app.py

import os 
import openai
from dotenv import load_dotenv


API_KEY = "k-3irlTPJi5IPkPkjheHfrT3BlbkFJf9ACVS3pHhacqjisnLWy"

# Write the API key to the .env file
with open(".env", "w") as env_file:
    env_file.write(f"openai_api_key={API_KEY}\n")
    
#echo openai_api_key="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" > .env

load_dotenv(".env")

openai.api_key = os.environ.get("openai_api_key")

os.environ["OPENAI_API_KEY"] = openai.api_key

with open("guide1.txt") as f:
    hitchhikersguide = f.read()

from langchain.text_splitter import CharacterTextSplitter

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n")
texts = text_splitter.split_text(hitchhikersguide)

from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

from langchain.vectorstores import Chroma

docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI

chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
query = "What makes towels important?"
chain.run(input_documents=docs, question=query)