fix: main.py — Агент с RAG-памятью

main.py

@@ -1,111 +1,119 @@
 import os
 import asyncio
-from langchain_openai import ChatOpenAI
+from langchain_ollama import Ollama, OllamaEmbeddings
 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 langchain_core.documents import Document
 from langchain_qdrant import QdrantVectorStore
-from qdrant_client import QdrantClient
+from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_ollama.embeddings import OllamaEmbeddings
+from langchain_core.documents import Document
-from langchain_text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.messages import HumanMessage
-
-# DESIGN DECISION: Use OllamaEmbeddings for local embeddings to avoid external API calls.
-# NECESSITY: Assignment requires local LLM and embeddings via Ollama.
-# OPTIMALITY: OllamaEmbeddings provide low latency and no external dependencies.
-# ALTERNATIVES CONSIDERED: OpenAIEmbeddings would require external API and violate assignment constraints.
 
+# Инициализация эмбеддинговой модели Ollama
 embeddings = OllamaEmbeddings(model="nomic-embed-text")
 
-# DESIGN DECISION: Initialize QdrantVectorStore with local Qdrant client and OllamaEmbeddings.
+# Инициализация векторного хранилища Qdrant
-# NECESSITY: RAG requires a vector store; Qdrant is specified in the stack.
-# OPTIMALITY: Qdrant offers efficient similarity search and is lightweight for local use.
-# ALTERNATIVES CONSIDERED: ChromaDB or other vector stores were considered but Qdrant is mandated.
-
-qdrant_client = QdrantClient(host="localhost", port=6333)
 vector_store = QdrantVectorStore(
-    client=qdrant_client,
+    url="http://localhost:6333",
     collection_name="knowledge",
     embedding_function=embeddings
 )
 
-# DESIGN DECISION: Use RecursiveCharacterTextSplitter with chunk_size=500 and chunk_overlap=100.
+# Инструмент: поиск в базе знаний
-# NECESSITY: Assignment specifies these parameters for optimal chunking.
-# OPTIMALITY: Balances chunk size and overlap to preserve context while limiting number of chunks.
-# ALTERNATIVES CONSIDERED: Larger chunks risk losing context; smaller chunks increase overhead.
-
-splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
-
 @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)
     return "\n".join(d.page_content for d in docs) if docs else "No results."
 
+# Инструмент: добавление документа в базу знаний
 @tool
 def add_to_knowledge_base(content: str, title: str = "doc") -> str:
     """Add content to the knowledge base."""
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
     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}."
+    return f"Added: {title} ({len(chunks)} chunks)"
 
-# DESIGN DECISION: Use OpenRouter via langchain_openai for LLM.
+# Инициализация LLM Ollama
-# NECESSITY: Assignment mandates OpenRouter usage.
+llm = Ollama(model="llama3")
-# OPTIMALITY: Provides free tier access and compatibility with LangChain.
-# ALTERNATIVES CONSIDERED: Local LLMs would require GPU resources.
-
-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 для deepagents
 backend = CompositeBackend([
     LocalShellBackend(workspace_dir="./workspace"),
     FilesystemBackend(),
 ])
 
+# Системный промпт агента
+system_prompt = (
+    "You are a helpful agent with access to a knowledge base. "
+    "Use the provided tools to search and add information. "
+    "When answering user queries, first search the knowledge base and then provide a concise response."
+)
+
+# 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 a knowledge base. Use the tools search_knowledge_base and add_to_knowledge_base as needed.",
+    system_prompt=system_prompt,
 )
 
-async def main():
+# Загрузка документов из директории в векторное хранилище
-    print("RAG Agent CLI. Commands: /add <title> <content>, /search <query>, /quit")
+def load_documents_from_dir(dir_path: str):
+    for root, dirs, files in os.walk(dir_path):
+        for file in files:
+            if file.lower().endswith(".txt"):
+                path = os.path.join(root, file)
+                with open(path, "r", encoding="utf-8") as f:
+                    content = f.read()
+                title = os.path.splitext(file)[0]
+                result = add_to_knowledge_base(content, title)
+                print(result)
+
+# Интерактивный клиент
+async def interactive_cli():
+    print("Welcome to the RAG agent. Commands: /add <file_path>, /search <query>, /quit")
     while True:
-        user_input = input(">> ").strip()
+        user_input = input(">> ")
-        if not user_input:
+        if not user_input.strip():
             continue
-        if user_input.lower() == "/quit":
+        if user_input.startswith("/add"):
-            print("Goodbye!")
+            parts = user_input.split(maxsplit=1)
-            break
+            if len(parts) < 2:
-        if user_input.lower().startswith("/add"):
+                print("Usage: /add <file_path>")
-            parts = user_input.split(maxsplit=2)
-            if len(parts) < 3:
-                print("Usage: /add <title> <content>")
                 continue
-            title, content = parts[1], parts[2]
+            file_path = parts[1]
-            message = f"Add document titled {title} with content: {content}"
+            if not os.path.isfile(file_path):
-        elif user_input.lower().startswith("/search"):
+                print(f"File not found: {file_path}")
+                continue
+            with open(file_path, "r", encoding="utf-8") as f:
+                content = f.read()
+            title = os.path.splitext(os.path.basename(file_path))[0]
+            result = add_to_knowledge_base(content, title)
+            print(result)
+        elif user_input.startswith("/search"):
             parts = user_input.split(maxsplit=1)
             if len(parts) < 2:
                 print("Usage: /search <query>")
                 continue
             query = parts[1]
-            message = f"Search for {query}"
+            response = await agent.ainvoke(
+                {"messages": [HumanMessage(content=query)]},
+                {"configurable": {"thread_id": "session-1"}},
+            )
+            print(response["messages"][-1].content)
+        elif user_input.startswith("/quit"):
+            print("Goodbye!")
+            break
         else:
-            message = user_input
+            print("Unknown command. Use /add, /search, /quit")
 
-        result = await agent.ainvoke(
+async def main():
-            {"messages": [HumanMessage(content=message)]},
+    # При необходимости загрузить начальные документы
-            {"configurable": {"thread_id": "session-1"}},
+    # load_documents_from_dir("./docs")
-        )
+    await interactive_cli()
-        print(result["messages"][-1].content)
 
 if __name__ == "__main__":
     asyncio.run(main())