Bump PIpelex

.cursor/rules/llms.mdc

@@ -55,7 +55,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```
 

.cursor/rules/python_standards.mdc

@@ -13,6 +13,7 @@ This document outlines the core coding standards, best practices, and quality co
     - Variable names should have a minimum length of 3 characters. No exceptions: name your `for` loop indexes like `index_foobar`, your exceptions `exc` or more specific like `validation_error` when there are several layers of exceptions, and use `for key, value in ...` for key/value pairs.
     - When looping on the keys of a dict, use `for key in the_dict` rather than `for key in the_dict.keys()` otherwise you won't pass linting.
     - Avoid inline for loops, unless it's ultra-simple and holds on oneline.
+    - If you have a variable that will get its value differently through different code paths, declare it first with a type, e.g. `pipe_code: str` but DO NOT give it a default value like `pipe_code: str = ""` unless it's really justified. We want the variable to be unbound until all paths are covered, and the linters will help us avoid bugs this way.
 
 ## Enums and tests
 
@@ -39,7 +40,7 @@ This document outlines the core coding standards, best practices, and quality co
     - Both have a title arg which is handy when logging/printing objects:
 
     ```python
-    log.debug("Hello, world!", title="Your first Pipelex log")
+    log.verbose("Hello, world!", title="Your first Pipelex log")
     pretty_print(output_object, title="Your first Pipelex output")
     ```
     - Both handle formatting json using Rich, pretty_print makes it prettier.

.cursor/rules/run_pipelex.mdc

@@ -81,7 +81,7 @@ pretty_print(gantt_chart, title="Gantt Chart")
 The input memory is a dictionary, where the key is the name of the input variable and the value provides details to make it a stuff object. The relevant definitions are:
 ```python
 StuffContentOrData = dict[str, Any] | StuffContent | list[Any] | str
-ImplicitMemory = dict[str, StuffContentOrData]
+PipelineInputs = dict[str, StuffContentOrData]
 ```
 As you can seen, we made it so different ways can be used to define that stuff using structured content or data.
 

.cursor/rules/write_pipelex.mdc

@@ -89,6 +89,26 @@ inputs = {
 
 - `output`: The name of the concept to output. The `ConceptName` should have the same name as the python class if you want structured output:
 
+### Input Multiplicity
+
+By default, inputs expect a single item. Use bracket notation to specify multiple items:
+
+```plx
+# Single item (default)
+inputs = { document = "Text" }
+
+# Variable list - indeterminate number of items
+inputs = { documents = "Text[]" }
+
+# Fixed count - exactly N items
+inputs = { comparison_items = "Image[2]" }
+```
+
+**Key points:**
+- No brackets = single item (default behavior)
+- Use `[]` for lists of unknown length
+- Use `[N]` (where N is an integer) when operation requires exact count (e.g., comparing 2 items)
+
 ## Structuring Models
 
 Once you've defined your concepts semantically (see "Concept Definitions" above), you need to specify their structure if they have fields.
@@ -355,22 +375,22 @@ prompt = "Analyze this data"
 
 ### Multiple Outputs
 
-Generate multiple outputs (fixed number):
+Generate multiple outputs (fixed number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-nb_output = 3  # Generate exactly 3 ideas
+output = "Idea[3]"  # Generate exactly 3 ideas
+prompt = "Generate 3 ideas"
 ```
 
-Generate multiple outputs (variable number):
+Generate multiple outputs (variable number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-multiple_output = true  # Let the LLM decide how many to generate
+output = "Idea[]"  # Let the LLM decide how many to generate
+prompt = "Generate ideas"
 ```
 
 ### Vision
@@ -621,8 +641,7 @@ Multiple Image Generation:
 type = "PipeImgGen"
 description = "Generate multiple image variations"
 inputs = { prompt = "ImgGenPrompt" }
-output = "Image"
-nb_output = 3
+output = "Image[3]"
 seed = "auto"
 ```
 
@@ -649,7 +668,6 @@ safety_tolerance = 3
 - `quality`: Image quality ("standard", "hd")
 
 **Output Configuration:**
-- `nb_output`: Number of images to generate
 - `aspect_ratio`: Image dimensions ("1:1", "16:9", "9:16", etc.)
 - `output_format`: File format ("png", "jpeg", "webp")
 - `background`: Background type ("default", "transparent")
@@ -803,7 +821,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```
 

.github/copilot-instructions.md

@@ -83,6 +83,26 @@ inputs = {
 
 - `output`: The name of the concept to output. The `ConceptName` should have the same name as the python class if you want structured output:
 
+#### Input Multiplicity
+
+By default, inputs expect a single item. Use bracket notation to specify multiple items:
+
+```plx
+## Single item (default)
+inputs = { document = "Text" }
+
+## Variable list - indeterminate number of items
+inputs = { documents = "Text[]" }
+
+## Fixed count - exactly N items
+inputs = { comparison_items = "Image[2]" }
+```
+
+**Key points:**
+- No brackets = single item (default behavior)
+- Use `[]` for lists of unknown length
+- Use `[N]` (where N is an integer) when operation requires exact count (e.g., comparing 2 items)
+
 ### Structuring Models
 
 Once you've defined your concepts semantically (see "Concept Definitions" above), you need to specify their structure if they have fields.
@@ -349,22 +369,22 @@ prompt = "Analyze this data"
 
 #### Multiple Outputs
 
-Generate multiple outputs (fixed number):
+Generate multiple outputs (fixed number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-nb_output = 3  # Generate exactly 3 ideas
+output = "Idea[3]"  # Generate exactly 3 ideas
+prompt = "Generate 3 ideas"
 ```
 
