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

main.py

@@ -1,178 +1,142 @@
-"""
-# main.py
-# FAQ bot using deepagents, ChromaDB and a single MCP‑style HTTP tool.
-# The agent decides whether to query the local knowledge base or the
-# external metadata service and annotates the answer with a `source` field.
-"""
 import os
 import asyncio
-import json
 from pathlib import Path
-from typing import List, Dict
+from langchain_community.embeddings import OllamaEmbeddings
-
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain_chroma import Chroma
 from langchain_core.documents import Document
-from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_openai import ChatOpenAI
 from langchain.tools import tool
 from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+from langchain_core.messages import HumanMessage
 
-# ---------------------------------------------------------------------------
+# ---------------------------
-# Configuration
+# 1. Embeddings & Chroma setup
-# ---------------------------------------------------------------------------
+# ---------------------------
-# Load API key from .env or environment variable
+# Using OllamaEmbeddings with nomic-embed-text as required by the "Исправить" section.
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+# The embeddings are used for both loading the FAQ and for the search tool.
-if not OPENAI_API_KEY:
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
-    raise RuntimeError("OPENAI_API_KEY not set")
-
-# LLM configuration – OpenRouter
-llm = ChatOpenAI(
-    model="openai/gpt-oss-20b:free",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
-    temperature=0.0,
-)
-
-# Embeddings – OpenRouter
-embeddings = OpenAIEmbeddings(
-    model="text-embedding-3-small",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
-)
-
-# ---------------------------------------------------------------------------
-# ChromaDB setup
-# ---------------------------------------------------------------------------
-CHROMA_PATH = Path("./chroma_faq")
-CHROMA_COLLECTION = "faq_collection"
 
+# Persistent Chroma collection for the FAQ knowledge base.
 vector_store = Chroma(
-    collection_name=CHROMA_COLLECTION,
+    collection_name="faq_collection",
     embedding_function=embeddings,
-    persist_directory=str(CHROMA_PATH),
+    persist_directory="./chroma_faq"
 )
 
-# Load markdown files into Chroma if not already persisted
+# ---------------------------
-if not CHROMA_PATH.exists() or not list(CHROMA_PATH.iterdir()):
+# 2. Load FAQ markdown files into Chroma
-    def load_faq_to_chroma(md_dir: str = "data"):
+# ---------------------------
-        md_path = Path(md_dir)
+
-        docs: List[Document] = []
+def load_faq_to_chroma(data_dir: str = "data"):
-        for file in md_path.glob("*.md"):
+    """Load all .md files from *data_dir* into the persistent Chroma collection.
-            text = file.read_text(encoding="utf-8")
+    Each file is split into documents with a simple line‑based splitter.
-            docs.append(Document(page_content=text, metadata={"source": file.name}))
+    """
+    data_path = Path(data_dir)
+    if not data_path.exists():
+        raise FileNotFoundError(f"Data directory {data_dir} not found")
+    docs = []
+    for md_file in data_path.glob("*.md"):
+        text = md_file.read_text(encoding="utf-8")
+        # Simple split by double newlines to create chunks
+        for i, chunk in enumerate(text.split("\n\n")):
+            docs.append(Document(page_content=chunk, metadata={"source": md_file.name, "chunk": i}))
     vector_store.add_documents(docs)
     vector_store.persist()
 
-    load_faq_to_chroma()
+# ---------------------------
-
+# 3. Tools
-# ---------------------------------------------------------------------------
+# ---------------------------
-# Tools
-# ---------------------------------------------------------------------------
 @tool
 def search_course_docs(query: str, k: int = 3) -> str:
-    """Search the local FAQ collection for relevant passages."""
+    """Search the FAQ knowledge base for relevant information."""
     docs = vector_store.similarity_search(query, k=k)
     if not docs:
-        return "No relevant information found in the course materials."
+        return "No relevant information found in the FAQ."
-    return "\n\n---\n\n".join(d.page_content for d in docs)
+    return "\n\n---\n\n".join(f"**{doc.metadata.get('source')}** (chunk {doc.metadata.get('chunk')}):\n{doc.page_content}" for doc in docs)
 
-# MCP‑style tool – simple HTTP GET to a local JSON file
-# For the purpose of this assignment we use a static JSON file
-# located at ./meta/course_meta.json
 @tool
 def fetch_course_meta(query: str) -> str:
-    """Return metadata that matches the query from a local JSON file."""
+    """Mock MCP‑style tool that returns course metadata.
-    meta_path = Path("./meta/course_meta.json")
+    In production this would perform an HTTP GET to an MCP server.
-    if not meta_path.exists():
+    Here we simply return a static JSON string based on the query.
-        return "Metadata file not found."
+    """
-    data = json.loads(meta_path.read_text(encoding="utf-8"))
+    # Simple static mapping for demo purposes
-    # Very naive matching: return any entry where the query is a substring
+    meta = {
-    matches = [item for item in data if query.lower() in item.get("title", "").lower()]
+        "schedule": "Monday 10:00-12:00, Wednesday 14:00-16:00",
-    if not matches:
+        "instructor": "Dr. Ivanov",
-        return "No matching metadata found."
+        "credits": "3"
-    return json.dumps(matches, indent=2)
+    }
+    key = query.lower().strip()
+    return meta.get(key, f"No metadata found for '{query}'.")
+
+# ---------------------------
+# 4. Agent setup with deepagents
+# ---------------------------
+# LLM via OpenRouter as per course requirement
+llm = ChatOpenAI(
+    model="openai/gpt-oss-20b:free",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+    temperature=0.0,
+)
 
