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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question answering over a group chat messages\n",
"In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search and ask questions over a group chat.\n",
"\n",
"View a working demo [here](https://twitter.com/thisissukh_/status/1647223328363679745)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python3 -m pip install --upgrade langchain deeplake openai tiktoken"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Add API keys"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"from langchain.document_loaders import PyPDFLoader, TextLoader\n",
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter\n",
"from langchain.vectorstores import DeepLake\n",
"from langchain.chains import ConversationalRetrievalChain, RetrievalQA\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI\n",
"\n",
"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')\n",
"os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')\n",
"os.environ['ACTIVELOOP_ORG'] = getpass.getpass('Activeloop Org:')\n",
"\n",
"org = os.environ['ACTIVELOOP_ORG']\n",
"embeddings = OpenAIEmbeddings()\n",
"\n",
"dataset_path = 'hub://' + org + '/data'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## 2. Create sample data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can generate a sample group chat conversation using ChatGPT with this prompt:\n",
"\n",
"```\n",
"Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.\n",
"```\n",
"\n",
"I've already generated such a chat in `messages.txt`. We can keep it simple and use this for our example.\n",
"\n",
"## 3. Ingest chat embeddings\n",
"\n",
"We load the messages in the text file, chunk and upload to ActiveLoop Vector store."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(\"messages.txt\") as f:\n",
" state_of_the_union = f.read()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"pages = text_splitter.split_text(state_of_the_union)\n",
"\n",
"text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
"texts = text_splitter.create_documents(pages)\n",
"\n",
"print (texts)\n",
"\n",
"dataset_path = 'hub://'+org+'/data'\n",
"embeddings = OpenAIEmbeddings()\n",
"db = DeepLake.from_documents(texts, embeddings, dataset_path=dataset_path, overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Ask questions\n",
"\n",
"Now we can ask a question and get an answer back with a semantic search:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)\n",
"\n",
"retriever = db.as_retriever()\n",
"retriever.search_kwargs['distance_metric'] = 'cos'\n",
"retriever.search_kwargs['k'] = 4\n",
"\n",
"qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=False)\n",
"\n",
"# What was the restaurant the group was talking about called?\n",
"query = input(\"Enter query:\")\n",
"\n",
"# The Hungry Lobster\n",
"ans = qa({\"query\": query})\n",
"\n",
"print(ans)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
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"file_extension": ".py",
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