Fix useful typo

Author: YoanSallami

README.md

@@ -46,7 +46,7 @@ Developping a successful LM application in a profesional context, beyond statele
 - **Ensuring the correctness of the LMs output**: Synalinks combines constrained structured output with In-Context RL to ensure both format and content correctness.
 - **Optimizing async processes**: Synalinks automatically optimizes your pipelines by detecting parallel processes.
 - **Assessing the performance of your application**: Synalinks provides built-in metrics and rewards to evaluate your workflows.
-- **Configuring Language & Embedding Models**: Seamlessly integrate multiple LM providers like Ollama, OpenAI, Anthropic, or Groq.
+- **Configuring Language & Embedding Models**: Seamlessly integrate multiple LM providers like Ollama, OpenAI, Anthropic, Mistral or Groq.
 - **Documenting your ML workflows**: Plot your workflows, training history, and evaluations; document everything.
 - **Versioning the prompts/pipelines**: Each program is serializable into JSON so you can version it with git.
 - **Deploying REST APIs**: Compatible out-of-the-box with FastAPI so your Data Scientists and Web Developers can stop tearing each other apart.
@@ -101,7 +101,7 @@ async def main():
         inputs=x0,
         outputs=x1,
         name="chain_of_thought",
-        description="Usefull to answer in a step by step manner.",
+        description="Useful to answer in a step by step manner.",
     )
 
 if __name__ == "__main__":
@@ -133,7 +133,7 @@ async def main():
         )
 
     class ChainOfThought(synalinks.Program):
-        """Usefull to answer in a step by step manner.
+        """Useful to answer in a step by step manner.
         
         The first line of the docstring is provided as description
         for the program if not provided in the `super().__init__()`.
@@ -230,7 +230,7 @@ async def main():
             ),
         ],
         name="chain_of_thought",
-        description="Usefull to answer in a step by step manner.",
+        description="Useful to answer in a step by step manner.",
     )
 
 if __name__ == "__main__":
@@ -245,7 +245,7 @@ To print a tabular summary of your program:
 program.summary()
 ```
 
-Or a plot (usefull to document your system):
+Or a plot (Useful to document your system):
 
 ```python
 synalinks.utils.plot_program(

docs/FAQ.md

@@ -119,7 +119,7 @@ Likewise, cached dataset files, such as those downloaded with `get_file()`, are
 
 ### How should I cite Synalinks?
 
-Please cite Synalinks if it is usefull in your research. Here is the bibtex entry to use:
+Please cite Synalinks if it is Useful in your research. Here is the bibtex entry to use:
 
 ```bibtex
 @misc{sallami2025synalinks,
@@ -142,7 +142,7 @@ We provide consulting, development and technical support for companies that want
 
 - **Sample**: A sample is one element of a dataset. For example, one DataModel is one sample.
 - **Batch**: A batch is a set of N samples. The samples in a batch are processed independently, in parallel. During training, a batch result in only one program update. A batch approximates the input distribution better than a single input. The larger the batch, the better the approximation; however a larger batch will take longer to process and still result in only one update.
-- **Epochs**: A epochs is an arbitrarly cutoff, generally defined as "one pass over the entire dataset", used to separate training into distinct phases, which is usefull for logging and periodic evaluation. When using `validation_split` or `validation_data` with the `fit` method of Synalinks programs, evaluation will be run at the end of every epoch.
+- **Epochs**: A epochs is an arbitrarly cutoff, generally defined as "one pass over the entire dataset", used to separate training into distinct phases, which is Useful for logging and periodic evaluation. When using `validation_split` or `validation_data` with the `fit` method of Synalinks programs, evaluation will be run at the end of every epoch.
 
 ---
 

docs/Synalinks API/Built-in Datasets/index.md

@@ -2,10 +2,10 @@
 
 The `synalinks.datasets` module provide a few datasets that can be used to debugging, evaluation or to create code examples.
 
-These datasets are leaked in nowadays LMs training data, which is a big concern in todays ML community, so they won't give you much information about the reasoning abilities of the underlying models. But they are still usefull as baseline to compare neuro-symbolic methods or when using small language models.
+These datasets are leaked in nowadays LMs training data, which is a big concern in todays ML community, so they won't give you much information about the reasoning abilities of the underlying models. But they are still useful as baseline to compare neuro-symbolic methods or when using small language models.
 
 ---
 
-- [GSM8K dataset](GSM8K.md): A dataset of 8.5K high quality linguistically diverse grade school math word problems. Usefull to evaluate reasoning capabilities.
+- [GSM8K dataset](GSM8K.md): A dataset of 8.5K high quality linguistically diverse grade school math word problems. Useful to evaluate reasoning capabilities.
 
-- [ARC-AGI dataset](ARC-AGI.md): A dataset of 400 different tasks about general artificial intelligence, as a program synthesis benchmark. Usefull to evaluate general reasoning abilities and program synthesis applications.
+- [ARC-AGI dataset](ARC-AGI.md): A dataset of 400 different tasks about general artificial intelligence, as a program synthesis benchmark. Useful to evaluate general reasoning abilities and program synthesis applications.

docs/index.md

@@ -48,7 +48,7 @@ async def main():
         inputs=x0,
         outputs=x1,
         name="chain_of_thought",
-        description="Usefull to answer in a step by step manner.",
+        description="Useful to answer in a step by step manner.",
     )
 
 if __name__ == "__main__":
@@ -80,7 +80,7 @@ async def main():
         )
 
     class ChainOfThought(synalinks.Program):
-        """Usefull to answer in a step by step manner.
+        """Useful to answer in a step by step manner.
         
         The first line of the docstring is provided as description
         for the program if not provided in the `super().__init__()`.
@@ -177,7 +177,7 @@ async def main():
             ),
         ],
         name="chain_of_thought",
-        description="Usefull to answer in a step by step manner.",
+        description="Useful to answer in a step by step manner.",
     )
 
 if __name__ == "__main__":
@@ -192,7 +192,7 @@ To print a tabular summary of your program:
 program.summary()
 ```
 
