removed qdrant

app.py

@@ -12,8 +12,6 @@ from aimakerspace.vectordatabase import VectorDatabase
 from aimakerspace.openai_utils.chatmodel import ChatOpenAI
 import chainlit as cl
 import fitz  # PyMuPDF for PDF reading
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import PointStruct, VectorParams, Distance
 
 system_template = """\
 Use the following context to answer a user's question. If you cannot find the answer in the context, say you don't know the answer."""
@@ -29,7 +27,7 @@ Question:
 user_role_prompt = UserRolePrompt(user_prompt_template)
 
 class RetrievalAugmentedQAPipeline:
-    def __init__(self, llm: ChatOpenAI(), vector_db_retriever) -> None:
+    def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:
         self.llm = llm
         self.vector_db_retriever = vector_db_retriever
 
@@ -87,29 +85,9 @@ def process_text_file(file: AskFileResponse):
     texts = text_splitter.split_texts(documents)
     return texts
 
-async def initialize_vector_db(choice, texts):
-    if choice == "current":
-        vector_db = VectorDatabase()
-        vector_db = await vector_db.abuild_from_list(texts)
-        return vector_db
-    elif choice == "qdrant":
-        client = QdrantClient(":memory:")  # Using an in-memory Qdrant instance for demonstration
-        client.recreate_collection(
-            collection_name="my_collection",
-            vectors_config=VectorParams(size=768, distance=Distance.COSINE)
-        )
-        points = [PointStruct(id=i, vector=[0.0] * 768, payload={"text": text}) for i, text in enumerate(texts)]
-        client.upsert(collection_name="my_collection", points=points)
-        return client
 
 @cl.on_chat_start
 async def on_chat_start():
-    # Prompt the user to select the vector database
-    user_choice = await cl.AskSelectMessage(
-        content="Which vector database would you like to use?",
-        options=["current", "qdrant"],
-    ).send()
-
     files = None
 
     # Wait for the user to upload a file
@@ -133,8 +111,9 @@ async def on_chat_start():
 
     print(f"Processing {len(texts)} text chunks")
 
-    # Initialize the selected vector database
-    vector_db = await initialize_vector_db(user_choice, texts)
+    # Create a dict vector store
+    vector_db = VectorDatabase()
+    vector_db = await vector_db.abuild_from_list(texts)
     
     chat_openai = ChatOpenAI()
 

requirements.txt

@@ -2,7 +2,6 @@ numpy
 chainlit==0.7.700
 openai
 pymupdf
-qdrant-client ==1.11.0
-h2==3.2.0
+