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app.py

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+#Manual Q&A bot powered by chatGPT plus langchain.ipynb
+#
+#reference:
+#https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8
+#Sung Kim Mar/09/2023
+#
+#Chat with Document(s) using OpenAI ChatGPT API and Text Embedding
+#How to chat with any documents, PDFs, and books using OpenAI ChatGPT API and Text Embedding
+
+# Import Python Packages
+import os
+import platform
+import openai
+import chromadb
+import langchain
+import magic
+import os
+import nltk
+
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import TokenTextSplitter
+from langchain.llms import OpenAI
+from langchain.chains import ChatVectorDBChain, VectorDBQA
+from langchain.document_loaders import GutenbergLoader
+from langchain.document_loaders import UnstructuredPDFLoader
+from langchain.document_loaders import UnstructuredURLLoader
+from langchain.document_loaders import OnlinePDFLoader
+from langchain.document_loaders import PDFMinerLoader
+from langchain.document_loaders import UnstructuredFileLoader
+
+disclaimer = ("""
+#### Disclaimer and Precautions
+######  Use of this tool is at your own risk. The creator cannot be held responsible for any harm caused by this tool.
+######  The creator is not responsible for any damages caused to users or third parties as a result of such content.
+""")
+
+# Configure Chroma
+persist_directory="/data/"
+
+#Convert local file to Embedding (txt file is better)
+loader = UnstructuredFileLoader('data/ChatGPT_S_ND.txt')
+paldoc = loader.load()
+
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+#OPENAI_API_KEY = os.getenv('openai_api_key')
+#OPENAI_API_KEY = "openai_api_key"
+
+#local file
+text_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=30)
+paldocs = text_splitter.split_documents(paldoc)
+
+embeddings = OpenAIEmbeddings()
+
+vStore = Chroma.from_documents(paldocs, embeddings)
+
+from langchain.chains import RetrievalQA
+from langchain.chat_models import ChatOpenAI
+
+# RetrievalQA
+model = RetrievalQA.from_chain_type(
+    llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo-16k", max_tokens = 256), 
+    chain_type="stuff",
+    retriever=vStore.as_retriever()
+)
+
+#open as a demo Web app using Gradio
+import gradio as gr
+# 関数を定義
+def askandanswer(question, Language): return model.run("Please create a simple answer to the question from  in " + Language + ". [Question] " + question) 
+# Webアプリを作成
+app = gr.Interface(fn=askandanswer,
+                   inputs= [gr.Textbox(placeholder="Please input query "),
+                            gr.Dropdown(["中文 Chinese", "英語 English"], label="言語 Language")],
+                   outputs="text",
+                  title="Chat with Document(s) using OpenAI ChatGPT API and Text Embedding",
+    description= "How to chat with any documents, PDFs, and books using OpenAI ChatGPT API and Text Embedding",
+    article= (disclaimer),
+)
+# Webアプリを起動
+app.launch()

requirements.txt

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+openai
+chromadb
+langchain
+tiktoken
+unstructured
+pdfminer-six
+pdf2image
+gradio