services should support both single and batch operations
4. Error Handling: Consistent error reporting across all new services
5. Backwards Compatibility: Existing schemas remain unchanged except for minor Field enhancements

Next Steps

1. Implement schema files in the new structure
2. Update existing services to recognize new schema types
3. Implement flow modules that use these schemas
4. Add gateway/rev-gateway endpoints for new services
5. Create unit tests for schema validation