diff --git a/.cursor/rules/llms.mdc b/.cursor/rules/llms.mdc
index a21831a..8babc76 100644
--- a/.cursor/rules/llms.mdc
+++ b/.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" }
 ```
 
diff --git a/.cursor/rules/python_standards.mdc b/.cursor/rules/python_standards.mdc
index 13d3c5a..2e2e0a9 100644
--- a/.cursor/rules/python_standards.mdc
+++ b/.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.
diff --git a/.cursor/rules/run_pipelex.mdc b/.cursor/rules/run_pipelex.mdc
index 5f29c45..eebaa22 100644
--- a/.cursor/rules/run_pipelex.mdc
+++ b/.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.
 
diff --git a/.cursor/rules/write_pipelex.mdc b/.cursor/rules/write_pipelex.mdc
index 6e3339c..841da97 100644
--- a/.cursor/rules/write_pipelex.mdc
+++ b/.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" }
 ```
 
diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md
index 4d612f7..b467128 100644
--- a/.github/copilot-instructions.md
+++ b/.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" }
 ```
 
diff --git a/.windsurfrules.md b/.windsurfrules.md
index 4d612f7..b467128 100644
--- a/.windsurfrules.md
+++ b/.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" }
 ```
 
diff --git a/AGENTS.md b/AGENTS.md
index 4d612f7..b467128 100644
--- a/AGENTS.md
+++ b/AGENTS.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" }
 ```
 
diff --git a/BLACKBOX_RULES.md b/BLACKBOX_RULES.md
index 4d612f7..b467128 100644
--- a/BLACKBOX_RULES.md
+++ b/BLACKBOX_RULES.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" }
 ```
 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b2fec6a..2c0a2fd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,9 @@
 # Changelog
 
+## [v0.4.0] - 2025-10-21
+
+- Bump `pipelex` to `v0.13.0`: See `Pipelex` changelog [here](https://docs.pipelex.com/changelog/)
+
 ## [v0.3.0] - 2025-10-15
 
 - Bump `pipelex` to `v0.12.0`: See `Pipelex` changelog [here](https://docs.pipelex.com/changelog/)
diff --git a/CLAUDE.md b/CLAUDE.md
index 4d612f7..b467128 100644
--- a/CLAUDE.md
+++ b/CLAUDE.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" }
 ```
 
diff --git a/pyproject.toml b/pyproject.toml
index 519158f..bdc2adb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "my-project"
-version = "0.3.0"
+version = "0.4.0"
 description = "Replace this with your project description"
 # authors = [{ name = "Your Name", email = "your.email@example.com" }]
 license = "MIT"
@@ -17,7 +17,7 @@ classifiers = [
 ]
 
 dependencies = [
-    "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal] @ git+https://github.com/Pipelex/pipelex.git",
+    "pipelex[mistralai,anthropic,google,google-genai,bedrock,fal]==0.13.0",
 ]
 
 [project.optional-dependencies]
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 789f55a..08acf11 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -103,15 +103,16 @@ annotated-types==0.7.0 \
     --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
     --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
     # via pydantic
-anthropic==0.52.1 \
-    --hash=sha256:807cee7ebc5503753da0403a77932decf5a4c036041ddda58b4edcdb2a3da551 \
-    --hash=sha256:da4a7c3aeac0170cb45a42dc3369ca1fcf2b3238edf845cb056505d4b0c42fcf
+anthropic==0.69.0 \
+    --hash=sha256:1f73193040f33f11e27c2cd6ec25f24fe7c3f193dc1c5cde6b7a08b18a16bcc5 \
+    --hash=sha256:c604d287f4d73640f40bd2c0f3265a2eb6ce034217ead0608f6b07a8bc5ae5f2
     # via pipelex
 anyio==4.9.0 \
     --hash=sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028 \
     --hash=sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c
     # via
     #   anthropic
+    #   google-genai
     #   httpx
     #   openai
 async-timeout==5.0.1 ; python_full_version < '3.11' \
@@ -161,57 +162,6 @@ certifi==2025.4.26 \
     #   httpcore
     #   httpx
     #   requests
-cffi==1.17.1 \
-    --hash=sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8 \
-    --hash=sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2 \
-    --hash=sha256:0f048dcf80db46f0098ccac01132761580d28e28bc0f78ae0d58048063317e15 \
-    --hash=sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36 \
-    --hash=sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824 \
-    --hash=sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17 \
-    --hash=sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf \
-    --hash=sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3 \
-    --hash=sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed \
-    --hash=sha256:45398b671ac6d70e67da8e4224a065cec6a93541bb7aebe1b198a61b58c7b702 \
-    --hash=sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1 \
-    --hash=sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8 \
-    --hash=sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903 \
-    --hash=sha256:5da5719280082ac6bd9aa7becb3938dc9f9cbd57fac7d2871717b1feb0902ab6 \
-    --hash=sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d \
-    --hash=sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e \
-    --hash=sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be \
-    --hash=sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683 \
-    --hash=sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9 \
-    --hash=sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c \
-    --hash=sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4 \
-    --hash=sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655 \
-    --hash=sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67 \
-    --hash=sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65 \
-    --hash=sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41 \
-    --hash=sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6 \
-    --hash=sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401 \
-    --hash=sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6 \
-    --hash=sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3 \
-    --hash=sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93 \
-    --hash=sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4 \
-    --hash=sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c \
-    --hash=sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0 \
-    --hash=sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3 \
-    --hash=sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff \
-    --hash=sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5 \
-    --hash=sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd \
-    --hash=sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f \
-    --hash=sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5 \
-    --hash=sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14 \
-    --hash=sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d \
-    --hash=sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382 \
-    --hash=sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e \
-    --hash=sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a \
-    --hash=sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4 \
-    --hash=sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99 \
-    --hash=sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b
-    # via
-    #   cryptography
-    #   pynacl
 charset-normalizer==3.4.2 \
     --hash=sha256:0c29de6a1a95f24b9a1aa7aefd27d2487263f00dfd55a77719b530788f75cff7 \
     --hash=sha256:0c8c57f84ccfc871a48a47321cfa49ae1df56cd1d965a09abe84066f6853b9c0 \
@@ -272,10 +222,6 @@ click==8.2.1 \
     --hash=sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202 \
     --hash=sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b
     # via typer
-cocode==0.1.2 \
-    --hash=sha256:32aba620170da4e1855394fdf37274bf443a98fba98f0d5da1d89a06f06aa8c6 \
-    --hash=sha256:63b7f9c51bd18c55f485051d714577a5b755498a20e6a9c69fa9ed734fb47801
