app.py

import os
from getpass import getpass

openai_api_key = os.getenv('OPENAI_API_KEY')
openai_api_key = openai_api_key



from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings

Settings.llm = OpenAI(model="gpt-3.5-turbo",temperature=0.4)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader("files").load_data()

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client

client = qdrant_client.QdrantClient(
    location=":memory:",
)

vector_store = QdrantVectorStore(
    collection_name = "paper",
    client=client,
    enable_hybrid=True,
    batch_size=20,
)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
)

query_engine = index.as_query_engine(
    vector_store_query_mode="hybrid"
)

from llama_index.core.memory import ChatMemoryBuffer

memory = ChatMemoryBuffer.from_defaults(token_limit=3000)

chat_engine = index.as_chat_engine(
    chat_mode="context",
    memory=memory,
    system_prompt=(
        "You are an AI assistant who answers the user questions"
    ),
)

import gradio as gr
def chat_with_ai(user_input, chat_history):
    response = chat_engine.chat(user_input)

    chat_history.append((user_input, str(response)))

    return chat_history, ""


def gradio_chatbot():
    with gr.Blocks() as demo:
        gr.Markdown("# Chat Interface for LlamaIndex")

        chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
        user_input = gr.Textbox(
            placeholder="Ask a question...", label="Enter your question"
        )

        submit_button = gr.Button("Send")

        chat_history = gr.State([])

        submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])

        user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])

    return demo

gradio_chatbot().launch(debug=True)