Clean up

council/prompt/llm_dataset.py

@@ -3,9 +3,10 @@
 import json
 import random
 from collections import defaultdict
-from typing import Any, Counter, DefaultDict, Dict, List, Mapping, Optional
+from typing import Any, Counter, DefaultDict, Dict, List, Mapping, Optional, Sequence
 
 import yaml
+from council.llm import LLMMessage, LLMMessageRole
 from council.utils import DataObject, DataObjectSpecBase
 
 
@@ -14,7 +15,7 @@ class LLMDatasetMessage:
     Represents a single chat message in a conversation.
     """
 
-    def __init__(self, role: str, content: str):
+    def __init__(self, role: LLMMessageRole, content: str):
         self.role = role
         self.content = content.strip()
 
@@ -24,7 +25,11 @@ def from_dict(cls, values: Dict[str, str]) -> LLMDatasetMessage:
         content = values.get("content")
         if role is None or content is None:
             raise ValueError("Both 'role' and 'content' must be defined for a message")
-        return LLMDatasetMessage(role, content)
+        return LLMDatasetMessage(LLMMessageRole(role), content)
+
+    @classmethod
+    def from_llm_message(cls, message: LLMMessage) -> LLMDatasetMessage:
+        return LLMDatasetMessage(role=message.role, content=message.content)
 
     def to_dict(self) -> Dict[str, str]:
         return {"role": self.role, "content": self.content}
@@ -35,20 +40,18 @@ class LLMDatasetConversation:
     Represents a conversation between user and assistant with optional labels.
     """
 
-    def __init__(self, messages: List[Dict[str, str]], labels: Optional[Mapping[str, str]]):
-        self.messages = [LLMDatasetMessage(msg["role"], msg["content"]) for msg in messages]
+    def __init__(self, messages: Sequence[LLMDatasetMessage], labels: Optional[Mapping[str, str]]):
+        self.messages = list(messages)
         self.labels: Dict[str, str] = dict(labels) if labels is not None else {}
 
     @classmethod
     def from_dict(cls, values: Dict[str, Any]) -> LLMDatasetConversation:
         messages = values.get("messages", [])
         if not messages:
             raise ValueError("Conversation must contain at least one message")
+        llm_dataset_messages = [LLMDatasetMessage.from_dict(message) for message in messages]
         labels = values.get("labels")
-        return LLMDatasetConversation(messages, labels)
-
-    def add_label(self, key: str, value: str):
-        self.labels[key] = value
+        return LLMDatasetConversation(llm_dataset_messages, labels)
 
     def to_dict(self) -> Dict[str, Any]:
         result: Dict[str, Any] = {"messages": [message.to_dict() for message in self.messages]}
@@ -158,7 +161,7 @@ def save_jsonl_messages(
         Args:
             path: Base path for saving the file(s)
             random_seed: If provided, will be used to shuffle dataset before saving (default: None)
-            val_split: If provided, fraction of data to use for validation and create separate files
+            val_split: If provided, fraction of data to use for validation and create separate files for train and val.
                        If None, saves all data to a single file (default: None)
 
         Examples: