fix(needs_fixes): 1 исправлений, 0 отстояно — main.py

main.py

@@ -9,144 +9,116 @@ from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 from langchain_core.messages import HumanMessage
 
-# ---------------------------
+# --------------------- Configuration ---------------------
-# Configuration
+BASE_DIR = Path(__file__).parent
-# ---------------------------
+DATA_DIR = BASE_DIR / "data"
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+CHROMA_DIR = BASE_DIR / "chroma_faq"
-if not OPENAI_API_KEY:
+MOCK_META_FILE = BASE_DIR / "course_meta.json"
-    raise RuntimeError("OPENAI_API_KEY not set in environment")
 
-# LLM via OpenRouter
+# --------------------- LLM and Embeddings ---------------------
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
     base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
+    api_key=os.getenv("OPENAI_API_KEY"),
     temperature=0.0,
 )
 
-# Embeddings via OpenRouter
 embeddings = OpenAIEmbeddings(
     model="text-embedding-3-small",
     base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
+    api_key=os.getenv("OPENAI_API_KEY"),
 )
 
-# Chroma vector store (persisted)
+# --------------------- Chroma Vector Store ---------------------
-CHROMA_PATH = Path("./chroma_faq")
 vector_store = Chroma(
     collection_name="faq_collection",
     embedding_function=embeddings,
-    persist_directory=str(CHROMA_PATH),
+    persist_directory=str(CHROMA_DIR),
 )
 
-# ---------------------------
+# --------------------- Tools ---------------------
-# Data loading
-# ---------------------------
-
-def load_faq_to_chroma(md_folder: str = "data"):
-    """Load all .md files from md_folder into ChromaDB.
-    Each file is split into chunks and added to the vector store.
-    """
-    md_path = Path(md_folder)
-    if not md_path.exists():
-        raise FileNotFoundError(f"Markdown folder {md_folder} not found")
-    docs = []
-    for md_file in md_path.glob("*.md"):
-        text = md_file.read_text(encoding="utf-8")
-        # Simple chunking: split by double newlines
-        chunks = [c.strip() for c in text.split("\n\n") if c.strip()]
-        for i, chunk in enumerate(chunks):
-            docs.append(Document(page_content=chunk, metadata={"source": md_file.name, "chunk": i}))
-    if docs:
-        vector_store.add_documents(docs)
-        vector_store.persist()
-    else:
-        print("No markdown files found to load.")
-
-# ---------------------------
-# Tools
-# ---------------------------
 @tool
 def search_course_docs(query: str, k: int = 3) -> str:
-    """Search the FAQ knowledge base for relevant information."""
+    """Search the local FAQ collection for relevant passages."""
     docs = vector_store.similarity_search(query, k=k)
     if not docs:
         return "No relevant information found in the course materials."
-    return "\n\n---\n\n".join(f"**{doc.metadata.get('source')}** (chunk {doc.metadata.get('chunk')}):\n{doc.page_content}" for doc in docs)
+    return "\n\n---\n\n".join(f"**{doc.metadata.get('title', 'Document')}**\n{doc.page_content}" for doc in docs)
 
 @tool
 def fetch_course_meta(query: str) -> str:
     """Mock MCP-style tool that returns course metadata.
-    In production this would perform an HTTP GET to an MCP server.
+    In production this would be an HTTP call to an MCP server.
-    Here we return a static JSON-like string for simplicity.
+    Here we read a local JSON file for simplicity.
     """
-    # Static mock data
+    import json
-    meta = {
+    if not MOCK_META_FILE.exists():
-        "schedule": {
+        return "Metadata source not available."
-            "Monday": "Lecture 1: Introduction",
+    with open(MOCK_META_FILE, "r", encoding="utf-8") as f:
-            "Wednesday": "Lecture 2: Advanced Topics",
+        data = json.load(f)
-            "Friday": "Lab Session"
+    # Simple keyword search in the metadata
-        },
+    results = [f"{k}: {v}" for k, v in data.items() if query.lower() in k.lower() or query.lower() in str(v).lower()]
-        "instructor": "Dr. Jane Doe",
+    return "\n".join(results) if results else "No metadata matches your query."
-        "credits": 3
-    }
-    return f"Course metadata: {meta}"
 
