115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
import asyncio, os
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from pathlib import Path
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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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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import httpx
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import json
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# ---------- LLM ----------
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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=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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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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# ---------- Chroma DB ----------
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CHROMA_PATH = Path("./chroma_faq")
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CHROMA_PATH.mkdir(exist_ok=True)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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# Load or create vector store
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if CHROMA_PATH.exists() and any(CHROMA_PATH.iterdir()):
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chroma = Chroma(persist_directory=str(CHROMA_PATH), embedding_function=embeddings)
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else:
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# Load markdown files
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docs = []
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for md_file in Path("data").glob("*.md"):
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text = md_file.read_text(encoding="utf-8")
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docs.append(text)
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chroma = Chroma.from_texts(docs, embedding=embeddings, persist_directory=str(CHROMA_PATH))
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chroma.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 documents in Chroma."""
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results = chroma.similarity_search(query, k=k)
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return "\n---\n".join(doc.page_content for doc in results) if results else "No relevant docs found."
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# ---------- MCP‑style tool ----------
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# For demo we use a static JSON file. In production this would be an HTTP call.
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META_JSON = Path("meta.json")
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if not META_JSON.exists():
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# Create a simple mock meta file
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META_JSON.write_text(json.dumps({
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"schedule": "Mon 10-12, Wed 14-16, Fri 9-11",
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"instructor": "Dr. Smith",
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"location": "Room 101"
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}))
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@tool
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def fetch_course_meta(query: str) -> str:
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"""Return course metadata matching the query keyword."""
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data = json.loads(META_JSON.read_text())
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# Simple keyword search in values
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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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return f"{key}: {value}"
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return "No metadata found for the query."
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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=(
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"You are a helpful FAQ bot for the course.\n"
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"If the question is about course content, use search_course_docs.\n"
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"If the question is about schedule, instructor, or location, use fetch_course_meta.\n"
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"Do not call both tools unless necessary.\n"
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"In your answer, prefix the source with 'source: chroma' or 'source: mcp_meta'."
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),
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)
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# ---------- CLI ----------
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PRESET_QUESTIONS = [
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"What is the main topic of the first lecture?",
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"How can I access the lecture slides?",
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"When is the next class?"
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]
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async def run_cli():
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print("--- FAQ Bot CLI ---")
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for i, q in enumerate(PRESET_QUESTIONS, 1):
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print(f"\nPreset {i}: {q}")
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=q)]},
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{"configurable": {"thread_id": f"preset-{i}"}},
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)
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print(result["messages"][-1].content)
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print("\nEnter your own question (or 'exit'): ")
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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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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=user_q)]},
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{"configurable": {"thread_id": "interactive"}},
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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(run_cli())
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