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Create QA_Chatbot_BM
Browse files- QA_Chatbot_BM +122 -0
QA_Chatbot_BM
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# install packages
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!pip install langchain openai chromadb tiktoken pypdf panel
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# import packages
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import os
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from langchain.chains import RetrievalQA
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from langchain.llms import OpenAI
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from langchain.document_loaders import TextLoader
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from langchain.document_loaders import PyPDFLoader
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from langchain.indexes import VectorstoreIndexCreator
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import Chroma
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import panel as pn
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import tempfile
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# sets Panel Framework
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pn.extension('texteditor', template="bootstap", sizing_mode='stretch_width')
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pn.state.template.param.update(
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main_max_width="690px",
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header_background="#F08080",
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)
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# set widgets
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file_input = pn.widgets.FileInput(width=300)
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openaikey = pn.widgets.PasswordInput(
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value="", placeholder="Enter your OpenAI API key here...", width=300
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)
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prompt = pn.widgets.TextEditor(
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value="", placeholder="Enter your questions here...", height=160, toolbar=False
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)
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run_button = pn.widgets.Button(name="Run!")
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select_k = pn.widgets.IntSlider(
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name="Number of relevant chunks", start=1, end=5, step=1, value=2
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)
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select_chain_type = pn.widgets.RadioButtonGroup(
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name='Chain type',
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options =['stuff', 'map_reduce', "refine", "map_rerank"]
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)
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widgets = pn.Row(
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pn.Column(prompt, run_button, margin=5),
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pn.Card(
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"Chain type:",
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pn.Column(select_chain_type, select_k),
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title="Advanced settings", margin=10
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), width=600
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)
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# define the question answering function
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def qa(file, query, chain_type, k):
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# load document
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loader = PyPDFLoader(file)
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documents = loader.load()
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# split the documents into chunks
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text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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texts = text_splitter.split_documents(documents)
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# select which embeddings we want to use
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embeddings = OpenAIEmbeddings()
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# create the vectorstore to use as the index
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db = Chroma.from_documents(texts, embeddings)
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# expose this index in a retriever interface
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retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": k})
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# to create a chain to answer questions
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qa = RetreivalQA.from_chain_type(
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llm = OPenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)
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result = qa({"query": query})
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print(result['result'])
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return result
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# store all Panel objects in a list
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convos = []
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def qa_result(_):
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os.environ["OPENAI_API_KEY"] = openaikey.value
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# save pdf file to a temp file
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if file_input.value is not None:
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file_input.save(temp.pdf)
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prompt_text = prompt.value
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if prompt_text:
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result = qa(file="temp.pdf", query=prompt_text, chain_type=select_chain_type.value, k=select_k.value)
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convos.extend([
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pn.Row(
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pn.panel("\U0001F60A", width=10),
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prompt_text,
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width=600
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),
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pn.Row(
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pn.panel(\"U0001F916", width=10),
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pn.Column(
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result["result"],
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"Relevant source text:",
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pn.pane.Markdown('\n--------------------------------------------------------------------\n'.join(doc.page_content for docu in result["source_documents"]))
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)
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)
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])
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return pn.Column(*convos, margin=15, width=575, min_height=400)
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# bind run button with the qa_result function
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qa_interactive = pn.panel(
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pn.bind(qa_result, run_button),
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loading_indicator=True,
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)
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output pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)
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# define the layout
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pn.Column(
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pn.pane.Markdown("""
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## \U0001F60A! Question Answering with your PDF file
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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".
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"""),
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pn.Row(file_input, openaikey)
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output,
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widgets
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).servable()
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