-    # via my-project
 colorama==0.4.6 ; sys_platform == 'win32' \
     --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
     --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
@@ -283,49 +229,6 @@ colorama==0.4.6 ; sys_platform == 'win32' \
     #   click
     #   pytest
     #   tqdm
-cryptography==45.0.7 \
-    --hash=sha256:06ce84dc14df0bf6ea84666f958e6080cdb6fe1231be2a51f3fc1267d9f3fb34 \
-    --hash=sha256:16ede8a4f7929b4b7ff3642eba2bf79aa1d71f24ab6ee443935c0d269b6bc513 \
-    --hash=sha256:18fcf70f243fe07252dcb1b268a687f2358025ce32f9f88028ca5c364b123ef5 \
-    --hash=sha256:1993a1bb7e4eccfb922b6cd414f072e08ff5816702a0bdb8941c247a6b1b287c \
-    --hash=sha256:1f3d56f73595376f4244646dd5c5870c14c196949807be39e79e7bd9bac3da63 \
-    --hash=sha256:258e0dff86d1d891169b5af222d362468a9570e2532923088658aa866eb11130 \
-    --hash=sha256:2f641b64acc00811da98df63df7d59fd4706c0df449da71cb7ac39a0732b40ae \
-    --hash=sha256:3808e6b2e5f0b46d981c24d79648e5c25c35e59902ea4391a0dcb3e667bf7443 \
-    --hash=sha256:3994c809c17fc570c2af12c9b840d7cea85a9fd3e5c0e0491f4fa3c029216d59 \
-    --hash=sha256:3be4f21c6245930688bd9e162829480de027f8bf962ede33d4f8ba7d67a00cee \
-    --hash=sha256:465ccac9d70115cd4de7186e60cfe989de73f7bb23e8a7aa45af18f7412e75bf \
-    --hash=sha256:48c41a44ef8b8c2e80ca4527ee81daa4c527df3ecbc9423c41a420a9559d0e27 \
-    --hash=sha256:4a862753b36620af6fc54209264f92c716367f2f0ff4624952276a6bbd18cbde \
-    --hash=sha256:4b1654dfc64ea479c242508eb8c724044f1e964a47d1d1cacc5132292d851971 \
-    --hash=sha256:4bd3e5c4b9682bc112d634f2c6ccc6736ed3635fc3319ac2bb11d768cc5a00d8 \
-    --hash=sha256:577470e39e60a6cd7780793202e63536026d9b8641de011ed9d8174da9ca5339 \
-    --hash=sha256:67285f8a611b0ebc0857ced2081e30302909f571a46bfa7a3cc0ad303fe015c6 \
-    --hash=sha256:7285a89df4900ed3bfaad5679b1e668cb4b38a8de1ccbfc84b05f34512da0a90 \
-    --hash=sha256:81823935e2f8d476707e85a78a405953a03ef7b7b4f55f93f7c2d9680e5e0691 \
-    --hash=sha256:8978132287a9d3ad6b54fcd1e08548033cc09dc6aacacb6c004c73c3eb5d3ac3 \
-    --hash=sha256:a20e442e917889d1a6b3c570c9e3fa2fdc398c20868abcea268ea33c024c4083 \
-    --hash=sha256:a24ee598d10befaec178efdff6054bc4d7e883f615bfbcd08126a0f4931c83a6 \
-    --hash=sha256:b04f85ac3a90c227b6e5890acb0edbaf3140938dbecf07bff618bf3638578cf1 \
-    --hash=sha256:b6a0e535baec27b528cb07a119f321ac024592388c5681a5ced167ae98e9fff3 \
-    --hash=sha256:bef32a5e327bd8e5af915d3416ffefdbe65ed975b646b3805be81b23580b57b8 \
-    --hash=sha256:bfb4c801f65dd61cedfc61a83732327fafbac55a47282e6f26f073ca7a41c3b2 \
-    --hash=sha256:c13b1e3afd29a5b3b2656257f14669ca8fa8d7956d509926f0b130b600b50ab7 \
-    --hash=sha256:c987dad82e8c65ebc985f5dae5e74a3beda9d0a2a4daf8a1115f3772b59e5141 \
-    --hash=sha256:ce7a453385e4c4693985b4a4a3533e041558851eae061a58a5405363b098fcd3 \
-    --hash=sha256:d0c5c6bac22b177bf8da7435d9d27a6834ee130309749d162b26c3105c0795a9 \
-    --hash=sha256:d97cf502abe2ab9eff8bd5e4aca274da8d06dd3ef08b759a8d6143f4ad65d4b4 \
-    --hash=sha256:dad43797959a74103cb59c5dac71409f9c27d34c8a05921341fb64ea8ccb1dd4 \
-    --hash=sha256:dd342f085542f6eb894ca00ef70236ea46070c8a13824c6bde0dfdcd36065b9b \
-    --hash=sha256:de58755d723e86175756f463f2f0bddd45cc36fbd62601228a3f8761c9f58252 \
-    --hash=sha256:f3df7b3d0f91b88b2106031fd995802a2e9ae13e02c36c1fc075b43f420f3a17 \
-    --hash=sha256:f5414a788ecc6ee6bc58560e85ca624258a55ca434884445440a810796ea0e0b \
-    --hash=sha256:fa26fa54c0a9384c27fcdc905a2fb7d60ac6e47d14bc2692145f2b3b1e2cfdbd
-    # via pyjwt
-deprecated==1.2.18 \
-    --hash=sha256:422b6f6d859da6f2ef57857761bfb392480502a64c3028ca9bbe86085d72115d \
-    --hash=sha256:bd5011788200372a32418f888e326a09ff80d0214bd961147cfed01b5c018eec
-    # via pygithub
 distro==1.9.0 \
     --hash=sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed \
     --hash=sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2
@@ -335,11 +238,9 @@ distro==1.9.0 \
 docstring-parser==0.16 \
     --hash=sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e \
     --hash=sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637
-    # via instructor
-et-xmlfile==2.0.0 \
-    --hash=sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa \
-    --hash=sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54
-    # via openpyxl
+    # via
+    #   anthropic
+    #   instructor
 eval-type-backport==0.2.2 \
     --hash=sha256:cb6ad7c393517f476f96d456d0412ea80f0a8cf96f6892834cd9340149111b0a \
     --hash=sha256:f0576b4cf01ebb5bd358d02314d31846af5e07678387486e2c798af0e7d849c1
@@ -456,11 +357,19 @@ frozenlist==1.6.0 \
 google-auth==2.40.2 \
     --hash=sha256:a33cde547a2134273226fa4b853883559947ebe9207521f7afc707efbf690f58 \
     --hash=sha256:f7e568d42eedfded58734f6a60c58321896a621f7c116c411550a4b4a13da90b
-    # via google-auth-oauthlib
+    # via
+    #   google-auth-oauthlib
+    #   google-genai
 google-auth-oauthlib==1.2.2 \
     --hash=sha256:11046fb8d3348b296302dd939ace8af0a724042e8029c1b872d87fabc9f41684 \
     --hash=sha256:fd619506f4b3908b5df17b65f39ca8d66ea56986e5472eb5978fd8f3786f00a2
     # via pipelex
+google-genai==1.41.0 \
+    --hash=sha256:111a3ee64c1a0927d3879faddb368234594432479a40c311e5fe4db338ca8778 \
+    --hash=sha256:134f861bb0ace4e34af0501ecb75ceee15f7662fd8120698cd185e8cb39f2800
+    # via
+    #   instructor
+    #   pipelex
 h11==0.16.0 \
     --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \
     --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86
@@ -475,6 +384,7 @@ httpx==0.28.1 \
     # via
     #   anthropic
     #   fal-client
+    #   google-genai
     #   mistralai
     #   openai
     #   pipelex
@@ -575,9 +485,13 @@ jsonpath-python==1.0.6 \
     --hash=sha256:1e3b78df579f5efc23565293612decee04214609208a2335884b3ee3f786b575 \
     --hash=sha256:dd5be4a72d8a2995c3f583cf82bf3cd1a9544cfdabf2d22595b67aff07349666
     # via mistralai
-kajson==0.3.0 \
-    --hash=sha256:5ea8e164cd7fc96877bf4038cf64c286ac4606814146c24dc9ecc4a2c6ac047d \
-    --hash=sha256:d8cc72875ebef6ea44c06c96fe63accf5b0721ca24ea1e593814dd45fd25bf15
+jsonref==1.1.0 \
+    --hash=sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552 \
+    --hash=sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9
+    # via instructor
+kajson==0.3.1 \
+    --hash=sha256:a170883f04a9c5e72a3831ce65720324b078483e0360e6f249400e1c4b8d9ffa \
+    --hash=sha256:c129b5dd66f757ed8077c49803ea290f91ba6f5699717eb97b65d960bcc83ac4
     # via pipelex
 markdown==3.8 \
     --hash=sha256:794a929b79c5af141ef5ab0f2f642d0f7b1872981250230e72682346f7cc90dc \
@@ -842,9 +756,7 @@ numpy==2.2.6 \
     --hash=sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249 \
     --hash=sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de \
     --hash=sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8
-    # via
-    #   pandas
-    #   types-networkx
+    # via types-networkx
 oauthlib==3.2.2 \
     --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
     --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
@@ -855,53 +767,12 @@ openai==1.82.1 \
     # via
     #   instructor
     #   pipelex
-openpyxl==3.1.5 \
-    --hash=sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2 \
-    --hash=sha256:cf0e3cf56142039133628b5acffe8ef0c12bc902d2aadd3e0fe5878dc08d1050
-    # via pipelex
 packaging==25.0 \
     --hash=sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 \
     --hash=sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f
     # via
     #   pytest
     #   pytest-sugar
-pandas==2.2.3 \
-    --hash=sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a \
-    --hash=sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d \
-    --hash=sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5 \
-    --hash=sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4 \
