adding answering service and config parapms, and content model

src/ctp_slack_bot/core/config.py

@@ -36,8 +36,20 @@ class Settings(BaseSettings):
     # OpenAI Configuration
     OPENAI_API_KEY: Optional[SecretStr] = None
 
-    
-    
+    # Chat Model Configuration
+    CHAT_MODEL: str = "gpt-3.5-turbo"
+    MAX_TOKENS: int = 150 
+    TEMPERATURE: float = 0.8 # Maximum tokens for response generation
+    SYSTEM_PROMPT: str = """
+    You are a helpful teaching assistant for a data science class. 
+    Based on the students question, you will be given context retreived from class transcripts and materials to answer their question. 
+    Your responses should be:
+        1. Accurate and based on the class content
+        2. Clear and educational
+        3. Concise but complete
+    If you're unsure about something, acknowledge it and suggest asking the professor.
+    """ 
+
     # Logging Configuration
     LOG_LEVEL: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = "INFO"
     LOG_FORMAT: Literal["text", "json"] = "json"

src/ctp_slack_bot/models/content.py

@@ -14,5 +14,6 @@ class RetreivedContext(BaseModel):
     contextual_text: str
     metadata_source: str
     similarity_score: float
-    in_reation_to_question: str = None
-    
+    
+    said_by: str = Optional[None]
+    in_reation_to_question: str = Optional[None]

src/ctp_slack_bot/services/AnswerQuestionService.py

@@ -0,0 +1,60 @@
+from pydantic import BaseModel, validator
+from typing import List, Optional, Tuple
+from ctp_slack_bot.core.config import settings
+import numpy as np
+from openai import OpenAI
+from ctp_slack_bot.models.slack import SlackMessage
+from ctp_slack_bot.models.content import RetreivedContext
+
+class GenerateAnswer():
+    """
+    Service for language model operations.
+    """
+    def __init__(self):
+        self.client = OpenAI(api_key=settings.OPENAI_API_KEY)
+
+    def generate_answer(self, question: SlackMessage, context: List[RetreivedContext]) -> str:
+            """Generate a response using OpenAI's API with retrieved context.
+            
+            Args:
+                question (str): The user's question
+                context (List[RetreivedContext]): List of RetreivedContext 
+                
+            Returns:
+                str: Generated answer
+            """
+            # Prepare context string from retrieved chunks
+            context_str = ""
+            for c in context:
+                context_str += f"{c.contextual_text}\n"
+
+            
+            # logger.info(f"Generating response for question: {question}")
+            # logger.info(f"Using {len(context)} context chunks")
+            
+            # Create messages for the chat completion
+            messages = [
+                {"role": "system", "content": settings.SYSTEM_PROMPT},
+                {"role": "user", "content": 
+                    f"""Student Auestion: {question.text}
+                    Context from class materials and transcripts: {context_str}              
+                    Please answer the Student Auestion based on the Context from class materials and transcripts. If the context doesn't contain relevant information, acknowledge that and suggest asking the professor."""}
+            ]
+            
+            # Generate response
+            response = self.client.chat.completions.create(
+                model=settings.CHAT_MODEL,
+                messages=messages,
+                max_tokens=settings.MAX_TOKENS,
+                temperature=settings.TEMPERATURE
+            )
+            
+            return response.choices[0].message.content
+
+
+
+### REMOVE BELOW, PUT SOMEWHERE IN TESTS BUT IDK WHERE YET
+# sm = SlackMessage(text="What is the capital of France?", channel_id="123", user_id="456", timestamp="789")
+# context = [RetreivedContext(contextual_text="The capital of France is Paris", metadata_source="class materials", similarity_score=0.95)]
+# a = GenerateAnswer()
+# a.generate_answer(sm, context)