fixes and modelgen insert

tools/modelgen/generator/__init__.py

@@ -0,0 +1,40 @@
+"""
+Generator - Stack-specific code generators for modelgen.
+
+Supported generators:
+- PydanticGenerator: Pydantic BaseModel classes
+- DjangoGenerator: Django ORM models
+- TypeScriptGenerator: TypeScript interfaces
+- ProtobufGenerator: Protocol Buffer definitions
+- PrismaGenerator: Prisma schema
+"""
+
+from typing import Dict, Type
+
+from .base import BaseGenerator
+from .django import DjangoGenerator
+from .prisma import PrismaGenerator
+from .protobuf import ProtobufGenerator
+from .pydantic import PydanticGenerator
+from .typescript import TypeScriptGenerator
+
+# Registry of available generators
+GENERATORS: Dict[str, Type[BaseGenerator]] = {
+    "pydantic": PydanticGenerator,
+    "django": DjangoGenerator,
+    "typescript": TypeScriptGenerator,
+    "ts": TypeScriptGenerator,  # Alias
+    "protobuf": ProtobufGenerator,
+    "proto": ProtobufGenerator,  # Alias
+    "prisma": PrismaGenerator,
+}
+
+__all__ = [
+    "BaseGenerator",
+    "PydanticGenerator",
+    "DjangoGenerator",
+    "TypeScriptGenerator",
+    "ProtobufGenerator",
+    "PrismaGenerator",
+    "GENERATORS",
+]

tools/modelgen/generator/base.py

@@ -0,0 +1,23 @@
+"""
+Base Generator
+
+Abstract base class for all code generators.
+"""
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import Any
+
+
+class BaseGenerator(ABC):
+    """Abstract base for code generators."""
+
+    @abstractmethod
+    def generate(self, models: Any, output_path: Path) -> None:
+        """Generate code for the given models to the specified path."""
+        pass
+
+    @abstractmethod
+    def file_extension(self) -> str:
+        """Return the file extension for this format."""
+        pass

tools/modelgen/generator/django.py

@@ -0,0 +1,268 @@
+"""
+Django Generator
+
+Generates Django ORM models from model definitions.
+"""
+
+import dataclasses as dc
+from enum import Enum
+from pathlib import Path
+from typing import Any, List, get_type_hints
+
+from ..helpers import format_opts, get_origin_name, get_type_name, unwrap_optional
+from ..loader.schema import EnumDefinition, ModelDefinition
+from ..types import DJANGO_SPECIAL, DJANGO_TYPES
+from .base import BaseGenerator
+
+
+class DjangoGenerator(BaseGenerator):
+    """Generates Django ORM model files."""
+
+    def file_extension(self) -> str:
+        return ".py"
+
+    def generate(self, models, output_path: Path) -> None:
+        """Generate Django models to output_path."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Handle different input types
+        if hasattr(models, "models"):
+            # SchemaLoader or similar
+            content = self._generate_from_definitions(
+                models.models, getattr(models, "enums", [])
+            )
+        elif isinstance(models, tuple):
+            # (models, enums) tuple
+            content = self._generate_from_definitions(models[0], models[1])
+        elif isinstance(models, list):
+            # List of dataclasses (MPR style)
+            content = self._generate_from_dataclasses(models)
+        else:
+            raise ValueError(f"Unsupported input type: {type(models)}")
+
+        output_path.write_text(content)
+
+    def _generate_from_definitions(
+        self, models: List[ModelDefinition], enums: List[EnumDefinition]
+    ) -> str:
+        """Generate from ModelDefinition objects."""
+        lines = self._generate_header()
+
+        # Generate enums as TextChoices
+        for enum_def in enums:
+            lines.extend(self._generate_text_choices(enum_def))
+            lines.append("")
+
+        # Generate models
+        for model_def in models:
+            lines.extend(self._generate_model_from_definition(model_def))
+            lines.extend(["", ""])
+
+        return "\n".join(lines)
+
+    def _generate_from_dataclasses(self, dataclasses: List[type]) -> str:
+        """Generate from Python dataclasses (MPR style)."""
+        lines = self._generate_header()
+
+        for cls in dataclasses:
+            lines.extend(self._generate_model_from_dataclass(cls))
+            lines.extend(["", ""])
+
+        return "\n".join(lines)
+
+    def _generate_header(self) -> List[str]:
+        """Generate file header."""
+        return [
+            '"""',
+            "Django ORM Models - GENERATED FILE",
+            "",
+            "Do not edit directly. Regenerate using modelgen.",
+            '"""',
+            "",
+            "import uuid",
+            "from django.db import models",
+            "",
+        ]
+
+    def _generate_text_choices(self, enum_def: EnumDefinition) -> List[str]:
+        """Generate Django TextChoices from EnumDefinition."""
