darknet_diaries_llm/main.py
2023-10-06 21:35:53 +02:00

52 lines
1.7 KiB
Python

from llama_index import (SimpleDirectoryReader, ServiceContext, StorageContext, PromptTemplate,
load_index_from_storage, Document, set_global_service_context)
from llama_index.node_parser import SimpleNodeParser
from llama_index import VectorStoreIndex
from llama_index.llms import OpenAI
import os
import re
llm = OpenAI(model="gpt-3.5-turbo", temperature=0, max_tokens=256)
service_context = ServiceContext.from_defaults(llm=llm)
set_global_service_context(service_context)
if not os.path.exists("./index/lock"):
documents = []
for filename in os.listdir("./data"):
episode_number = re.search(r'\d+', filename).group()
with open("./data/" + filename, 'r') as f:
content = f.read()
document = Document(
text=content,
metadata={
"episode_number": episode_number
}
)
documents = SimpleDirectoryReader('./data').load_data()
parser = SimpleNodeParser.from_defaults()
nodes = parser.get_nodes_from_documents(documents)
index = VectorStoreIndex(nodes, show_progress=True)
index.storage_context.persist(persist_dir="./index")
open("./index/lock", 'a').close()
else:
storage_context = StorageContext.from_defaults(persist_dir="./index")
index = load_index_from_storage(storage_context)
template = (
"You are now an expert on the Darknet Diaries podcast. \n"
"Please answer this question by referring to the podcast: {query_str}\n"
)
qa_template = PromptTemplate(template)
query_engine = index.as_query_engine(text_qa_template=qa_template)
while True:
try:
user_prompt = input("Prompt: ")
response = query_engine.query(user_prompt)
print(response)
except KeyboardInterrupt:
break