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Added AwaDB vector store, which is a wrapper over the AwaDB, that can be used as a vector storage and has an efficient similarity search. Added integration tests for the vector store Added jupyter notebook with the example Delete a unneeded empty file and resolve the conflict(https://github.com/hwchase17/langchain/pull/5886) Please check, Thanks! @dev2049 @hwchase17 --------- <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> --------- Co-authored-by: ljeagle <vincent_jieli@yeah.net> Co-authored-by: vincent <awadb.vincent@gmail.com>
195 lines
4.4 KiB
Plaintext
195 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "833c4789",
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"metadata": {},
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"source": [
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"# AwaDB\n",
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"[AwaDB](https://github.com/awa-ai/awadb) is an AI Native database for the search and storage of embedding vectors used by LLM Applications.\n",
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"This notebook shows how to use functionality related to the AwaDB."
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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": null,
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"id": "252930ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install awadb"
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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": null,
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"id": "f2b71a47",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import AwaDB\n",
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"from langchain.document_loaders import TextLoader"
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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": null,
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"id": "49be0bac",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TextLoader('../../../state_of_the_union.txt')\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size= 100, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(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": null,
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"id": "18714278",
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"metadata": {},
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"outputs": [],
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"source": [
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"db = AwaDB.from_documents(docs)\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs = db.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": null,
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"id": "62b7a4c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a9b4be48",
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"metadata": {},
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"source": [
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence."
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]
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},
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{
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"cell_type": "markdown",
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"id": "87fec6b5",
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"metadata": {},
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"source": [
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"## Similarity search with score"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17231924",
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"metadata": {},
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"source": [
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"The returned distance score is between 0-1. 0 is dissimilar, 1 is the most similar"
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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": null,
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"id": "f40ddae1",
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = db.similarity_search_with_score(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": null,
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"id": "f0045583",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(docs[0])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8c2da99d",
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"metadata": {},
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"source": [
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"(Document(page_content='And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}), 0.561813814013747)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b49fb59",
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"metadata": {},
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"source": [
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"## Restore the table created and added data before"
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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": null,
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"id": "1bfa6e25",
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"metadata": {},
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"outputs": [],
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"source": [
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"AwaDB automatically persists added document data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2a0f3b35",
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"metadata": {},
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"source": [
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"If you can restore the table you created and added before, you can just do this as below:"
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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": null,
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"id": "1fd4b5b0",
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"metadata": {},
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"outputs": [],
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"source": [
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"awadb_client = awadb.Client()\n",
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"ret = awadb_client.Load('langchain_awadb')\n",
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"if ret : print('awadb load table success')\n",
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"else:\n",
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" print('awadb load table failed')"
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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": null,
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"id": "5ae9a9dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"awadb load table success"
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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": "3.11.1"
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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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