+        lines = [
+            f"class {enum_def.name}(models.TextChoices):",
+        ]
+        for name, value in enum_def.values:
+            label = name.replace("_", " ").title()
+            lines.append(f'    {name} = "{value}", "{label}"')
+        return lines
+
+    def _generate_model_from_definition(self, model_def: ModelDefinition) -> List[str]:
+        """Generate Django model from ModelDefinition."""
+        docstring = model_def.docstring or model_def.name
+        lines = [
+            f"class {model_def.name}(models.Model):",
+            f'    """{docstring.strip().split(chr(10))[0]}"""',
+            "",
+        ]
+
+        for field in model_def.fields:
+            django_field = self._resolve_field_type(
+                field.name, field.type_hint, field.default, field.optional
+            )
+            lines.append(f"    {field.name} = {django_field}")
+
+        # Add Meta and __str__
+        lines.extend(
+            [
+                "",
+                "    class Meta:",
+                '        ordering = ["-created_at"]'
+                if any(f.name == "created_at" for f in model_def.fields)
+                else "        pass",
+                "",
+                "    def __str__(self):",
+            ]
+        )
+
+        # Determine __str__ return
+        field_names = [f.name for f in model_def.fields]
+        if "filename" in field_names:
+            lines.append("        return self.filename")
+        elif "name" in field_names:
+            lines.append("        return self.name")
+        else:
+            lines.append("        return str(self.id)")
+
+        return lines
+
+    def _generate_model_from_dataclass(self, cls: type) -> List[str]:
+        """Generate Django model from a dataclass (MPR style)."""
+        docstring = cls.__doc__ or cls.__name__
+        lines = [
+            f"class {cls.__name__}(models.Model):",
+            f'    """{docstring.strip().split(chr(10))[0]}"""',
+            "",
+        ]
+
+        hints = get_type_hints(cls)
+        fields = {f.name: f for f in dc.fields(cls)}
+
+        # Check for enums and add Status inner class if needed
+        for type_hint in hints.values():
+            base, _ = unwrap_optional(type_hint)
+            if isinstance(base, type) and issubclass(base, Enum):
+                lines.append("    class Status(models.TextChoices):")
+                for member in base:
+                    label = member.name.replace("_", " ").title()
+                    lines.append(f'        {member.name} = "{member.value}", "{label}"')
+                lines.append("")
+                break
+
+        # Generate fields
+        for name, type_hint in hints.items():
+            if name.startswith("_"):
+                continue
+            field = fields.get(name)
+            default = dc.MISSING
+            if field and field.default is not dc.MISSING:
+                default = field.default
+            django_field = self._resolve_field_type(name, type_hint, default, False)
+            lines.append(f"    {name} = {django_field}")
+
+        # Add Meta and __str__
+        lines.extend(
+            [
+                "",
+                "    class Meta:",
+                '        ordering = ["-created_at"]'
+                if "created_at" in hints
+                else "        pass",
+                "",
+                "    def __str__(self):",
+            ]
+        )
+
+        if "filename" in hints:
+            lines.append("        return self.filename")
+        elif "name" in hints:
+            lines.append("        return self.name")
+        else:
+            lines.append("        return str(self.id)")
+
+        return lines
+
+    def _resolve_field_type(
+        self, name: str, type_hint: Any, default: Any, optional: bool
+    ) -> str:
+        """Resolve Python type to Django field."""
+        # Special fields
+        if name in DJANGO_SPECIAL:
+            return DJANGO_SPECIAL[name]
+
+        base, is_optional = unwrap_optional(type_hint)
+        optional = optional or is_optional
+        origin = get_origin_name(base)
+        type_name = get_type_name(base)
+        opts = format_opts(optional)
+
+        # Container types
+        if origin == "dict":
+            return DJANGO_TYPES["dict"]
+        if origin == "list":
+            return DJANGO_TYPES["list"]
+
+        # UUID / datetime
+        if type_name == "UUID":
+            return DJANGO_TYPES["UUID"].format(opts=opts)
+        if type_name == "datetime":
+            return DJANGO_TYPES["datetime"].format(opts=opts)
+
+        # Enum
+        if isinstance(base, type) and issubclass(base, Enum):
+            extra = []
+            if optional:
+                extra.append("null=True, blank=True")
+            if default is not dc.MISSING and isinstance(default, Enum):
+                extra.append(f"default=Status.{default.name}")
+            return DJANGO_TYPES["enum"].format(
+                opts=", " + ", ".join(extra) if extra else ""
+            )
+
+        # Text fields (based on name heuristics)
+        if base is str and any(
+            x in name for x in ("message", "comments", "description")
+        ):
+            return DJANGO_TYPES["text"]
+
+        # BigInt fields
+        if base is int and name in ("file_size", "bitrate"):
+            return DJANGO_TYPES["bigint"].format(opts=opts)
+
+        # String with max_length
+        if base is str:
+            max_length = 1000 if "path" in name else 500 if "filename" in name else 255
+            return DJANGO_TYPES[str].format(
+                max_length=max_length, opts=", " + opts if opts else ""
+            )
+
+        # Integer
+        if base is int:
+            extra = [opts] if opts else []
+            if default is not dc.MISSING and not callable(default):
+                extra.append(f"default={default}")
+            return DJANGO_TYPES[int].format(opts=", ".join(extra))
+
+        # Float
+        if base is float:
+            extra = [opts] if opts else []
+            if default is not dc.MISSING and not callable(default):
+                extra.append(f"default={default}")
+            return DJANGO_TYPES[float].format(opts=", ".join(extra))
+
+        # Boolean
+        if base is bool:
+            default_val = default if default is not dc.MISSING else False
+            return DJANGO_TYPES[bool].format(default=default_val)
+
+        # Fallback to CharField
+        return DJANGO_TYPES[str].format(
+            max_length=255, opts=", " + opts if opts else ""
+        )

tools/modelgen/generator/prisma.py

@@ -0,0 +1,173 @@
+"""
+Prisma Generator
+
+Generates Prisma schema from model definitions.