-    --hash=sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0 \
-    --hash=sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32 \
-    --hash=sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28 \
-    --hash=sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f \
-    --hash=sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348 \
-    --hash=sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18 \
-    --hash=sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468 \
-    --hash=sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5 \
-    --hash=sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667 \
-    --hash=sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645 \
-    --hash=sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13 \
-    --hash=sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3 \
-    --hash=sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d \
-    --hash=sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb \
-    --hash=sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3 \
-    --hash=sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039 \
-    --hash=sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8 \
-    --hash=sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd \
-    --hash=sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659 \
-    --hash=sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57 \
-    --hash=sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4 \
-    --hash=sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a \
-    --hash=sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9 \
-    --hash=sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42 \
-    --hash=sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2 \
-    --hash=sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc \
-    --hash=sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698 \
-    --hash=sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed \
-    --hash=sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015 \
-    --hash=sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24 \
-    --hash=sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319
-    # via pipelex
 pathspec==0.12.1 \
     --hash=sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08 \
     --hash=sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712
@@ -978,10 +849,10 @@ pillow==11.2.1 \
     --hash=sha256:fdec757fea0b793056419bca3e9932eb2b0ceec90ef4813ea4c1e072c389eb28 \
     --hash=sha256:fe15238d3798788d00716637b3d4e7bb6bde18b26e5d08335a96e88564a36b6b
     # via pipelex
-pipelex @ git+https://github.com/Pipelex/pipelex.git@9d93639f6abe0beeb485138a78ae14042537cf17
-    # via
-    #   cocode
-    #   my-project
+pipelex==0.13.0 \
+    --hash=sha256:357fe4e5a61814deac3dd87de9c650f7a842ba81f8603d92a5097a688e5456bd \
+    --hash=sha256:70abab4d8e27c768898e7037317e9f869677cf2e66ad542c4d5e04d6ead8d5c3
+    # via my-project
 pluggy==1.6.0 \
     --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \
     --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746
@@ -1086,15 +957,12 @@ pyasn1-modules==0.4.2 \
     --hash=sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a \
     --hash=sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6
     # via google-auth
-pycparser==2.22 \
-    --hash=sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6 \
-    --hash=sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc
-    # via cffi
 pydantic==2.10.6 \
     --hash=sha256:427d664bf0b8a2b34ff5dd0f5a18df00591adcee7198fbd71981054cef37b584 \
     --hash=sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236
     # via
     #   anthropic
+    #   google-genai
     #   instructor
     #   kajson
     #   mistralai
@@ -1169,30 +1037,10 @@ pydantic-core==2.27.2 \
     # via
     #   instructor
     #   pydantic
-pygithub==2.4.0 \
-    --hash=sha256:6601e22627e87bac192f1e2e39c6e6f69a43152cfb8f307cee575879320b3051 \
-    --hash=sha256:81935aa4bdc939fba98fee1cb47422c09157c56a27966476ff92775602b9ee24
-    # via cocode
 pygments==2.19.1 \
     --hash=sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f \
     --hash=sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c
     # via rich
-pyjwt==2.10.1 \
-    --hash=sha256:3cc5772eb20009233caf06e9d8a0577824723b44e6648ee0a2aedb6cf9381953 \
-    --hash=sha256:dcdd193e30abefd5debf142f9adfcdd2b58004e644f25406ffaebd50bd98dacb
-    # via pygithub
-pynacl==1.5.0 \
-    --hash=sha256:06b8f6fa7f5de8d5d2f7573fe8c863c051225a27b61e6860fd047b1775807858 \
-    --hash=sha256:0c84947a22519e013607c9be43706dd42513f9e6ae5d39d3613ca1e142fba44d \
-    --hash=sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93 \
-    --hash=sha256:401002a4aaa07c9414132aaed7f6836ff98f59277a234704ff66878c2ee4a0d1 \
-    --hash=sha256:52cb72a79269189d4e0dc537556f4740f7f0a9ec41c1322598799b0bdad4ef92 \
-    --hash=sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff \
-    --hash=sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba \
-    --hash=sha256:a36d4a9dda1f19ce6e03c9a784a2921a4b726b02e1c736600ca9c22029474394 \
-    --hash=sha256:a422368fc821589c228f4c49438a368831cb5bbc0eab5ebe1d7fac9dded6567b \
-    --hash=sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543
-    # via pygithub
 pypdfium2==4.30.0 \
     --hash=sha256:0dfa61421b5eb68e1188b0b2231e7ba35735aef2d867d86e48ee6cab6975195e \
     --hash=sha256:119b2969a6d6b1e8d55e99caaf05290294f2d0fe49c12a3f17102d01c441bd29 \
@@ -1208,9 +1056,9 @@ pypdfium2==4.30.0 \
     --hash=sha256:f1f78d2189e0ddf9ac2b7a9b9bd4f0c66f54d1389ff6c17e9fd9dc034d06eb3f \
     --hash=sha256:f33bd79e7a09d5f7acca3b0b69ff6c8a488869a7fab48fdf400fec6e20b9c8be
     # via pipelex
-pyright==1.1.398 \
-    --hash=sha256:0a70bfd007d9ea7de1cf9740e1ad1a40a122592cfe22a3f6791b06162ad08753 \
-    --hash=sha256:357a13edd9be8082dc73be51190913e475fa41a6efb6ec0d4b7aab3bc11638d8
+pyright==1.1.406 \
+    --hash=sha256:1d81fb43c2407bf566e97e57abb01c811973fdb21b2df8df59f870f688bdca71 \
+    --hash=sha256:c4872bc58c9643dac09e8a2e74d472c62036910b3bd37a32813989ef7576ea2c
     # via my-project
 pytest==8.3.5 \
     --hash=sha256:c69214aa47deac29fad6c2a4f590b9c4a9fdb16a403176fe154b79c0b4d4d820 \
@@ -1234,15 +1082,10 @@ python-dateutil==2.9.0.post0 \
     #   aiobotocore
     #   botocore
     #   mistralai
-    #   pandas
 python-dotenv==1.1.0 \
     --hash=sha256:41f90bc6f5f177fb41f53e87666db362025010eb28f60a01c9143bfa33a2b2d5 \
     --hash=sha256:d7c01d9e2293916c18baf562d95698754b0dbbb5e74d457c45d4f6561fb9d55d
     # via pipelex
-pytz==2025.2 \
-    --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \
-    --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00
-    # via pandas
 pyyaml==6.0.2 \
     --hash=sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48 \
     --hash=sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086 \
@@ -1286,8 +1129,8 @@ requests==2.32.3 \
     --hash=sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760 \
     --hash=sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6
     # via
+    #   google-genai
     #   instructor
-    #   pygithub
     #   requests-oauthlib
 requests-oauthlib==2.0.0 \
     --hash=sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36 \
@@ -1350,50 +1193,59 @@ sniffio==1.3.1 \
 tenacity==9.1.2 \
     --hash=sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb \
     --hash=sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138
-    # via instructor
+    # via
+    #   google-genai
+    #   instructor
 termcolor==3.1.0 \
     --hash=sha256:591dd26b5c2ce03b9e43f391264626557873ce1d379019786f99b0c2bee140aa \
     --hash=sha256:6a6dd7fbee581909eeec6a756cff1d7f7c376063b14e4a298dc4980309e55970
     # via pytest-sugar
-toml==0.10.2 \
