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import openai
import gradio as gr
from langchain.retriever import RetrievalQA
from langchain.chains.question_answering import load_qa_cha
from langchain.llms import OpenAI
from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
# Initialize OpenAI API key
openai.api_key = "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"
# Load text from file
loader = TextLoader("Dropsheets.txt")
documents = loader.load()
# split the documents into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# select embeddings
embeddings = OpenAIEmbeddings()
# create the vectorestore to use as the index
db = Chroma.from_documents(texts, embeddings)
# expose this index in a retriever interface
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
# Define OpenAI GPT-3.5 model function
## def generate_text(query):
# response = openai.Completion.create(
# engine="text-davinci-002",
# temperature=0,
# max_tokens=7000,
# prompt=prompt
# )
# return response.choices[0].text.strip()
# Create Gradio interface
input_text = gr.Textbox(label="Enter prompt", type="text")
output_text = gr.Textbox(label="AI response", type="text")
demo = gr.Interface(
fn = None,
inputs=input_text,
outputs=output_text,
title="AI Chatbot for PlanetTogether Knowledge Base",
description="Ask a question about the PlanetTogether APS:",
examples=[["How do you create an Alternate Path?"]],
theme="default"
)
# create a chain to answer questions
qa = RetrievalQA.from_chain_type(
llm=OpenAI(), chain_type="stuff", retriever=retriever)
result = qa({"query": query})
retriever.get_relevant_documents(query)
# Launch demo
demo.launch()
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