made corrections to the bugs

app.py

@@ -1,28 +1,28 @@
 import streamlit as st
 from openai import OpenAI
-from sentence_transformers import SentenceTransformer
 import os
+import json
 from dotenv import load_dotenv
-import numpy as np
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.schema import Document
 from langchain_community.llms import HuggingFaceHub
-from langchain.chains import RetrievalQA, LLMChain
-from langchain.prompts import PromptTemplate
+from langchain.chains import RetrievalQA
 from langchain_community.embeddings import HuggingFaceEmbeddings
 from langchain_community.vectorstores import Chroma
-from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers.document_compressors import LLMChainExtractor
+from tqdm import tqdm
+import random
 
+# Load environment variables
 load_dotenv()
 
-# Initialize the OpenAI client for Hugging Face
-client = OpenAI(
-    base_url="https://api-inference.huggingface.co/v1",
-    api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')
-)
+# Constants
+CHUNK_SIZE = 8192
+CHUNK_OVERLAP = 200
+BATCH_SIZE = 100
+RETRIEVER_K = 4
+VECTORSTORE_PATH = "./vectorstore"
 
-# Create supported models
+# Model information
 model_links = {
     "Meta-Llama-3.1-8B": "meta-llama/Meta-Llama-3.1-8B-Instruct",
     "Mistral-7B-Instruct-v0.3": "mistralai/Mistral-7B-Instruct-v0.3",
@@ -30,128 +30,119 @@ model_links = {
 
 model_info = {
     "Meta-Llama-3.1-8B": {
-        'description': """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.
+        "description": """The Llama (3.1) model is a **Large Language Model (LLM)** that's able to have question and answer interactions.
         \nIt was created by the [**Meta's AI**](https://llama.meta.com/) team and has over **8 billion parameters.**\n""",
-        'logo': 'llama_logo.gif'
+        "logo": "llama_logo.gif",
     },
     "Mistral-7B-Instruct-v0.3": {
-        'description': """The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
+        "description": """The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
         \nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over **7 billion parameters.**\n""",
-        'logo': 'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'
+        "logo": "https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp",
     },
 }
 
 # Random dog images for error message
-random_dog = ["randomdog.jpg"]
+random_dogs = ["randomdog.jpg", "randomdog2.jpg", "randomdog3.jpg"]  # Add more as needed
 
