add: main.py
This commit is contained in:
@@ -1,73 +1,22 @@
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import os
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import asyncio
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import json
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from pathlib import Path
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from typing import List
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import httpx
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from pathlib import Path
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_chroma import Chroma
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from langchain_core.documents import Document
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from langchain.tools import tool
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langchain_chroma import Chroma
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from langchain_ollama import OllamaEmbeddings
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# ---------------------
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# 1. Chroma DB helpers
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# ---------------------
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CHROMA_PATH = Path("./chroma_faq")
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DATA_DIR = Path("./data")
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# --------------------- Configuration ---------------------
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BASE_DIR = Path(__file__).parent
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DATA_DIR = BASE_DIR / "data"
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CHROMA_DIR = BASE_DIR / "chroma_faq"
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META_JSON = BASE_DIR / "course_meta.json"
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def load_faq_to_chroma() -> None:
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"""Load all .md files from data/ into a persistent Chroma store."""
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if CHROMA_PATH.exists():
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# already loaded
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return
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CHROMA_PATH.mkdir(parents=True, exist_ok=True)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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db = Chroma.from_folder(str(DATA_DIR), embedding=embeddings, persist_directory=str(CHROMA_PATH))
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db.persist()
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@tool
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def search_course_docs(query: str, k: int = 3) -> str:
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"""Search local course FAQ documents in Chroma DB."""
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load_faq_to_chroma()
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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db = Chroma(persist_directory=str(CHROMA_PATH), embedding=embeddings)
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docs = db.similarity_search(query, k=k)
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if not docs:
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return "No relevant FAQ found."
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return "\n\n---\n\n".join(doc.page_content for doc in docs)
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# ---------------------
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# 2. MCP‑style tool
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# ---------------------
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# For demo we use a local JSON file served by python -m http.server
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# The file is located at ./meta/course_meta.json
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META_URL = "http://localhost:8000/course_meta.json"
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Fetch course metadata (e.g., schedule) from a mock MCP server."""
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try:
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response = httpx.get(META_URL, timeout=5.0)
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response.raise_for_status()
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data = response.json()
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except Exception as e:
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return f"Error fetching metadata: {e}"
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# Simple keyword search in the JSON
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results: List[str] = []
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for key, value in data.items():
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if query.lower() in key.lower() or query.lower() in str(value).lower():
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results.append(f"{key}: {value}")
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return "\n".join(results) if results else "No metadata matches your query."
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# ---------------------
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# 3. Agent setup
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# ---------------------
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# --------------------- LLM & Embeddings ---------------------
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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@@ -75,56 +24,100 @@ llm = ChatOpenAI(
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temperature=0.0,
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)
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-small",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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# --------------------- Chroma DB ---------------------
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vector_store = Chroma(
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collection_name="faq_collection",
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embedding_function=embeddings,
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persist_directory=str(CHROMA_DIR),
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)
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# Load markdown files into Chroma if not already loaded
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if not CHROMA_DIR.exists() or not any(CHROMA_DIR.iterdir()):
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docs = []
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for md_file in DATA_DIR.glob("*.md"):
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text = md_file.read_text(encoding="utf-8")
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docs.append(Document(page_content=text, metadata={"source": md_file.name}))
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vector_store.add_documents(docs)
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vector_store.persist()
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# --------------------- Tools ---------------------
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@tool
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def search_course_docs(query: str) -> str:
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"""Search the FAQ knowledge base for relevant information."""
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results = vector_store.similarity_search(query, k=3)
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if not results:
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return "No relevant information found in the course materials."
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return "\n---\n".join(f"{doc.metadata.get('source', 'unknown')}\n{doc.page_content}" for doc in results)
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Fetch course metadata (e.g., schedule) from a local JSON mock."""
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if not META_JSON.exists():
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return "Metadata file not found."
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data = json.loads(META_JSON.read_text(encoding="utf-8"))
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# Simple keyword search in the metadata
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matches = [f"{k}: {v}" for k, v in data.items() if query.lower() in k.lower() or query.lower() in str(v).lower()]
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return "\n".join(matches) if matches else "No metadata matches the query."
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# --------------------- Backend ---------------------
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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SYSTEM_PROMPT = (
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"You are a helpful FAQ assistant for the course.\n"
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"When a user asks a question, first decide whether the answer is best found in the local FAQ documents or in the course metadata.\n"
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"If the answer is in the FAQ, use the tool `search_course_docs`.\n"
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"If the answer requires schedule or other metadata, use the tool `fetch_course_meta`.\n"
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"Do not call both tools unless absolutely necessary.\n"
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"In your final response, prepend the source: `source: chroma` or `source: mcp_meta`."
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)
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# --------------------- Agent ---------------------
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agent = create_deep_agent(
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model=llm,
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tools=[search_course_docs, fetch_course_meta],
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backend=backend,
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system_prompt=SYSTEM_PROMPT,
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system_prompt=(
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"You are a helpful FAQ assistant for the course.\n"
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"When a user asks about course content, use the search_course_docs tool.\n"
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"When a user asks about schedule, metadata, or other non‑content info, use fetch_course_meta.\n"
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"Do not use both tools unless absolutely necessary.\n"
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"In your final answer, prefix the source with 'source: chroma' or 'source: mcp_meta'."
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),
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)
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# ---------------------
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# 4. CLI
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# ---------------------
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PRESET_QUESTIONS = [
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"What is the deadline for the final project?", # FAQ
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"When does the next lecture on deep learning start?", # metadata
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"Explain the concept of attention mechanism.", # FAQ
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# --------------------- CLI ---------------------
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SAMPLE_QUESTIONS = [
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"What topics are covered in the first lecture?", # should hit chroma
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"Explain the concept of tokenization in NLP.", # chroma
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"When is the next class scheduled?", # meta
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]
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async def run_agent(question: str) -> str:
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=question)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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return result["messages"][-1].content
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async def run_interactive():
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print("Welcome to the Course FAQ Bot! Type 'exit' to quit.")
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while True:
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user_input = input("\nYou: ")
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if user_input.lower() in {"exit", "quit"}:
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break
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result = await agent.ainvoke(
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{"messages": [{"role": "user", "content": user_input}]},
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{"configurable": {"thread_id": "interactive-session"}},
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)
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print("\nAssistant:", result["messages"][-1]["content"])
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async def run_samples():
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for q in SAMPLE_QUESTIONS:
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print("\nQuestion:", q)
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result = await agent.ainvoke(
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{"messages": [{"role": "user", "content": q}]},
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{"configurable": {"thread_id": "sample-session"}},
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)
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print("Answer:", result["messages"][-1]["content"])
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async def main():
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print("--- FAQ Bot Demo ---\n")
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for i, q in enumerate(PRESET_QUESTIONS, 1):
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print(f"Q{i}: {q}")
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ans = await run_agent(q)
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print(f"A{i}: {ans}\n")
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print("Enter your own question (or 'exit' to quit):")
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while True:
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user_q = input("> ")
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if user_q.lower() in {"exit", "quit"}:
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break
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ans = await run_agent(user_q)
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print(ans)
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# Run sample questions first
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await run_samples()
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# Then enter interactive mode
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await run_interactive()
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if __name__ == "__main__":
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asyncio.run(main())
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