-Generate multiple outputs (variable number):
+Generate multiple outputs (variable number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-multiple_output = true  # Let the LLM decide how many to generate
+output = "Idea[]"  # Let the LLM decide how many to generate
+prompt = "Generate ideas"
 ```
 
 #### Vision
@@ -615,8 +635,7 @@ Multiple Image Generation:
 type = "PipeImgGen"
 description = "Generate multiple image variations"
 inputs = { prompt = "ImgGenPrompt" }
-output = "Image"
-nb_output = 3
+output = "Image[3]"
 seed = "auto"
 ```
 
@@ -643,7 +662,6 @@ safety_tolerance = 3
 - `quality`: Image quality ("standard", "hd")
 
 **Output Configuration:**
-- `nb_output`: Number of images to generate
 - `aspect_ratio`: Image dimensions ("1:1", "16:9", "9:16", etc.)
 - `output_format`: File format ("png", "jpeg", "webp")
 - `background`: Background type ("default", "transparent")
@@ -797,7 +815,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```
 
@@ -907,7 +925,7 @@ pretty_print(gantt_chart, title="Gantt Chart")
 The input memory is a dictionary, where the key is the name of the input variable and the value provides details to make it a stuff object. The relevant definitions are:
 ```python
 StuffContentOrData = dict[str, Any] | StuffContent | list[Any] | str
-ImplicitMemory = dict[str, StuffContentOrData]
+PipelineInputs = dict[str, StuffContentOrData]
 ```
 As you can seen, we made it so different ways can be used to define that stuff using structured content or data.
 
@@ -1105,7 +1123,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```
 

.windsurfrules.md

@@ -83,6 +83,26 @@ inputs = {
 
 - `output`: The name of the concept to output. The `ConceptName` should have the same name as the python class if you want structured output:
 
+#### Input Multiplicity
+
+By default, inputs expect a single item. Use bracket notation to specify multiple items:
+
+```plx
+## Single item (default)
+inputs = { document = "Text" }
+
+## Variable list - indeterminate number of items
+inputs = { documents = "Text[]" }
+
+## Fixed count - exactly N items
+inputs = { comparison_items = "Image[2]" }
+```
+
+**Key points:**
+- No brackets = single item (default behavior)
+- Use `[]` for lists of unknown length
+- Use `[N]` (where N is an integer) when operation requires exact count (e.g., comparing 2 items)
+
 ### Structuring Models
 
 Once you've defined your concepts semantically (see "Concept Definitions" above), you need to specify their structure if they have fields.
@@ -349,22 +369,22 @@ prompt = "Analyze this data"
 
 #### Multiple Outputs
 
-Generate multiple outputs (fixed number):
+Generate multiple outputs (fixed number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-nb_output = 3  # Generate exactly 3 ideas
+output = "Idea[3]"  # Generate exactly 3 ideas
+prompt = "Generate 3 ideas"
 ```
 
-Generate multiple outputs (variable number):
+Generate multiple outputs (variable number) - use bracket notation:
 ```plx
 [pipe.generate_ideas]
 type = "PipeLLM"
 description = "Generate ideas"
-output = "Idea"
-multiple_output = true  # Let the LLM decide how many to generate
+output = "Idea[]"  # Let the LLM decide how many to generate
+prompt = "Generate ideas"
 ```
 
 #### Vision
@@ -615,8 +635,7 @@ Multiple Image Generation:
 type = "PipeImgGen"
 description = "Generate multiple image variations"
 inputs = { prompt = "ImgGenPrompt" }
-output = "Image"
-nb_output = 3
+output = "Image[3]"
 seed = "auto"
 ```
 
@@ -643,7 +662,6 @@ safety_tolerance = 3
 - `quality`: Image quality ("standard", "hd")
 
 **Output Configuration:**
-- `nb_output`: Number of images to generate
 - `aspect_ratio`: Image dimensions ("1:1", "16:9", "9:16", etc.)
 - `output_format`: File format ("png", "jpeg", "webp")
 - `background`: Background type ("default", "transparent")
@@ -797,7 +815,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```
 
@@ -907,7 +925,7 @@ pretty_print(gantt_chart, title="Gantt Chart")
 The input memory is a dictionary, where the key is the name of the input variable and the value provides details to make it a stuff object. The relevant definitions are:
 ```python
 StuffContentOrData = dict[str, Any] | StuffContent | list[Any] | str
-ImplicitMemory = dict[str, StuffContentOrData]
+PipelineInputs = dict[str, StuffContentOrData]
 ```
 As you can seen, we made it so different ways can be used to define that stuff using structured content or data.
 
@@ -1105,7 +1123,7 @@ Presets are meant to record the choice of an llm with its hyper parameters (temp
 
 Examples:
 ```toml
-llm_to_reason = { model = "base-claude", temperature = 1 }
+llm_for_complex_reasoning = { model = "base-claude", temperature = 1 }
 llm_to_extract_invoice = { model = "claude-3-7-sonnet", temperature = 0.1, max_tokens = "auto" }
 ```