"""
Vector store initialization using Qdrant in-memory.

The vector store is used by the search tool to perform semantic similarity search.
"""
import os
from pathlib import Path
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
from langchain_openai import OpenAIEmbeddings

# Embedding model compatible with OpenRouter API (used by BroJS LLM)
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
)

# In-memory Qdrant client – no external server required
client = QdrantClient(":memory:")
client.create_collection(
    "knowledge",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
vector_store = QdrantVectorStore(client=client, collection_name="knowledge", embedding=embeddings)

# Helper to add documents – used in examples
from langchain_core.documents import Document

def add_documents(docs: list[Document]):
    """Add a list of :class:`~langchain_core.documents.Document` objects to the store."""
    vector_store.add_documents(docs)

# Example documents – can be extended by users
if __name__ == "__main__":
    docs = [
        Document(page_content="LangChain is a framework for building applications powered by language models.", metadata={"title": "LangChain Overview"}),
        Document(page_content="Qdrant is an open-source vector database that stores embeddings and performs similarity search efficiently.", metadata={"title": "Qdrant Documentation"}),
    ]
    add_documents(docs)
    print("Added example documents to Qdrant in-memory store")