fix: apply code formatting and linting fixes

- Apply Black code formatting to all Python files
- Fix import sorting with isort
- Resolve all Flake8 linting issues
- Fix MyPy type checking errors
- Remove unused imports and variables
- Fix line length violations and formatting inconsistencies
- Add proper type annotations for global variables
- Add test_env to .gitignore

Author: Neverdecel

.gitignore

@@ -27,3 +27,4 @@ node_modules/
 *.tmp
 plan.md
 metadata.npy
+test_env/

app.py

@@ -1,8 +1,9 @@
-import streamlit as st
 import logging
-from typing import Dict, List
+
+import streamlit as st
 from openai import OpenAI
-from coderag.config import OPENAI_API_KEY, OPENAI_CHAT_MODEL
+
+from coderag.config import OPENAI_API_KEY
 from prompt_flow import execute_rag_flow
 
 # Configure logging for Streamlit
@@ -23,9 +24,7 @@
 
 # Set page config
 st.set_page_config(
-    page_title="CodeRAG: Your Coding Assistant",
-    page_icon="🤖",
-    layout="wide"
+    page_title="CodeRAG: Your Coding Assistant", page_icon="🤖", layout="wide"
 )
 
 st.title("🤖 CodeRAG: Your Coding Assistant")
@@ -40,20 +39,20 @@
 # Sidebar with controls
 with st.sidebar:
     st.header("Controls")
-    
+
     if st.button("🗑️ Clear Conversation", type="secondary"):
         st.session_state.messages = []
         st.session_state.conversation_context = []
         st.rerun()
-    
+
     # Status indicators
     st.header("Status")
     if client:
         st.success("✅ OpenAI Connected")
     else:
         st.error("❌ OpenAI Not Connected")
         st.error("Please check your API key in .env file")
-    
+
     # Conversation stats
     if st.session_state.messages:
         st.info(f"💬 {len(st.session_state.messages)} messages in conversation")
@@ -68,73 +67,100 @@
 
 # Chat input with validation
 if not client:
-    st.warning("⚠️ OpenAI client not available. Please configure your API key to use the assistant.")
+    st.warning(
+        "⚠️ OpenAI client not available. Please configure your API key to use "
+        "the assistant."
+    )
     st.stop()
 
 if prompt := st.chat_input("What is your coding question?", disabled=not client):
     # Validate input
     if not prompt.strip():
         st.warning("Please enter a valid question.")
         st.stop()
-        
+
     # Add user message
     st.session_state.messages.append({"role": "user", "content": prompt})
     # Add to conversation context for better continuity
     st.session_state.conversation_context.append(f"User: {prompt}")
-    
+
     with st.chat_message("user"):
         st.markdown(prompt)
 
     with st.chat_message("assistant"):
         message_placeholder = st.empty()
-        
+
         # Show loading indicator
         with st.spinner("🔍 Searching codebase and generating response..."):
             try:
                 # Execute RAG flow with error handling
                 response = execute_rag_flow(prompt)
-                
+
                 # Check if response indicates an error
-                if response.startswith("Error:") or "error occurred" in response.lower():
+                if (
+                    response.startswith("Error:")
+                    or "error occurred" in response.lower()
+                ):
                     message_placeholder.error(response)
                 else:
                     message_placeholder.markdown(response)
-                    
+
                 full_response = response
-                
+
             except Exception as e:
                 error_message = f"Unexpected error: {str(e)}"
                 logger.error(f"Streamlit error: {error_message}")
                 message_placeholder.error(error_message)
                 full_response = error_message
 
         # Add assistant response to session
-        st.session_state.messages.append({"role": "assistant", "content": full_response})
+        st.session_state.messages.append(
+            {"role": "assistant", "content": full_response}
+        )
         # Add to conversation context
-        st.session_state.conversation_context.append(f"Assistant: {full_response[:200]}...")  # Truncate for context
-        
+        st.session_state.conversation_context.append(
+            f"Assistant: {full_response[:200]}..."
+        )  # Truncate for context
+
         # Keep conversation context manageable (last 10 exchanges)
         if len(st.session_state.conversation_context) > 20:
-            st.session_state.conversation_context = st.session_state.conversation_context[-20:]
+            st.session_state.conversation_context = (
+                st.session_state.conversation_context[-20:]
+            )
 