-Or a plot (usefull to document your system):
+Or a plot (Useful to document your system):
 
 ```python
 synalinks.utils.plot_program(

synalinks/src/modules/agents/react.py

@@ -180,7 +180,6 @@ def __init__(
         )
 
     def build(self, inputs_schema):
-        print(f"call build with: {inputs_schema}")
         inputs = Input(schema=inputs_schema)
         asyncio.get_event_loop().run_until_complete(self.initialize(inputs))
         self.built = True

synalinks/src/modules/core/branch.py

@@ -57,7 +57,7 @@ class AnswerWithCritique(synalinks.DataModel):
             inputs=x0,
             outputs=x3,
             name="adaptative_chain_of_thought",
-            description="Usefull to answer step by step only when needed",
+            description="Useful to answer step by step only when needed",
         )
 
     if __name__ == "__main__":

synalinks/src/modules/core/branch_test.py

@@ -49,7 +49,7 @@ class AnswerWithCritique(DataModel):
             inputs=x0,
             outputs=x3,
             name="adaptative_chain_of_thought",
-            description="Usefull to answer step by step only when needed",
+            description="Useful to answer step by step only when needed",
         )
 
         decision_response = (

synalinks/src/modules/core/generator.py

@@ -167,7 +167,7 @@ class AnswerWithCritique(synalinks.DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought_with_critique",
-            description="Usefull to answer step by step and evaluate your answer",
+            description="Useful to answer step by step and evaluate your answer",
         )
 
     if __name__ == "__main__":

synalinks/src/programs/program.py

@@ -52,7 +52,7 @@ class AnswerWithRationale(synalinks.DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
     if __name__ == "__main__":
@@ -81,7 +81,7 @@ class AnswerWithRationale(synalinks.DataModel):
             answer: str
 
         class ChainOfThought(synalinks.Program):
-            \"""Usefull to answer in a step by step manner.
+            \"""Useful to answer in a step by step manner.
 
             The first line of the docstring is provided as description for the program
             if not provided in the `super().__init__()`. In a similar way the name is
@@ -143,7 +143,7 @@ class AnswerWithRationale(synalinks.DataModel):
                 ),
             ],
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
     if __name__ == "__main__":

synalinks/src/programs/program_test.py

@@ -35,7 +35,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         state_tree = program.get_state_tree()
@@ -77,7 +77,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         state_tree = program.get_state_tree()
@@ -107,7 +107,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         program.compile(
@@ -157,7 +157,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         program.compile(
@@ -193,7 +193,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         program.compile(
@@ -233,7 +233,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         program.compile(
@@ -276,7 +276,7 @@ class AnswerWithRationale(DataModel):
             inputs=x0,
             outputs=x1,
             name="chain_of_thought",
-            description="Usefull to answer in a step by step manner.",
+            description="Useful to answer in a step by step manner.",
         )
 
         program.compile(