+"""
+
+from enum import Enum
+from pathlib import Path
+from typing import Any, List, get_type_hints
+
+from ..helpers import get_origin_name, get_type_name, unwrap_optional
+from ..loader.schema import EnumDefinition, ModelDefinition
+from ..types import PRISMA_SPECIAL, PRISMA_TYPES
+from .base import BaseGenerator
+
+
+class PrismaGenerator(BaseGenerator):
+    """Generates Prisma schema files."""
+
+    def file_extension(self) -> str:
+        return ".prisma"
+
+    def generate(self, models, output_path: Path) -> None:
+        """Generate Prisma schema to output_path."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Handle different input types
+        if hasattr(models, "models"):
+            # SchemaLoader
+            content = self._generate_from_definitions(
+                models.models, getattr(models, "enums", [])
+            )
+        elif isinstance(models, tuple):
+            # (models, enums) tuple
+            content = self._generate_from_definitions(models[0], models[1])
+        elif isinstance(models, list):
+            # List of dataclasses (MPR style)
+            content = self._generate_from_dataclasses(models)
+        else:
+            raise ValueError(f"Unsupported input type: {type(models)}")
+
+        output_path.write_text(content)
+
+    def _generate_from_definitions(
+        self, models: List[ModelDefinition], enums: List[EnumDefinition]
+    ) -> str:
+        """Generate from ModelDefinition objects."""
+        lines = self._generate_header()
+
+        # Generate enums
+        for enum_def in enums:
+            lines.extend(self._generate_enum(enum_def))
+            lines.append("")
+
+        # Generate models
+        for model_def in models:
+            lines.extend(self._generate_model_from_definition(model_def))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_from_dataclasses(self, dataclasses: List[type]) -> str:
+        """Generate from Python dataclasses (MPR style)."""
+        lines = self._generate_header()
+
+        # Collect and generate enums first
+        enums_generated = set()
+        for cls in dataclasses:
+            hints = get_type_hints(cls)
+            for type_hint in hints.values():
+                base, _ = unwrap_optional(type_hint)
+                if isinstance(base, type) and issubclass(base, Enum):
+                    if base.__name__ not in enums_generated:
+                        lines.extend(self._generate_enum_from_python(base))
+                        lines.append("")
+                        enums_generated.add(base.__name__)
+
+        # Generate models
+        for cls in dataclasses:
+            lines.extend(self._generate_model_from_dataclass(cls))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_header(self) -> List[str]:
+        """Generate file header with datasource and generator."""
+        return [
+            "// Prisma Schema - GENERATED FILE",
+            "//",
+            "// Do not edit directly. Regenerate using modelgen.",
+            "",
+            "generator client {",
+            '  provider = "prisma-client-py"',
+            "}",
+            "",
+            "datasource db {",
+            '  provider = "postgresql"',
+            '  url      = env("DATABASE_URL")',
+            "}",
+            "",
+        ]
+
+    def _generate_enum(self, enum_def: EnumDefinition) -> List[str]:
+        """Generate Prisma enum from EnumDefinition."""
+        lines = [f"enum {enum_def.name} {{"]
+        for name, _ in enum_def.values:
+            lines.append(f"  {name}")
+        lines.append("}")
+        return lines
+
+    def _generate_enum_from_python(self, enum_cls: type) -> List[str]:
+        """Generate Prisma enum from Python Enum."""
+        lines = [f"enum {enum_cls.__name__} {{"]
+        for member in enum_cls:
+            lines.append(f"  {member.name}")
+        lines.append("}")
+        return lines
+
+    def _generate_model_from_definition(self, model_def: ModelDefinition) -> List[str]:
+        """Generate Prisma model from ModelDefinition."""
+        lines = [f"model {model_def.name} {{"]
+
+        for field in model_def.fields:
+            prisma_type = self._resolve_type(
+                field.name, field.type_hint, field.optional
+            )
+            lines.append(f"  {field.name} {prisma_type}")
+
+        lines.append("}")
+        return lines
+
+    def _generate_model_from_dataclass(self, cls: type) -> List[str]:
+        """Generate Prisma model from a dataclass."""
