added OpenAI support and markdown in the chat window

local_rag.py

@@ -1,5 +1,5 @@
 """
-Local RAG setup with LangChain, Ollama, and FAISS
+Local RAG setup with LangChain, Ollama/OpenAI, and FAISS
 Minimal dependencies, simple code
 """
 import os
@@ -9,34 +9,51 @@ from langchain_community.document_loaders import PyPDFLoader, TextLoader
 from langchain_community.vectorstores import FAISS
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_ollama import ChatOllama
+from langchain_openai import ChatOpenAI
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 
 
 class LocalRAG:
-    def __init__(self, vectorstore_path="./vectorstore", ollama_model="mistral:7b"):
-        """Initialize local RAG system"""
+    def __init__(
+        self,
+        vectorstore_path="./vectorstore",
+        llm_provider="ollama",
+        ollama_model="gpt-oss:20b",
+        openai_model="gpt-5.2",
+        ollama_base_url="http://localhost:11434",
+    ):
+        """Initialize local RAG system. llm_provider: 'ollama' or 'openai'."""
         self.vectorstore_path = vectorstore_path
-        self.ollama_model = ollama_model
-        
+        self.llm_provider = llm_provider
+
         # Embeddings
         print("Loading embeddings model...")
         self.embeddings = HuggingFaceEmbeddings(
             model_name="sentence-transformers/all-MiniLM-L6-v2"
         )
-        
+
         # Text splitter
         self.text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=2000,
             chunk_overlap=400
         )
-        
-        # Ollama LLM
-        print(f"Connecting to Ollama (model: {ollama_model})...")
-        self.llm = ChatOllama(
-            model=ollama_model,
-            base_url="http://localhost:11434"
-        )
-        
+
+        # LLM (Ollama or OpenAI)
+        if llm_provider == "openai":
+            api_key = os.environ.get("OPENAI_API_KEY")
+            if not api_key:
+                raise ValueError(
+                    "OPENAI_API_KEY environment variable is required when llm_provider='openai'"
+                )
+            print(f"Using OpenAI (model: {openai_model})...")
+            self.llm = ChatOpenAI(model=openai_model, api_key=api_key)
+        else:
+            print(f"Using Ollama (model: {ollama_model})...")
+            self.llm = ChatOllama(
+                model=ollama_model,
+                base_url=ollama_base_url
+            )
+
         # Vector store (load if exists, otherwise None)
         self.vectorstore = None
         self._load_vectorstore()