OpenRouter MCP Server

from typing import Optional from qdrant_client import AsyncQdrantClient, models class QdrantConnector: """ Encapsulates the connection to a Qdrant server and all the methods to interact with it. :param qdrant_url: The URL of the Qdrant server. :param qdrant_api_key: The API key to use for the Qdrant server. :param collection_name: The name of the collection to use. :param fastembed_model_name: The name of the FastEmbed model to use. :param qdrant_local_path: The path to the storage directory for the Qdrant client, if local mode is used. """ def __init__( self, qdrant_url: Optional[str], qdrant_api_key: Optional[str], collection_name: str, fastembed_model_name: str, qdrant_local_path: Optional[str] = None, ): self._qdrant_url = qdrant_url.rstrip("/") if qdrant_url else None self._qdrant_api_key = qdrant_api_key self._collection_name = collection_name self._fastembed_model_name = fastembed_model_name # For the time being, FastEmbed models are the only supported ones. # A list of all available models can be found here: # https://qdrant.github.io/fastembed/examples/Supported_Models/ self._client = AsyncQdrantClient(location=qdrant_url, api_key=qdrant_api_key, path=qdrant_local_path) self._client.set_model(fastembed_model_name) async def store_memory(self, information: str): """ Store a memory in the Qdrant collection. :param information: The information to store. """ await self._client.add( self._collection_name, documents=[information], ) async def find_memories(self, query: str) -> list[str]: """ Find memories in the Qdrant collection. If there are no memories found, an empty list is returned. :param query: The query to use for the search. :return: A list of memories found. """ collection_exists = await self._client.collection_exists(self._collection_name) if not collection_exists: return [] search_results = await self._client.query( self._collection_name, query_text=query, limit=10, ) return [result.document for result in search_results]