fix(needs_fixes): 1 исправлений, 0 отстояно — main.py
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@@ -9,144 +9,116 @@ from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from langchain_core.messages import HumanMessage
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# ---------------------------
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# Configuration
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# ---------------------------
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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if not OPENAI_API_KEY:
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raise RuntimeError("OPENAI_API_KEY not set in environment")
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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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MOCK_META_FILE = BASE_DIR / "course_meta.json"
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# LLM via OpenRouter
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# --------------------- LLM and 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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api_key=OPENAI_API_KEY,
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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# Embeddings via OpenRouter
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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=OPENAI_API_KEY,
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api_key=os.getenv("OPENAI_API_KEY"),
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)
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# Chroma vector store (persisted)
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CHROMA_PATH = Path("./chroma_faq")
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# --------------------- Chroma Vector Store ---------------------
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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_PATH),
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persist_directory=str(CHROMA_DIR),
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)
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# ---------------------------
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# Data loading
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# ---------------------------
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def load_faq_to_chroma(md_folder: str = "data"):
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"""Load all .md files from md_folder into ChromaDB.
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Each file is split into chunks and added to the vector store.
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"""
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md_path = Path(md_folder)
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if not md_path.exists():
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raise FileNotFoundError(f"Markdown folder {md_folder} not found")
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docs = []
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for md_file in md_path.glob("*.md"):
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text = md_file.read_text(encoding="utf-8")
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# Simple chunking: split by double newlines
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chunks = [c.strip() for c in text.split("\n\n") if c.strip()]
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for i, chunk in enumerate(chunks):
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docs.append(Document(page_content=chunk, metadata={"source": md_file.name, "chunk": i}))
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if docs:
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vector_store.add_documents(docs)
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vector_store.persist()
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else:
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print("No markdown files found to load.")
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# ---------------------------
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# Tools
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# ---------------------------
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# --------------------- Tools ---------------------
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@tool
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def search_course_docs(query: str, k: int = 3) -> str:
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"""Search the FAQ knowledge base for relevant information."""
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"""Search the local FAQ collection for relevant passages."""
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docs = vector_store.similarity_search(query, k=k)
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if not docs:
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return "No relevant information found in the course materials."
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return "\n\n---\n\n".join(f"**{doc.metadata.get('source')}** (chunk {doc.metadata.get('chunk')}):\n{doc.page_content}" for doc in docs)
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return "\n\n---\n\n".join(f"**{doc.metadata.get('title', 'Document')}**\n{doc.page_content}" for doc in docs)
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Mock MCP-style tool that returns course metadata.
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In production this would perform an HTTP GET to an MCP server.
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Here we return a static JSON-like string for simplicity.
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In production this would be an HTTP call to an MCP server.
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Here we read a local JSON file for simplicity.
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"""
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# Static mock data
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meta = {
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"schedule": {
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"Monday": "Lecture 1: Introduction",
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"Wednesday": "Lecture 2: Advanced Topics",
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"Friday": "Lab Session"
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},
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"instructor": "Dr. Jane Doe",
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"credits": 3
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}
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return f"Course metadata: {meta}"
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import json
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if not MOCK_META_FILE.exists():
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return "Metadata source not available."
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with open(MOCK_META_FILE, "r", encoding="utf-8") as f:
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data = json.load(f)
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# Simple keyword search in the metadata
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results = [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(results) if results else "No metadata matches your query."
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# ---------------------------
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# Backend setup
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# ---------------------------
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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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# ---------------------------
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# Agent creation
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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="You are a helpful FAQ bot for the course. Use the search_course_docs tool for questions about lecture materials, and fetch_course_meta for questions about schedule or metadata. In your answer, clearly indicate the source: either 'chroma' or 'mcp_meta'. Do not use both tools unless necessary.",
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system_prompt=(
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"You are a helpful FAQ bot for the course.\n"
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"Use the search_course_docs tool for questions about lecture materials.\n"
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"Use the fetch_course_meta tool for questions about schedule or metadata.\n"
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"Do not call 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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# CLI
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# ---------------------------
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# --------------------- Data Loading ---------------------
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async def load_faq_to_chroma():
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"""Load all .md files from data/ into the Chroma collection."""
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if not DATA_DIR.exists():
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print("Data directory not found.")
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return
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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={"title": md_file.stem}))
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if docs:
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vector_store.add_documents(docs)
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vector_store.persist()
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print(f"Loaded {len(docs)} documents into Chroma.")
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else:
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print("No markdown files found in data/.")
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# --------------------- CLI ---------------------
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PRESET_QUESTIONS = [
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"What topics are covered in Lecture 1?", # should hit chroma
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"What is the deadline for the final project?", # chroma
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"Explain the concept of tokenization in NLP.", # chroma
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"When is the next lab session?", # should hit mcp_meta
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"When is the next lecture scheduled?", # mcp_meta
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]
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async def run_agent(question: str, thread_id: str = "session-1"):
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=question)]},
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{"configurable": {"thread_id": thread_id}},
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)
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# The last message is the agent's reply
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reply = result["messages"][-1].content
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print(f"\nQ: {question}\nA: {reply}\n")
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async def main():
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# Load data if not already loaded
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if not CHROMA_PATH.exists() or not any(CHROMA_PATH.iterdir()):
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print("Loading FAQ data into Chroma...")
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load_faq_to_chroma()
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else:
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print("Chroma database already loaded.")
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# Run preset questions
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async def run_cli():
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await load_faq_to_chroma()
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print("\n--- FAQ Bot CLI ---\n")
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for i, q in enumerate(PRESET_QUESTIONS, 1):
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await run_agent(q, thread_id=f"preset-{i}")
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# Interactive mode
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print("Enter your own questions (type 'exit' to quit):")
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print(f"{i}. {q}")
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print("\nEnter your own question (or 'exit' to quit):")
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while True:
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user_input = input("> ")
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if user_input.lower() in {"exit", "quit"}:
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break
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await run_agent(user_input, thread_id="interactive")
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_input)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(main())
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asyncio.run(run_cli())
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