+        lines = [f"model {cls.__name__} {{"]
+
+        for name, type_hint in get_type_hints(cls).items():
+            if name.startswith("_"):
+                continue
+            prisma_type = self._resolve_type(name, type_hint, False)
+            lines.append(f"  {name} {prisma_type}")
+
+        lines.append("}")
+        return lines
+
+    def _resolve_type(self, name: str, type_hint: Any, optional: bool) -> str:
+        """Resolve Python type to Prisma type string."""
+        # Special fields
+        if name in PRISMA_SPECIAL:
+            return PRISMA_SPECIAL[name]
+
+        base, is_optional = unwrap_optional(type_hint)
+        optional = optional or is_optional
+        origin = get_origin_name(base)
+        type_name = get_type_name(base)
+
+        # Container types
+        if origin == "dict" or origin == "list":
+            result = PRISMA_TYPES.get(origin, "Json")
+            return f"{result}?" if optional else result
+
+        # UUID / datetime
+        if type_name in ("UUID", "datetime"):
+            result = PRISMA_TYPES.get(type_name, "String")
+            return f"{result}?" if optional else result
+
+        # Enum
+        if isinstance(base, type) and issubclass(base, Enum):
+            result = base.__name__
+            return f"{result}?" if optional else result
+
+        # Basic types
+        result = PRISMA_TYPES.get(base, "String")
+        return f"{result}?" if optional else result

tools/modelgen/generator/protobuf.py

@@ -0,0 +1,168 @@
+"""
+Protobuf Generator
+
+Generates Protocol Buffer definitions from model definitions.
+"""
+
+from pathlib import Path
+from typing import Any, List, get_type_hints
+
+from ..helpers import get_origin_name, unwrap_optional
+from ..loader.schema import GrpcServiceDefinition, ModelDefinition
+from ..types import PROTO_RESOLVERS
+from .base import BaseGenerator
+
+
+class ProtobufGenerator(BaseGenerator):
+    """Generates Protocol Buffer definition files."""
+
+    def file_extension(self) -> str:
+        return ".proto"
+
+    def generate(self, models, output_path: Path) -> None:
+        """Generate protobuf definitions to output_path."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Handle different input types
+        if hasattr(models, "grpc_messages"):
+            # SchemaLoader with gRPC definitions
+            content = self._generate_from_loader(models)
+        elif isinstance(models, tuple) and len(models) >= 3:
+            # (messages, service_def) tuple
+            content = self._generate_from_definitions(models[0], models[1])
+        elif isinstance(models, list):
+            # List of dataclasses (MPR style)
+            content = self._generate_from_dataclasses(models)
+        else:
+            raise ValueError(f"Unsupported input type: {type(models)}")
+
+        output_path.write_text(content)
+
+    def _generate_from_loader(self, loader) -> str:
+        """Generate from SchemaLoader."""
+        messages = loader.grpc_messages
+        service = loader.grpc_service
+
+        lines = self._generate_header(
+            service.package if service else "service",
+            service.name if service else "Service",
+            service.methods if service else [],
+        )
+
+        for model_def in messages:
+            lines.extend(self._generate_message_from_definition(model_def))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_from_definitions(
+        self, messages: List[ModelDefinition], service: GrpcServiceDefinition
+    ) -> str:
+        """Generate from ModelDefinition objects."""
+        lines = self._generate_header(service.package, service.name, service.methods)
+
+        for model_def in messages:
+            lines.extend(self._generate_message_from_definition(model_def))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_from_dataclasses(self, dataclasses: List[type]) -> str:
+        """Generate from Python dataclasses (MPR style)."""
+        lines = self._generate_header("service", "Service", [])
+
+        for cls in dataclasses:
+            lines.extend(self._generate_message_from_dataclass(cls))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_header(
+        self, package: str, service_name: str, methods: List[dict]
+    ) -> List[str]:
+        """Generate file header with service definition."""
+        lines = [
+            "// Protocol Buffer Definitions - GENERATED FILE",
+            "//",
+            "// Do not edit directly. Regenerate using modelgen.",
+            "",
+            'syntax = "proto3";',
+            "",
+            f"package {package};",
+            "",
+        ]
+
+        if methods:
+            lines.append(f"service {service_name} {{")
+            for m in methods:
+                req = (
+                    m["request"].__name__
+                    if hasattr(m["request"], "__name__")
+                    else str(m["request"])
+                )
+                resp = (
+                    m["response"].__name__
+                    if hasattr(m["response"], "__name__")
+                    else str(m["response"])
+                )
+                returns = f"stream {resp}" if m.get("stream_response") else resp
+                lines.append(f"    rpc {m['name']}({req}) returns ({returns});")
+            lines.extend(["}", ""])
+
+        return lines
+
+    def _generate_message_from_definition(
+        self, model_def: ModelDefinition
+    ) -> List[str]:
+        """Generate proto message from ModelDefinition."""