-# ---------------------------
+# --------------------- Backend ---------------------
-# Backend setup
-# ---------------------------
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-# ---------------------------
+# --------------------- Agent ---------------------
-# Agent creation
-# ---------------------------
 agent = create_deep_agent(
     model=llm,
     tools=[search_course_docs, fetch_course_meta],
     backend=backend,
-    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 or metadata. In your answer, clearly indicate the source: either 'chroma' or 'mcp_meta'. Do not use both tools unless necessary.",
+    system_prompt=(
+        "You are a helpful FAQ bot for the course.\n"
+        "Use the search_course_docs tool for questions about lecture materials.\n"
+        "Use the fetch_course_meta tool for questions about schedule or metadata.\n"
+        "Do not call both tools unless absolutely necessary.\n"
+        "In your final answer, prefix the source with 'source: chroma' or 'source: mcp_meta'."
+    ),
 )
 
-# ---------------------------
+# --------------------- Data Loading ---------------------
-# CLI
+async def load_faq_to_chroma():
-# ---------------------------
+    """Load all .md files from data/ into the Chroma collection."""
+    if not DATA_DIR.exists():
+        print("Data directory not found.")
+        return
+    docs = []
+    for md_file in DATA_DIR.glob("*.md"):
+        text = md_file.read_text(encoding="utf-8")
+        docs.append(Document(page_content=text, metadata={"title": md_file.stem}))
+    if docs:
+        vector_store.add_documents(docs)
+        vector_store.persist()
+        print(f"Loaded {len(docs)} documents into Chroma.")
+    else:
+        print("No markdown files found in data/.")
+
+# --------------------- CLI ---------------------
 PRESET_QUESTIONS = [
-    "What topics are covered in Lecture 1?",  # should hit chroma
+    "What is the deadline for the final project?",  # chroma
     "Explain the concept of tokenization in NLP.",  # chroma
-    "When is the next lab session?",  # should hit mcp_meta
+    "When is the next lecture scheduled?",  # mcp_meta
 ]
 
-async def run_agent(question: str, thread_id: str = "session-1"):
+async def run_cli():
-    result = await agent.ainvoke(
+    await load_faq_to_chroma()
-        {"messages": [HumanMessage(content=question)]},
+    print("\n--- FAQ Bot CLI ---\n")
-        {"configurable": {"thread_id": thread_id}},
-    )
-    # The last message is the agent's reply
-    reply = result["messages"][-1].content
-    print(f"\nQ: {question}\nA: {reply}\n")
-
-async def main():
-    # Load data if not already loaded
-    if not CHROMA_PATH.exists() or not any(CHROMA_PATH.iterdir()):
-        print("Loading FAQ data into Chroma...")
-        load_faq_to_chroma()
-    else:
-        print("Chroma database already loaded.")
-
-    # Run preset questions
     for i, q in enumerate(PRESET_QUESTIONS, 1):
-        await run_agent(q, thread_id=f"preset-{i}")
+        print(f"{i}. {q}")
-
+    print("\nEnter your own question (or 'exit' to quit):")
-    # Interactive mode
-    print("Enter your own questions (type 'exit' to quit):")
     while True:
         user_input = input("> ")
         if user_input.lower() in {"exit", "quit"}:
             break
-        await run_agent(user_input, thread_id="interactive")
+        result = await agent.ainvoke(
+            {"messages": [HumanMessage(content=user_input)]},
+            {"configurable": {"thread_id": "session-1"}},
+        )
+        print(result["messages"][-1].content)
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(run_cli())