forked from Archives/langchain
249 lines
6.0 KiB
Plaintext
249 lines
6.0 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "05859721",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Question Answering\n",
|
|
"\n",
|
|
"This notebook walks through how to use LangChain for question answering over a list of documents. It covers three different types of chaings: `stuff`, `map_reduce`, and `refine`. For a more in depth explanation of what these chain types are, see [here](../../explanation/combine_docs.md)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "726f4996",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Prepare Data\n",
|
|
"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)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "17fcbc0f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
|
"from langchain.text_splitter import CharacterTextSplitter\n",
|
|
"from langchain.vectorstores.faiss import FAISS\n",
|
|
"from langchain.docstore.document import Document"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"id": "291f0117",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"with open('../state_of_the_union.txt') as f:\n",
|
|
" state_of_the_union = f.read()\n",
|
|
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
|
"texts = text_splitter.split_text(state_of_the_union)\n",
|
|
"\n",
|
|
"embeddings = OpenAIEmbeddings()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"id": "fd9666a9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"docsearch = FAISS.from_texts(texts, embeddings)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "d1eaf6e6",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"query = \"What did the president say about Justice Breyer\"\n",
|
|
"docs = docsearch.similarity_search(query)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "a16e3453",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from langchain.chains.question_answering import load_qa_chain\n",
|
|
"from langchain.llms import OpenAI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f78787a0",
|
|
"metadata": {},
|
|
"source": [
|
|
"### The `stuff` Chain\n",
|
|
"\n",
|
|
"This sections shows results of using the `stuff` Chain to do question answering."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "180fd4c1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"stuff\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "d145ae31",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"docs = [Document(page_content=t) for t in texts[:3]]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "77fdf1aa",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'output_text': ' The president did not mention Justice Breyer.'}"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"query = \"What did the president say about Justice Breyer\"\n",
|
|
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "91522e29",
|
|
"metadata": {},
|
|
"source": [
|
|
"### The `map_reduce` Chain\n",
|
|
"\n",
|
|
"This sections shows results of using the `map_reduce` Chain to do question answering."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "b0060f51",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"map_reduce\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "fbdb9137",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'output_text': ' The president did not mention Justice Breyer.'}"
|
|
]
|
|
},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"query = \"What did the president say about Justice Breyer\"\n",
|
|
"chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6ea50ad0",
|
|
"metadata": {},
|
|
"source": [
|
|
"### The `refine` Chain\n",
|
|
"\n",
|
|
"This sections shows results of using the `refine` Chain to do question answering."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "fb167057",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"chain = load_qa_chain(OpenAI(temperature=0), chain_type=\"refine\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "d8b5286e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'output_text': \"\\n\\nThe president did not mention Justice Breyer in his speech to the European Parliament about building a coalition of freedom-loving nations to confront Putin, unifying European allies, countering Russia's lies with truth, and enforcing powerful economic sanctions.\"}"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"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": "49e9c6d7",
|
|
"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": "3.10.8"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|