+        lines = [f"message {model_def.name} {{"]
+
+        if not model_def.fields:
+            lines.append("    // Empty")
+        else:
+            for i, field in enumerate(model_def.fields, 1):
+                proto_type, optional = self._resolve_type(field.type_hint)
+                prefix = (
+                    "optional "
+                    if optional and not proto_type.startswith("repeated")
+                    else ""
+                )
+                lines.append(f"    {prefix}{proto_type} {field.name} = {i};")
+
+        lines.append("}")
+        return lines
+
+    def _generate_message_from_dataclass(self, cls: type) -> List[str]:
+        """Generate proto message from a dataclass."""
+        lines = [f"message {cls.__name__} {{"]
+
+        hints = get_type_hints(cls)
+        if not hints:
+            lines.append("    // Empty")
+        else:
+            for i, (name, type_hint) in enumerate(hints.items(), 1):
+                proto_type, optional = self._resolve_type(type_hint)
+                prefix = (
+                    "optional "
+                    if optional and not proto_type.startswith("repeated")
+                    else ""
+                )
+                lines.append(f"    {prefix}{proto_type} {name} = {i};")
+
+        lines.append("}")
+        return lines
+
+    def _resolve_type(self, type_hint: Any) -> tuple[str, bool]:
+        """Resolve Python type to proto type. Returns (type, is_optional)."""
+        base, optional = unwrap_optional(type_hint)
+        origin = get_origin_name(base)
+
+        # Look up resolver
+        resolver = PROTO_RESOLVERS.get(origin) or PROTO_RESOLVERS.get(base)
+
+        if resolver:
+            result = resolver(base)
+            is_repeated = result.startswith("repeated")
+            return result, optional and not is_repeated
+
+        return "string", optional

tools/modelgen/generator/pydantic.py

@@ -0,0 +1,427 @@
+"""
+Pydantic Generator
+
+Generates Pydantic BaseModel classes from model definitions.
+"""
+
+from enum import Enum
+from pathlib import Path
+from typing import Any, List, get_type_hints
+
+from ..helpers import get_origin_name, get_type_name, unwrap_optional
+from ..loader.schema import EnumDefinition, FieldDefinition, ModelDefinition
+from ..types import PYDANTIC_RESOLVERS
+from .base import BaseGenerator
+
+
+class PydanticGenerator(BaseGenerator):
+    """Generates Pydantic model files."""
+
+    def file_extension(self) -> str:
+        return ".py"
+
+    def generate(self, models, output_path: Path) -> None:
+        """Generate Pydantic models to output_path."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Detect input type and generate accordingly
+        if hasattr(models, "get_shared_component"):
+            # ConfigLoader (soleprint config)
+            content = self._generate_from_config(models)
+        elif hasattr(models, "models"):
+            # SchemaLoader
+            content = self._generate_from_definitions(
+                models.models, getattr(models, "enums", [])
+            )
+        elif isinstance(models, tuple):
+            # (models, enums) tuple from extractor
+            content = self._generate_from_definitions(models[0], models[1])
+        elif isinstance(models, list):
+            # List of dataclasses (MPR style)
+            content = self._generate_from_dataclasses(models)
+        else:
+            raise ValueError(f"Unsupported input type: {type(models)}")
+
+        output_path.write_text(content)
+
+    def _generate_from_definitions(
+        self, models: List[ModelDefinition], enums: List[EnumDefinition]
+    ) -> str:
+        """Generate from ModelDefinition objects (schema/extract mode)."""
+        lines = self._generate_header()
+
+        # Generate enums
+        for enum_def in enums:
+            lines.extend(self._generate_enum(enum_def))
+            lines.append("")
+
+        # Generate models
+        for model_def in models:
+            lines.extend(self._generate_model_from_definition(model_def))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_from_dataclasses(self, dataclasses: List[type]) -> str:
+        """Generate from Python dataclasses (MPR style)."""
+        lines = self._generate_header()
+
+        # Collect and generate enums first
+        enums_generated = set()
+        for cls in dataclasses:
+            hints = get_type_hints(cls)
+            for type_hint in hints.values():
+                base, _ = unwrap_optional(type_hint)
+                if isinstance(base, type) and issubclass(base, Enum):
+                    if base.__name__ not in enums_generated:
+                        lines.extend(self._generate_enum_from_python(base))
+                        lines.append("")
+                        enums_generated.add(base.__name__)
+
+        # Generate models
+        for cls in dataclasses:
+            lines.extend(self._generate_model_from_dataclass(cls))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_header(self) -> List[str]:
+        """Generate file header."""
+        return [
+            '"""',
+            "Pydantic Models - GENERATED FILE",
+            "",
+            "Do not edit directly. Regenerate using modelgen.",
+            '"""',
+            "",
+            "from datetime import datetime",
+            "from enum import Enum",
+            "from typing import Any, Dict, List, Optional",
+            "from uuid import UUID",
+            "",
+            "from pydantic import BaseModel, Field",
+            "",
+        ]
+
+    def _generate_enum(self, enum_def: EnumDefinition) -> List[str]:
+        """Generate Pydantic enum from EnumDefinition."""
