121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
import os
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import asyncio
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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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# ----------------- Configuration -----------------
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# Load API key from .env or environment variable
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os.environ.setdefault("OPENAI_API_KEY", os.getenv("OPENAI_API_KEY", ""))
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# LLM via OpenRouter
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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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# Embeddings for Chroma
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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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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 (idempotent)
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DATA_DIR = Path("./data")
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if not CHROMA_PATH.exists() or not any(CHROMA_PATH.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 local FAQ collection for relevant passages."""
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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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"""Mock MCP-style tool that returns course metadata.
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In production this would be an HTTP call to an MCP server.
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Here we return a static JSON-like string based on the query.
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"""
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meta = {
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"schedule": "Mon 10-12, Wed 14-16, Fri 9-11",
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"instructor": "Dr. Ivanov",
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"credits": 3,
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}
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# Simple keyword matching
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if "schedule" in query.lower():
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return f"Course schedule: {meta['schedule']}"
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if "instructor" in query.lower():
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return f"Instructor: {meta['instructor']}"
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if "credits" in query.lower():
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return f"Credits: {meta['credits']}"
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return "No metadata matches your 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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# ----------------- 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, instructor, or credits. Do not use both tools unless necessary. In your final answer, prefix the source with 'source: chroma' or 'source: mcp_meta'.",
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)
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# ----------------- CLI -----------------
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PRESET_QUESTIONS = [
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"What topics are covered in lecture 3?",
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"When is the next class?",
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"Who is the instructor for this course?",
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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=\"{}\")".format(question)]},
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{"configurable": {"thread_id": thread_id}},
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)
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# The agent returns a dict with 'messages'; extract last content
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content = result["messages"][-1].content
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print(f"\nQ: {question}\nA: {content}\n")
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async def main():
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print("--- FAQ Bot Demo ---")
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for i, q in enumerate(PRESET_QUESTIONS, 1):
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await run_agent(q, thread_id=f"demo-{i}")
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print("Enter your own question (or 'exit' to quit):")
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while True:
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q = input("> ")
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if q.lower() in {"exit", "quit"}:
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break
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await run_agent(q, thread_id="interactive")
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if __name__ == "__main__":
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asyncio.run(main())
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