-    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
-    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
-    # via pipelex
-tomli==2.2.1 ; python_full_version < '3.11' \
-    --hash=sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6 \
-    --hash=sha256:02abe224de6ae62c19f090f68da4e27b10af2b93213d36cf44e6e1c5abd19fdd \
-    --hash=sha256:286f0ca2ffeeb5b9bd4fcc8d6c330534323ec51b2f52da063b11c502da16f30c \
-    --hash=sha256:2d0f2fdd22b02c6d81637a3c95f8cd77f995846af7414c5c4b8d0545afa1bc4b \
-    --hash=sha256:33580bccab0338d00994d7f16f4c4ec25b776af3ffaac1ed74e0b3fc95e885a8 \
-    --hash=sha256:400e720fe168c0f8521520190686ef8ef033fb19fc493da09779e592861b78c6 \
-    --hash=sha256:40741994320b232529c802f8bc86da4e1aa9f413db394617b9a256ae0f9a7f77 \
-    --hash=sha256:465af0e0875402f1d226519c9904f37254b3045fc5084697cefb9bdde1ff99ff \
-    --hash=sha256:4a8f6e44de52d5e6c657c9fe83b562f5f4256d8ebbfe4ff922c495620a7f6cea \
-    --hash=sha256:4e340144ad7ae1533cb897d406382b4b6fede8890a03738ff1683af800d54192 \
-    --hash=sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249 \
-    --hash=sha256:6972ca9c9cc9f0acaa56a8ca1ff51e7af152a9f87fb64623e31d5c83700080ee \
-    --hash=sha256:7fc04e92e1d624a4a63c76474610238576942d6b8950a2d7f908a340494e67e4 \
-    --hash=sha256:889f80ef92701b9dbb224e49ec87c645ce5df3fa2cc548664eb8a25e03127a98 \
-    --hash=sha256:8d57ca8095a641b8237d5b079147646153d22552f1c637fd3ba7f4b0b29167a8 \
-    --hash=sha256:8dd28b3e155b80f4d54beb40a441d366adcfe740969820caf156c019fb5c7ec4 \
-    --hash=sha256:9316dc65bed1684c9a98ee68759ceaed29d229e985297003e494aa825ebb0281 \
-    --hash=sha256:a198f10c4d1b1375d7687bc25294306e551bf1abfa4eace6650070a5c1ae2744 \
-    --hash=sha256:a38aa0308e754b0e3c67e344754dff64999ff9b513e691d0e786265c93583c69 \
-    --hash=sha256:a92ef1a44547e894e2a17d24e7557a5e85a9e1d0048b0b5e7541f76c5032cb13 \
-    --hash=sha256:ac065718db92ca818f8d6141b5f66369833d4a80a9d74435a268c52bdfa73140 \
-    --hash=sha256:b82ebccc8c8a36f2094e969560a1b836758481f3dc360ce9a3277c65f374285e \
-    --hash=sha256:c954d2250168d28797dd4e3ac5cf812a406cd5a92674ee4c8f123c889786aa8e \
-    --hash=sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc \
-    --hash=sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff \
-    --hash=sha256:d3f5614314d758649ab2ab3a62d4f2004c825922f9e370b29416484086b264ec \
-    --hash=sha256:d920f33822747519673ee656a4b6ac33e382eca9d331c87770faa3eef562aeb2 \
-    --hash=sha256:db2b95f9de79181805df90bedc5a5ab4c165e6ec3fe99f970d0e302f384ad222 \
-    --hash=sha256:e59e304978767a54663af13c07b3d1af22ddee3bb2fb0618ca1593e4f593a106 \
-    --hash=sha256:e85e99945e688e32d5a35c1ff38ed0b3f41f43fad8df0bdf79f72b2ba7bc5272 \
-    --hash=sha256:ece47d672db52ac607a3d9599a9d48dcb2f2f735c6c2d1f34130085bb12b112a \
-    --hash=sha256:f4039b9cbc3048b2416cc57ab3bda989a6fcf9b36cf8937f01a6e731b64f80d7
+tomli==2.3.0 \
+    --hash=sha256:00b5f5d95bbfc7d12f91ad8c593a1659b6387b43f054104cda404be6bda62456 \
+    --hash=sha256:0a154a9ae14bfcf5d8917a59b51ffd5a3ac1fd149b71b47a3a104ca4edcfa845 \
+    --hash=sha256:0c95ca56fbe89e065c6ead5b593ee64b84a26fca063b5d71a1122bf26e533999 \
+    --hash=sha256:0eea8cc5c5e9f89c9b90c4896a8deefc74f518db5927d0e0e8d4a80953d774d0 \
+    --hash=sha256:1cb4ed918939151a03f33d4242ccd0aa5f11b3547d0cf30f7c74a408a5b99878 \
+    --hash=sha256:4021923f97266babc6ccab9f5068642a0095faa0a51a246a6a02fccbb3514eaf \
+    --hash=sha256:4c2ef0244c75aba9355561272009d934953817c49f47d768070c3c94355c2aa3 \
+    --hash=sha256:4dc4ce8483a5d429ab602f111a93a6ab1ed425eae3122032db7e9acf449451be \
+    --hash=sha256:4f195fe57ecceac95a66a75ac24d9d5fbc98ef0962e09b2eddec5d39375aae52 \
+    --hash=sha256:5192f562738228945d7b13d4930baffda67b69425a7f0da96d360b0a3888136b \
+    --hash=sha256:5e01decd096b1530d97d5d85cb4dff4af2d8347bd35686654a004f8dea20fc67 \
+    --hash=sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549 \
+    --hash=sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba \
+    --hash=sha256:73ee0b47d4dad1c5e996e3cd33b8a76a50167ae5f96a2607cbe8cc773506ab22 \
+    --hash=sha256:74bf8464ff93e413514fefd2be591c3b0b23231a77f901db1eb30d6f712fc42c \
+    --hash=sha256:792262b94d5d0a466afb5bc63c7daa9d75520110971ee269152083270998316f \
+    --hash=sha256:7b0882799624980785240ab732537fcfc372601015c00f7fc367c55308c186f6 \
+    --hash=sha256:883b1c0d6398a6a9d29b508c331fa56adbcdff647f6ace4dfca0f50e90dfd0ba \
+    --hash=sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45 \
+    --hash=sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f \
+    --hash=sha256:940d56ee0410fa17ee1f12b817b37a4d4e4dc4d27340863cc67236c74f582e77 \
+    --hash=sha256:97d5eec30149fd3294270e889b4234023f2c69747e555a27bd708828353ab606 \
+    --hash=sha256:a0e285d2649b78c0d9027570d4da3425bdb49830a6156121360b3f8511ea3441 \
+    --hash=sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0 \
+    --hash=sha256:a4ea38c40145a357d513bffad0ed869f13c1773716cf71ccaa83b0fa0cc4e42f \
+    --hash=sha256:a56212bdcce682e56b0aaf79e869ba5d15a6163f88d5451cbde388d48b13f530 \
+    --hash=sha256:ad805ea85eda330dbad64c7ea7a4556259665bdf9d2672f5dccc740eb9d3ca05 \
+    --hash=sha256:b273fcbd7fc64dc3600c098e39136522650c49bca95df2d11cf3b626422392c8 \
+    --hash=sha256:b5870b50c9db823c595983571d1296a6ff3e1b88f734a4c8f6fc6188397de005 \
+    --hash=sha256:b74a0e59ec5d15127acdabd75ea17726ac4c5178ae51b85bfe39c4f8a278e879 \
+    --hash=sha256:be71c93a63d738597996be9528f4abe628d1adf5e6eb11607bc8fe1a510b5dae \
+    --hash=sha256:c22a8bf253bacc0cf11f35ad9808b6cb75ada2631c2d97c971122583b129afbc \
+    --hash=sha256:c4665508bcbac83a31ff8ab08f424b665200c0e1e645d2bd9ab3d3e557b6185b \
+    --hash=sha256:c5f3ffd1e098dfc032d4d3af5c0ac64f6d286d98bc148698356847b80fa4de1b \
+    --hash=sha256:cebc6fe843e0733ee827a282aca4999b596241195f43b4cc371d64fc6639da9e \
+    --hash=sha256:d1381caf13ab9f300e30dd8feadb3de072aeb86f1d34a8569453ff32a7dea4bf \
+    --hash=sha256:d7d86942e56ded512a594786a5ba0a5e521d02529b3826e7761a05138341a2ac \
+    --hash=sha256:e31d432427dcbf4d86958c184b9bfd1e96b5b71f8eb17e6d02531f434fd335b8 \
+    --hash=sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b \
+    --hash=sha256:f85209946d1fe94416debbb88d00eb92ce9cd5266775424ff81bc959e001acaf \
+    --hash=sha256:feb0dacc61170ed7ab602d3d972a58f14ee3ee60494292d384649a3dc38ef463 \
+    --hash=sha256:ff72b71b5d10d22ecb084d345fc26f42b5143c5533db5e2eaba7d2d335358876
     # via
     #   mypy
+    #   pipelex
     #   pytest
 tomlkit==0.13.3 \
     --hash=sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1 \
@@ -1433,14 +1285,6 @@ types-awscrt==0.27.2 \
     --hash=sha256:49a045f25bbd5ad2865f314512afced933aed35ddbafc252e2268efa8a787e4e \
     --hash=sha256:acd04f57119eb15626ab0ba9157fc24672421de56e7bd7b9f61681fedee44e91
     # via botocore-stubs
-types-beautifulsoup4==4.12.0.20250516 \
-    --hash=sha256:5923399d4a1ba9cc8f0096fe334cc732e130269541d66261bb42ab039c0376ee \
-    --hash=sha256:aa19dd73b33b70d6296adf92da8ab8a0c945c507e6fb7d5db553415cc77b417e
-    # via my-project
-types-html5lib==1.1.11.20250516 \
-    --hash=sha256:5e407b14b1bd2b9b1107cbd1e2e19d4a0c46d60febd231c7ab7313d7405663c1 \
-    --hash=sha256:65043a6718c97f7d52567cc0cdf41efbfc33b1f92c6c0c5e19f60a7ec69ae720
-    # via types-beautifulsoup4
 types-markdown==3.8.0.20250415 \
     --hash=sha256:98ab13587d1177769d93e55586d3dc97047df75bc6e37ce4074666f5dd4212ba \
     --hash=sha256:b41abed474a303ba300e3a4cf6f27eda339219124a59d529a158203570007776
@@ -1457,20 +1301,12 @@ types-pyyaml==6.0.12.20250516 \
     --hash=sha256:8478208feaeb53a34cb5d970c56a7cd76b72659442e733e268a94dc72b2d0530 \
     --hash=sha256:9f21a70216fc0fa1b216a8176db5f9e0af6eb35d2f2932acb87689d03a5bf6ba
     # via my-project
-types-requests==2.32.0.20250515 \