+        lines = [f"class {enum_def.name}(str, Enum):"]
+        for name, value in enum_def.values:
+            lines.append(f'    {name} = "{value}"')
+        return lines
+
+    def _generate_enum_from_python(self, enum_cls: type) -> List[str]:
+        """Generate Pydantic enum from Python Enum."""
+        lines = [f"class {enum_cls.__name__}(str, Enum):"]
+        for member in enum_cls:
+            lines.append(f'    {member.name} = "{member.value}"')
+        return lines
+
+    def _generate_model_from_definition(self, model_def: ModelDefinition) -> List[str]:
+        """Generate Pydantic model from ModelDefinition."""
+        docstring = model_def.docstring or model_def.name
+        lines = [
+            f"class {model_def.name}(BaseModel):",
+            f'    """{docstring.strip().split(chr(10))[0]}"""',
+        ]
+
+        if not model_def.fields:
+            lines.append("    pass")
+        else:
+            for field in model_def.fields:
+                py_type = self._resolve_type(field.type_hint, field.optional)
+                default = self._format_default(field.default, field.optional)
+                lines.append(f"    {field.name}: {py_type}{default}")
+
+        return lines
+
+    def _generate_model_from_dataclass(self, cls: type) -> List[str]:
+        """Generate Pydantic model from a dataclass."""
+        import dataclasses as dc
+
+        docstring = cls.__doc__ or cls.__name__
+        lines = [
+            f"class {cls.__name__}(BaseModel):",
+            f'    """{docstring.strip().split(chr(10))[0]}"""',
+        ]
+
+        hints = get_type_hints(cls)
+        fields = {f.name: f for f in dc.fields(cls)}
+
+        for name, type_hint in hints.items():
+            if name.startswith("_"):
+                continue
+
+            field = fields.get(name)
+            default_val = dc.MISSING
+            if field:
+                if field.default is not dc.MISSING:
+                    default_val = field.default
+
+            py_type = self._resolve_type(type_hint, False)
+            default = self._format_default(default_val, "Optional" in py_type)
+            lines.append(f"    {name}: {py_type}{default}")
+
+        return lines
+
+    def _resolve_type(self, type_hint: Any, optional: bool) -> str:
+        """Resolve Python type to Pydantic type string."""
+        base, is_optional = unwrap_optional(type_hint)
+        optional = optional or is_optional
+        origin = get_origin_name(base)
+        type_name = get_type_name(base)
+
+        # Look up resolver
+        resolver = (
+            PYDANTIC_RESOLVERS.get(origin)
+            or PYDANTIC_RESOLVERS.get(type_name)
+            or PYDANTIC_RESOLVERS.get(base)
+            or (
+                PYDANTIC_RESOLVERS["enum"]
+                if isinstance(base, type) and issubclass(base, Enum)
+                else None
+            )
+        )
+
+        result = resolver(base) if resolver else "str"
+        return f"Optional[{result}]" if optional else result
+
+    def _format_default(self, default: Any, optional: bool) -> str:
+        """Format default value for field."""
+        import dataclasses as dc
+
+        if optional:
+            return " = None"
+        if default is dc.MISSING or default is None:
+            return ""
+        if isinstance(default, str):
+            return f' = "{default}"'
+        if isinstance(default, Enum):
+            return f" = {default.__class__.__name__}.{default.name}"
+        if callable(default):
+            return " = Field(default_factory=list)" if "list" in str(default) else ""
+        return f" = {default!r}"
+
+    def _generate_from_config(self, config) -> str:
+        """Generate from ConfigLoader (soleprint config.json mode)."""
