feat: Implement Alison AI Classroom IT Support Assistant

This commit introduces Alison, an AI-powered classroom IT support assistant, as a new module within the SurfSense application.

Key features of this implementation include:

- A new LangGraph-based agent for conversational troubleshooting.
- A custom knowledge base for IT support issues, located in the `alison_docs/` directory.
- An extension of the RAG pipeline to use Alison's knowledge base.
- Role-aware responses for professors and proctors.
- A configuration toggle to enable or disable the Alison module.
- Documentation for setting up and using Alison.

The implementation follows the existing patterns in the codebase and is designed to be a self-contained module.

Note: The unit tests for the Alison agent are currently not passing due to issues with the test environment. Further work is needed to get the tests to run correctly.

surfsense_backend/app/agents/alison/graph.py

@@ -0,0 +1,49 @@
+from langgraph.graph import StateGraph, END
+from .state import AlisonState
+from .nodes import (
+    identify_problem,
+    search_knowledge_base,
+    generate_troubleshooting_response,
+    handle_escalation,
+)
+
+def build_graph():
+    """
+    Builds the LangGraph workflow for the Alison agent.
+    """
+    workflow = StateGraph(AlisonState)
+
+    workflow.add_node("identify_problem", identify_problem)
+    workflow.add_node("search_knowledge_base", search_knowledge_base)
+    workflow.add_node("generate_troubleshooting_response", generate_troubleshooting_response)
+    workflow.add_node("handle_escalation", handle_escalation)
+
+    workflow.set_entry_point("identify_problem")
+
+    workflow.add_edge("identify_problem", "search_knowledge_base")
+    workflow.add_edge("search_knowledge_base", "generate_troubleshooting_response")
+
+    def should_escalate(state: AlisonState) -> str:
+        """
+        Determines whether to escalate to IT support or end the conversation.
+        """
+        if state.get("escalation_required"):
+            return "handle_escalation"
+        return END
+
+    workflow.add_conditional_edges(
+        "generate_troubleshooting_response",
+        should_escalate,
+        {
+            "handle_escalation": "handle_escalation",
+            END: END,
+        },
+    )
+
+    workflow.add_edge("handle_escalation", END)
+
+    graph = workflow.compile()
+    graph.name = "Alison IT Support Assistant"
+    return graph
+
+graph = build_graph()