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import os
from typing import TypedDict, Annotated, List
import operator
from langchain_core.messages import BaseMessage, HumanMessage
from langchain_huggingface import HuggingFaceEmbeddings, ChatHuggingFace
from langchain_community.vectorstores import FAISS
from langchain.tools.retriever import create_retriever_tool
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolExecutor
# --- Configuration ---
SAVE_PATH = "/data/faiss_index"
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
# Recommended to use a powerful model for agentic tasks
LLM_REPO_ID = "mistralai/Mixtral-8x7B-Instruct-v0.1"
# --- Agent State Definition ---
class AgentState(TypedDict):
"""Defines the state of the agent graph, tracking messages."""
messages: Annotated, operator.add]
# --- Knowledge Base and Tools Setup ---
def create_agent_system():
"""
Initializes the entire agent system, including the knowledge base retriever,
specialized tools, and the LangGraph-based agent executor.
"""
print("Initializing Agent System...")
# 1. Load the Knowledge Base
if not os.path.exists(SAVE_PATH):
raise FileNotFoundError(
f"FAISS index not found at {SAVE_PATH}. "
"Please run knowledge_base.py first to create it."
)
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
vector_store = FAISS.load_local(SAVE_PATH, embeddings, allow_dangerous_deserialization=True)
retriever = vector_store.as_retriever(search_kwargs={'k': 3})
# 2. Create Specialized Retriever Tools for each Agent
# The tool descriptions are crucial as they guide the Orchestrator agent.
academic_tool = create_retriever_tool(
retriever,
"academic_retriever",
"Searches for information about academics, including curriculum, study materials, timetables, RGPV links, exam papers, and Moodle/e-Library info."
)
administrative_tool = create_retriever_tool(
retriever,
"administrative_retriever",
"Searches for information about college administration, including fees, scholarships, admissions, rules, regulations, and grievance policies."
)
campus_services_tool = create_retriever_tool(
retriever,
"campus_services_retriever",
"Searches for information about campus services like library hours, bus routes, lab availability, sports facilities, and special academies (Cisco, AWS)."
)
student_life_tool = create_retriever_tool(
retriever,
"student_life_retriever",
"Searches for information about student life, including upcoming events, clubs, cultural festivals, and how to submit complaints or raise issues."
)
tools = [academic_tool, administrative_tool, campus_services_tool, student_life_tool]
tool_executor = ToolExecutor(tools)
# 3. Initialize the LLM
# This requires the HUGGINGFACEHUB_API_TOKEN to be set as a secret in the Space.
llm = ChatHuggingFace(
repo_id=LLM_REPO_ID,
task="text-generation",
model_kwargs={
"max_new_tokens": 1024,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
# Bind the tools to the LLM so it knows how to call them
llm_with_tools = llm.bind_tools(tools)
# 4. Define the LangGraph Nodes
def agent_node(state):
"""The primary node that invokes the LLM to decide the next action."""
response = llm_with_tools.invoke(state["messages"])
return {"messages": [response]}
def tool_node(state):
"""Executes the tool called by the agent and returns the result."""
tool_calls = state["messages"][-1].tool_calls
tool_messages = tool_executor.batch(tool_calls)
return {"messages": tool_messages}
def should_continue(state):
"""Conditional edge logic: decides whether to continue or end."""
if state["messages"][-1].tool_calls:
return "continue"
return "end"
# 5. Build the Graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
"agent",
should_continue,
{"continue": "tools", "end": END}
)
workflow.add_edge("tools", "agent")
# 6. Compile the graph into a runnable app
agent_executor = workflow.compile()
print("Agent System Initialized Successfully.")
return agent_executor
def run_query(agent_executor, query, user_info):
"""
Runs a query through the agent system, providing user context.
"""
# Prepend a system message with user context for better personalization
system_message = (
"You are the 'GGITS Digital Campus Assistant,' a helpful, professional, and reliable AI assistant. "
f"You are currently assisting a '{user_info['role']}' named {user_info['email']}. "
"Use the available tools to find the most relevant and accurate information from the college's knowledge base. "
"If you cannot find an answer, state that the information is not available in your current knowledge base."
)
messages = [
HumanMessage(content=system_message),
HumanMessage(content=query)
]
# The `stream` method provides real-time output as the agent works
final_response = None
for chunk in agent_executor.stream({"messages": messages}):
if "messages" in chunk:
final_response = chunk["messages"][-1]
return final_response.content if final_response else "I'm sorry, I couldn't process your request." |