+        # Get component names from config
+        config_comp = config.get_shared_component("config")
+        data_comp = config.get_shared_component("data")
+
+        data_flow_sys = config.get_system("data_flow")
+        doc_sys = config.get_system("documentation")
+        exec_sys = config.get_system("execution")
+
+        connector_comp = config.get_component("data_flow", "connector")
+        pulse_comp = config.get_component("data_flow", "composed")
+
+        pattern_comp = config.get_component("documentation", "pattern")
+        doc_composed = config.get_component("documentation", "composed")
+
+        tool_comp = config.get_component("execution", "utility")
+        monitor_comp = config.get_component("execution", "watcher")
+        cabinet_comp = config.get_component("execution", "container")
+        exec_composed = config.get_component("execution", "composed")
+
+        return f'''"""
+Pydantic models - Generated from {config.framework.name}.config.json
+
+DO NOT EDIT MANUALLY - Regenerate from config
+"""
+
+from enum import Enum
+from typing import List, Literal, Optional
+
+from pydantic import BaseModel, Field
+
+
+class Status(str, Enum):
+    PENDING = "pending"
+    PLANNED = "planned"
+    BUILDING = "building"
+    DEV = "dev"
+    LIVE = "live"
+    READY = "ready"
+
+
+class System(str, Enum):
+    {data_flow_sys.name.upper()} = "{data_flow_sys.name}"
+    {doc_sys.name.upper()} = "{doc_sys.name}"
+    {exec_sys.name.upper()} = "{exec_sys.name}"
+
+
+class ToolType(str, Enum):
+    APP = "app"
+    CLI = "cli"
+
+
+# === Shared Components ===
+
+
+class {config_comp.title}(BaseModel):
+    """{config_comp.description}. Shared across {data_flow_sys.name}, {exec_sys.name}."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    config_path: Optional[str] = None
+
+
+class {data_comp.title}(BaseModel):
+    """{data_comp.description}. Shared across all systems."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    source_template: Optional[str] = None
+    data_path: Optional[str] = None
+
+
+# === System-Specific Components ===
+
+
+class {connector_comp.title}(BaseModel):
+    """{connector_comp.description} ({data_flow_sys.name})."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    system: Literal["{data_flow_sys.name}"] = "{data_flow_sys.name}"
+    mock: Optional[bool] = None
+    description: Optional[str] = None
+
+
+class {pattern_comp.title}(BaseModel):
+    """{pattern_comp.description} ({doc_sys.name})."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    template_path: Optional[str] = None
+    system: Literal["{doc_sys.name}"] = "{doc_sys.name}"
+
+
+class {tool_comp.title}(BaseModel):
+    """{tool_comp.description} ({exec_sys.name})."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    system: Literal["{exec_sys.name}"] = "{exec_sys.name}"
+    type: Optional[ToolType] = None
+    description: Optional[str] = None
+    path: Optional[str] = None
+    url: Optional[str] = None
+    cli: Optional[str] = None
+
+
+class {monitor_comp.title}(BaseModel):
+    """{monitor_comp.description} ({exec_sys.name})."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    system: Literal["{exec_sys.name}"] = "{exec_sys.name}"
+
+
+class {cabinet_comp.title}(BaseModel):
+    """{cabinet_comp.description} ({exec_sys.name})."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    tools: List[{tool_comp.title}] = Field(default_factory=list)
+    system: Literal["{exec_sys.name}"] = "{exec_sys.name}"
+
+
+# === Composed Types ===
+
+
+class {pulse_comp.title}(BaseModel):
+    """{pulse_comp.description} ({data_flow_sys.name}). Formula: {pulse_comp.formula}."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    {connector_comp.name}: Optional[{connector_comp.title}] = None
+    {config_comp.name}: Optional[{config_comp.title}] = None
+    {data_comp.name}: Optional[{data_comp.title}] = None
+    system: Literal["{data_flow_sys.name}"] = "{data_flow_sys.name}"
+
+
+class {doc_composed.title}(BaseModel):
+    """{doc_composed.description} ({doc_sys.name}). Formula: {doc_composed.formula}."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    template: Optional[{pattern_comp.title}] = None
+    {data_comp.name}: Optional[{data_comp.title}] = None
+    output_{data_comp.name}: Optional[{data_comp.title}] = None
+    system: Literal["{doc_sys.name}"] = "{doc_sys.name}"
+
+
+class {exec_composed.title}(BaseModel):
+    """{exec_composed.description} ({exec_sys.name}). Formula: {exec_composed.formula}."""
+
+    name: str  # Unique identifier
+    slug: str  # URL-friendly identifier
+    title: str  # Display title for UI
+    status: Optional[Status] = None
+    cabinet: Optional[{cabinet_comp.title}] = None
+    {config_comp.name}: Optional[{config_comp.title}] = None
+    {data_comp.plural}: List[{data_comp.title}] = Field(default_factory=list)
+    system: Literal["{exec_sys.name}"] = "{exec_sys.name}"
+
+
+# === Collection wrappers for JSON files ===
+
+
+class {config_comp.title}Collection(BaseModel):
+    items: List[{config_comp.title}] = Field(default_factory=list)
+
+
+class {data_comp.title}Collection(BaseModel):
+    items: List[{data_comp.title}] = Field(default_factory=list)
+
+
+class {connector_comp.title}Collection(BaseModel):
+    items: List[{connector_comp.title}] = Field(default_factory=list)
+
+
+class {pattern_comp.title}Collection(BaseModel):
+    items: List[{pattern_comp.title}] = Field(default_factory=list)
+
+
+class {tool_comp.title}Collection(BaseModel):
+    items: List[{tool_comp.title}] = Field(default_factory=list)
+
+
+class {monitor_comp.title}Collection(BaseModel):
+    items: List[{monitor_comp.title}] = Field(default_factory=list)
+
+
+class {cabinet_comp.title}Collection(BaseModel):
+    items: List[{cabinet_comp.title}] = Field(default_factory=list)
+
+
+class {pulse_comp.title}Collection(BaseModel):
+    items: List[{pulse_comp.title}] = Field(default_factory=list)
+
+
+class {doc_composed.title}Collection(BaseModel):
+    items: List[{doc_composed.title}] = Field(default_factory=list)
+
+
+class {exec_composed.title}Collection(BaseModel):
+    items: List[{exec_composed.title}] = Field(default_factory=list)
+'''

tools/modelgen/generator/typescript.py

@@ -0,0 +1,144 @@
+"""
+TypeScript Generator
+
+Generates TypeScript interfaces from model definitions.
