| from langchain.embeddings.openai import OpenAIEmbeddings |
| from langchain.vectorstores import Chroma |
| from langchain.text_splitter import CharacterTextSplitter |
| from langchain.chains.question_answering import load_qa_chain |
| from langchain.llms import OpenAI |
| import os |
|
|
| with open("Harry_Potter1 _Sorcerer's_Stone.txt") as f: |
| harry_potter_text = f.read() |
|
|
| text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n") |
| texts = text_splitter.split_text(harry_potter_text) |
|
|
| embeddings = OpenAIEmbeddings() |
|
|
| docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever() |
|
|
| chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff") |
|
|
| def make_inference(query): |
| docs = docsearch.get_relevant_documents(query) |
| return(chain.run(input_documents=docs, question=query)) |
|
|
| if __name__ == "__main__": |
| |
| import gradio as gr |
|
|
| gr.Interface( |
| make_inference, |
| [ |
| gr.inputs.Textbox(lines=2, label="Query"), |
| ], |
| gr.outputs.Textbox(label="Response"), |
| title="Query My Document📄", |
| description="Query My Document📄: is a tool that allows you to ask questions about a document. In this case - J.k. Rowling's: Harry Potter and the Sorcerer's Stone.", |
| ).launch() |
