Update app.py

app.py

@@ -6,8 +6,53 @@ import requests
 import gradio as gr
 from dotenv import load_dotenv
 from datasets import load_dataset
-from langchain_core.documents import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+try:
+    from langchain_core.documents import Document
+except ImportError:
+    try:
+        from langchain.docstore.document import Document
+    except ImportError:
+        try:
+            from langchain.schema import Document
+        except ImportError:
+            # Fallback: Create a simple Document class
+            class Document:
+                def __init__(self, page_content: str, metadata: dict = None):
+                    self.page_content = page_content
+                    self.metadata = metadata or {}
+
+# Import RecursiveCharacterTextSplitter with fallback
+RecursiveCharacterTextSplitter = None
+
+try:
+    from langchain.text_splitter import RecursiveCharacterTextSplitter
+    print("✅ Using langchain.text_splitter.RecursiveCharacterTextSplitter")
+except ImportError:
+    try:
+        from langchain_text_splitters import RecursiveCharacterTextSplitter
+        print("✅ Using langchain_text_splitters.RecursiveCharacterTextSplitter")
+    except ImportError:
+        print("⚠️ Using fallback RecursiveCharacterTextSplitter")
+        # Fallback: Simple text splitter
+        class RecursiveCharacterTextSplitter:
+            def __init__(self, chunk_size=500, chunk_overlap=50, **kwargs):
+                self.chunk_size = chunk_size
+                self.chunk_overlap = chunk_overlap
+                print(f"📝 Initialized fallback text splitter with chunk_size={chunk_size}")
+            
+            def split_documents(self, documents):
+                """Simple document splitting fallback"""
+                print(f"🔄 Splitting {len(documents)} documents using fallback method...")
+                result = []
+                for doc in documents:
+                    text = doc.page_content
+                    # Simple chunking
+                    for i in range(0, len(text), self.chunk_size - self.chunk_overlap):
+                        chunk = text[i:i + self.chunk_size]
+                        if chunk.strip():
+                            result.append(Document(page_content=chunk, metadata=doc.metadata))
+                print(f"✅ Split into {len(result)} chunks")
+                return result
 from langchain_community.retrievers import BM25Retriever
 
 # Load environment variables
@@ -624,6 +669,5 @@ if __name__ == "__main__":
     print("   - search_cve_database: Search CVE knowledge base")
     print("   - get_nvd_cve_details: Fetch detailed CVE info from NVD")
     print("   - 🆕 search_and_fetch_cve_details: Smart combined RAG + NVD analysis")
-    print("   - simple_cve_search: Simple CVE search for multi-agent workflow")
     
     demo.launch(mcp_server=True)