from langchain_openai import ChatOpenAI
from pydantic import SecretStr
from langchain.tools import tool
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_ollama import OllamaEmbeddings
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.agents import create_agent
import os

# ---------- LLM ----------
llm = ChatOpenAI(
    model="openai/gpt-oss-20b",
    base_url='https://platform.brojs.ru/jrnl-bh/api/inference/v1',
    api_key=SecretStr("jrnl_30283ab953615cbb6846ff9940a1eedce0b76d7b2f59a2394f29e74643e6a90d"),
    temperature=0.7,
)

# ---------- Embeddings ----------
embeddings = OllamaEmbeddings(model="nomic-embed-text")

# ---------- Qdrant ----------
client = QdrantClient(":memory:")
collection_name = "knowledge_base"

try:
    client.get_collection(collection_name)
except Exception:
    # Use a typical embedding size for nomic-embed-text (768)
    client.create_collection(
        collection_name=collection_name,
        vectors_config=VectorParams(size=768, distance=Distance.COSINE),
    )

vector_store = QdrantVectorStore(
    client=client,
    collection_name=collection_name,
    embedding=embeddings,
)

# ---------- Text splitter ----------
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)

# ---------- Tools ----------
@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Search the knowledge base for relevant documents."""
    docs_with_score = vector_store.similarity_search_with_score(query, k=max_results)
    if not docs_with_score:
        return "No results found."
    return "\n".join(
        f"{i+1}. {doc.page_content[:200]}..."
        for i, (doc, _) in enumerate(docs_with_score)
    )

@tool
def add_to_knowledge_base(content: str, title: str = "") -> str:
    """Add a new document to the knowledge base."""
    chunks = splitter.split_text(content)
    docs = [Document(page_content=c, metadata={"title": title}) for c in chunks]
    vector_store.add_documents(docs)
    return f"Added {len(chunks)} chunks under title '{title}'."

# ---------- Agent ----------
system_prompt = """
You are an assistant that can search and add information to a knowledge base.
Use the tools `search_knowledge_base` and `add_to_knowledge_base` as needed.
"""

agent = create_agent(
    model=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    system_prompt=system_prompt,
)

# ---------- CLI ----------
def load_directory(path: str):
    """Load all text files from a directory into the knowledge base."""
    for root, _, files in os.walk(path):
        for file in files:
            if file.lower().endswith(".txt"):
                with open(os.path.join(root, file), encoding="utf-8") as f:
                    content = f.read()
                add_to_knowledge_base(content=content, title=file)

def main():
    print("Welcome to the RAG agent. Commands: /add <file>, /search <query>, /load <dir>, /quit")
    while True:
        try:
            inp = input("> ").strip()
        except EOFError:
            break
        if not inp:
            continue
        if inp.lower() in ("/quit", "exit"):
            print("Goodbye!")
            break
        if inp.startswith("/add "):
            _, file_path = inp.split(maxsplit=1)
            try:
                with open(file_path, encoding="utf-8") as f:
                    content = f.read()
                print(add_to_knowledge_base(content=content, title=os.path.basename(file_path)))
            except Exception as e:
                print(f"Error adding file: {e}")
        elif inp.startswith("/search "):
            _, query = inp.split(maxsplit=1)
            print(search_knowledge_base(query=query))
        elif inp.startswith("/load "):
            _, dir_path = inp.split(maxsplit=1)
            load_directory(dir_path)
            print(f"Loaded documents from {dir_path}")
        else:
            # Regular conversation
            response = agent.invoke({"messages": [{"role": "human", "content": inp}]})
            msg = response["messages"][-1]
            print(msg.content)

if __name__ == "__main__":
    main()