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{
"cells": [
{
"cell_type": "markdown",
"id": "04815d1b-44ee-4bd3-878e-fa0c3bf9fa7f",
"metadata": {
"tags": [],
"id": "04815d1b-44ee-4bd3-878e-fa0c3bf9fa7f"
},
"source": [
"# LangChain QA Panel App\n",
"\n",
"This notebook shows how to make this app:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a181568b-9cde-4a55-a853-4d2a41dbfdad",
"metadata": {
"tags": [],
"id": "a181568b-9cde-4a55-a853-4d2a41dbfdad"
},
"outputs": [],
"source": [
"#!pip install langchain openai chromadb tiktoken pypdf panel\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a464409-d064-4766-a9cb-5119f6c4b8f5",
"metadata": {
"tags": [],
"id": "9a464409-d064-4766-a9cb-5119f6c4b8f5"
},
"outputs": [],
"source": [
"import os \n",
"from langchain.chains import RetrievalQA\n",
"from langchain.llms import OpenAI\n",
"from langchain.document_loaders import TextLoader\n",
"from langchain.document_loaders import PyPDFLoader\n",
"from langchain.indexes import VectorstoreIndexCreator\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.embeddings import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"import panel as pn\n",
"import tempfile\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2d07ea5-9ff2-4c96-a8dc-92895d870b73",
"metadata": {
"tags": [],
"id": "b2d07ea5-9ff2-4c96-a8dc-92895d870b73"
},
"outputs": [],
"source": [
"pn.extension('texteditor', template=\"bootstrap\", sizing_mode='stretch_width')\n",
"pn.state.template.param.update(\n",
" main_max_width=\"690px\",\n",
" header_background=\"#F08080\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "763db4d0-3436-41d3-8b0f-e66ce16468cd",
"metadata": {
"tags": [],
"id": "763db4d0-3436-41d3-8b0f-e66ce16468cd"
},
"outputs": [],
"source": [
"file_input = pn.widgets.FileInput(width=300)\n",
"\n",
"openaikey = pn.widgets.PasswordInput(\n",
" value=\"\", placeholder=\"Enter your OpenAI API Key here...\", width=300\n",
")\n",
"prompt = pn.widgets.TextEditor(\n",
" value=\"\", placeholder=\"Enter your questions here...\", height=160, toolbar=False\n",
")\n",
"run_button = pn.widgets.Button(name=\"Run!\")\n",
"\n",
"select_k = pn.widgets.IntSlider(\n",
" name=\"Number of relevant chunks\", start=1, end=5, step=1, value=2\n",
")\n",
"select_chain_type = pn.widgets.RadioButtonGroup(\n",
" name='Chain type', \n",
" options=['stuff', 'map_reduce', \"refine\", \"map_rerank\"]\n",
")\n",
"\n",
"widgets = pn.Row(\n",
" pn.Column(prompt, run_button, margin=5),\n",
" pn.Card(\n",
" \"Chain type:\",\n",
" pn.Column(select_chain_type, select_k),\n",
" title=\"Advanced settings\", margin=10\n",
" ), width=600\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b83cc06-3401-498f-8f84-8a98370f3121",
"metadata": {
"tags": [],
"id": "9b83cc06-3401-498f-8f84-8a98370f3121"
},
"outputs": [],
"source": [
"def qa(file, query, chain_type, k):\n",
" # load document\n",
" loader = PyPDFLoader(file)\n",
" documents = loader.load()\n",
" # split the documents into chunks\n",
" text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
" texts = text_splitter.split_documents(documents)\n",
" # select which embeddings we want to use\n",
" embeddings = OpenAIEmbeddings()\n",
" # create the vectorestore to use as the index\n",
" db = Chroma.from_documents(texts, embeddings)\n",
" # expose this index in a retriever interface\n",
" retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": k})\n",
" # create a chain to answer questions \n",
" qa = RetrievalQA.from_chain_type(\n",
" llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)\n",
" result = qa({\"query\": query})\n",
" print(result['result'])\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2722f43b-daf6-4d17-a842-41203ae9b140",
"metadata": {
"tags": [],
"id": "2722f43b-daf6-4d17-a842-41203ae9b140"
},
"outputs": [],
"source": [
"# result = qa(\"example.pdf\", \"what is the total number of AI publications?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60e1b3d3-c0d2-4260-ae0c-26b03f1b8824",
"metadata": {
"id": "60e1b3d3-c0d2-4260-ae0c-26b03f1b8824"
},
"outputs": [],
"source": [
"convos = [] # store all panel objects in a list\n",
"\n",
"def qa_result(_):\n",
" os.environ[\"OPENAI_API_KEY\"] = openaikey.value\n",
" \n",
" # save pdf file to a temp file \n",
" if file_input.value is not None:\n",
" file_input.save(\"/.cache/temp.pdf\")\n",
" \n",
" prompt_text = prompt.value\n",
" if prompt_text:\n",
" result = qa(file=\"/.cache/temp.pdf\", query=prompt_text, chain_type=select_chain_type.value, k=select_k.value)\n",
" convos.extend([\n",
" pn.Row(\n",
" pn.panel(\"\\U0001F60A\", width=10),\n",
" prompt_text,\n",
" width=600\n",
" ),\n",
" pn.Row(\n",
" pn.panel(\"\\U0001F916\", width=10),\n",
" pn.Column(\n",
" result[\"result\"],\n",
" \"Relevant source text:\",\n",
" pn.pane.Markdown('\\n--------------------------------------------------------------------\\n'.join(doc.page_content for doc in result[\"source_documents\"]))\n",
" )\n",
" )\n",
" ])\n",
" #return convos\n",
" return pn.Column(*convos, margin=15, width=575, min_height=400)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3a70857-0b98-4f62-a9c0-b62ca42b474c",
"metadata": {
"tags": [],
"id": "c3a70857-0b98-4f62-a9c0-b62ca42b474c"
},
"outputs": [],
"source": [
"qa_interactive = pn.panel(\n",
" pn.bind(qa_result, run_button),\n",
" loading_indicator=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "228e2b42-b1ed-43af-b923-031a70241ab0",
"metadata": {
"tags": [],
"id": "228e2b42-b1ed-43af-b923-031a70241ab0"
},
"outputs": [],
"source": [
"output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b0ec253-2bcd-4f91-96d8-d8456e900a58",
"metadata": {
"tags": [],
"id": "1b0ec253-2bcd-4f91-96d8-d8456e900a58"
},
"outputs": [],
"source": [
"# layout\n",
"pn.Column(\n",
" pn.pane.Markdown(\"\"\"\n",
" ## \\U0001F60A! Question Answering with your PDF file\n",
" \n",
" 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\".\n",
" \n",
" \"\"\"),\n",
" pn.Row(file_input,openaikey),\n",
" output,\n",
" widgets\n",
"\n",
").servable()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.10"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 5
} |