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,
)