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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt_tab to\n",
"[nltk_data] /Users/ryanrodriguez/nltk_data...\n",
"[nltk_data] Package punkt_tab is already up-to-date!\n",
"[nltk_data] Downloading package averaged_perceptron_tagger_eng to\n",
"[nltk_data] /Users/ryanrodriguez/nltk_data...\n",
"[nltk_data] Package averaged_perceptron_tagger_eng is already up-to-\n",
"[nltk_data] date!\n"
]
}
],
"source": [
"from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain.chains import create_retrieval_chain\n",
"from langchain.chains.combine_documents import create_stuff_documents_chain\n",
"from backend.app.vectorstore import get_vector_db"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"system_role_prompt = \"\"\"\n",
" You are a helpful assistant that generates questions based on a given context.\n",
"\"\"\"\n",
"\n",
"user_role_prompt = \"\"\"\n",
" Based on the following context about {query}, generate 5 relevant and specific questions.\n",
" Make sure the questions can be answered using only the provided context.\n",
"\n",
" Context: {context}\n",
"\n",
" Generate 5 questions that test understanding of the material in the context.\n",
" \n",
" Return only a json object with the following format:\n",
" {{\n",
" \"questions\": [\"question1\", \"question2\", \"question3\", \"question4\", \"question5\"]\n",
" }}\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"chat_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", system_role_prompt),\n",
" (\"user\", user_role_prompt)\n",
"])\n",
"\n",
"openai_chat_model = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0.7)\n",
"\n",
"retriever = get_vector_db().as_retriever(search_kwargs={\"k\": 2})\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.runnables import RunnablePassthrough\n",
"from langchain_core.output_parsers import StrOutputParser\n",
"\n",
"simple_rag = (\n",
" {\"context\": retriever, \"query\": RunnablePassthrough(), \"num_questions\": RunnablePassthrough()}\n",
" | chat_prompt\n",
" | openai_chat_model\n",
" | StrOutputParser()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"raw_result = simple_rag.invoke(\"RAG\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['What are the two main components of a typical RAG application?',\n",
" 'What is the purpose of the indexing component in a RAG application?',\n",
" \"What are the steps involved in the 'Load' phase of indexing?\",\n",
" 'Why is splitting text into smaller chunks important in the context of RAG applications?',\n",
" 'How does the retrieval and generation component of a RAG application process user queries?']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"result = json.loads(raw_result)\n",
"result[\"questions\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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