-# Define the available models
-models = [key for key in model_links.keys()]
+# Set up embeddings
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-# Create the sidebar with the dropdown for model selection
-selected_model = st.sidebar.selectbox("Select Model", models)
-
-# Function to load and process documents
 def load_and_process_documents(file_path):
-    with open(file_path, 'r') as file:
-        content = file.read()
-    
-    doc = Document(page_content=content, metadata={"source": file_path})
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=8192, chunk_overlap=200)
-    splits = text_splitter.split_documents([doc])
-    
-    return splits
-
-# Function to set up the advanced RAG pipeline
-@st.cache_resource
-def setup_advanced_rag_pipeline(model_name):
-    # Load and process documents
-    splits = load_and_process_documents("index_training.json")  # Replace with your document path
-    
-    # Set up InstructorEmbeddings
-    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-    
-    # Create vectorstore
-    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
-    
-    # Set up language model
-    llm = HuggingFaceHub(repo_id=model_links[model_name], model_kwargs={"temperature": 0.5, "max_length": 4000})
-    
-    def load_and_process_json(file_path):
-    with open(file_path, 'r') as file:
-        data = json.load(file)
-    
-    documents = data.get("documents", [])
-    
-    if not documents:
-        raise ValueError("No valid documents found in JSON file.")
-    
-    # Create Document objects
-    doc_objects = [
-        Document(
-            page_content=doc["content"],
-            metadata={"title": doc["title"], "id": doc["id"]}
-        ) for doc in documents
-    ]
-    
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
-    splits = text_splitter.split_documents(doc_objects)
-    
-    return splits
-
-    def get_vectorstore(file_path):
-        # Check if vectorstore already exists
+    """Load and process documents from a JSON file."""
+    try:
+        with open(file_path, "r") as file:
+            data = json.load(file)
+
+        documents = data.get("documents", [])
+
+        if not documents:
+            raise ValueError("No valid documents found in JSON file.")
+
+        doc_objects = [
+            Document(
+                page_content=doc["content"],
+                metadata={"title": doc["title"], "id": doc["id"]},
+            )
+            for doc in documents
+        ]
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP
+        )
+        splits = text_splitter.split_documents(doc_objects)
+
+        return splits
+    except Exception as e:
+        st.error(f"Error loading documents: {str(e)}")
+        return []
+
+def get_vectorstore(file_path):
+    """Get or create a vectorstore."""
+    try:
         if os.path.exists(VECTORSTORE_PATH):
             print("Loading existing vectorstore...")
-            return Chroma(persist_directory=VECTORSTORE_PATH, embedding_function=embeddings)
-        
+            return Chroma(
+                persist_directory=VECTORSTORE_PATH, embedding_function=embeddings
+            )
+
         print("Creating new vectorstore...")
-        splits = load_and_process_json(file_path)
-        
-        # Process in batches
+        splits = load_and_process_documents(file_path)
+
         vectorstore = None
         for i in tqdm(range(0, len(splits), BATCH_SIZE), desc="Processing batches"):
-            batch = splits[i:i+BATCH_SIZE]
+            batch = splits[i : i + BATCH_SIZE]
             if vectorstore is None:
-                vectorstore = Chroma.from_documents(documents=batch, embedding=embeddings, persist_directory=VECTORSTORE_PATH)
+                vectorstore = Chroma.from_documents(
+                    documents=batch,
+                    embedding=embeddings,
+                    persist_directory=VECTORSTORE_PATH,
+                )
             else:
                 vectorstore.add_documents(documents=batch)
-        
+
         vectorstore.persist()
         return vectorstore
-    
-    def setup_rag_pipeline(file_path):
+    except Exception as e:
+        st.error(f"Error creating vectorstore: {str(e)}")
+        return None
+
+@st.cache_resource(hash_funcs={"builtins.tuple": lambda _: None})
+def setup_rag_pipeline(file_path, model_name, temperature):
+    """Set up the RAG pipeline."""
+    try:
         vectorstore = get_vectorstore(file_path)
+        if vectorstore is None:
+            raise ValueError("Failed to create or load vectorstore.")
+
+        llm = HuggingFaceHub(
+            repo_id=model_links[model_name],
+            model_kwargs={"temperature": temperature, "max_length": 4000},
+        )
+
         return RetrievalQA.from_chain_type(
             llm=llm,
             chain_type="stuff",
             retriever=vectorstore.as_retriever(search_kwargs={"k": RETRIEVER_K}),
-            return_source_documents=True
+            return_source_documents=True,
         )
-    
-    # Create RetrievalQA chain
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=compression_retriever,
-        return_source_documents=True
-    )
-    
-    return qa_chain
+    except Exception as e:
+        st.error(f"Error setting up RAG pipeline: {str(e)}")
+        return None
 
 # Streamlit app
-st.header('Liahona.AI')
+st.header("Liahona.AI")
 
 # Sidebar for model selection
-st.markdown(f'_powered_ by ***:violet[{selected_model}]***')
+selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))
+st.markdown(f"_powered_ by ***:violet[{selected_model}]***")
 
 # Temperature slider
-temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
+temperature = st.sidebar.slider("Select a temperature value", 0.0, 1.0, 0.5)
 
 # Display model info
 st.sidebar.write(f"You're now chatting with **{selected_model}**")
-st.sidebar.markdown(model_info[selected_model]['description'])
-st.sidebar.image(model_info[selected_model]['logo'])
+st.sidebar.markdown(model_info[selected_model]["description"])
+st.sidebar.image(model_info[selected_model]["logo"])
 st.sidebar.markdown("*Generated content may be inaccurate or false.*")
 
 # Initialize chat history
@@ -164,7 +155,7 @@ for message in st.session_state.messages:
         st.markdown(message["content"])
 
 # Set up advanced RAG pipeline
-qa_chain = setup_advanced_rag_pipeline(selected_model)
+qa_chain = setup_rag_pipeline("index_training.json", selected_model, temperature)
 
 # Chat input
 if prompt := st.chat_input("Type message here..."):
@@ -176,16 +167,19 @@ if prompt := st.chat_input("Type message here..."):
     # Generate and display assistant response
     with st.chat_message("assistant"):
         try:
+            if qa_chain is None:
+                raise ValueError("RAG pipeline is not properly set up.")
+            
             result = qa_chain({"query": prompt})
             response = result["result"]
             st.write(response)
-            
+
         except Exception as e:
             response = """😵‍💫 Looks like someone unplugged something!
             \n Either the model space is being updated or something is down.
             \n"""
             st.write(response)
-            random_dog_pick = 'https://random.dog/' + random_dog[np.random.randint(len(random_dog))]
+            random_dog_pick = random.choice(random_dogs)
             st.image(random_dog_pick)
             st.write("This was the error message:")
             st.write(str(e))