Initial commit from Create Llama

backend/app/api/services/suggestion.py

@@ -0,0 +1,48 @@
+from typing import List
+
+from app.api.routers.models import Message
+from llama_index.core.prompts import PromptTemplate
+from llama_index.core.settings import Settings
+from pydantic import BaseModel
+
+NEXT_QUESTIONS_SUGGESTION_PROMPT = PromptTemplate(
+    "You're a helpful assistant! Your task is to suggest the next question that user might ask. "
+    "\nHere is the conversation history"
+    "\n---------------------\n{conversation}\n---------------------"
+    "Given the conversation history, please give me $number_of_questions questions that you might ask next!"
+)
+N_QUESTION_TO_GENERATE = 3
+
+
+class NextQuestions(BaseModel):
+    """A list of questions that user might ask next"""
+
+    questions: List[str]
+
+
+class NextQuestionSuggestion:
+    @staticmethod
+    async def suggest_next_questions(
+        messages: List[Message],
+        number_of_questions: int = N_QUESTION_TO_GENERATE,
+    ) -> List[str]:
+        # Reduce the cost by only using the last two messages
+        last_user_message = None
+        last_assistant_message = None
+        for message in reversed(messages):
+            if message.role == "user":
+                last_user_message = f"User: {message.content}"
+            elif message.role == "assistant":
+                last_assistant_message = f"Assistant: {message.content}"
+            if last_user_message and last_assistant_message:
+                break
+        conversation: str = f"{last_user_message}\n{last_assistant_message}"
+
+        output: NextQuestions = await Settings.llm.astructured_predict(
+            NextQuestions,
+            prompt=NEXT_QUESTIONS_SUGGESTION_PROMPT,
+            conversation=conversation,
+            nun_questions=number_of_questions,
+        )
+
+        return output.questions