tegwick
36c20f37d0
Some checks failed
Test Suite / unit-tests (3.11) (push) Has been cancelled
Test Suite / unit-tests (3.12) (push) Has been cancelled
Test Suite / code-quality (push) Has been cancelled
Test Suite / security-scan (push) Has been cancelled
Test Suite / integration-tests (push) Has been cancelled
Test Suite / e2e-tests (push) Has been cancelled
Test Suite / performance-tests (push) Has been cancelled
Test Suite / test-summary (push) Has been cancelled
Stage 1 — Decouple:
- Move RunConfig + LLMResponse to markitect/llm/models.py (canonical)
- Move LLMAdapter + Mock/ErrorLLMAdapter to markitect/llm/adapter.py
- markitect/prompts/execution/models.py and llm_adapter.py become re-export shims
- All 4 adapters + factory.py updated to import from markitect.llm.*
- Parameterize app_name in toml_config.py (resolve_llm, get_default_layers,
get_preference_layers): paths and env var now derived from app_name arg
- Add tests/test_llm_isolation.py: 7 isolation + backward-compat tests
Stage 2 — Extract:
- Standalone llm-connect package created at ~/llm-connect/
- All 18 llm files copied; markitect.* imports replaced with llm_connect.*
- LLMError base inlined in llm_connect/exceptions.py (no markitect dep)
- llm-connect installed into markitect-venv; declared in pyproject.toml
Smoke test: markitect llm-check succeeds (live Gemini API call).
Backward compat: markitect.prompts.execution.{models,llm_adapter} still work.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
"""
|
|
Shared data models for LLM execution.
|
|
|
|
These classes are the canonical definitions; they are re-exported by
|
|
markitect.prompts.execution.models for backward compatibility.
|
|
"""
|
|
|
|
from dataclasses import dataclass, field
|
|
from typing import Dict, Any
|
|
|
|
|
|
@dataclass
|
|
class RunConfig:
|
|
"""
|
|
Configuration for prompt execution.
|
|
|
|
Attributes:
|
|
model_name: LLM model to use
|
|
temperature: Model temperature (0.0-1.0)
|
|
max_tokens: Maximum tokens to generate
|
|
model_params: Additional model parameters
|
|
max_depth: Maximum generation depth for nested runs
|
|
skip_if_exists: Skip if identical InputBundleHash exists
|
|
timeout_seconds: Execution timeout
|
|
"""
|
|
model_name: str = "gpt-4"
|
|
temperature: float = 0.7
|
|
max_tokens: int = 2000
|
|
model_params: Dict[str, Any] = field(default_factory=dict)
|
|
max_depth: int = 3
|
|
skip_if_exists: bool = True
|
|
timeout_seconds: int = 300
|
|
|
|
def to_dict(self) -> Dict[str, Any]:
|
|
"""Convert to dictionary."""
|
|
return {
|
|
"model_name": self.model_name,
|
|
"temperature": self.temperature,
|
|
"max_tokens": self.max_tokens,
|
|
"model_params": self.model_params,
|
|
"max_depth": self.max_depth,
|
|
"skip_if_exists": self.skip_if_exists,
|
|
"timeout_seconds": self.timeout_seconds,
|
|
}
|
|
|
|
@classmethod
|
|
def from_dict(cls, data: Dict[str, Any]) -> "RunConfig":
|
|
"""Create from dictionary."""
|
|
return cls(
|
|
model_name=data.get("model_name", "gpt-4"),
|
|
temperature=data.get("temperature", 0.7),
|
|
max_tokens=data.get("max_tokens", 2000),
|
|
model_params=data.get("model_params", {}),
|
|
max_depth=data.get("max_depth", 3),
|
|
skip_if_exists=data.get("skip_if_exists", True),
|
|
timeout_seconds=data.get("timeout_seconds", 300),
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class LLMResponse:
|
|
"""
|
|
Response from LLM execution.
|
|
|
|
Attributes:
|
|
content: Generated content
|
|
model: Model used
|
|
usage: Token usage statistics
|
|
finish_reason: Why generation stopped
|
|
metadata: Additional response metadata
|
|
"""
|
|
content: str
|
|
model: str
|
|
usage: Dict[str, int] = field(default_factory=dict)
|
|
finish_reason: str = "stop"
|
|
metadata: Dict[str, Any] = field(default_factory=dict)
|
|
|
|
def to_dict(self) -> Dict[str, Any]:
|
|
"""Convert to dictionary."""
|
|
return {
|
|
"content": self.content,
|
|
"model": self.model,
|
|
"usage": self.usage,
|
|
"finish_reason": self.finish_reason,
|
|
"metadata": self.metadata,
|
|
}
|