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

# ---------- LLM and embeddings ----------
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 = OllamaEmbeddings(model_name="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=500, chunk_overlap=50)

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

@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Search the knowledge base for relevant information."""
    results = vector_store.similarity_search_with_score(query, k=max_results)
    if not results:
        return "No relevant documents found."
    reply = ""
    for i, (doc, score) in enumerate(results, start=1):
        reply += f"{i}. ({score:.2f}) {doc.metadata.get('title', 'Untitled')}: {doc.page_content[:200]}...\n"
    return reply.strip()

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

# ---------- 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
        if inp.startswith("/add"):
            parts = inp.split(maxsplit=2)
            if len(parts) < 3:
                print("Usage: /add <title> <content>")
                continue
            title, content = parts[1], parts[2]
            res_msg = agent.invoke({"messages": [{"role": "human", "content": f"/add {title} {content}"}]})
            for msg in res_msg["messages"]:
                if hasattr(msg, "tool_calls"):
                    print(msg.tool_calls[0]["output"])
        elif inp.startswith("/search"):
            query = inp[len("/search"):].strip()
            if not query:
                print("Usage: /search <query>")
                continue
            res_msg = agent.invoke({"messages": [{"role": "human", "content": f"/search {query}"}]})
            for msg in res_msg["messages"]:
                if hasattr(msg, "tool_calls"):
                    print(msg.tool_calls[0]["output"])
        else:
            print("Unknown command. Use /add, /search, or /quit.")

if __name__ == "__main__":
    main()