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

# ---------- 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 ----------
client = QdrantClient(":memory:")
client.create_collection(
    collection_name="knowledge_base",
    vectors_config=VectorParams(size=embeddings.embedding_size, distance=Distance.COSINE),
)
vector_store = QdrantVectorStore(
    client=client,
    collection_name="knowledge_base",
    embedding=embeddings,
)

# ---------- Text splitter ----------
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)

# ---------- 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."
    result_lines = [
        f"{i+1}. {doc.page_content[:200]}..." for i, (doc, _) in enumerate(docs_with_score)
    ]
    return "\n".join(result_lines)

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

# ---------- Agent ----------
agent = create_agent(
    model=llm,
    tools=[search_knowledge_base, add_to_knowledge_base],
    system_prompt="You are an assistant that can search and add documents to the knowledge base.",
)

# ---------- CLI ----------
def main():
    print("RAG Agent CLI. Commands: /add <title> <content>, /search <query>, /quit")
    while True:
        try:
            inp = input("> ").strip()
        except EOFError:
            break
        if not inp:
            continue
        if inp.lower() in ("quit", "exit"):
            print("Bye!")
            break

        # Add document
        if inp.startswith("/add "):
            parts = inp[5:].split(None, 1)
            if len(parts) != 2:
                print("Usage: /add <title> <content>")
                continue
            title, content = parts
            res = agent.invoke({"messages": [{"role": "human", "content": f"Add document '{title}'"}]})
            for msg in res["messages"]:
                if hasattr(msg, "tool_calls"):
                    print(msg.content)
            continue

        # Search query
        if inp.startswith("/search "):
            query = inp[8:].strip()
            res = agent.invoke({"messages": [{"role": "human", "content": f"Search: {query}"}]})
            for msg in res["messages"]:
                if hasattr(msg, "tool_calls"):
                    print(msg.content)
            continue

        # General chat
        res = agent.invoke({"messages": [{"role": "human", "content": inp}]})
        for msg in res["messages"]:
            if hasattr(msg, "content"):
                print(msg.content)

if __name__ == "__main__":
    main()