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219 lines
6.1 KiB
Plaintext
219 lines
6.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "efc5be67",
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"metadata": {},
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"source": [
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"# VectorDB Question Answering with Sources\n",
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"\n",
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"This notebook goes over how to do question-answering with sources over a vector database. It does this by using the `VectorDBQAWithSourcesChain`, which does the lookup of the documents from a vector database. "
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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": "1c613960",
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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.embeddings.cohere import CohereEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
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"from langchain.vectorstores import Chroma"
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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": "17d1306e",
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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": 5,
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"id": "0e745d99",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Running Chroma using direct local API.\n",
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"Using DuckDB in-memory for database. Data will be transient.\n",
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"Exiting: Cleaning up .chroma directory\n"
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]
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}
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],
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"source": [
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"docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{\"source\": f\"{i}-pl\"} for i in range(len(texts))])"
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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": "8aa571ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import VectorDBQAWithSourcesChain"
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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": "aa859d4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import OpenAI\n",
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"\n",
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"chain = VectorDBQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"stuff\", vectorstore=docsearch)"
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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": "8ba36fa7",
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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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"{'answer': ' The president thanked Justice Breyer for his service and mentioned his legacy of excellence.\\n',\n",
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" 'sources': '30-pl'}"
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]
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},
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"execution_count": 8,
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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({\"question\": \"What did the president say about Justice Breyer\"}, 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": "718ecbda",
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"metadata": {},
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"source": [
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"## Chain Type\n",
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"You can easily specify different chain types to load and use in the VectorDBQAWithSourcesChain chain. For a more detailed walkthrough of these types, please see [this notebook](qa_with_sources.ipynb).\n",
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"\n",
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"There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `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": 8,
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"id": "8b35b30a",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = VectorDBQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type=\"map_reduce\", vectorstore=docsearch)"
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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": "58bd424f",
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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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"{'answer': ' The president honored Justice Stephen Breyer for his service.\\n',\n",
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" 'sources': '30-pl'}"
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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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"chain({\"question\": \"What did the president say about Justice Breyer\"}, 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": "21e14eed",
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"metadata": {},
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"source": [
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"The above way allows you to really simply change the chain_type, but it does provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](qa_with_sources.ipynb)) and then pass that directly to the the VectorDBQA chain with the `combine_documents_chain` parameter. For example:"
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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": "af35f0c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains.qa_with_sources import load_qa_with_sources_chain\n",
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"qa_chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type=\"stuff\")\n",
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"qa = VectorDBQAWithSourcesChain(combine_documents_chain=qa_chain, vectorstore=docsearch)"
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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": "c91fdc8a",
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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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"{'answer': ' The president honored Justice Stephen Breyer for his service.\\n',\n",
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" 'sources': '30-pl'}"
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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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"qa({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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