fix(needs_fixes): 1 исправлений, 0 отстояно — main.py
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@@ -1,124 +1,142 @@
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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 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 typing import List
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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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# ----------------- 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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# --- Embeddings and vector store (Qdrant + Ollama) ---
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from langchain_ollama import OllamaEmbeddings
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from langchain_qdrant import Qdrant
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from langchain_core.documents import Document
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# LLM via OpenRouter
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# Load OpenRouter key for LLM (required by Ollama embeddings as well)
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OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
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# LLM configuration (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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api_key=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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# Embeddings via Ollama (nomic-embed-text)
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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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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# Qdrant client (in‑memory for demo; replace with host/port for prod)
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qdrant_client = Qdrant(
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collection_name="faq_collection",
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url="http://localhost:6333", # default Qdrant local URL
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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 list(CHROMA_PATH.iterdir()):
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docs = []
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# --- Data loading and indexing ---
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DATA_DIR = Path("data")
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CHROMA_PERSIST = Path("./qdrant_faq") # not used directly but kept for compatibility
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def load_faq_to_qdrant() -> None:
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"""Read all .md files from DATA_DIR, chunk them, embed and store in Qdrant."""
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if not DATA_DIR.exists():
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print("Data directory not found. Create 'data/' with .md files.")
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return
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docs: List[Document] = []
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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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# Simple split by double newlines as a naive chunker
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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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# Add to Qdrant
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qdrant_client.add_documents(docs)
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print(f"Indexed {len(docs)} chunks into Qdrant.")
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# ----------------- Tools -----------------
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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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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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results = vector_store.similarity_search(query, k=3)
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results = qdrant_client.similarity_search(query, k=k)
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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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return "\n\n---\n\n".join(r.page_content for r in results)
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# Mock MCP tool – static JSON data
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COURSE_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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"location": "Room 101",
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}
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# MCP-style tool: fetch metadata from a static JSON file
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META_FILE = Path("meta.json")
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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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For example, query="schedule" returns the schedule string.
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"""Return course metadata that matches the query.
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For demo purposes, we load a static JSON file and perform a simple keyword search.
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"""
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key = query.lower().strip()
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return COURSE_META.get(key, f"No metadata found for '{query}'.")
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if not META_FILE.exists():
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return "Metadata file not found."
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data = json.loads(META_FILE.read_text(encoding="utf-8"))
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# Very naive matching: return entries where query is a substring of any value
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matches = []
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for key, value in data.items():
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if isinstance(value, str) and query.lower() in value.lower():
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matches.append(f"{key}: {value}")
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if not matches:
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return "No metadata matches your query."
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return "\n".join(matches)
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# ----------------- Backend -----------------
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# --- Agent setup ---
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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=(
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"You are a helpful FAQ assistant for the course.\n"
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"When answering a question, first decide whether the answer comes from the course materials (use search_course_docs)\n"
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"or from course metadata (use fetch_course_meta).\n"
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"You are a helpful FAQ bot for a course.\n"
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"When a user asks about course materials, use the search_course_docs tool.\n"
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"When a user asks about schedule, metadata, or other non‑material info, use fetch_course_meta.\n"
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"Do not call both tools unless absolutely necessary.\n"
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"In your final answer, prepend 'source: chroma' or 'source: mcp_meta' to indicate the origin."
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"In your final answer, prepend 'source: chroma' if you used search_course_docs,\n"
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"or 'source: mcp_meta' if you used fetch_course_meta."
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),
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)
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# ----------------- CLI -----------------
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# --- CLI ---
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PRESET_QUESTIONS = [
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"What topics are covered in the first lecture?", # should hit chroma
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"Who is the instructor for this course?", # should hit mcp_meta
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"When is the next class?", # should hit chroma (or meta if schedule)
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"What topics are covered in the first lecture?",
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"How can I access the lecture slides?",
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"What is the schedule for the next week?",
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]
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async def run_question(question: str):
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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": "session-1"}},
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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 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 interactive():
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print("Enter a 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_question(q)
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return result["messages"][-1].content
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async def main():
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print("Running preset questions...")
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for q in PRESET_QUESTIONS:
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await run_question(q)
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await interactive()
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# Ensure the vector store is populated
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load_faq_to_qdrant()
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print("\n--- 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_q = input("> ")
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if user_q.lower() in {"exit", "quit"}:
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break
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answer = await run_agent(user_q, thread_id="interactive")
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print(answer)
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if __name__ == "__main__":
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asyncio.run(main())
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