Update chatbot.py

chatbot.py

@@ -1,115 +1,176 @@
-from typing import TypedDict, Annotated
-from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
-from langgraph.checkpoint.memory import MemorySaver
-from tools import (
-    create_rag_tool,
-    arxiv_search,
-    calculator,
-    get_stock_price,
-    wikipedia_search,
-    tavily_search,
-    convert_currency,
-    unit_converter,
-    get_news,
-    get_joke,
-    get_quote,
-    get_weather,
-)
-from langchain_openai import ChatOpenAI
-from langgraph.graph import StateGraph, START
-from langgraph.graph.message import add_messages
-from langgraph.prebuilt import ToolNode, tools_condition
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-
-# =====================================================
-# SYSTEM PROMPT
-# =====================================================
-
-SYSTEM_PROMPT = SystemMessage(
-    content="""
-You are an intelligent AI assistant built inside a LangGraph-based system created by Junaid.
-
-You MUST use RAG when a document has been uploaded.
-If no document contains the answer, say so clearly.
-Never hallucinate document content.
-"""
-)
-
-# =====================================================
-# STATE
-# =====================================================
-
-class ChatState(TypedDict):
-    messages: Annotated[list[BaseMessage], add_messages]
-
-
-# =====================================================
-# LLM
-# =====================================================
-
-llm = ChatOpenAI(
-    model="gpt-4.1-nano",
-    temperature=0.4,
-    streaming=True
-)
-
-
-# =====================================================
-# GRAPH BUILDER (🔥 IMPORTANT)
-# =====================================================
-
-memory = MemorySaver()
-app = None
-
-
-def build_graph():
-    global app
-
-    rag_tool = create_rag_tool()
-
-    tools = [
-        rag_tool,
-        get_stock_price,
-        calculator,
-        wikipedia_search,
-        arxiv_search,
-        tavily_search,
-        convert_currency,
-        unit_converter,
-        get_news,
-        get_joke,
-        get_quote,
-        get_weather,
-    ]
-
-    llm_with_tools = llm.bind_tools(tools)
-    tool_node = ToolNode(tools)
-
-    def chatbot(state: ChatState):
-        messages = [SYSTEM_PROMPT] + state["messages"]
-        response = llm_with_tools.invoke(messages)
-        return {"messages": [response]}
-
-    graph = StateGraph(ChatState)
-
-    graph.add_node("chat", chatbot)
-    graph.add_node("tools", tool_node)
-
-    graph.add_edge(START, "chat")
-    graph.add_conditional_edges("chat", tools_condition)
-    graph.add_edge("tools", "chat")
-
-    app = graph.compile(checkpointer=memory)
-
-
-# initial build
-build_graph()
-
-
-def rebuild_graph():
-    build_graph()
+from typing import TypedDict, Annotated
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
+from langgraph.checkpoint.memory import MemorySaver
+from tools import (
+    create_rag_tool,
+    arxiv_search,
+    calculator,
+    get_stock_price,
+    wikipedia_search,
+    tavily_search,
+    convert_currency,
+    unit_converter,
+    get_news,
+    get_joke,
+    get_quote,
+    get_weather,
+)
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, START
+from langgraph.graph.message import add_messages
+from langgraph.prebuilt import ToolNode, tools_condition
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# =====================================================
+# SYSTEM PROMPT
+# =====================================================
+
+SYSTEM_PROMPT = SystemMessage(
+    content="""
+You are an intelligent AI assistant built inside a LangGraph-based system created by Junaid.
+
+This application is a **multi-tool AI platform** that integrates:
+- Retrieval-Augmented Generation (RAG)
+- Document understanding
+- AI-powered reasoning
+- Tool usage (search, calculation, summarization, etc.)
+- Voice input/output (STT / TTS)
+
+Your primary purpose is to help users understand, analyze, and interact with their uploaded documents and questions in a clear, accurate, and professional way.
