Update app.py

app.py

@@ -38,14 +38,14 @@ llm = ChatOpenAI(model="gpt-4o", temperature=0, openai_api_key=OPENAI_API_KEY)
 # === INPUT SCHEMAS ===
 
 class Query(BaseModel):
-    question: str
+    message: str
 
 class ProjectRequest(BaseModel):
     userLoginId: int
     orgId: int
 
 class AgentQuery(BaseModel):
-    question: str
+    message: str
     userLoginId: Optional[int] = None
     orgId: Optional[int] = None
     auth_token: Optional[str] = None
@@ -123,7 +123,7 @@ def search_documents(query: str) -> str:
     """Search through ingested documents and get relevant information.
     
     Args:
-        query: The search query or question about the documents
+        query: The search query or message about the documents
         
     Returns:
         Relevant information from the documents with sources
@@ -154,13 +154,13 @@ def search_documents(query: str) -> str:
         context = "\n\n".join(context_texts)
         unique_sources = list(set(sources))
         
-        # Use the LLM directly to answer the question based on context
-        prompt = f"""Based on the following context, answer the question: {query}
+        # Use the LLM directly to answer the message based on context
+        prompt = f"""Based on the following context, answer the message: {query}
 
 Context:
 {context}
 
-Please provide a comprehensive answer based on the context above. If the context doesn't contain enough information to answer the question, say so clearly."""
+Please provide a comprehensive answer based on the context above. If the context doesn't contain enough information to answer the message, say so clearly."""
 
         response = llm.invoke(prompt)
         
@@ -212,8 +212,8 @@ def get_user_projects(userLoginId: str) -> str:
 document_search_tool = Tool(
     name="document_search",
     description="""Use this tool to search through ingested documents and get relevant information from the knowledge base. 
-    Perfect for answering questions about uploaded documents, manuals, or any content that was previously stored.
-    Input should be a search query or question about the documents.""",
+    Perfect for answering messages about uploaded documents, manuals, or any content that was previously stored.
+    Input should be a search query or message about the documents.""",
     func=search_documents
 )
 
@@ -234,7 +234,7 @@ agent_prompt = ChatPromptTemplate.from_messages([
 2. **Project Management**: Get list of user projects and project information
 
 Your capabilities:
-- Answer questions about documents using the document search tool
+- Answer messages about documents using the document search tool
 - Help users find their projects and project information
 - Provide general assistance and information
 - Use appropriate tools based on user queries
@@ -247,7 +247,7 @@ Guidelines:
 - If you're unsure which tool to use, you can ask for clarification
 - Provide helpful, accurate, and well-formatted responses
 
-Remember: Always use the most appropriate tool based on the user's question to provide the best possible answer."""),
+Remember: Always use the most appropriate tool based on the user's message to provide the best possible answer."""),
     ("user", "{input}"),
     MessagesPlaceholder(variable_name="agent_scratchpad"),
 ])
@@ -268,7 +268,7 @@ def chat_with_agent(query: AgentQuery):
     """Main agent endpoint - handles both document search and project queries intelligently"""
     try:
         # Prepare the input for the agent
-        agent_input = query.question
+        agent_input = query.message
         
         # If user provided credentials, add them to the context
         
@@ -282,14 +282,14 @@ def chat_with_agent(query: AgentQuery):
         result = agent_executor.invoke({"input": agent_input})
         
         return {
-            "question": query.question,
+            "message": query.message,
             "answer": result["output"],
             "agent_used": True
         }
         
     except Exception as e:
         return {
-            "question": query.question,
+            "message": query.message,
             "answer": f"An error occurred: {str(e)}",
             "agent_used": True
         }
@@ -298,15 +298,15 @@ def chat_with_agent(query: AgentQuery):
 def chat_documents_only(query: Query):
     """Direct document search without agent"""
     try:
-        result = search_documents(query.question)
+        result = search_documents(query.message)
         return {
-            "question": query.question,
+            "message": query.message,
             "answer": result,
             "tool_used": "document_search"
         }
     except Exception as e:
         return {
-            "question": query.question,
+            "message": query.message,
             "answer": f"An error occurred: {str(e)}",
             "tool_used": "document_search"
         }
@@ -339,4 +339,3 @@ def list_projects(request: ProjectRequest):
 @app.get("/health")
 def health():
     return {"status": "ok", "tools": ["document_search", "project_list"], "agent": "active"}
-