fix: main.py — Агент с RAG-памятью

main.py

@@ -1,20 +1,14 @@
 import os
 import asyncio
 from dotenv import load_dotenv
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
 from langchain_core.messages import HumanMessage
-from langchain_core.documents import Document
-from langchain.tools import tool
 from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
-from langchain_qdrant import QdrantVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from qdrant_client import QdrantClient
+from tools import search_knowledge_base, add_to_knowledge_base
 
-# Загрузка переменных окружения
 load_dotenv()
 
-# Инициализация LLM
 llm = ChatOpenAI(
     model="openai/gpt-oss-20b:free",
     base_url="https://openrouter.ai/api/v1",
@@ -22,90 +16,43 @@ llm = ChatOpenAI(
     temperature=0.0,
 )
 
-# Инициализация эмбеддингов
-embeddings = OpenAIEmbeddings(
-    model="text-embedding-3-small",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=os.getenv("OPENAI_API_KEY"),
-)
-
-# Инициализация Qdrant
-client = QdrantClient(url="http://localhost:6333")
-collection_name = "knowledge_base"
-vector_store = QdrantVectorStore(
-    client=client,
-    collection_name=collection_name,
-    embeddings=embeddings,
-)
-
-# Чанкинг
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-
-# Инструмент поиска
-@tool
-def search_knowledge_base(query: str, max_results: int) -> str:
-    """Semantic search in the knowledge base."""
-    docs = vector_store.similarity_search(query, k=max_results)
-    if not docs:
-        return "No results found."
-    return "\n".join(doc.page_content for doc in docs)
-
-# Инструмент добавления
-@tool
-def add_to_knowledge_base(content: str, title: str) -> str:
-    """Add content to the knowledge base."""
-    chunks = splitter.split_text(content)
-    docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
-    vector_store.add_documents(docs)
-    return f"Added {len(docs)} chunks for {title}."
-
-# Backend для deepagents
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
-# Создание агента
 agent = create_deep_agent(
     model=llm,
     tools=[search_knowledge_base, add_to_knowledge_base],
     backend=backend,
-    system_prompt="You are a helpful agent with access to a knowledge base. Use the tools to search and add information.",
+    system_prompt="You are a helpful knowledge assistant. Use the tools to search and add documents.",
 )
 
-async def main():
-    print("Interactive agent. Commands: /add, /search, /quit")
+async def interactive_loop():
+    thread_id = "interactive-session"
+    print("Welcome to RAG Agent. Commands: /add <title> <content>, /search <query>, /quit")
     while True:
-        try:
-            user_input = input("> ").strip()
-        except EOFError:
+        user_input = input(">> ")
+        if user_input.strip() == "/quit":
+            print("Goodbye.")
             break
-        if not user_input:
-            continue
         if user_input.startswith("/add"):
-            title = input("Title: ").strip()
-            content = input("Content: ").strip()
-            result = add_to_knowledge_base(content, title)
-            print(result)
-        elif user_input.startswith("/search"):
-            query = input("Query: ").strip()
-            max_str = input("Max results (int): ").strip()
             try:
-                max_results = int(max_str)
+                _, title, content = user_input.split(" ", 2)
             except ValueError:
-                max_results = 3
-            result = search_knowledge_base(query, max_results)
-            print(result)
-        elif user_input.startswith("/quit"):
-            print("Goodbye!")
-            break
+                print("Usage: /add <title> <content>")
+                continue
+            message = HumanMessage(content=f"Add document titled '{title}' with content: {content}")
+        elif user_input.startswith("/search"):
+            query = user_input[len("/search"):].strip()
+            message = HumanMessage(content=f"Search knowledge base for: {query}")
         else:
-            # обычный диалог с агентом
-            response = await agent.ainvoke(
-                {"messages": [HumanMessage(content=user_input)]},
-                {"configurable": {"thread_id": "session-1"}},
-            )
-            print(response["messages"][-1].content)
+            message = HumanMessage(content=user_input)
+        result = await agent.ainvoke(
+            {"messages": [message]},
+            {"configurable": {"thread_id": thread_id}},
+        )
+        print(result["messages"][-1].content)
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    asyncio.run(interactive_loop())