fix(needs_fixes): 1 исправлений, 0 отстояно — main.py
This commit is contained in:
@@ -1,178 +1,142 @@
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"""
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# main.py
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# FAQ bot using deepagents, ChromaDB and a single MCP‑style HTTP tool.
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# The agent decides whether to query the local knowledge base or the
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# external metadata service and annotates the answer with a `source` field.
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"""
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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, Dict
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from langchain_community.embeddings import OllamaEmbeddings
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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_core.messages import HumanMessage, SystemMessage
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from langchain_openai import ChatOpenAI
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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_core.messages import HumanMessage
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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# Load API key from .env or environment variable
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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")
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# ---------------------------
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# 1. Embeddings & Chroma setup
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# ---------------------------
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# Using OllamaEmbeddings with nomic-embed-text as required by the "Исправить" section.
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# The embeddings are used for both loading the FAQ and for the search tool.
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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# LLM configuration – OpenRouter
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# Persistent Chroma collection for the FAQ knowledge base.
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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="./chroma_faq"
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)
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# ---------------------------
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# 2. Load FAQ markdown files into Chroma
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# ---------------------------
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def load_faq_to_chroma(data_dir: str = "data"):
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"""Load all .md files from *data_dir* into the persistent Chroma collection.
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Each file is split into documents with a simple line‑based splitter.
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"""
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data_path = Path(data_dir)
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if not data_path.exists():
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raise FileNotFoundError(f"Data directory {data_dir} not found")
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docs = []
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for md_file in data_path.glob("*.md"):
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text = md_file.read_text(encoding="utf-8")
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# Simple split by double newlines to create chunks
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for i, chunk in enumerate(text.split("\n\n")):
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docs.append(Document(page_content=chunk, metadata={"source": md_file.name, "chunk": i}))
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vector_store.add_documents(docs)
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vector_store.persist()
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# ---------------------------
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# 3. Tools
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# ---------------------------
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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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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 FAQ."
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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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@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 simply return a static JSON string based on the query.
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"""
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# Simple static mapping for demo purposes
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meta = {
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"schedule": "Monday 10:00-12:00, Wednesday 14:00-16:00",
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"instructor": "Dr. Ivanov",
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"credits": "3"
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}
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key = query.lower().strip()
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return meta.get(key, f"No metadata found for '{query}'.")
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# ---------------------------
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# 4. Agent setup with deepagents
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# ---------------------------
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# LLM via OpenRouter as per course requirement
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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 – 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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)
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# ---------------------------------------------------------------------------
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# ChromaDB setup
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# ---------------------------------------------------------------------------
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CHROMA_PATH = Path("./chroma_faq")
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CHROMA_COLLECTION = "faq_collection"
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vector_store = Chroma(
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collection_name=CHROMA_COLLECTION,
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embedding_function=embeddings,
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persist_directory=str(CHROMA_PATH),
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)
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# Load markdown files into Chroma if not already persisted
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if not CHROMA_PATH.exists() or not list(CHROMA_PATH.iterdir()):
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def load_faq_to_chroma(md_dir: str = "data"):
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md_path = Path(md_dir)
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docs: List[Document] = []
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for file in md_path.glob("*.md"):
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text = file.read_text(encoding="utf-8")
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docs.append(Document(page_content=text, metadata={"source": file.name}))
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vector_store.add_documents(docs)
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vector_store.persist()
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load_faq_to_chroma()
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# ---------------------------------------------------------------------------
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# Tools
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# ---------------------------------------------------------------------------
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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 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(d.page_content for d in docs)
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# MCP‑style tool – simple HTTP GET to a local JSON file
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# For the purpose of this assignment we use a static JSON file
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# located at ./meta/course_meta.json
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Return metadata that matches the query from a local JSON file."""
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meta_path = Path("./meta/course_meta.json")
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if not meta_path.exists():
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return "Metadata file not found."
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data = json.loads(meta_path.read_text(encoding="utf-8"))
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# Very naive matching: return any entry where the query is a substring
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matches = [item for item in data if query.lower() in item.get("title", "").lower()]
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if not matches:
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return "No matching metadata found."
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return json.dumps(matches, indent=2)
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# ---------------------------------------------------------------------------
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# Backend for deepagents
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# ---------------------------------------------------------------------------
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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 definition
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# ---------------------------------------------------------------------------
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# System prompt instructs the agent to choose the appropriate tool and
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# to annotate the answer with a `source` field.
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SYSTEM_PROMPT = (
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"You are an FAQ assistant for a course.\n"
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"If the user asks about course materials, use the `search_course_docs` tool.\n"
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"If the user asks about schedule or metadata, use the `fetch_course_meta` tool.\n"
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"Respond in JSON format with two fields: `answer` (string) and `source` (either `chroma` or `mcp_meta`).\n"
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"Do not call both tools unless absolutely necessary."
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# System prompt instructs the agent to choose the appropriate tool and to label the source.
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system_prompt = (
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"You are a helpful FAQ assistant.\n"
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"When a user asks a question about course materials, use the tool `search_course_docs`.\n"
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"When a user asks about schedule, instructor, or credits, use the tool `fetch_course_meta`.\n"
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"Do not call both tools unless necessary.\n"
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"In your final answer, prepend the source label: `source: chroma` or `source: mcp_meta`."
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)
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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=system_prompt,
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)
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# ---------------------------------------------------------------------------
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# CLI helpers
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# ---------------------------------------------------------------------------
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# ---------------------------
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# 5. CLI
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# ---------------------------
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PRESET_QUESTIONS = [
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"What topics are covered in the first lecture?", # chroma
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"How can I access the lecture slides?", # chroma
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"What is the schedule for the next week?", # mcp_meta
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"What topics are covered in the first lecture?",
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"Who is the instructor for this course?",
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"When is the next class?"
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]
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async def run_interactive():
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print("FAQ Bot – type your question (or 'exit' to quit).\n")
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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 response
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return result["messages"][-1].content
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async def main():
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# Ensure FAQ is loaded
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load_faq_to_chroma()
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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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answer = await run_agent(q, thread_id=f"demo-{i}")
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print(f"A{i}: {answer}\n")
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# Interactive mode
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print("Enter your own questions (type '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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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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# The agent returns a list of messages; the last is the assistant
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assistant_msg = result["messages"][-1].content
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try:
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data = json.loads(assistant_msg)
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print(f"\nAnswer: {data['answer']}\nSource: {data['source']}\n")
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except Exception:
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print("\nUnexpected response format:\n", assistant_msg)
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answer = await run_agent(user_input, thread_id="interactive")
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print(answer)
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async def run_presets():
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for q in PRESET_QUESTIONS:
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print(f"\nQuestion: {q}")
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=q)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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assistant_msg = result["messages"][-1].content
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try:
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data = json.loads(assistant_msg)
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print(f"Answer: {data['answer']}\nSource: {data['source']}")
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except Exception:
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print("Unexpected response format:\n", assistant_msg)
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# ---------------------------------------------------------------------------
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# Entry point
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser(description="FAQ Bot CLI")
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parser.add_argument("--presets", action="store_true", help="Run preset questions")
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args = parser.parse_args()
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if args.presets:
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asyncio.run(run_presets())
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else:
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asyncio.run(run_interactive())
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asyncio.run(main())
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