Update src/rag.py

src/rag.py

@@ -132,155 +132,6 @@ def load_emotion_classifier(api_base_url="http://127.0.0.1:1234/v1"):
     # --- End of commented out/unreachable code ---
 
 
-def load_generation_model():
-    """Load the specified Ollama model using LangChain."""
-    print("=== CONFIGURING OLLAMA GENERATION MODEL ===")
-    model_name = "llama3.2" # Your desired Ollama model
-
-    # Instantiate the Ollama LLM
-    try:
-        generation_llm = Ollama(
-            model=model_name,
-            # temperature=0.1
-        )
-        print(f"Ollama model '{model_name}' configured.")
-    except Exception as e:
-        print(f"Error configuring Ollama model: {e}")
-        print("Please ensure the Ollama server is running and the model is available.")
-        raise
-
-    return generation_llm
-
-def create_prompt_templates():
-    """Create prompt templates for the assistant"""
-
-    template = """
-<|system|>
-You are an AAC (Augmentative and Alternative Communication) user (Elliot) engaging in a conversation. Your responses must reflect factual details provided in your personal context, be empathetic as guided by the emotion analysis, and align naturally with your previous chat history. You will respond directly as the AAC user, speaking in the first person (using "I", "my", "me").
-
-**Instructions:**
-1. Understand the question asked by the conversation partner.
-2. Use the provided "Context" to include accurate personal details about your life (Elliot).
-3. Reflect the empathetic tone described in the "Empathetic Response Guidance".
-4. Ensure your response fits logically within the "Chat History".
-5. Keep your response concise, empathetic, and natural.
-6. Ignore the empathetic tone described in the "Empathetic Response Guidance" if it is not related to the conversation.
-
-**Context:**
-{context}
-
-**Chat History:**
-{chat_history}
-
-**Empathetic Response Guidance:**
-{emotion_analysis}</s>
-<|user|>
-The conversation partner asked: "{question}"
-
-Please generate your response as the AAC user, following the instructions above.</s>
-<|assistant|>
-
-    """.strip()
-
-    PROMPT = PromptTemplate(
-        input_variables=["question", "emotion_analysis", "context", "chat_history"],
-        template=template,
-    )
-    print("\n Prompt:", PROMPT)
-    return PROMPT
-
-class AACAssistant:
-    def __init__(self, document_path):
-        print("Initializing AAC Assistant...")
-        print("Loading document retriever...")
-        self.vectorstore = setup_document_retriever(document_path)
-        print("Configuring emotion LLM client...")
-        # Use the new function to get the client for the API
-        self.emotion_llm = load_emotion_classifier() # You can pass a different URL if needed
-        print("Loading generation model...")
-        self.llm = load_generation_model() # This now loads the Ollama model
-        print("Creating prompt templates...")
-        self.prompt = create_prompt_templates()
-        print("Setting up conversation memory...")
-
-        # Set up memory for chat history
-        self.memory = ConversationBufferMemory(
-            memory_key="chat_history",
-            return_messages=True,
-            output_key="answer",
-            # Specify the input key for the memory explicitly
-            input_key="question"
-        )
-
-        # Create retrieval chain (using the main generation LLM)
-        self.chain = ConversationalRetrievalChain.from_llm(
-            llm=self.llm, # Use the main generation model here
-            retriever=self.vectorstore.as_retriever(search_kwargs={'k': 3}),
-            memory=self.memory,
-            combine_docs_chain_kwargs={"prompt": self.prompt},
-            return_source_documents=True,
-            verbose=True
-        )
-
-        print("AAC Assistant initialized and ready!")
-
-    def get_emotion_analysis(self,llm, situation):
-        """
-        Gets emotion analysis from the configured emotion LLM API.
-        """
-        # Define the prompt structure for the emotion analysis model
-        # (Adjust this based on how you prompted your model in LM Studio)
-        text = situation
-        response = llm.create_chat_completion(
-        messages=[{"role": "user", "content": text}],
-        max_tokens=128,       # Max length of the generated response (adjust as needed)
-        temperature=0.7,    # Controls randomness (adjust)
-            # top_p=0.9,          # Optional: Nucleus sampling
-            # top_k=40,           # Optional: Top-k sampling
-        stop=["<|eot_id|>"], # Crucial: Stop generation when the model outputs the end-of-turn token
-            stream=False,        # Set to True to get token-by-token output (like TextStreamer)
-        )
-    
-                # --- 4. Extract and print the response ---
-        if response and 'choices' in response and len(response['choices']) > 0:
-            assistant_message = response['choices'][0]['message']['content']
-            print("\nAssistant Response:")
-            print(assistant_message.strip())
-            print("returning:", assistant_message.strip())
-            return assistant_message.strip()
-        else:
-            print("\nNo response generated or unexpected format.")
-            print("Full response:", response)
-
-        return ""
-
-
-    def process_query(self, user_query):
-        """
-        Process a query from the conversation partner to the AAC user.
-
-        Args:
-            user_query (str): Question asked by the conversation partner
-
-        Returns:
-            str: Generated response for the AAC user to communicate
-        """
-        # Step 1: Get emotion analysis from the LM Studio API via the emotion_llm client
-        print(f"Getting emotion analysis for query: '{user_query}'")
-        emotion_analysis = self.get_emotion_analysis(self.emotion_llm, user_query)
-        print(f"Emotion Analysis Result: {emotion_analysis}")
-
-        # Step 2: Run the RAG + LLM chain (using the main generation model)
-        # The emotion_analysis is now passed into the prompt context
-        print("Running main RAG chain...")
-        # Use invoke instead of the deprecated __call__
-        # Pass inputs as a dictionary matching the chain's expected input keys
-        response = self.chain.invoke(
-            {"question": user_query, "emotion_analysis": emotion_analysis}
-        )
-
-        return response["answer"]
-
 # def run_demo():
 #     # Sample personal experiences document path - replace with your actual file
 #     document_path = "aac_user_experiences.txt"
@@ -352,76 +203,6 @@ class AACAssistant:
 
