mirror of
https://github.com/hwchase17/langchain
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687 lines
26 KiB
Plaintext
687 lines
26 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "05859721",
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"metadata": {},
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"source": [
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"# Question Answering\n",
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"\n",
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"This notebook walks through how to use LangChain for question answering over a list of documents. It covers four different types of chaings: `stuff`, `map_reduce`, `refine`, `map-rerank`. For a more in depth explanation of what these chain types are, see [here](../combine_docs.md)."
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]
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},
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{
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"cell_type": "markdown",
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"id": "726f4996",
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"metadata": {},
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"source": [
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"## Prepare Data\n",
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"First we prepare the data. For this example we do similarity search over a vector database, but these documents could be fetched in any manner (the point of this notebook to highlight what to do AFTER you fetch the documents)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "17fcbc0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores.faiss import FAISS\n",
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"from langchain.docstore.document import Document\n",
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"from langchain.prompts import PromptTemplate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "291f0117",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('../../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"texts = text_splitter.split_text(state_of_the_union)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "fd9666a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"docsearch = FAISS.from_texts(texts, embeddings)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d1eaf6e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Justice Breyer\"\n",
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"docs = docsearch.similarity_search(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a16e3453",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains.question_answering import load_qa_chain\n",
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"from langchain.llms import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f64b7f8",
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"metadata": {},
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"source": [
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"## Quickstart\n",
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"If you just want to get started as quickly as possible, this is the recommended way to do it:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "fd9e6190",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
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"query = \"What did the president say about Justice Breyer\"\n",
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"chain.run(input_documents=docs, question=query)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eea01309",
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"metadata": {},
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"source": [
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"If you want more control and understanding over what is happening, please see the information below."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f78787a0",
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"metadata": {},
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"source": [
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"## The `stuff` Chain\n",
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"\n",
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"This sections shows results of using the `stuff` Chain to do question answering."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "180fd4c1",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "77fdf1aa",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'output_text': ' The president said that he was honoring Justice Breyer for his service to the country and that he was a Constitutional scholar, Army veteran, and retiring Justice of the United States Supreme Court.'}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query = \"What did the president say about Justice Breyer\"\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "84794d4c",
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"metadata": {},
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"source": [
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"**Custom Prompts**\n",
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"\n",
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"You can also use your own prompts with this chain. In this example, we will respond in Italian."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "5558c9e0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera come giudice della Corte Suprema degli Stati Uniti.'}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
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"\n",
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"{context}\n",
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"\n",
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"Question: {question}\n",
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"Answer in Italian:\"\"\"\n",
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"PROMPT = PromptTemplate(\n",
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" template=prompt_template, input_variables=[\"context\", \"question\"]\n",
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")\n",
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\", prompt=PROMPT)\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "91522e29",
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"metadata": {},
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"source": [
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"## The `map_reduce` Chain\n",
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"\n",
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"This sections shows results of using the `map_reduce` Chain to do question answering."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "b0060f51",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "fbdb9137",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query = \"What did the president say about Justice Breyer\"\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "31478d32",
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"metadata": {},
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"source": [
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"**Intermediate Steps**\n",
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"\n",
