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import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass("Enter your 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("/content/new_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 = chat_history + [(user_input, str(response))]
return chat_history, ""
def gradio_chatbot():
with gr.Blocks() as demo:
gr.Markdown("# Gradio 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)
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