-    --hash=sha256:09c8b63c11318cb2460813871aaa48b671002e59fda67ca909e9883777787581 \
-    --hash=sha256:f8eba93b3a892beee32643ff836993f15a785816acca21ea0ffa006f05ef0fb2
-    # via my-project
 types-s3transfer==0.13.0 \
     --hash=sha256:203dadcb9865c2f68fb44bc0440e1dc05b79197ba4a641c0976c26c9af75ef52 \
     --hash=sha256:79c8375cbf48a64bff7654c02df1ec4b20d74f8c5672fc13e382f593ca5565b3
     # via
     #   boto3-stubs
     #   types-aioboto3
-types-toml==0.10.8.20240310 \
-    --hash=sha256:3d41501302972436a6b8b239c850b26689657e25281b48ff0ec06345b8830331 \
-    --hash=sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d
-    # via my-project
 typing-extensions==4.13.2 \
     --hash=sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c \
     --hash=sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef
@@ -1479,6 +1315,7 @@ typing-extensions==4.13.2 \
     #   anyio
     #   boto3-stubs
     #   exceptiongroup
+    #   google-genai
     #   multidict
     #   mypy
     #   openai
@@ -1486,7 +1323,6 @@ typing-extensions==4.13.2 \
     #   polyfactory
     #   pydantic
     #   pydantic-core
-    #   pygithub
     #   pyright
     #   rich
     #   typer
@@ -1502,17 +1338,67 @@ typing-inspect==0.9.0 \
 tzdata==2025.2 \
     --hash=sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 \
     --hash=sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9
-    # via
-    #   faker
-    #   pandas
+    # via faker
 urllib3==2.4.0 \
     --hash=sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466 \
     --hash=sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813
     # via
     #   botocore
-    #   pygithub
     #   requests
-    #   types-requests
+websockets==15.0.1 \
+    --hash=sha256:0701bc3cfcb9164d04a14b149fd74be7347a530ad3bbf15ab2c678a2cd3dd9a2 \
+    --hash=sha256:0a34631031a8f05657e8e90903e656959234f3a04552259458aac0b0f9ae6fd9 \
+    --hash=sha256:0af68c55afbd5f07986df82831c7bff04846928ea8d1fd7f30052638788bc9b5 \
+    --hash=sha256:0c9e74d766f2818bb95f84c25be4dea09841ac0f734d1966f415e4edfc4ef1c3 \
+    --hash=sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8 \
+    --hash=sha256:0fdfe3e2a29e4db3659dbd5bbf04560cea53dd9610273917799f1cde46aa725e \
+    --hash=sha256:1009ee0c7739c08a0cd59de430d6de452a55e42d6b522de7aa15e6f67db0b8e1 \
+    --hash=sha256:1234d4ef35db82f5446dca8e35a7da7964d02c127b095e172e54397fb6a6c256 \
+    --hash=sha256:16b6c1b3e57799b9d38427dda63edcbe4926352c47cf88588c0be4ace18dac85 \
+    --hash=sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375 \
+    --hash=sha256:27ccee0071a0e75d22cb35849b1db43f2ecd3e161041ac1ee9d2352ddf72f065 \
+    --hash=sha256:39c1fec2c11dc8d89bba6b2bf1556af381611a173ac2b511cf7231622058af41 \
+    --hash=sha256:3be571a8b5afed347da347bfcf27ba12b069d9d7f42cb8c7028b5e98bbb12597 \
+    --hash=sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f \
+    --hash=sha256:3d00075aa65772e7ce9e990cab3ff1de702aa09be3940d1dc88d5abf1ab8a09c \
+    --hash=sha256:3e90baa811a5d73f3ca0bcbf32064d663ed81318ab225ee4f427ad4e26e5aff3 \
+    --hash=sha256:4c2529b320eb9e35af0fa3016c187dffb84a3ecc572bcee7c3ce302bfeba52bf \
+    --hash=sha256:54479983bd5fb469c38f2f5c7e3a24f9a4e70594cd68cd1fa6b9340dadaff7cf \
+    --hash=sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4 \
+    --hash=sha256:5756779642579d902eed757b21b0164cd6fe338506a8083eb58af5c372e39d9a \
+    --hash=sha256:592f1a9fe869c778694f0aa806ba0374e97648ab57936f092fd9d87f8bc03665 \
+    --hash=sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22 \
+    --hash=sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675 \
+    --hash=sha256:5d54b09eba2bada6011aea5375542a157637b91029687eb4fdb2dab11059c1b4 \
+    --hash=sha256:5df592cd503496351d6dc14f7cdad49f268d8e618f80dce0cd5a36b93c3fc08d \
+    --hash=sha256:64dee438fed052b52e4f98f76c5790513235efaa1ef7f3f2192c392cd7c91b65 \
+    --hash=sha256:66dd88c918e3287efc22409d426c8f729688d89a0c587c88971a0faa2c2f3792 \
+    --hash=sha256:678999709e68425ae2593acf2e3ebcbcf2e69885a5ee78f9eb80e6e371f1bf57 \
+    --hash=sha256:693f0192126df6c2327cce3baa7c06f2a117575e32ab2308f7f8216c29d9e2e3 \
+    --hash=sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151 \
+    --hash=sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d \
+    --hash=sha256:76d1f20b1c7a2fa82367e04982e708723ba0e7b8d43aa643d3dcd404d74f1475 \
+    --hash=sha256:823c248b690b2fd9303ba00c4f66cd5e2d8c3ba4aa968b2779be9532a4dad431 \
+    --hash=sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee \
+    --hash=sha256:8dd8327c795b3e3f219760fa603dcae1dcc148172290a8ab15158cf85a953413 \
+    --hash=sha256:8fdc51055e6ff4adeb88d58a11042ec9a5eae317a0a53d12c062c8a8865909e8 \
+    --hash=sha256:ac1e5c9054fe23226fb11e05a6e630837f074174c4c2f0fe442996112a6de4fb \
+    --hash=sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205 \
+    --hash=sha256:b359ed09954d7c18bbc1680f380c7301f92c60bf924171629c5db97febb12f04 \
+    --hash=sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa \
+    --hash=sha256:c338ffa0520bdb12fbc527265235639fb76e7bc7faafbb93f6ba80d9c06578a9 \
+    --hash=sha256:cad21560da69f4ce7658ca2cb83138fb4cf695a2ba3e475e0559e05991aa8122 \
+    --hash=sha256:d50fd1ee42388dcfb2b3676132c78116490976f1300da28eb629272d5d93e905 \
+    --hash=sha256:d5f6b181bb38171a8ad1d6aa58a67a6aa9d4b38d0f8c5f496b9e42561dfc62fe \
+    --hash=sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b \
+    --hash=sha256:d99e5546bf73dbad5bf3547174cd6cb8ba7273062a23808ffea025ecb1cf8562 \
+    --hash=sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561 \
+    --hash=sha256:e8b56bdcdb4505c8078cb6c7157d9811a85790f2f2b3632c7d1462ab5783d215 \
+    --hash=sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931 \
+    --hash=sha256:f29d80eb9a9263b8d109135351caf568cc3f80b9928bccde535c235de55c22d9 \
+    --hash=sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f \
+    --hash=sha256:fcd5cf9e305d7b8338754470cf69cf81f420459dbae8a3b40cee57417f4614a7
+    # via google-genai
 wrapt==1.17.2 \
     --hash=sha256:0a6e821770cf99cc586d33833b2ff32faebdbe886bd6322395606cf55153246c \
     --hash=sha256:0b929ac182f5ace000d459c59c2c9c33047e20e935f8e39371fa6e3b85d56f4a \
@@ -1571,9 +1457,7 @@ wrapt==1.17.2 \
     --hash=sha256:ec89ed91f2fa8e3f52ae53cd3cf640d6feff92ba90d62236a81e4e563ac0e991 \
     --hash=sha256:f09b286faeff3c750a879d336fb6d8713206fc97af3adc14def0cdd349df6000 \
     --hash=sha256:ff04ef6eec3eee8a5efef2401495967a916feaa353643defcc03fc74fe213b58
-    # via
-    #   aiobotocore
-    #   deprecated
+    # via aiobotocore
 yarl==1.20.0 \
     --hash=sha256:04d8cfb12714158abf2618f792c77bc5c3d8c5f37353e79509608be4f18705c9 \
     --hash=sha256:06d06c9d5b5bc3eb56542ceeba6658d31f54cf401e8468512447834856fb0e61 \
diff --git a/requirements.txt b/requirements.txt
index 0cdedbb..d766d96 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -103,15 +103,16 @@ annotated-types==0.7.0 \
     --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
     --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
     # via pydantic
-anthropic==0.52.1 \
-    --hash=sha256:807cee7ebc5503753da0403a77932decf5a4c036041ddda58b4edcdb2a3da551 \
-    --hash=sha256:da4a7c3aeac0170cb45a42dc3369ca1fcf2b3238edf845cb056505d4b0c42fcf
+anthropic==0.69.0 \
+    --hash=sha256:1f73193040f33f11e27c2cd6ec25f24fe7c3f193dc1c5cde6b7a08b18a16bcc5 \
+    --hash=sha256:c604d287f4d73640f40bd2c0f3265a2eb6ce034217ead0608f6b07a8bc5ae5f2
     # via pipelex
 anyio==4.9.0 \
     --hash=sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028 \
     --hash=sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c
     # via
     #   anthropic
+    #   google-genai
     #   httpx
     #   openai
 async-timeout==5.0.1 ; python_full_version < '3.11' \
@@ -225,11 +226,9 @@ distro==1.9.0 \
 docstring-parser==0.16 \