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"We can also return the intermediate steps for `map_reduce` chains, should we want to inspect them. This is done with the `return_map_steps` variable."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "452c8680",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "90b47a75",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'intermediate_steps': [' \"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.\"',\n",
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" ' None',\n",
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" ' None',\n",
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" ' None'],\n",
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" 'output_text': ' The president said, \"Justice Breyer, thank you for your service.\"'}"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93c51102",
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"metadata": {},
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"source": [
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"**Custom Prompts**\n",
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"\n",
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"You can also use your own prompts with this chain. In this example, we will respond in Italian."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "af03a578",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'intermediate_steps': [\"\\nStasera vorrei onorare qualcuno che ha dedicato la sua vita a servire questo paese: il giustizia Stephen Breyer - un veterano dell'esercito, uno studioso costituzionale e un giustizia in uscita della Corte Suprema degli Stati Uniti. Giustizia Breyer, grazie per il tuo servizio.\",\n",
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" '\\nNessun testo pertinente.',\n",
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" \"\\nCome ho detto l'anno scorso, soprattutto ai nostri giovani americani transgender, avrò sempre il tuo sostegno come tuo Presidente, in modo che tu possa essere te stesso e raggiungere il tuo potenziale donato da Dio.\",\n",
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" '\\nNella mia amministrazione, i guardiani sono stati accolti di nuovo. Stiamo andando dietro ai criminali che hanno rubato miliardi di dollari di aiuti di emergenza destinati alle piccole imprese e a milioni di americani. E stasera, annuncio che il Dipartimento di Giustizia nominerà un procuratore capo per la frode pandemica.'],\n",
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" 'output_text': ' Non conosco la risposta alla tua domanda su cosa abbia detto il Presidente riguardo al Giustizia Breyer.'}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"question_prompt_template = \"\"\"Use the following portion of a long document to see if any of the text is relevant to answer the question. \n",
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"Return any relevant text translated into italian.\n",
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"{context}\n",
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"Question: {question}\n",
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"Relevant text, if any, in Italian:\"\"\"\n",
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"QUESTION_PROMPT = PromptTemplate(\n",
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" template=question_prompt_template, input_variables=[\"context\", \"question\"]\n",
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")\n",
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"\n",
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"combine_prompt_template = \"\"\"Given the following extracted parts of a long document and a question, create a final answer italian. \n",
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"If you don't know the answer, just say that you don't know. Don't try to make up an answer.\n",
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"\n",
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"QUESTION: {question}\n",
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"=========\n",
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"{summaries}\n",
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"=========\n",
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"Answer in Italian:\"\"\"\n",
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"COMBINE_PROMPT = PromptTemplate(\n",
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" template=combine_prompt_template, input_variables=[\"summaries\", \"question\"]\n",
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")\n",
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\", return_map_steps=True, question_prompt=QUESTION_PROMPT, combine_prompt=COMBINE_PROMPT)\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ea50ad0",
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"metadata": {},
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"source": [
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"## The `refine` Chain\n",
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"\n",
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"This sections shows results of using the `refine` Chain to do question answering."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "fb167057",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "d8b5286e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query = \"What did the president say about Justice Breyer\"\n",
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f95dfb2e",
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"metadata": {},
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"source": [
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"**Intermediate Steps**\n",
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"\n",
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"We can also return the intermediate steps for `refine` chains, should we want to inspect them. This is done with the `return_refine_steps` variable."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "a5c64200",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "817546ac",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'intermediate_steps': ['\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country and his legacy of excellence.',\n",
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" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice.',\n",
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" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act.',\n",
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" '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'],\n",
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" 'output_text': '\\n\\nThe president said that he wanted to honor Justice Breyer for his dedication to serving the country, his legacy of excellence, and his commitment to advancing liberty and justice, as well as for his commitment to protecting the rights of LGBTQ+ Americans and his support for the bipartisan Equality Act. He also mentioned his plan to lower costs to give families a fair shot, lower the deficit, and go after criminals who stole pandemic relief funds. He also announced that the Justice Department will name a chief prosecutor for pandemic fraud.'}"
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]
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},
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"execution_count": 15,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4f0bcae4",
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||
