removing qdrant from app

app.py

@@ -1,50 +1,22 @@
-import os, tempfile
+import os
+import tempfile
 from typing import List
 from chainlit.types import AskFileResponse
 from aimakerspace.text_utils import CharacterTextSplitter, TextFileLoader
 from aimakerspace.openai_utils.prompts import UserRolePrompt, SystemRolePrompt
+from aimakerspace.vectordatabase import VectorDatabase
 from aimakerspace.openai_utils.chatmodel import ChatOpenAI
-from aimakerspace.openai_utils.embedding import EmbeddingModel
 import chainlit as cl
 from PyPDF2 import PdfReader
-from qdrant_client import QdrantClient
-from qdrant_client.http import models
 
 system_template = "Use the following context to answer a users question. If you cannot find the answer in the context, say you don't know the answer."
 system_role_prompt = SystemRolePrompt(system_template)
+
 user_prompt_template = "Context:\n{context}\n\nQuestion:\n{question}"
 user_role_prompt = UserRolePrompt(user_prompt_template)
 
-class QdrantVectorStore:
-    def __init__(self, collection_name="my_collection"):
-        self.client = QdrantClient(":memory:")
-        self.collection_name = collection_name
-        self.embedding_model = EmbeddingModel()
-
-    async def abuild_from_list(self, texts: List[str]):
-        self.client.recreate_collection(
-            collection_name=self.collection_name,
-            vectors_config=models.VectorParams(size=1536, distance=models.Distance.COSINE),
-        )
-        for i, text in enumerate(texts):
-            vector = await self.embedding_model.aembed_query(text)
-            self.client.upsert(
-                collection_name=self.collection_name,
-                points=[models.PointStruct(id=i, vector=vector, payload={"text": text})]
-            )
-        return self
-
-    def search_by_text(self, query: str, k: int = 4):
-        vector = self.embedding_model.embed_query(query)
-        results = self.client.search(
-            collection_name=self.collection_name,
-            query_vector=vector,
-            limit=k
-        )
-        return [(hit.payload["text"], hit.score) for hit in results]
-
 class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: QdrantVectorStore) -> None:
+    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
         self.llm = llm
         self.vector_db_retriever = vector_db_retriever
 
@@ -98,7 +70,7 @@ async def on_chat_start():
     texts = process_file(file)
     print(f"Processing {len(texts)} text chunks")
 
-    vector_db = QdrantVectorStore()
+    vector_db = VectorDatabase()
     vector_db = await vector_db.abuild_from_list(texts)
     
     chat_openai = ChatOpenAI()