     --hash=sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e \
     --hash=sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637
-    # via instructor
-et-xmlfile==2.0.0 \
-    --hash=sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa \
-    --hash=sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54
-    # via openpyxl
+    # via
+    #   anthropic
+    #   instructor
 eval-type-backport==0.2.2 \
     --hash=sha256:cb6ad7c393517f476f96d456d0412ea80f0a8cf96f6892834cd9340149111b0a \
     --hash=sha256:f0576b4cf01ebb5bd358d02314d31846af5e07678387486e2c798af0e7d849c1
@@ -344,11 +343,19 @@ frozenlist==1.6.0 \
 google-auth==2.40.2 \
     --hash=sha256:a33cde547a2134273226fa4b853883559947ebe9207521f7afc707efbf690f58 \
     --hash=sha256:f7e568d42eedfded58734f6a60c58321896a621f7c116c411550a4b4a13da90b
-    # via google-auth-oauthlib
+    # via
+    #   google-auth-oauthlib
+    #   google-genai
 google-auth-oauthlib==1.2.2 \
     --hash=sha256:11046fb8d3348b296302dd939ace8af0a724042e8029c1b872d87fabc9f41684 \
     --hash=sha256:fd619506f4b3908b5df17b65f39ca8d66ea56986e5472eb5978fd8f3786f00a2
     # via pipelex
+google-genai==1.41.0 \
+    --hash=sha256:111a3ee64c1a0927d3879faddb368234594432479a40c311e5fe4db338ca8778 \
+    --hash=sha256:134f861bb0ace4e34af0501ecb75ceee15f7662fd8120698cd185e8cb39f2800
+    # via
+    #   instructor
+    #   pipelex
 h11==0.16.0 \
     --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \
     --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86
@@ -363,6 +370,7 @@ httpx==0.28.1 \
     # via
     #   anthropic
     #   fal-client
+    #   google-genai
     #   mistralai
     #   openai
     #   pipelex
@@ -459,9 +467,13 @@ jsonpath-python==1.0.6 \
     --hash=sha256:1e3b78df579f5efc23565293612decee04214609208a2335884b3ee3f786b575 \
     --hash=sha256:dd5be4a72d8a2995c3f583cf82bf3cd1a9544cfdabf2d22595b67aff07349666
     # via mistralai
-kajson==0.3.0 \
-    --hash=sha256:5ea8e164cd7fc96877bf4038cf64c286ac4606814146c24dc9ecc4a2c6ac047d \
-    --hash=sha256:d8cc72875ebef6ea44c06c96fe63accf5b0721ca24ea1e593814dd45fd25bf15
+jsonref==1.1.0 \
+    --hash=sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552 \
+    --hash=sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9
+    # via instructor
+kajson==0.3.1 \
+    --hash=sha256:a170883f04a9c5e72a3831ce65720324b078483e0360e6f249400e1c4b8d9ffa \
+    --hash=sha256:c129b5dd66f757ed8077c49803ea290f91ba6f5699717eb97b65d960bcc83ac4
     # via pipelex
 markdown==3.8 \
     --hash=sha256:794a929b79c5af141ef5ab0f2f642d0f7b1872981250230e72682346f7cc90dc \
@@ -636,63 +648,6 @@ networkx==3.5 ; python_full_version >= '3.11' \
     --hash=sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec \
     --hash=sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037
     # via pipelex
-numpy==2.2.6 \
-    --hash=sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff \
-    --hash=sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47 \
-    --hash=sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84 \
-    --hash=sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d \
-    --hash=sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6 \
-    --hash=sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f \
-    --hash=sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b \
-    --hash=sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49 \
-    --hash=sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163 \
-    --hash=sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571 \
-    --hash=sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42 \
-    --hash=sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff \
-    --hash=sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491 \
-    --hash=sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4 \
-    --hash=sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566 \
-    --hash=sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf \
-    --hash=sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40 \
-    --hash=sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd \
-    --hash=sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06 \
-    --hash=sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282 \
-    --hash=sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680 \
-    --hash=sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db \
-    --hash=sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3 \
-    --hash=sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90 \
-    --hash=sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1 \
-    --hash=sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289 \
-    --hash=sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab \
-    --hash=sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c \
-    --hash=sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d \
-    --hash=sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb \
-    --hash=sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d \
-    --hash=sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a \
-    --hash=sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf \
-    --hash=sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1 \
-    --hash=sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2 \
-    --hash=sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a \
-    --hash=sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543 \
-    --hash=sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00 \
-    --hash=sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c \
-    --hash=sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f \
-    --hash=sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd \
-    --hash=sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868 \
-    --hash=sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303 \
-    --hash=sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83 \
-    --hash=sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3 \
-    --hash=sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d \
-    --hash=sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87 \
-    --hash=sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa \
-    --hash=sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f \
-    --hash=sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae \
-    --hash=sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda \
-    --hash=sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915 \
-    --hash=sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249 \
-    --hash=sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de \
-    --hash=sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8
-    # via pandas
 oauthlib==3.2.2 \
     --hash=sha256:8139f29aac13e25d502680e9e19963e83f16838d48a0d71c287fe40e7067fbca \
     --hash=sha256:9859c40929662bec5d64f34d01c99e093149682a3f38915dc0655d5a633dd918
@@ -703,47 +658,6 @@ openai==1.82.1 \
     # via
     #   instructor
     #   pipelex
-openpyxl==3.1.5 \
-    --hash=sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2 \
-    --hash=sha256:cf0e3cf56142039133628b5acffe8ef0c12bc902d2aadd3e0fe5878dc08d1050
-    # via pipelex
-pandas==2.2.3 \
-    --hash=sha256:062309c1b9ea12a50e8ce661145c6aab431b1e99530d3cd60640e255778bd43a \
-    --hash=sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d \
-    --hash=sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5 \
-    --hash=sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4 \
-    --hash=sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0 \
-    --hash=sha256:29401dbfa9ad77319367d36940cd8a0b3a11aba16063e39632d98b0e931ddf32 \
-    --hash=sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28 \
-    --hash=sha256:37e0aced3e8f539eccf2e099f65cdb9c8aa85109b0be6e93e2baff94264bdc6f \
