Add main.py

main.py

@@ -0,0 +1,82 @@
+"""Entry point for the FAQ bot.
+
+The script performs the following steps:
+1. Loads FAQ markdown files into a persistent Chroma vector store.
+2. Creates an agent with a system prompt that routes queries to the correct tool.
+3. Provides a simple CLI with three preset questions (two for the FAQ store and one for metadata).
+4. Allows interactive querying until the user types /quit.
+"""
+
+import os
+from typing import List
+
+from langchain_ollama import ChatOllama
+from langchain.agents import create_agent
+from langchain.tools import BaseTool
+
+from config import CHROMA_PERSIST_DIR
+from tools import search_course_docs_tool, fetch_course_meta_tool
+from vector_store import load_faq_to_chroma
+
+# ---------------------------------------------------------------------------
+# 1. Load data into Chroma
+# ---------------------------------------------------------------------------
+print("Loading FAQ data into Chroma...")
+load_faq_to_chroma()
+print("Data loaded.")
+
+# ---------------------------------------------------------------------------
+# 2. Define system prompt
+# ---------------------------------------------------------------------------
+SYSTEM_PROMPT = (
+    "You are an assistant that answers questions about the course. "
+    "If the question is about lecture materials, use the tool "
+    "search_course_docs. If the question is about course schedule or "
+    "metadata, use fetch_course_meta. After using a tool, answer the user and "
+    "include a source tag: 'source: chroma' or 'source: mcp_meta'. "
+    "Do not use both tools unless the question explicitly requires it."
+)
+
+# ---------------------------------------------------------------------------
+# 3. Create LLM and agent
+# ---------------------------------------------------------------------------
+llm = ChatOllama(model="llama3", temperature=0.2)
+
+# Gather tools
+TOOLS: List[BaseTool] = [search_course_docs_tool, fetch_course_meta_tool]
+
+agent = create_agent(model=llm, tools=TOOLS, system_prompt=SYSTEM_PROMPT)
+
+# ---------------------------------------------------------------------------
+# 4. CLI with preset questions
+# ---------------------------------------------------------------------------
+PRESET_QUESTIONS = [
+    "What topics are covered in Lecture 5?",  # Should use chroma
+    "Explain the concept of recursion as described in the notes.",  # chroma
+    "What is the schedule for the next week?",  # mcp_meta
+]
+
+print("\nPreset questions: (type /quit to exit)\n")
+for i, q in enumerate(PRESET_QUESTIONS, 1):
+    print(f"{i}. {q}")
+print()
+
+while True:
+    user_input = input("You: ")
+    if user_input.strip().lower() in {"/quit", "exit", "q"}:
+        print("Goodbye!")
+        break
+    # If user types a number, use preset
+    if user_input.isdigit() and 1 <= int(user_input) <= len(PRESET_QUESTIONS):
+        query = PRESET_QUESTIONS[int(user_input) - 1]
+    else:
+        query = user_input
+    # Invoke agent
+    try:
+        response = agent.invoke({"messages": [{"role": "user", "content": query}]})
+        # The response is a dict with 'messages' list
+        assistant_msg = response["messages"][-1]["content"]
+        print(f"Assistant: {assistant_msg}\n")
+    except Exception as e:
+        print(f"Error: {e}\n")
+""