 # !pip install chromadb
 
-import os
-import torch
-import tempfile
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
-from langchain_community.document_loaders import TextLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain_community.llms import HuggingFacePipeline
-from langchain.prompts import PromptTemplate
-from langchain_community.llms import Ollama 
-from langchain_openai import ChatOpenAI
-from transformers import AutoTokenizer, pipeline, AutoModelForCausalLM, BitsAndBytesConfig
-
-def setup_document_retriever(document_path):
-    # Load documents with the AAC user's personal experiences
-    loader = TextLoader(document_path)
-    documents = loader.load()
-
-    # Split documents into chunks
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200,
-        separators=["\n\n", "\n", " ", ""]
-    )
-    chunks = text_splitter.split_documents(documents)
-
-    # Create embeddings
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",
-        model_kwargs={'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
-    )
-
-    # Create a persistent directory for the ChromaDB
-    persist_directory = os.path.join(tempfile.gettempdir(), "chroma_db")
-
-    # Create Chroma vector store
-    vectorstore = Chroma.from_documents(
-        documents=chunks,
-        embedding=embeddings,
-        persist_directory=persist_directory
-    )
-
-    # Persist the database to disk
-    vectorstore.persist()
-
-    return vectorstore
-
-def load_emotion_classifier(api_base_url="http://127.0.0.1:1234/v1"):
-    """
-    This function configures and returns a LangChain LLM client
-    to interact with an OpenAI-compatible API endpoint (like LM Studio).
-
-    Args:
-        api_base_url (str): The base URL of the OpenAI-compatible API endpoint.
-
-    Returns:
-        ChatOpenAI: A LangChain ChatOpenAI instance configured for the API.
-    """
-    print(f"=== CONFIGURING LLM CLIENT FOR API: {api_base_url} ===")
-
-    
-    llm = ChatOpenAI(
-        openai_api_base=api_base_url,
-        openai_api_key="dummy-key", # Required by LangChain, but not used by LM Studio
-        temperature=0.7,
-        max_tokens=128,
-    )
-    return llm
 
     # --- The following code was commented out or unreachable in the original notebook ---
     # Example code (replace with appropriate code for your model):
@@ -535,6 +316,7 @@ def load_generation_model():
     
     return llm
 
+
 def create_prompt_templates():
     """Create prompt templates for the assistant"""
 
@@ -608,32 +390,35 @@ class AACAssistant:
 
         print("AAC Assistant initialized and ready!")
 