-    --hash=sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348 \
-    --hash=sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18 \
-    --hash=sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468 \
-    --hash=sha256:3fc6873a41186404dad67245896a6e440baacc92f5b716ccd1bc9ed2995ab2c5 \
-    --hash=sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667 \
-    --hash=sha256:56534ce0746a58afaf7942ba4863e0ef81c9c50d3f0ae93e9497d6a41a057645 \
-    --hash=sha256:59ef3764d0fe818125a5097d2ae867ca3fa64df032331b7e0917cf5d7bf66b13 \
-    --hash=sha256:5de54125a92bb4d1c051c0659e6fcb75256bf799a732a87184e5ea503965bce3 \
-    --hash=sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d \
-    --hash=sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb \
-    --hash=sha256:63cc132e40a2e084cf01adf0775b15ac515ba905d7dcca47e9a251819c575ef3 \
-    --hash=sha256:66108071e1b935240e74525006034333f98bcdb87ea116de573a6a0dccb6c039 \
-    --hash=sha256:6dfcb5ee8d4d50c06a51c2fffa6cff6272098ad6540aed1a76d15fb9318194d8 \
-    --hash=sha256:7c2875855b0ff77b2a64a0365e24455d9990730d6431b9e0ee18ad8acee13dbd \
-    --hash=sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659 \
-    --hash=sha256:86976a1c5b25ae3f8ccae3a5306e443569ee3c3faf444dfd0f41cda24667ad57 \
-    --hash=sha256:a5a1595fe639f5988ba6a8e5bc9649af3baf26df3998a0abe56c02609392e0a4 \
-    --hash=sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a \
-    --hash=sha256:b1d432e8d08679a40e2a6d8b2f9770a5c21793a6f9f47fdd52c5ce1948a5a8a9 \
-    --hash=sha256:b8661b0238a69d7aafe156b7fa86c44b881387509653fdf857bebc5e4008ad42 \
-    --hash=sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2 \
-    --hash=sha256:c124333816c3a9b03fbeef3a9f230ba9a737e9e5bb4060aa2107a86cc0a497fc \
-    --hash=sha256:cd8d0c3be0515c12fed0bdbae072551c8b54b7192c7b1fda0ba56059a0179698 \
-    --hash=sha256:d9c45366def9a3dd85a6454c0e7908f2b3b8e9c138f5dc38fed7ce720d8453ed \
-    --hash=sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015 \
-    --hash=sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24 \
-    --hash=sha256:fffb8ae78d8af97f849404f21411c95062db1496aeb3e56f146f0355c9989319
-    # via pipelex
 pillow==11.2.1 \
     --hash=sha256:036e53f4170e270ddb8797d4c590e6dd14d28e15c7da375c18978045f7e6c37b \
     --hash=sha256:062b7a42d672c45a70fa1f8b43d1d38ff76b63421cbbe7f88146b39e8a558d91 \
@@ -816,7 +730,9 @@ pillow==11.2.1 \
     --hash=sha256:fdec757fea0b793056419bca3e9932eb2b0ceec90ef4813ea4c1e072c389eb28 \
     --hash=sha256:fe15238d3798788d00716637b3d4e7bb6bde18b26e5d08335a96e88564a36b6b
     # via pipelex
-pipelex @ git+https://github.com/Pipelex/pipelex.git@9d93639f6abe0beeb485138a78ae14042537cf17
+pipelex==0.13.0 \
+    --hash=sha256:357fe4e5a61814deac3dd87de9c650f7a842ba81f8603d92a5097a688e5456bd \
+    --hash=sha256:70abab4d8e27c768898e7037317e9f869677cf2e66ad542c4d5e04d6ead8d5c3
     # via my-project
 polyfactory==2.21.0 \
     --hash=sha256:9483b764756c8622313d99f375889b1c0d92f09affb05742d7bcfa2b5198d8c5 \
@@ -923,6 +839,7 @@ pydantic==2.10.6 \
     --hash=sha256:ca5daa827cce33de7a42be142548b0096bf05a7e7b365aebfa5f8eeec7128236
     # via
     #   anthropic
+    #   google-genai
     #   instructor
     #   kajson
     #   mistralai
@@ -1023,15 +940,10 @@ python-dateutil==2.9.0.post0 \
     #   aiobotocore
     #   botocore
     #   mistralai
-    #   pandas
 python-dotenv==1.1.0 \
     --hash=sha256:41f90bc6f5f177fb41f53e87666db362025010eb28f60a01c9143bfa33a2b2d5 \
     --hash=sha256:d7c01d9e2293916c18baf562d95698754b0dbbb5e74d457c45d4f6561fb9d55d
     # via pipelex
-pytz==2025.2 \
-    --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \
-    --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00
-    # via pandas
 pyyaml==6.0.2 \
     --hash=sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48 \
     --hash=sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086 \
@@ -1075,6 +987,7 @@ requests==2.32.3 \
     --hash=sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760 \
     --hash=sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6
     # via
+    #   google-genai
     #   instructor
     #   requests-oauthlib
 requests-oauthlib==2.0.0 \
@@ -1118,10 +1031,52 @@ sniffio==1.3.1 \
 tenacity==9.1.2 \
     --hash=sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb \
     --hash=sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138
-    # via instructor
-toml==0.10.2 \
-    --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \
-    --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f
+    # via
+    #   google-genai
+    #   instructor
+tomli==2.3.0 \
+    --hash=sha256:00b5f5d95bbfc7d12f91ad8c593a1659b6387b43f054104cda404be6bda62456 \
+    --hash=sha256:0a154a9ae14bfcf5d8917a59b51ffd5a3ac1fd149b71b47a3a104ca4edcfa845 \
+    --hash=sha256:0c95ca56fbe89e065c6ead5b593ee64b84a26fca063b5d71a1122bf26e533999 \
+    --hash=sha256:0eea8cc5c5e9f89c9b90c4896a8deefc74f518db5927d0e0e8d4a80953d774d0 \
+    --hash=sha256:1cb4ed918939151a03f33d4242ccd0aa5f11b3547d0cf30f7c74a408a5b99878 \
+    --hash=sha256:4021923f97266babc6ccab9f5068642a0095faa0a51a246a6a02fccbb3514eaf \
+    --hash=sha256:4c2ef0244c75aba9355561272009d934953817c49f47d768070c3c94355c2aa3 \
+    --hash=sha256:4dc4ce8483a5d429ab602f111a93a6ab1ed425eae3122032db7e9acf449451be \
+    --hash=sha256:4f195fe57ecceac95a66a75ac24d9d5fbc98ef0962e09b2eddec5d39375aae52 \
+    --hash=sha256:5192f562738228945d7b13d4930baffda67b69425a7f0da96d360b0a3888136b \
+    --hash=sha256:5e01decd096b1530d97d5d85cb4dff4af2d8347bd35686654a004f8dea20fc67 \
+    --hash=sha256:64be704a875d2a59753d80ee8a533c3fe183e3f06807ff7dc2232938ccb01549 \
+    --hash=sha256:70a251f8d4ba2d9ac2542eecf008b3c8a9fc5c3f9f02c56a9d7952612be2fdba \
+    --hash=sha256:73ee0b47d4dad1c5e996e3cd33b8a76a50167ae5f96a2607cbe8cc773506ab22 \
+    --hash=sha256:74bf8464ff93e413514fefd2be591c3b0b23231a77f901db1eb30d6f712fc42c \
+    --hash=sha256:792262b94d5d0a466afb5bc63c7daa9d75520110971ee269152083270998316f \
+    --hash=sha256:7b0882799624980785240ab732537fcfc372601015c00f7fc367c55308c186f6 \
+    --hash=sha256:883b1c0d6398a6a9d29b508c331fa56adbcdff647f6ace4dfca0f50e90dfd0ba \
+    --hash=sha256:88bd15eb972f3664f5ed4b57c1634a97153b4bac4479dcb6a495f41921eb7f45 \
+    --hash=sha256:8a35dd0e643bb2610f156cca8db95d213a90015c11fee76c946aa62b7ae7e02f \
+    --hash=sha256:940d56ee0410fa17ee1f12b817b37a4d4e4dc4d27340863cc67236c74f582e77 \
+    --hash=sha256:97d5eec30149fd3294270e889b4234023f2c69747e555a27bd708828353ab606 \
+    --hash=sha256:a0e285d2649b78c0d9027570d4da3425bdb49830a6156121360b3f8511ea3441 \
+    --hash=sha256:a1f7f282fe248311650081faafa5f4732bdbfef5d45fe3f2e702fbc6f2d496e0 \
+    --hash=sha256:a4ea38c40145a357d513bffad0ed869f13c1773716cf71ccaa83b0fa0cc4e42f \
+    --hash=sha256:a56212bdcce682e56b0aaf79e869ba5d15a6163f88d5451cbde388d48b13f530 \
+    --hash=sha256:ad805ea85eda330dbad64c7ea7a4556259665bdf9d2672f5dccc740eb9d3ca05 \
+    --hash=sha256:b273fcbd7fc64dc3600c098e39136522650c49bca95df2d11cf3b626422392c8 \
+    --hash=sha256:b5870b50c9db823c595983571d1296a6ff3e1b88f734a4c8f6fc6188397de005 \
+    --hash=sha256:b74a0e59ec5d15127acdabd75ea17726ac4c5178ae51b85bfe39c4f8a278e879 \
+    --hash=sha256:be71c93a63d738597996be9528f4abe628d1adf5e6eb11607bc8fe1a510b5dae \
+    --hash=sha256:c22a8bf253bacc0cf11f35ad9808b6cb75ada2631c2d97c971122583b129afbc \
+    --hash=sha256:c4665508bcbac83a31ff8ab08f424b665200c0e1e645d2bd9ab3d3e557b6185b \
+    --hash=sha256:c5f3ffd1e098dfc032d4d3af5c0ac64f6d286d98bc148698356847b80fa4de1b \
+    --hash=sha256:cebc6fe843e0733ee827a282aca4999b596241195f43b4cc371d64fc6639da9e \
+    --hash=sha256:d1381caf13ab9f300e30dd8feadb3de072aeb86f1d34a8569453ff32a7dea4bf \
+    --hash=sha256:d7d86942e56ded512a594786a5ba0a5e521d02529b3826e7761a05138341a2ac \
