Update documentation

Author: YoanSallami

README.md

@@ -151,6 +151,8 @@ async def main():
             )
 
         async def call(self, inputs, training=False):
+            if not inputs:
+                return None
             x = await self.answer(inputs, training=training)
             return x
 

api_gen.py

@@ -1,3 +1,7 @@
+# Modified from: keras/api_gen.py
+# Original authors: François Chollet et al. (Keras Team)
+# License Apache 2.0: (c) 2025 Yoan Sallami (Synalinks Team)
+
 """Script to generate synalinks public API in `synalinks/api` directory.
 
 Usage:

coverage-badge.svg

@@ -8,7 +8,7 @@ First, let's see how a production-ready project is structured.
 
 In this tutorial, we will focus only on the backend of your application.
 
-For the purpose of this tutorial, we will skip authentification. There is different ways of handling it, but the most easy one is using JWT tokens combined with Google Oauth. But because is is higly dependent on your business usecase/frontend, we will not handle it here.
+For the purpose of this tutorial, we will skip authentification. But because is is higly dependent on your business usecase/frontend, we will not handle it here.
 
 ### Project Structure
 
@@ -25,7 +25,7 @@ demo/
 │   └── ... (your frontend code)
 ├── scripts/
 │   ├── export_program.py
-│   └── train.py
+│   └── train.py (refer to the code examples to learn how to train programs)
 ├── docker-compose.yml
 ├── .env.backend
 ├── .end.frontend
@@ -91,14 +91,19 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-# Load your program
-program = synalinks.Program.load("checkpoint.program.json")
+# The dictionary mapping the name of your custom modules to the class
+custom_modules = {}
 
+# Load your program
+program = synalinks.Program.load(
+    "checkpoint.program.json",
+    custom_modules=custom_modules,
+)
 
 @app.post("/v1/chat_completion")
 async def chat_completion(messages: synalinks.ChatMessages):
     result = await program(messages)
-    return result.json() if result else None
+    return result.json()
 
 
 if __name__ == "__main__":
@@ -113,11 +118,6 @@ if __name__ == "__main__":
 
 For obvious reasons, you will need to have a separate logic to train your application. This script will specify the program architecture, training and evaluation procedure and will end up saving your program into a serializable JSON format.
 
-```python title="train.py"
-import synalinks
-
-```
-
 To ease the migration, we'll also make a small script that export the trained program into our backend folder.
 
 ```python title="export_program.py"
@@ -187,16 +187,6 @@ services:
     image: ghcr.io/mlflow/mlflow:latest
     ports:
       - "5000:5000"
-  frontend:
-    build:
-      context: ./frontend
-      dockerfile: Dockerfile
-    ports:
-      - "3000:3000"
-    depends_on:
-      - backend
-    env_file:
-      - .env.frontend
   backend:
     build:
       context: ./backend
@@ -216,6 +206,6 @@ cd demo
 docker compose up
 ```
 
-### Testing your application
+### Testing your application backend
 
 Open you browser to `http://127.0.0.1/docs` and test your API

docs/Deployment/Building a REST API.md

@@ -1,2 +1,2 @@
 
-::: synalinks.src.backend.pydantic.DataModel
+::: synalinks.src.backend.pydantic.core

docs/Deployment/index.md

@@ -1,2 +1,2 @@
 
-::: synalinks.src.backend.common.json_data_model.JsonDataModel
+::: synalinks.src.backend.common.json_data_model

docs/Synalinks API/Data Models API/The DataModel class.md

@@ -1,2 +1,2 @@
 
-::: synalinks.src.backend.common.symbolic_data_model.SymbolicDataModel
+::: synalinks.src.backend.common.symbolic_data_model

docs/Synalinks API/Data Models API/The JsonDataModel class.md

@@ -4,4 +4,4 @@
 - [Generator module](Generator module.md)
 - [Decision module](Decision module.md)
 - [Action module](Action module.md)
-- [Router module](Router module.md)
+- [Branch module](Branch module.md)

docs/Synalinks API/Data Models API/The SymbolicDataModel class.md

@@ -6,23 +6,35 @@ A module instance is callable, much like a function:
 
 ``` py
 import synalinks
+import asyncio
 
-class Query(synalinks.DataModel):
-    query: str
+async def main():
+    class Query(synalinks.DataModel):
+        query: str = synalinks.Field(
+            description="The user query",
+        )
 
-class ChainOfThought(synalinks.DataModel):
-    thinking: str
-    answer: str
+    class AnswerWithThinking(synalinks.DataModel):
+        thinking: str = synalinks.Field(
+            description="Your step by step thinking",
+        )
+        answer: str = synalinks.Field(
+            description="The correct answer",
+        )
 
-language_model = LanguageModel("ollama_chat/deepseek-r1")
+    language_model = LanguageModel("ollama_chat/deepseek-r1")
 
-generator = synalinks.Generator(
-    data_model=ChainOfThought,
-    language_model=language_model,
-)
+    generator = synalinks.Generator(
+        data_model=AnswerWithThinking,
+        language_model=language_model,
+    )
 
-inputs = Query(query="What is the capital of France?")
-outputs = generator(inputs)
+    inputs = Query(query="What is the capital of France?")
+    outputs = await generator(inputs)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
 ```
 
 ## Modules API overview
@@ -44,8 +56,8 @@ outputs = generator(inputs)
 ### Merging Modules
 
 - [Concat Module](Merging Modules/Concat module.md)
-- [Logical And](Merging Modules/Logical And module.md)
-- [Logical Or](Merging Modules/Logical Or module.md)
+- [Logical And](Merging Modules/And module.md)
+- [Logical Or](Merging Modules/Or module.md)
 
 ---