fix: main.py — Агент с RAG-памятью

main.py

@@ -1,135 +1,41 @@
-import os
 import asyncio
-from pathlib import Path
-from langchain_openai import ChatOpenAI, OpenAIEmbeddings
-from langchain_core.documents import Document
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_qdrant import QdrantVectorStore
-from langchain.tools import tool
-from deepagents import create_deep_agent
-from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
 from langchain_core.messages import HumanMessage
+from rag_agent import agent, add_to_knowledge_base, search_knowledge_base
 
-# Configuration
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-if not OPENAI_API_KEY:
-    raise RuntimeError("OPENAI_API_KEY not set in environment")
-
-# LLM via 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 via OpenRouter
-embeddings = OpenAIEmbeddings(
-    model="text-embedding-3-small",
-    base_url="https://openrouter.ai/api/v1",
-    api_key=OPENAI_API_KEY,
-)
-
-# Qdrant client and vector store
-from qdrant_client import QdrantClient
-qdrant_client = QdrantClient(url="http://localhost:6333")
-collection_name = "knowledge_base"
-vector_store = QdrantVectorStore(
-    client=qdrant_client,
-    collection_name=collection_name,
-    embedding_function=embeddings,
-)
-
-# Text splitter
-splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-
-# Tool: search knowledge base
-@tool
-def search_knowledge_base(query: str, max_results: int = 3) -> str:
-    """Search the knowledge base for relevant information."""
-    docs = vector_store.similarity_search(query, k=max_results)
-    if not docs:
-        return "No results found."
-    return "\n\n".join(f"Title: {doc.metadata.get('title', 'unknown')}\n{doc.page_content}" for doc in docs)
-
-# Tool: add to knowledge base
-@tool
-def add_to_knowledge_base(content: str, title: str = "document") -> 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 from '{title}'."
-
-# Backend for deepagents
-backend = CompositeBackend([
-    LocalShellBackend(workspace_dir="./workspace"),
-    FilesystemBackend(),
-])
-
-# Agent
-agent = create_deep_agent(
-    model=llm,
-    tools=[search_knowledge_base, add_to_knowledge_base],
-    backend=backend,
-    system_prompt="You are a helpful knowledge assistant. Use the provided tools to search and add information.",
-)
-
-# Helper: load documents from a directory
-def load_documents_from_dir(directory: str):
-    dir_path = Path(directory)
-    if not dir_path.is_dir():
-        raise ValueError(f"Directory {directory} does not exist.")
-    for file_path in dir_path.rglob("*"):
-        if file_path.is_file() and file_path.suffix.lower() in {".txt", ".md", ".py", ".json"}:
-            content = file_path.read_text(encoding="utf-8")
-            title = file_path.stem
-            add_to_knowledge_base(content, title)
-
-# Interactive CLI
 async def interactive_loop():
-    print("RAG Agent CLI. Commands: /add <file_path>, /search <query>, /quit")
+    print("Welcome to the RAG agent. Type /add to add a document, /search to query, /quit to exit.")
+    thread_id = "session-1"
     while True:
         user_input = input(">> ").strip()
-        if not user_input:
-            continue
         if user_input.lower() == "/quit":
-            print("Exiting.")
+            print("Goodbye!")
             break
-        if user_input.startswith("/add "):
+        elif user_input.lower() == "/add":
-            _, file_path = user_input.split(maxsplit=1)
+            title = input("Title: ").strip()
-            try:
+            print("Enter content (end with a single line containing only 'END'):")
-                content = Path(file_path).read_text(encoding="utf-8")
+            lines = []
-                title = Path(file_path).stem
+            while True:
+                line = input()
+                if line.strip() == "END":
+                    break
+                lines.append(line)
+            content = "\n".join(lines)
             result = add_to_knowledge_base(content, title)
             print(result)
-            except Exception as e:
+        elif user_input.lower() == "/search":
-                print(f"Error adding file: {e}")
+            query = input("Query: ").strip()
-            continue
+            result = search_knowledge_base(query)
-        if user_input.startswith("/search "):
+            print("Search results:")
-            _, query = user_input.split(maxsplit=1)
-            result = search_knowledge_base(query, max_results=3)
             print(result)
-            continue
+        else:
-        # Treat as normal message to agent
+            messages = [HumanMessage(content=user_input)]
-        try:
             response = await agent.ainvoke(
-                {"messages": [HumanMessage(content=user_input)]},
+                {"messages": messages},
-                {"configurable": {"thread_id": "session-1"}},
+                {"configurable": {"thread_id": thread_id}},
             )
             print(response["messages"][-1].content)
-        except Exception as e:
-            print(f"Agent error: {e}")
 
 def main():
-    # Optional: load initial docs from a folder
-    init_dir = os.getenv("INIT_DOCS_DIR")
-    if init_dir:
-        try:
-            load_documents_from_dir(init_dir)
-            print(f"Loaded documents from {init_dir}")
-        except Exception as e:
-            print(f"Failed to load initial docs: {e}")
     asyncio.run(interactive_loop())
 
 if __name__ == "__main__":