Fix frontend connection: use relative API path

Dockerfile

@@ -26,7 +26,9 @@ COPY --chown=appuser:appuser . .
 EXPOSE 7860
 
 USER appuser
-RUN chmod -R 777 /app/data && chmod -R 777 /app/chroma_db
+# RUN chmod -R 777 /app/data && chmod -R 777 /app/chroma_db
+COPY ./data /app/data
+RUN chmod -R 777 /app/data
 # Update Healthcheck in Dockerfile
 HEALTHCHECK --interval=30s --timeout=3s \
     CMD curl --fail http://localhost:7860/health || exit 1

rag_with_memory.py

@@ -39,7 +39,7 @@ class MemoryRAG:
             
             # 1. Load and chunk documents
             loader = DirectoryLoader(docs_path, glob="**/*.md",
-                                     loader_cls=TextLoader,show_progress=True, use_multithreading=True)
+                                     loader_cls=TextLoader, silent_errors=False)
             docs = loader.load()
             logger.info(f"RAG DATABASE STATUS: Loaded {len(docs)} documents from {docs_path}")
             if not docs:

tools.py

@@ -1,60 +1,116 @@
-"""
-    This tools working correctly
-"""
+# """
+#     This tools working correctly
+# """
+
+# import os
+# from langchain_core.tools import tool
+# from pydantic import BaseModel, Field
+# from rag_with_memory import MemoryRAG
+# import glob
+# from loguru import logger
+
+# possible_paths = [
+#     "/app/data/knowledge_base",
+#     "./data/knowledge_base",
+#     "./backend/data/knowledge_base"
+# ]
+
+# KNOWLEDGE_BASE_PATH = None
+# for p in possible_paths:
+#     # Check if path exists AND contains .md files
+#     if os.path.exists(p) and glob.glob(os.path.join(p, "*.md")):
+#         KNOWLEDGE_BASE_PATH = p
+#         break
+
+# if not KNOWLEDGE_BASE_PATH:
+#     logger.critical("No .md files found in any knowledge base path!")
+#     rag_engine = None
+
+# else:
+#     logger.info(f"Knowledge Base detected at: {KNOWLEDGE_BASE_PATH}")
+#     try:
+#         rag_engine = MemoryRAG(docs_path=KNOWLEDGE_BASE_PATH)
+#         logger.success("RAG Engine initialized successfully.")
+#     except Exception as e:
+#         logger.exception(f"Failed to initialize MemoryRAG: {e}")
+#         rag_engine = None
+
+# class KnowledgeBaseInput(BaseModel):
+#     query: str = Field(description="User's question about coffee products, resets, warranty, installation safety, maintenance procedures, or troubleshooting guide.")
+
+# @tool(args_schema=KnowledgeBaseInput, return_direct=True)
+# def knowledge_base_search(query: str) -> str:
+#     """Search product documentation and FAQs to provide accurate answers about company products."""
+    
+#     if not rag_engine:
+#         logger.warning(f"Search attempted but RAG engine is None. Query: {query}")
+#         return "I'm sorry, my internal knowledge base is currently offline."
+
+#     try:
+#         result = rag_engine.query(query, session_id="agent_tool_session")
+        
+#         answer = result.get("answer", "")
+#         if not answer or "error" in answer.lower():
+#             return "I couldn't find specific information about that in the knowledge base."
+            
+#         return answer
+    
+#     except Exception as e:
+#         logger.error(f"Error during RAG query: {e}")
+#         return "I encountered a technical error while searching documents."
+
+
+
+
+
+
+
 
 import os
 from langchain_core.tools import tool
 from pydantic import BaseModel, Field
 from rag_with_memory import MemoryRAG
-import glob
 from loguru import logger
 
-possible_paths = [
-    "/app/data/knowledge_base",
-    "./data/knowledge_base",
-    "./backend/data/knowledge_base"
-]
-
-KNOWLEDGE_BASE_PATH = None
-for p in possible_paths:
-    # Check if path exists AND contains .md files
-    if os.path.exists(p) and glob.glob(os.path.join(p, "*.md")):
-        KNOWLEDGE_BASE_PATH = p
-        break
-
-if not KNOWLEDGE_BASE_PATH:
-    logger.critical("No .md files found in any knowledge base path!")
-    rag_engine = None
-
-else:
-    logger.info(f"Knowledge Base detected at: {KNOWLEDGE_BASE_PATH}")
-    try:
-        rag_engine = MemoryRAG(docs_path=KNOWLEDGE_BASE_PATH)
-        logger.success("RAG Engine initialized successfully.")
-    except Exception as e:
-        logger.exception(f"Failed to initialize MemoryRAG: {e}")
-        rag_engine = None
+# Global variable to hold the engine
+_rag_engine = None
+
+def get_rag_engine():
+    global _rag_engine
+    if _rag_engine is None:
+        # ABSOLUTE PATH for Docker 2026
+        target_path = "/app/data/knowledge_base"
+        
+        # Verify folder exists and isn't empty
+        if not os.path.exists(target_path) or not os.listdir(target_path):
+            logger.error(f"KNOWLEDGE BASE MISSING at {target_path}")
+            return None
+            
+        try:
+            logger.info("Initializing MemoryRAG engine...")
+            _rag_engine = MemoryRAG(docs_path=target_path)
+            logger.success("MemoryRAG engine is now ONLINE.")
+        except Exception as e:
+            logger.error(f"MemoryRAG Initialization failed: {e}")
+            return None
+    return _rag_engine
 
 class KnowledgeBaseInput(BaseModel):
-    query: str = Field(description="User's question about coffee products, resets, warranty, installation safety, maintenance procedures, or troubleshooting guide.")
+    query: str = Field(description="The search query.")
 
 @tool(args_schema=KnowledgeBaseInput, return_direct=True)
 def knowledge_base_search(query: str) -> str:
-    """Search product documentation and FAQs to provide accurate answers about company products."""
+    """Search the company knowledge base for answers."""
+    engine = get_rag_engine()
     
-    if not rag_engine:
-        logger.warning(f"Search attempted but RAG engine is None. Query: {query}")
-        return "I'm sorry, my internal knowledge base is currently offline."
+    if engine is None:
+        logger.error("Tool called but RAG engine remains None.")
+        return "I'm sorry, I'm currently unable to access my internal documentation."
 
     try:
-        result = rag_engine.query(query, session_id="agent_tool_session")
-        
-        answer = result.get("answer", "")
-        if not answer or "error" in answer.lower():
-            return "I couldn't find specific information about that in the knowledge base."
-            
-        return answer
-    
+        # LOGIC FIX: Always return a string for the Agent to read
+        result = engine.query(query, session_id="tool_session")
+        return result.get("answer", "No information found.")
     except Exception as e:
-        logger.error(f"Error during RAG query: {e}")
-        return "I encountered a technical error while searching documents."
+        logger.error(f"Tool execution failed: {e}")
+        return "I encountered an error while searching."