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import logging
import sys
import gradio as gr
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index import OpenAIEmbedding
import os
from llama_index.memory import ChatMemoryBuffer
import time
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
def load_data():
return SimpleDirectoryReader("./subfolder_0/").load_data()
def build_index(documents):
for document in documents:
# Add a delay here
time.sleep(5)
index = GPTVectorStoreIndex.from_documents([document])
return index
def query_index(index, similarity_top_k=3, streaming=True):
memory = ChatMemoryBuffer.from_defaults(token_limit=4000)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory,
system_prompt=(
"generate detailed answers, but dont be lengthy"
),
)
return chat_engine # return the query engine instance # return the query engine instance
data = load_data()
index = build_index(data)
query_engine = query_index(index) # initialize the query engine
def get_response(text,history=None):
# Use the initialized query engine to perform the query
response = str(query_engine.chat(text))
return response
t = gr.ChatInterface(get_response, analytics_enabled=True)
t.launch(debug=True, share=True)
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