+"""
+
+from enum import Enum
+from pathlib import Path
+from typing import Any, List, get_type_hints
+
+from ..helpers import get_origin_name, get_type_name, unwrap_optional
+from ..loader.schema import EnumDefinition, FieldDefinition, ModelDefinition
+from ..types import TS_RESOLVERS
+from .base import BaseGenerator
+
+
+class TypeScriptGenerator(BaseGenerator):
+    """Generates TypeScript interface files."""
+
+    def file_extension(self) -> str:
+        return ".ts"
+
+    def generate(self, models, output_path: Path) -> None:
+        """Generate TypeScript types to output_path."""
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Handle different input types
+        if hasattr(models, "models"):
+            # SchemaLoader
+            content = self._generate_from_definitions(
+                models.models, getattr(models, "enums", [])
+            )
+        elif isinstance(models, tuple):
+            # (models, enums) tuple
+            content = self._generate_from_definitions(models[0], models[1])
+        elif isinstance(models, list):
+            # List of dataclasses (MPR style)
+            content = self._generate_from_dataclasses(models)
+        else:
+            raise ValueError(f"Unsupported input type: {type(models)}")
+
+        output_path.write_text(content)
+
+    def _generate_from_definitions(
+        self, models: List[ModelDefinition], enums: List[EnumDefinition]
+    ) -> str:
+        """Generate from ModelDefinition objects."""
+        lines = self._generate_header()
+
+        # Generate enums as union types
+        for enum_def in enums:
+            values = " | ".join(f'"{v}"' for _, v in enum_def.values)
+            lines.append(f"export type {enum_def.name} = {values};")
+        lines.append("")
+
+        # Generate interfaces
+        for model_def in models:
+            lines.extend(self._generate_interface_from_definition(model_def))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_from_dataclasses(self, dataclasses: List[type]) -> str:
+        """Generate from Python dataclasses (MPR style)."""
+        lines = self._generate_header()
+
+        # Collect and generate enums first
+        enums_generated = set()
+        for cls in dataclasses:
+            hints = get_type_hints(cls)
+            for type_hint in hints.values():
+                base, _ = unwrap_optional(type_hint)
+                if isinstance(base, type) and issubclass(base, Enum):
+                    if base.__name__ not in enums_generated:
+                        values = " | ".join(f'"{m.value}"' for m in base)
+                        lines.append(f"export type {base.__name__} = {values};")
+                        enums_generated.add(base.__name__)
+        lines.append("")
+
+        # Generate interfaces
+        for cls in dataclasses:
+            lines.extend(self._generate_interface_from_dataclass(cls))
+            lines.append("")
+
+        return "\n".join(lines)
+
+    def _generate_header(self) -> List[str]:
+        """Generate file header."""
+        return [
+            "/**",
+            " * TypeScript Types - GENERATED FILE",
+            " *",
+            " * Do not edit directly. Regenerate using modelgen.",
+            " */",
+            "",
+        ]
+
+    def _generate_interface_from_definition(
+        self, model_def: ModelDefinition
+    ) -> List[str]:
+        """Generate TypeScript interface from ModelDefinition."""
+        lines = [f"export interface {model_def.name} {{"]
+
+        for field in model_def.fields:
+            ts_type = self._resolve_type(field.type_hint, field.optional)
+            lines.append(f"  {field.name}: {ts_type};")
+
+        lines.append("}")
+        return lines
+
+    def _generate_interface_from_dataclass(self, cls: type) -> List[str]:
+        """Generate TypeScript interface from a dataclass."""
+        lines = [f"export interface {cls.__name__} {{"]
+
+        for name, type_hint in get_type_hints(cls).items():
+            if name.startswith("_"):
+                continue
+            ts_type = self._resolve_type(type_hint, False)
+            lines.append(f"  {name}: {ts_type};")
+
+        lines.append("}")
+        return lines
+
+    def _resolve_type(self, type_hint: Any, optional: bool) -> str:
+        """Resolve Python type to TypeScript type string."""
+        base, is_optional = unwrap_optional(type_hint)
+        optional = optional or is_optional
+        origin = get_origin_name(base)
+        type_name = get_type_name(base)
+
+        # Look up resolver
+        resolver = (
+            TS_RESOLVERS.get(origin)
+            or TS_RESOLVERS.get(type_name)
+            or TS_RESOLVERS.get(base)
+            or (
+                TS_RESOLVERS["enum"]
+                if isinstance(base, type) and issubclass(base, Enum)
+                else None
+            )
+        )
+
+        result = resolver(base) if resolver else "string"
+        return f"{result} | null" if optional else result