fix: rag_agent.py — Агент с RAG-памятью

rag_agent.py

@@ -0,0 +1,75 @@
+import os
+from langchain_ollama import ChatOllama, OllamaEmbeddings
+from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from langchain.tools import tool
+from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
+from deepagents import create_deep_agent
+
+# Настройки окружения
+OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://localhost:11434")
+QDRANT_HOST = os.getenv("QDRANT_HOST", "http://localhost:6333")
+
+# Эмбеддинги
+embeddings = OllamaEmbeddings(
+    model="nomic-embed-text",
+    base_url=OLLAMA_HOST
+)
+
+# Векторная база
+vector_store = QdrantVectorStore(
+    url=QDRANT_HOST,
+    collection_name="knowledge",
+    embedding_function=embeddings
+)
+
+# Чанкинг
+chunker = RecursiveCharacterTextSplitter(
+    chunk_size=500,
+    chunk_overlap=50
+)
+
+@tool
+def add_to_knowledge_base(content: str, title: str = "doc") -> str:
+    """Add content to the knowledge base."""
+    chunks = chunker.split_text(content)
+    docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
+    vector_store.add_documents(docs)
+    return f"Added: {title} with {len(chunks)} chunks."
+
+@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."
+    return "\n".join(f"{i+1}. {doc.page_content}" for i, doc in enumerate(docs))
+
+# LLM
+llm = ChatOllama(
+    model="llama3",
+    base_url=OLLAMA_HOST
+)
+
+# Backend
+backend = CompositeBackend([
+    LocalShellBackend(workspace_dir="./workspace"),
+    FilesystemBackend(),
+])
+
+# Системный промпт
+system_prompt = (
+    "You are a helpful agent with access to a knowledge base. "
+    "Use the tools to search and add knowledge. "
+    "When you need to retrieve information, call search_knowledge_base. "
+    "When you need to store new information, call add_to_knowledge_base."
+)
+
+# Агент
+agent = create_deep_agent(
+    model=llm,
+    tools=[add_to_knowledge_base, search_knowledge_base],
+    backend=backend,
+    system_prompt=system_prompt,
+)