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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # !pip install langchain openai chromadb tiktoken pypdf panel | |
| # In[ ]: | |
| import os | |
| from langchain.chains import RetrievalQA | |
| from langchain.llms import OpenAI | |
| from langchain.document_loaders import TextLoader | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.indexes import VectorstoreIndexCreator | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.embeddings import OpenAIEmbeddings | |
| from langchain.vectorstores import Chroma | |
| import panel as pn | |
| import tempfile | |
| # In[ ]: | |
| pn.extension('texteditor', template="bootstrap", sizing_mode='stretch_width') | |
| pn.state.template.param.update( | |
| main_max_width="690px", | |
| header_background="#F08080", | |
| ) | |
| # In[3]: | |
| file_input = pn.widgets.FileInput(width=300) | |
| openaikey = pn.widgets.PasswordInput( | |
| value="", placeholder="Enter your OpenAI API Key here...", width=300 | |
| ) | |
| prompt = pn.widgets.TextEditor( | |
| value="", placeholder="Enter your questions here...", height=160, toolbar=False | |
| ) | |
| run_button = pn.widgets.Button(name="Run!") | |
| select_k = pn.widgets.IntSlider( | |
| name="Number of relevant chunks", start=1, end=5, step=1, value=2 | |
| ) | |
| select_chain_type = pn.widgets.RadioButtonGroup( | |
| name='Chain type', | |
| options=['stuff', 'map_reduce', "refine", "map_rerank"] | |
| ) | |
| widgets = pn.Row( | |
| pn.Column(prompt, run_button, margin=5), | |
| pn.Card( | |
| "Chain type:", | |
| pn.Column(select_chain_type, select_k), | |
| title="Advanced settings", margin=10 | |
| ), width=600 | |
| ) | |
| # In[4]: | |
| def qa(file, query, chain_type, k): | |
| # load document | |
| loader = PyPDFLoader(file) | |
| documents = loader.load() | |
| # split the documents into chunks | |
| text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | |
| texts = text_splitter.split_documents(documents) | |
| # select which embeddings we want to use | |
| 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": k}) | |
| # create a chain to answer questions | |
| qa = RetrievalQA.from_chain_type( | |
| llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True) | |
| result = qa({"query": query}) | |
| print(result['result']) | |
| return result | |
| # In[6]: | |
| convos = [] # store all panel objects in a list | |
| def qa_result(_): | |
| os.environ["OPENAI_API_KEY"] = openaikey.value | |
| # save pdf file to a temp file | |
| if file_input.value is not None: | |
| file_input.save("/.cache/temp.pdf") | |
| prompt_text = prompt.value | |
| if prompt_text: | |
| result = qa(file="/.cache/temp.pdf", query=prompt_text, | |
| chain_type=select_chain_type.value, k=select_k.value) | |
| convos.extend([ | |
| pn.Row( | |
| pn.panel("\U0001F60A", width=10), | |
| prompt_text, | |
| width=600 | |
| ), | |
| pn.Row( | |
| pn.panel("\U0001F916", width=10), | |
| pn.Column( | |
| result["result"], | |
| "Relevant source text:", | |
| pn.pane.Markdown('\n--------------------------------------------------------------------\n'.join( | |
| doc.page_content for doc in result["source_documents"])) | |
| ) | |
| ) | |
| ]) | |
| # return convos | |
| return pn.Column(*convos, margin=15, width=575, min_height=400) | |
| # In[7]: | |
| qa_interactive = pn.panel( | |
| pn.bind(qa_result, run_button), | |
| loading_indicator=True, | |
| ) | |
| # In[8]: | |
| output = pn.WidgetBox('*Output will show up here:*', | |
| qa_interactive, width=630, scroll=True) | |
| # In[9]: | |
| # layout | |
| pn.Column( | |
| pn.pane.Markdown(""" | |
| ## \U0001F60A! Question Answering with your PDF file | |
| 1) Upload a PDF. 2) Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account). 3) Type a question and click "Run". | |
| """), | |
| pn.Row(file_input, openaikey), | |
| output, | |
| widgets | |
| ).servable() | |