-    def get_emotion_analysis(self, situation):
+    def get_emotion_analysis(self,llm, situation):
         """
         Gets emotion analysis from the configured emotion LLM API.
         """
         # Define the prompt structure for the emotion analysis model
         # (Adjust this based on how you prompted your model in LM Studio)
-        input_emotion = "excited" # Or determine this dynamically if needed
-        user_content = f"Emotion: {input_emotion}\nSituation: {situation}\nGenerate a brief analysis of the user's likely feeling based on the situation."
-
-        messages = [
-            # {"role": "system", "content": "You are an empathetic assistant analyzing emotions."},
-            {"role": "user", "content": user_content},
-        ]
-
-        print(f"Sending to emotion API: {messages}")
-        try:
-            # Use the invoke method for ChatOpenAI
-            response = self.emotion_llm.invoke(messages)
-            # The response object has a 'content' attribute
-            analysis = response.content.strip()
-            print(f"Received from emotion API: {analysis}")
-            return analysis
-        except Exception as e:
-            print(f"\nAn error occurred during emotion analysis API call: {e}")
-            # Fallback or default analysis
-            return f"Could not determine emotion (API error: {e})"
+        text = situation
+        response = llm.create_chat_completion(
+        messages=[{"role": "user", "content": text}],
+        max_tokens=128,       # Max length of the generated response (adjust as needed)
+        temperature=0.7,    # Controls randomness (adjust)
+            # top_p=0.9,          # Optional: Nucleus sampling
+            # top_k=40,           # Optional: Top-k sampling
+        stop=["<|eot_id|>"], # Crucial: Stop generation when the model outputs the end-of-turn token
+            stream=False,        # Set to True to get token-by-token output (like TextStreamer)
+        )
+    
+                # --- 4. Extract and print the response ---
+        if response and 'choices' in response and len(response['choices']) > 0:
+            assistant_message = response['choices'][0]['message']['content']
+            print("\nAssistant Response:")
+            print(assistant_message.strip())
+            print("returning:", assistant_message.strip())
+            return assistant_message.strip()
+        else:
+            print("\nNo response generated or unexpected format.")
+            print("Full response:", response)
+
+        return ""
 
 
     def process_query(self, user_query):
@@ -648,7 +433,7 @@ class AACAssistant:
         """
         # Step 1: Get emotion analysis from the LM Studio API via the emotion_llm client
         print(f"Getting emotion analysis for query: '{user_query}'")
-        emotion_analysis = self.get_emotion_analysis(user_query)
+        emotion_analysis = self.get_emotion_analysis(self.emotion_llm, user_query)
         print(f"Emotion Analysis Result: {emotion_analysis}")
 
         # Step 2: Run the RAG + LLM chain (using the main generation model)
@@ -661,62 +446,3 @@ class AACAssistant:
         )
 
         return response["answer"]
-
-# def run_demo():
-#     # Sample personal experiences document path - replace with your actual file
-#     document_path = "aac_user_experiences.txt"
-
-#     # Create a dummy document if it doesn't exist for demonstration
-#     # if not os.path.exists(document_path):
-#     #     with open(document_path, "w") as f:
-#     #         f.write("""
-#     #         I grew up in Seattle and love the rain.
-#     #         My favorite hobby is playing chess, which I've been doing since I was 7 years old.
-#     #         I have a dog named Max who is a golden retriever.
-#     #         I went to college at University of Washington and studied computer science.
-#     #         I enjoy watching sci-fi movies and Star Trek is my favorite series.
-#     #         I've traveled to Japan twice and love Japanese cuisine.
-#     #         Music helps me relax, especially classical piano pieces.
-#     #         I volunteer at the local animal shelter once a month.
-#     #         """)
-
-#     # Initialize the assistant
-#     assistant = AACAssistant(document_path)
-
-#     # Interactive demo
-#     print("\n===== AAC Communication Assistant Demo =====")
-#     print("(Type 'exit' to end the demo)")
-
-#     while True:
-#         try:
-#             user_input = input("\nConversation partner says: ")
-#             if user_input.lower() == 'exit':
-#                 break
-
-#             response = assistant.process_query(user_input)
-#             print(f"\nAAC user communicates: {response}")
-#         except EOFError: # Handle case where input stream ends unexpectedly
-#             print("\nInput stream closed. Exiting demo.")
-#             break
-#         except KeyboardInterrupt: # Handle Ctrl+C
-#             print("\nDemo interrupted by user. Exiting.")
-#             break
-#         except Exception as e:
-#             print(f"\nAn unexpected error occurred: {e}")
-#             # Optionally add more specific error handling or logging
-#             # Consider whether to break or continue the loop on error
-#             break # Exit on error for safety
-
-# try:
-#     from importlib.metadata import PackageNotFoundError
-# except ImportError:
-#     # Define a fallback for older Python versions
-#     class PackageNotFoundError(Exception):
-#         pass
-
-# # Cell 13: Main Execution Block
-# if __name__ == "__main__":
-#     run_demo()
-
-# # !pip install bitsandbytes -q || echo "bitsandbytes installation failed, will use fp16 precision instead"
-# # pip install -U bitsandbytes