Remove unused pipe_type: PipeType

Add Changelog for v0.5.0

Author: lchoquel

CHANGELOG.md

@@ -1,16 +1,36 @@
 # Changelog
 
-## [v0.5.0] - 2025-07-07
+## [v0.5.0] - 2025-07-01
 
-Draft:
+### Highlight: Vibe Coding an AI workflow becomes a reality
 
-- `WorkingMemoryFactory`: added method `make_for_dry_run`: Takes an array of tuple (`stuff_name`, `concept_code`, `class_structure`) and generates with `polyfactory` a working memory with fake objects.
-- Completed dry run for `PipeCondition`, `PipeParallel`, `PipeBatch`. Added static validation for those as well. 
-- Added a config in the DryRunConfig: `allowed_to_fail_pipes` enables to list pipes that are allowed to fail the dry run, for example the pipe explicitly coded for creating an infinite loop 
-- `ConceptLibrary`: Added method `is_concept_code_legal`, that checks that the domain attached to the concept exists.
-- Added `pipe_type: PipeType` to pipes classes (CONTROLLER/OPERATOR)
-- `PipeLibrary`:  added method `dry_run_all_pipes` that runs the dry_run for all pipes.
-- Added validation of TOML files: Raise an error when there are trailing whitespaces, compilation blockers...
+**Create AI workflows from natural language without writing code** - The combination of Pipelex's declarative language, comprehensive Cursor rules, and robust validation tools enables AI assistants to autonomously iterate on pipelines until all errors are resolved and workflows are ready to run.
+
+### Added
+
+- **Complete Dry Run & Static Validation System** - A comprehensive validation framework that catches configuration and pipeline errors before any expensive inference operations.
+- **WorkingMemoryFactory Enhancement**: New `make_for_dry_run()` method creates working memory with realistic mock objects for zero-cost pipeline testing
+- **Enhanced Dry Run System**: Complete dry run support for all pipe controllers (`PipeCondition`, `PipeParallel`, `PipeBatch`) with mock data generation using `polyfactory`
+- **Comprehensive Static Validation**: Enhanced static validation with configurable error handling for missing/extraneous input variables and domain validation
+- **TOML File Validation**: Automatic detection and prevention of trailing whitespaces, formatting issues, and compilation blockers in pipeline files
+- **Pipeline Testing Framework**: New `dry_run_all_pipes()` method enables comprehensive testing of entire pipeline libraries
+- **Enhanced Library Loading**: Improved error handling and validation during TOML file loading with proper exception propagation
+
+### Configuration
+
+- **Dry Run Configuration**: New `allowed_to_fail_pipes` setting allows specific pipes (like infinite loop examples that fail on purpose) to be excluded from dry run validation
+- **Static Validation Control**: Configurable error reactions (`raise`, `log`, `ignore`) for different validation error types
+
+### Documentation & Development Experience
+
+- **Cursor Rules Enhancement**: Comprehensive pipe controller documentation covering `PipeSequence`, `PipeCondition`, `PipeBatch`, and `PipeParallel`, improved PipeOperator documentation for `PipeLLM`, `PipeOCR`
+- **Pipeline Validation CLI**: Enhanced `pipelex validate` command with better error reporting and validation coverage
+- **Improved Error Messages**: Better formatting and context for pipeline configuration errors
+
+### Changed
+
+- **OCR Input Standardization**: Changed OCR pipe input parameter naming to consistently use `ocr_input` for both image and PDF inputs, improving consistency across the API
+- **Error Message Improvements**: Updated PipeCondition error messages to reference `expression_template` instead of deprecated `expression_jinja2`
 
 ## [v0.4.11] - 2025-06-30
 

pipelex/core/pipe_abstract.py

@@ -12,17 +12,11 @@
 from pipelex.types import StrEnum
 
 
-class PipeType(StrEnum):
-    OPERATOR = "operator"
-    CONTROLLER = "controller"
-
-
 class PipeAbstract(ABC, BaseModel):
     model_config = ConfigDict(strict=True, extra="forbid")
 
     code: str
     domain: str
-    pipe_type: PipeType
     definition: Optional[str] = None
     # TODO: support auto (implicit) input, it makes sense for pipe controllers
     inputs: PipeInputSpec = Field(default_factory=PipeInputSpec)

pipelex/pipe_controllers/pipe_controller.py

@@ -4,16 +4,14 @@
 from typing_extensions import override
 
 from pipelex import log
-from pipelex.core.pipe_abstract import PipeAbstract, PipeType
+from pipelex.core.pipe_abstract import PipeAbstract
 from pipelex.core.pipe_output import PipeOutput
 from pipelex.core.pipe_run_params import PipeRunMode, PipeRunParams
 from pipelex.core.working_memory import WorkingMemory
 from pipelex.pipeline.job_metadata import JobMetadata
 
 
 class PipeController(PipeAbstract):
-    pipe_type: PipeType = PipeType.CONTROLLER
-
     @override
     async def run_pipe(
         self,

pipelex/pipe_operators/pipe_operator.py

@@ -4,7 +4,7 @@
 from typing_extensions import override
 
 from pipelex import log
-from pipelex.core.pipe_abstract import PipeAbstract, PipeType
+from pipelex.core.pipe_abstract import PipeAbstract
 from pipelex.core.pipe_output import PipeOutput
 from pipelex.core.pipe_run_params import PipeRunMode, PipeRunParams
 from pipelex.core.working_memory import WorkingMemory
@@ -14,8 +14,6 @@
 
 
 class PipeOperator(PipeAbstract):
-    pipe_type: PipeType = PipeType.OPERATOR
-
     @override
     async def run_pipe(
         self,

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "pipelex"
-version = "0.4.12"
+version = "0.5.0"
 description = "Pipelex is an open-source dev tool based on a simple declarative language that lets you define replicable, structured, composable LLM pipelines."
 authors = [{ name = "Evotis S.A.S.", email = "evotis@pipelex.com" }]
 maintainers = [{ name = "Pipelex staff", email = "oss@pipelex.com" }]