+    --hash=sha256:e31d432427dcbf4d86958c184b9bfd1e96b5b71f8eb17e6d02531f434fd335b8 \
+    --hash=sha256:e95b1af3c5b07d9e643909b5abbec77cd9f1217e6d0bca72b0234736b9fb1f1b \
+    --hash=sha256:f85209946d1fe94416debbb88d00eb92ce9cd5266775424ff81bc959e001acaf \
+    --hash=sha256:feb0dacc61170ed7ab602d3d972a58f14ee3ee60494292d384649a3dc38ef463 \
+    --hash=sha256:ff72b71b5d10d22ecb084d345fc26f42b5143c5533db5e2eaba7d2d335358876
     # via pipelex
 tomlkit==0.13.3 \
     --hash=sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1 \
@@ -1144,6 +1099,7 @@ typing-extensions==4.13.2 \
     #   anthropic
     #   anyio
     #   exceptiongroup
+    #   google-genai
     #   multidict
     #   openai
     #   pipelex
@@ -1160,15 +1116,67 @@ typing-inspect==0.9.0 \
 tzdata==2025.2 \
     --hash=sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 \
     --hash=sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9
-    # via
-    #   faker
-    #   pandas
+    # via faker
 urllib3==2.4.0 \
     --hash=sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466 \
     --hash=sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813
     # via
     #   botocore
     #   requests
+websockets==15.0.1 \
+    --hash=sha256:0701bc3cfcb9164d04a14b149fd74be7347a530ad3bbf15ab2c678a2cd3dd9a2 \
+    --hash=sha256:0a34631031a8f05657e8e90903e656959234f3a04552259458aac0b0f9ae6fd9 \
+    --hash=sha256:0af68c55afbd5f07986df82831c7bff04846928ea8d1fd7f30052638788bc9b5 \
+    --hash=sha256:0c9e74d766f2818bb95f84c25be4dea09841ac0f734d1966f415e4edfc4ef1c3 \
+    --hash=sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8 \
+    --hash=sha256:0fdfe3e2a29e4db3659dbd5bbf04560cea53dd9610273917799f1cde46aa725e \
+    --hash=sha256:1009ee0c7739c08a0cd59de430d6de452a55e42d6b522de7aa15e6f67db0b8e1 \
+    --hash=sha256:1234d4ef35db82f5446dca8e35a7da7964d02c127b095e172e54397fb6a6c256 \
+    --hash=sha256:16b6c1b3e57799b9d38427dda63edcbe4926352c47cf88588c0be4ace18dac85 \
+    --hash=sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375 \
+    --hash=sha256:27ccee0071a0e75d22cb35849b1db43f2ecd3e161041ac1ee9d2352ddf72f065 \
+    --hash=sha256:39c1fec2c11dc8d89bba6b2bf1556af381611a173ac2b511cf7231622058af41 \
+    --hash=sha256:3be571a8b5afed347da347bfcf27ba12b069d9d7f42cb8c7028b5e98bbb12597 \
+    --hash=sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f \
+    --hash=sha256:3d00075aa65772e7ce9e990cab3ff1de702aa09be3940d1dc88d5abf1ab8a09c \
+    --hash=sha256:3e90baa811a5d73f3ca0bcbf32064d663ed81318ab225ee4f427ad4e26e5aff3 \
+    --hash=sha256:4c2529b320eb9e35af0fa3016c187dffb84a3ecc572bcee7c3ce302bfeba52bf \
+    --hash=sha256:54479983bd5fb469c38f2f5c7e3a24f9a4e70594cd68cd1fa6b9340dadaff7cf \
+    --hash=sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4 \
+    --hash=sha256:5756779642579d902eed757b21b0164cd6fe338506a8083eb58af5c372e39d9a \
+    --hash=sha256:592f1a9fe869c778694f0aa806ba0374e97648ab57936f092fd9d87f8bc03665 \
+    --hash=sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22 \
+    --hash=sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675 \
+    --hash=sha256:5d54b09eba2bada6011aea5375542a157637b91029687eb4fdb2dab11059c1b4 \
+    --hash=sha256:5df592cd503496351d6dc14f7cdad49f268d8e618f80dce0cd5a36b93c3fc08d \
+    --hash=sha256:64dee438fed052b52e4f98f76c5790513235efaa1ef7f3f2192c392cd7c91b65 \
+    --hash=sha256:66dd88c918e3287efc22409d426c8f729688d89a0c587c88971a0faa2c2f3792 \
+    --hash=sha256:678999709e68425ae2593acf2e3ebcbcf2e69885a5ee78f9eb80e6e371f1bf57 \
+    --hash=sha256:693f0192126df6c2327cce3baa7c06f2a117575e32ab2308f7f8216c29d9e2e3 \
+    --hash=sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151 \
+    --hash=sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d \
+    --hash=sha256:76d1f20b1c7a2fa82367e04982e708723ba0e7b8d43aa643d3dcd404d74f1475 \
+    --hash=sha256:823c248b690b2fd9303ba00c4f66cd5e2d8c3ba4aa968b2779be9532a4dad431 \
+    --hash=sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee \
+    --hash=sha256:8dd8327c795b3e3f219760fa603dcae1dcc148172290a8ab15158cf85a953413 \
+    --hash=sha256:8fdc51055e6ff4adeb88d58a11042ec9a5eae317a0a53d12c062c8a8865909e8 \
+    --hash=sha256:ac1e5c9054fe23226fb11e05a6e630837f074174c4c2f0fe442996112a6de4fb \
+    --hash=sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205 \
+    --hash=sha256:b359ed09954d7c18bbc1680f380c7301f92c60bf924171629c5db97febb12f04 \
+    --hash=sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa \
+    --hash=sha256:c338ffa0520bdb12fbc527265235639fb76e7bc7faafbb93f6ba80d9c06578a9 \
+    --hash=sha256:cad21560da69f4ce7658ca2cb83138fb4cf695a2ba3e475e0559e05991aa8122 \
+    --hash=sha256:d50fd1ee42388dcfb2b3676132c78116490976f1300da28eb629272d5d93e905 \
+    --hash=sha256:d5f6b181bb38171a8ad1d6aa58a67a6aa9d4b38d0f8c5f496b9e42561dfc62fe \
+    --hash=sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b \
+    --hash=sha256:d99e5546bf73dbad5bf3547174cd6cb8ba7273062a23808ffea025ecb1cf8562 \
+    --hash=sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561 \
+    --hash=sha256:e8b56bdcdb4505c8078cb6c7157d9811a85790f2f2b3632c7d1462ab5783d215 \
+    --hash=sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931 \
+    --hash=sha256:f29d80eb9a9263b8d109135351caf568cc3f80b9928bccde535c235de55c22d9 \
+    --hash=sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f \
+    --hash=sha256:fcd5cf9e305d7b8338754470cf69cf81f420459dbae8a3b40cee57417f4614a7
+    # via google-genai
 wrapt==1.17.2 \
     --hash=sha256:0a6e821770cf99cc586d33833b2ff32faebdbe886bd6322395606cf55153246c \
     --hash=sha256:0b929ac182f5ace000d459c59c2c9c33047e20e935f8e39371fa6e3b85d56f4a \
diff --git a/uv.lock b/uv.lock
index 69fff16..4032549 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1019,7 +1019,7 @@ wheels = [
 
 [[package]]
 name = "my-project"
-version = "0.3.0"
+version = "0.4.0"
 source = { virtual = "." }
 dependencies = [
     { name = "pipelex", extra = ["anthropic", "bedrock", "fal", "google", "google-genai", "mistralai"] },
@@ -1046,7 +1046,7 @@ dev = [
 requires-dist = [
     { name = "boto3-stubs", marker = "extra == 'dev'", specifier = ">=1.35.24" },
     { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.11.2" },
-    { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], git = "https://github.com/Pipelex/pipelex.git" },
+    { name = "pipelex", extras = ["mistralai", "anthropic", "google", "google-genai", "bedrock", "fal"], specifier = "==0.13.0" },
     { name = "pyright", marker = "extra == 'dev'", specifier = ">=1.1.405" },
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.3.3" },
     { name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.24.0" },
@@ -1332,8 +1332,8 @@ wheels = [
 
 [[package]]
 name = "pipelex"
-version = "0.12.0"
-source = { git = "https://github.com/Pipelex/pipelex.git#cc0ae6e4b30ff960544c5f2d4728d8b0df52fd46" }
+version = "0.13.0"
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
     { name = "backports-strenum", marker = "python_full_version < '3.11'" },
@@ -1361,6 +1361,10 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "yattag" },
 ]
+sdist = { url = "https://files.pythonhosted.org/packages/66/16/6f680fa5f44e6414ec0a3ac1588f0c3b0187c743fb81d540d12f61fa66eb/pipelex-0.13.0.tar.gz", hash = "sha256:70abab4d8e27c768898e7037317e9f869677cf2e66ad542c4d5e04d6ead8d5c3", size = 300496, upload-time = "2025-10-21T23:56:53.053Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ff/32/edb52bda84fc57e42579362d6260f3e3fda3f6c60e8c5e853b6ba946b358/pipelex-0.13.0-py3-none-any.whl", hash = "sha256:357fe4e5a61814deac3dd87de9c650f7a842ba81f8603d92a5097a688e5456bd", size = 477616, upload-time = "2025-10-21T23:56:51.543Z" },
+]
 
 [package.optional-dependencies]
 anthropic = [