add main.py
This commit is contained in:
@@ -1,5 +1,10 @@
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import asyncio, os
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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 langchain.tools import tool
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@@ -7,10 +12,62 @@ 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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# ---------------------
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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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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 = 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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@@ -18,97 +75,56 @@ llm = ChatOpenAI(
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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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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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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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system_prompt=SYSTEM_PROMPT,
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)
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# ---------- CLI ----------
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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 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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"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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]
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async def run_cli():
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print("--- FAQ Bot CLI ---")
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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 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"\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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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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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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ans = await run_agent(user_q)
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print(ans)
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if __name__ == "__main__":
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asyncio.run(run_cli())
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asyncio.run(main())
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