Refactor code and update PromptingMixin configuration

nanlabs · Feb 26, 2024 · d361df3 · d361df3
1 parent 34d4499
commit d361df3
Show file tree

Hide file tree

Showing 3 changed files with 44 additions and 41 deletions.
diff --git a/neurons/validators/validator.py b/neurons/validators/validator.py
@@ -56,7 +56,7 @@ async def forward(self):
         - Rewarding the miners
         - Updating the scores
         """
-        # TODO(developer): Rewrite this function based on your protocol definition.
+
         return await forward(self)
 
 

diff --git a/prompting/protocol.py b/prompting/protocol.py
@@ -59,50 +59,54 @@ class PromptingMixin(BaseModel):
     """
 
     class Config:
-        validate_assignment = True
+        """
+        Pydantic model configuration class for Prompting. This class sets validation of attribute assignment as True.
+        validate_assignment set to True means the pydantic model will validate attribute assignments on the class.
+        """
 
-        character_info: str = Field(
-            ...,
-            title="Character Info",
-            description="Information about the LLM.",
-            allow_mutation=False,
-        )
-        criteria: List[str] = Field(
-            ...,
-            title="Criteria",
-            description="Criteria guiding the LLM's responses.",
-            allow_mutation=False,
-        )
-        messages: List[Message] = Field(
-            ...,
-            title="Messages",
-            description="Dialogue history of the chat session.",
-            allow_mutation=False,
-        )
-        completion: str = Field(
-            "",
-            title="Completion",
-            description="Latest response or completion status of the chat.",
-        )
+        validate_assignment = True
 
-        def add_message(self, content: str):
-            """
-            Adds a new message to the dialogue history.
+    character_info: str = Field(
+        ...,
+        title="Character Info",
+        description="Information about the LLM.",
+        allow_mutation=False,
+    )
+    criteria: List[str] = Field(
+        ...,
+        title="Criteria",
+        description="Criteria guiding the LLM's responses.",
+        allow_mutation=False,
+    )
+    messages: List[Message] = Field(
+        ...,
+        title="Messages",
+        description="Dialogue history of the chat session.",
+        allow_mutation=False,
+    )
+    completion: str = Field(
+        "",
+        title="Completion",
+        description="Latest response or completion status of the chat.",
+    )
 
-            Parameters:
-                content (str): The content of the message to be added.
-            """
-            self.messages.append(Message(content=content))
+    def add_message(self, content: str):
+        """
+        Adds a new message to the dialogue history.
 
-        def update_completion(self, completion: str):
-            """
-            Updates the completion status of the chat.
+        Parameters:
+            content (str): The content of the message to be added.
+        """
+        self.messages.append(Message(content=content))
 
-            Parameters:
-                completion (str): The new completion status or LLM's response.
-            """
-            self.completion = completion
+    def update_completion(self, completion: str):
+        """
+        Updates the completion status of the chat.
 
+        Parameters:
+            completion (str): The new completion status or LLM's response.
+        """
+        self.completion = completion
 
 class Prompting(PromptingMixin, bt.Synapse):
     """

diff --git a/prompting/validator/forward.py b/prompting/validator/forward.py
@@ -38,15 +38,14 @@ async def forward(self):
     miner_uids = get_random_uids(self, k=self.config.neuron.sample_size)
 
     # Assuming Synapse provides certain functionalities required for integration
+    # TODO(developer): Define the Synapse instance for our use case.
     prompting = Prompting(
         character_info="GPT-4, for engaging and informative conversations.",
         criteria=["Ensure accuracy.", "Maintain a friendly tone."],
         messages=[],
     )
 
-    # Interacting with the LLM
     prompting.add_message("Tell me a joke.")
-    prompting.update_completion("Why did the computer go to the doctor? Because it had a virus!")
 
     # The dendrite client queries the network.
     responses = await self.dendrite(