"metadata": {},
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||
"source": [
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"**Custom Prompts**\n",
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||
"\n",
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"You can also use your own prompts with this chain. In this example, we will respond in Italian."
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||
]
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},
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||
{
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"cell_type": "code",
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"execution_count": 14,
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"id": "6664bda7",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'intermediate_steps': ['\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito.',\n",
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" \"\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani.\",\n",
|
||
" \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\",\n",
|
||
" \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\"],\n",
|
||
" 'output_text': \"\\n\\nIl presidente ha detto che Justice Breyer ha dedicato la sua vita al servizio di questo paese e ha onorato la sua carriera. Ha anche detto che la sua nomina di Circuit Court of Appeals Judge Ketanji Brown Jackson continuerà il suo eccezionale lascito. Ha sottolineato che la sua esperienza come avvocato di alto livello in pratica privata, come ex difensore federale pubblico e come membro di una famiglia di educatori e agenti di polizia, la rende una costruttrice di consenso. Ha anche sottolineato che, dalla sua nomina, ha ricevuto un ampio sostegno, dall'Ordine Fraterno della Polizia a ex giudici nominati da democratici e repubblicani. Ha inoltre sottolineato che la nomina di Justice Breyer è un passo importante verso l'uguaglianza per tutti gli americani, in partic\"}"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"refine_prompt_template = (\n",
|
||
" \"The original question is as follows: {question}\\n\"\n",
|
||
" \"We have provided an existing answer: {existing_answer}\\n\"\n",
|
||
" \"We have the opportunity to refine the existing answer\"\n",
|
||
" \"(only if needed) with some more context below.\\n\"\n",
|
||
" \"------------\\n\"\n",
|
||
" \"{context_str}\\n\"\n",
|
||
" \"------------\\n\"\n",
|
||
" \"Given the new context, refine the original answer to better \"\n",
|
||
" \"answer the question. \"\n",
|
||
" \"If the context isn't useful, return the original answer. Reply in Italian.\"\n",
|
||
")\n",
|
||
"refine_prompt = PromptTemplate(\n",
|
||
" input_variables=[\"question\", \"existing_answer\", \"context_str\"],\n",
|
||
" template=refine_prompt_template,\n",
|
||
")\n",
|
||
"\n",
|
||
"\n",
|
||
"initial_qa_template = (\n",
|
||
" \"Context information is below. \\n\"\n",
|
||
" \"---------------------\\n\"\n",
|
||
" \"{context_str}\"\n",
|
||
" \"\\n---------------------\\n\"\n",
|
||
" \"Given the context information and not prior knowledge, \"\n",
|
||
" \"answer the question: {question}\\nYour answer should be in Italian.\\n\"\n",
|
||
")\n",
|
||
"initial_qa_prompt = PromptTemplate(\n",
|
||
" input_variables=[\"context_str\", \"question\"], template=initial_qa_template\n",
|
||
")\n",
|
||
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\", return_refine_steps=True,\n",
|
||
" question_prompt=initial_qa_prompt, refine_prompt=refine_prompt)\n",
|
||
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "521a77cb",
|
||
"metadata": {},
|
||
"source": [
|
||
"## The `map-rerank` Chain\n",
|
||
"\n",
|
||
"This sections shows results of using the `map-rerank` Chain to do question answering with sources."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "e2bfe203",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "5c28880c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"query = \"What did the president say about Justice Breyer\"\n",
|
||
"results = chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "80ac2db3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. '"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results[\"output_text\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "b428fcb9",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'answer': ' The president thanked Justice Breyer for his service and honored him for dedicating his life to serving the country. ',\n",
|
||
" 'score': '100'},\n",
|
||
" {'answer': \" The president said that Justice Breyer is a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that since she's been nominated, she's received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and that she is a consensus builder.\",\n",
|
||
" 'score': '100'},\n",
|
||
" {'answer': ' The president did not mention Justice Breyer in this context.',\n",
|
||
" 'score': '0'},\n",
|
||
" {'answer': ' The president did not mention Justice Breyer in the given context. ',\n",
|
||
" 'score': '0'}]"
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results[\"intermediate_steps\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5e47a818",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Custom Prompts**\n",
|
||
"\n",
|
||
"You can also use your own prompts with this chain. In this example, we will respond in Italian."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "41b83cd8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'intermediate_steps': [{'answer': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.',\n",
|
||
" 'score': '100'},\n",
|
||
" {'answer': ' Il presidente non ha detto nulla sulla Giustizia Breyer.',\n",
|
||
" 'score': '100'},\n",
|
||
" {'answer': ' Non so.', 'score': '0'},\n",
|
||
" {'answer': ' Il presidente non ha detto nulla sulla giustizia Breyer.',\n",
|
||
" 'score': '100'}],\n",
|
||
" 'output_text': ' Il presidente ha detto che Justice Breyer ha dedicato la sua vita a servire questo paese e ha onorato la sua carriera.'}"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from langchain.prompts.base import RegexParser\n",
|
||
"\n",
|
||
"output_parser = RegexParser(\n",
|
||
" regex=r\"(.*?)\\nScore: (.*)\",\n",
|
||
" output_keys=[\"answer\", \"score\"],\n",
|
||
")\n",
|
||
"\n",
|
||
"prompt_template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
|
||
"\n",
|
||
"In addition to giving an answer, also return a score of how fully it answered the user's question. This should be in the following format:\n",
|
||
"\n",
|
||
"Question: [question here]\n",
|
||
"Helpful Answer In Italian: [answer here]\n",
|
||
"Score: [score between 0 and 100]\n",
|
||
"\n",
|
||
"Begin!\n",
|
||
"\n",
|
||
"Context:\n",
|
||
"---------\n",
|
||
"{context}\n",
|
||
"---------\n",
|
||
"Question: {question}\n",
|
||
"Helpful Answer In Italian:\"\"\"\n",
|
||
"PROMPT = PromptTemplate(\n",
|
||
" template=prompt_template,\n",
|
||
" input_variables=[\"context\", \"question\"],\n",
|
||
" output_parser=output_parser,\n",
|
||
")\n",
|
||
"\n",
|
||
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_rerank\", return_intermediate_steps=True, prompt=PROMPT)\n",
|
||
"query = \"What did the president say about Justice Breyer\"\n",
|
||
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e0f0bbdf",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"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": "3.10.9"
|
||
},
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|