 # Footer with helpful information
 if not st.session_state.messages:
     st.markdown("---")
     st.markdown("### 💡 Tips for better results:")
-    st.markdown("""
+    st.markdown(
+        """
     - Ask specific questions about your code
     - Mention file names or functions you're interested in
     - Request explanations, improvements, or debugging help
     - Ask about code patterns or best practices
-    """)
-    
+    """
+    )
+
     st.markdown("### 🚀 Example queries:")
     col1, col2 = st.columns(2)
     with col1:
         if st.button("📝 Explain the indexing process"):
-            st.session_state.messages.append({"role": "user", "content": "Explain how the FAISS indexing works in this codebase"})
+            st.session_state.messages.append(
+                {
+                    "role": "user",
+                    "content": "Explain how the FAISS indexing works in this codebase",
+                }
+            )
             st.rerun()
     with col2:
         if st.button("🐛 Help debug search issues"):
-            st.session_state.messages.append({"role": "user", "content": "How can I debug issues with code search not returning results?"})
-            st.rerun()
+            st.session_state.messages.append(
+                {
+                    "role": "user",
+                    "content": (
+                        "How can I debug issues with code search not returning "
+                        "results?"
+                    ),
+                }
+            )
+            st.rerun()

coderag/__init__.py

@@ -1 +1 @@
-# __init__.py
+# __init__.py

coderag/config.py

@@ -1,4 +1,5 @@
 import os
+
 from dotenv import load_dotenv
 
 # Load environment variables from the .env file
@@ -7,17 +8,21 @@
 # === Environment Variables ===
 # OpenAI API key and model settings (loaded from .env)
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-OPENAI_EMBEDDING_MODEL = os.getenv("OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002")  # Default to ada-002
+OPENAI_EMBEDDING_MODEL = os.getenv(
+    "OPENAI_EMBEDDING_MODEL", "text-embedding-ada-002"
+)  # Default to ada-002
 OPENAI_CHAT_MODEL = os.getenv("OPENAI_CHAT_MODEL", "gpt-4")  # Default to GPT-4
 
 # Embedding dimension (from .env or fallback)
 EMBEDDING_DIM = int(os.getenv("EMBEDDING_DIM", 1536))  # Default to 1536 if not in .env
 
 # Project directory (from .env)
-WATCHED_DIR = os.getenv("WATCHED_DIR", os.path.join(os.getcwd(), 'CodeRAG'))
+WATCHED_DIR = os.getenv("WATCHED_DIR", os.path.join(os.getcwd(), "CodeRAG"))
 
 # Path to FAISS index (from .env or fallback)
-FAISS_INDEX_FILE = os.getenv("FAISS_INDEX_FILE", os.path.join(WATCHED_DIR, 'coderag_index.faiss'))
+FAISS_INDEX_FILE = os.getenv(
+    "FAISS_INDEX_FILE", os.path.join(WATCHED_DIR, "coderag_index.faiss")
+)
 
 # === Project-Specific Configuration ===
 # Define the root directory of the project

coderag/embeddings.py

@@ -1,7 +1,9 @@
 import logging
 from typing import Optional
-from openai import OpenAI
+
 import numpy as np
+from openai import OpenAI
+
 from coderag.config import OPENAI_API_KEY, OPENAI_EMBEDDING_MODEL
 
 logger = logging.getLogger(__name__)
@@ -16,36 +18,37 @@
     logger.error(f"Failed to initialize OpenAI client: {e}")
     client = None
 
+
 def generate_embeddings(text: str) -> Optional[np.ndarray]:
     """Generate embeddings using OpenAI's embedding API.
-    
+
     Args:
         text: The input text to generate embeddings for
-        
+
     Returns:
         numpy array of embeddings or None if generation fails
     """
     if not client:
         logger.error("OpenAI client not initialized")
         return None
-        
+
     if not text or not text.strip():
         logger.warning("Empty text provided for embedding generation")
         return None
-    
+
     try:
         logger.debug(f"Generating embeddings for text of length: {len(text)}")
         response = client.embeddings.create(
             model=OPENAI_EMBEDDING_MODEL,
-            input=[text.strip()]  # Input should be a list of strings
+            input=[text.strip()],  # Input should be a list of strings
         )
-        
+
         # Extract the embedding from the response
         embeddings = response.data[0].embedding
-        result = np.array(embeddings).astype('float32').reshape(1, -1)
+        result = np.array(embeddings).astype("float32").reshape(1, -1)
         logger.debug(f"Successfully generated embeddings with shape: {result.shape}")
         return result
-        
+
     except Exception as e:
         logger.error(f"Failed to generate embeddings: {str(e)}")
-        return None
+        return None