-# ---------------------------------------------------------------------------
-# Backend for deepagents
-# ---------------------------------------------------------------------------
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-# ---------------------------------------------------------------------------
+# System prompt instructs the agent to choose the appropriate tool and to label the source.
-# Agent definition
+system_prompt = (
-# ---------------------------------------------------------------------------
+    "You are a helpful FAQ assistant.\n"
-# System prompt instructs the agent to choose the appropriate tool and
+    "When a user asks a question about course materials, use the tool `search_course_docs`.\n"
-# to annotate the answer with a `source` field.
+    "When a user asks about schedule, instructor, or credits, use the tool `fetch_course_meta`.\n"
-SYSTEM_PROMPT = (
+    "Do not call both tools unless necessary.\n"
-    "You are an FAQ assistant for a course.\n"
+    "In your final answer, prepend the source label: `source: chroma` or `source: mcp_meta`."
-    "If the user asks about course materials, use the `search_course_docs` tool.\n"
-    "If the user asks about schedule or metadata, use the `fetch_course_meta` tool.\n"
-    "Respond in JSON format with two fields: `answer` (string) and `source` (either `chroma` or `mcp_meta`).\n"
-    "Do not call both tools unless absolutely necessary."
 )
 
 agent = create_deep_agent(
     model=llm,
     tools=[search_course_docs, fetch_course_meta],
     backend=backend,
-    system_prompt=SYSTEM_PROMPT,
+    system_prompt=system_prompt,
 )
 
-# ---------------------------------------------------------------------------
+# ---------------------------
-# CLI helpers
+# 5. CLI
-# ---------------------------------------------------------------------------
+# ---------------------------
 PRESET_QUESTIONS = [
-    "What topics are covered in the first lecture?",  # chroma
+    "What topics are covered in the first lecture?",
-    "How can I access the lecture slides?",          # chroma
+    "Who is the instructor for this course?",
-    "What is the schedule for the next week?",        # mcp_meta
+    "When is the next class?"
 ]
 
-async def run_interactive():
+async def run_agent(question: str, thread_id: str = "session-1"):
-    print("FAQ Bot – type your question (or 'exit' to quit).\n")
+    result = await agent.ainvoke(
+        {"messages": [HumanMessage(content=question)]},
+        {"configurable": {"thread_id": thread_id}},
+    )
+    # The last message is the agent's response
+    return result["messages"][-1].content
+
+async def main():
+    # Ensure FAQ is loaded
+    load_faq_to_chroma()
+
+    print("--- FAQ Bot Demo ---\n")
+    for i, q in enumerate(PRESET_QUESTIONS, 1):
+        print(f"Q{i}: {q}")
+        answer = await run_agent(q, thread_id=f"demo-{i}")
+        print(f"A{i}: {answer}\n")
+
+    # Interactive mode
+    print("Enter your own questions (type 'exit' to quit):")
     while True:
         user_input = input("> ")
         if user_input.lower() in {"exit", "quit"}:
             break
-        result = await agent.ainvoke(
+        answer = await run_agent(user_input, thread_id="interactive")
-            {"messages": [HumanMessage(content=user_input)]},
+        print(answer)
-            {"configurable": {"thread_id": "session-1"}},
-        )
-        # The agent returns a list of messages; the last is the assistant
-        assistant_msg = result["messages"][-1].content
-        try:
-            data = json.loads(assistant_msg)
-            print(f"\nAnswer: {data['answer']}\nSource: {data['source']}\n")
-        except Exception:
-            print("\nUnexpected response format:\n", assistant_msg)
 
-async def run_presets():
-    for q in PRESET_QUESTIONS:
-        print(f"\nQuestion: {q}")
-        result = await agent.ainvoke(
-            {"messages": [HumanMessage(content=q)]},
-            {"configurable": {"thread_id": "session-1"}},
-        )
-        assistant_msg = result["messages"][-1].content
-        try:
-            data = json.loads(assistant_msg)
-            print(f"Answer: {data['answer']}\nSource: {data['source']}")
-        except Exception:
-            print("Unexpected response format:\n", assistant_msg)
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
 if __name__ == "__main__":
-    import argparse
+    asyncio.run(main())
-
-    parser = argparse.ArgumentParser(description="FAQ Bot CLI")
-    parser.add_argument("--presets", action="store_true", help="Run preset questions")
-    args = parser.parse_args()
-
-    if args.presets:
-        asyncio.run(run_presets())
-    else:
-        asyncio.run(run_interactive())