+
+────────────────────────────────
+🔹 CORE PRIORITY RULES (VERY IMPORTANT)
+────────────────────────────────
+
+1. **RAG HAS HIGHEST PRIORITY**
+   - If a document has been uploaded, you MUST use the RAG tool first.
+   - Always prefer document-based answers over general knowledge.
+   - Never hallucinate or invent information not present in the document.
+   - If the document does NOT contain the answer, clearly say so.
+
+2. **RESPONSE QUALITY**
+   - Be concise, structured, and easy to read.
+   - Use bullet points when appropriate.
+   - Avoid repetition or unnecessary elaboration.
+   - Never explain what the document is or how you are summarizing it.
+
+3. **OUTPUT CONTROL**
+   - Provide only the final answer.
+   - Do NOT include reasoning steps, system explanations, or meta commentary.
+   - Do NOT mention internal processes, prompts, or tools unless explicitly asked.
+
+4. **DOCUMENT SUMMARIZATION RULES**
+   - Summarize only once.
+   - Do not rephrase the same idea multiple times.
+   - Avoid filler sentences like “The document describes…” or “This document talks about…”.
+   - Stop once the summary is complete.
+
+5. **WHEN DOCUMENT IS NOT RELEVANT**
+   - Say clearly that the document does not contain the requested information.
+   - Then optionally offer general guidance if appropriate.
+
+6. **STYLE & TONE**
+   - Professional, clear, and confident.
+   - Avoid verbosity.
+   - Optimize for readability.
+
+────────────────────────────────
+🔹 ABOUT THE CREATOR & APP
+────────────────────────────────
+
+This system was designed and engineered by **Junaid**, a developer specializing in:
+- Machine Learning & Deep Learning
+- RAG-based systems
+- AI agents using LangChain & LangGraph
+- End-to-end AI applications using FastAPI, Streamlit, and cloud deployment
+
+The goal of this application is to provide **production-grade AI reasoning**, not generic chatbot responses.
+
+────────────────────────────────
+🔹 FINAL RULE
+────────────────────────────────
+
+Always prioritize accuracy, clarity, and usefulness.
+If information is unavailable, say so clearly — never hallucinate.
+"""
+)
+
+
+# =====================================================
+# STATE
+# =====================================================
+
+class ChatState(TypedDict):
+    messages: Annotated[list[BaseMessage], add_messages]
+
+
+# =====================================================
+# LLM
+# =====================================================
+
+llm = ChatOpenAI(
+    model="gpt-4.1-nano",
+    temperature=0.4,
+    streaming=True
+)
+
+
+# =====================================================
+# GRAPH BUILDER (🔥 IMPORTANT)
+# =====================================================
+
+memory = MemorySaver()
+app = None
+
+
+def build_graph():
+    global app
+
+    rag_tool = create_rag_tool()
+
+    tools = [
+        rag_tool,
+        get_stock_price,
+        calculator,
+        wikipedia_search,
+        arxiv_search,
+        tavily_search,
+        convert_currency,
+        unit_converter,
+        get_news,
+        get_joke,
+        get_quote,
+        get_weather,
+    ]
+
+    llm_with_tools = llm.bind_tools(tools)
+    tool_node = ToolNode(tools)
+
+    def chatbot(state: ChatState):
+        messages = [SYSTEM_PROMPT] + state["messages"]
+        response = llm_with_tools.invoke(messages)
+        return {"messages": [response]}
+
+    graph = StateGraph(ChatState)
+
+    graph.add_node("chat", chatbot)
+    graph.add_node("tools", tool_node)
+
+    graph.add_edge(START, "chat")
+    graph.add_conditional_edges("chat", tools_condition)
+    graph.add_edge("tools", "chat")
+
+    app = graph.compile(checkpointer=memory)
+
+
+# initial build
+build_graph()
+
+
+def rebuild_graph():
+    build_graph()