Delete obsolete qdrant_store.py

qdrant_store.py

@@ -1,37 +0,0 @@
-"""Wrapper around QdrantVectorStore using Ollama embeddings.
-
-This module is intentionally lightweight and can be imported from any
-context without requiring the caller to set up a package structure.
-"""
-
-import os
-import sys
-
-# Ensure the current directory is in sys.path so that relative imports work
-sys.path.append(os.path.dirname(__file__))
-
-from langchain_ollama import OllamaEmbeddings
-from langchain_qdrant import QdrantVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from typing import List, Dict
-
-class QdrantStore:
-    """Wrapper around QdrantVectorStore using Ollama embeddings."""
-
-    def __init__(self, host: str = "localhost", port: int = 6333, collection_name: str = "knowledge_base"):
-        self.embeddings = OllamaEmbeddings(model="nomic-embed-text")
-        self.store = QdrantVectorStore(
-            url=f"http://{host}:{port}",
-            collection_name=collection_name,
-            embeddings=self.embeddings,
-        )
-
-    def add_document(self, content: str, title: str) -> None:
-        splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-        chunks = splitter.split_text(content)
-        metadatas = [{"title": title, "source": title} for _ in chunks]
-        self.store.add_texts(chunks, metadatas=metadatas)
-
-    def search(self, query: str, k: int = 5) -> List[Dict]:
-        results = self.store.similarity_search(query, k=k)
-        return [{"content": doc.page_content, "metadata": doc.metadata} for doc in results]