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langchain/cookbook/code-analysis-deeplake.ipynb
Erick Friis b1fa726377
docs: langchain-openai (#15513) 
Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI
Embeddings from `langchain_openai`

There are likely more

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2024-01-06 15:54:48 -08:00

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
"In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Design"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Prepare data:\n",
" 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
" 2. Prepare questions.\n",
" 3. Get answers running the chain.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Integration preparations"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to set up keys for external services and install necessary python libraries."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#!python3 -m pip install --upgrade langchain deeplake openai"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
"\n",
"For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
"# Please manually enter OpenAI Key"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"activeloop_token = getpass(\"Activeloop Token:\")\n",
"os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
"\n",
"If you want to use files from different repo, change `root_dir` to the root dir of your repo."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CITATION.cff MIGRATE.md README.md libs\t poetry.toml\n",
"LICENSE Makefile\t docs\t poetry.lock pyproject.toml\n"
]
}
],
"source": [
"!ls \"../../../../../../libs\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2554\n"
]
}
],
"source": [
"from langchain_community.document_loaders import TextLoader\n",
"\n",
"root_dir = \"../../../../../../libs\"\n",
"\n",
"docs = []\n",
"for dirpath, dirnames, filenames in os.walk(root_dir):\n",
" for file in filenames:\n",
" if file.endswith(\".py\") and \"*venv/\" not in dirpath:\n",
" try:\n",
" loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
" docs.extend(loader.load_and_split())\n",
" except Exception:\n",
" pass\n",
"print(f\"{len(docs)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, chunk the files"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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"Created a chunk of size 2023, which is longer than the specified 1000\n",
"Created a chunk of size 1216, which is longer than the specified 1000\n",
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"Created a chunk of size 1354, which is longer than the specified 1000\n",
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"Created a chunk of size 1144, which is longer than the specified 1000\n",
"Created a chunk of size 1509, which is longer than the specified 1000\n",
"Created a chunk of size 1003, which is longer than the specified 1000\n",
"Created a chunk of size 1025, which is longer than the specified 1000\n",
"Created a chunk of size 1197, which is longer than the specified 1000\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"8244\n"
]
}
],
"source": [
"from langchain.text_splitter import CharacterTextSplitter\n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(docs)\n",
"print(f\"{len(texts)}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Then embed chunks and upload them to the DeepLake.\n",
"\n",
"This can take several minutes. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your Deep Lake dataset has been successfully created!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset(path='hub://adilkhan/langchain-code', tensors=['embedding', 'id', 'metadata', 'text'])\n",
"\n",
" tensor htype shape dtype compression\n",
" ------- ------- ------- ------- ------- \n",
" embedding embedding (8244, 1536) float32 None \n",
" id text (8244, 1) str None \n",
" metadata json (8244, 1) str None \n",
" text text (8244, 1) str None \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"data": {
"text/plain": [
"<langchain_community.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_community.vectorstores import DeepLake\n",
"\n",
"username = \"<USERNAME_OR_ORG>\"\n",
"\n",
"\n",
"db = DeepLake.from_documents(\n",
" texts, embeddings, dataset_path=f\"hub://{username}/langchain-code\", overwrite=True\n",
")\n",
"db"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"`Optional`: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# from langchain_community.vectorstores import DeepLake\n",
"\n",
"# db = DeepLake.from_documents(\n",
"# texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
"# )\n",
"# db"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question Answering\n",
"First load the dataset, construct the retriever, then construct the Conversational Chain"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Deep Lake Dataset in hub://adilkhan/langchain-code already exists, loading from the storage\n"
]
}
],
"source": [
"db = DeepLake(\n",
" dataset_path=f\"hub://{username}/langchain-code\",\n",
" read_only=True,\n",
" embedding=embeddings,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = db.as_retriever()\n",
"retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
"retriever.search_kwargs[\"fetch_k\"] = 20\n",
"retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
"retriever.search_kwargs[\"k\"] = 20"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def filter(x):\n",
" # filter based on source code\n",
" if \"something\" in x[\"text\"].data()[\"value\"]:\n",
" return False\n",
"\n",
" # filter based on path e.g. extension\n",
" metadata = x[\"metadata\"].data()[\"value\"]\n",
" return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
"\n",
"\n",
"### turn on below for custom filtering\n",
"# retriever.search_kwargs['filter'] = filter"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chains import ConversationalRetrievalChain\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(\n",
" model_name=\"gpt-3.5-turbo-0613\"\n",
") # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
"qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-> **Question**: What is the class hierarchy? \n",
"\n",
"**Answer**: The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
" \n",
"\n",
"-> **Question**: What classes are derived from the Chain class? \n",
"\n",
"**Answer**: The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FalkorDBQAChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
" \n",
"\n",
"-> **Question**: What kind of retrievers does LangChain have? \n",
"\n",
"**Answer**: The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureCognitiveSearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever \n",
"\n"
]
}
],
"source": [
"questions = [\n",
" \"What is the class hierarchy?\",\n",
" \"What classes are derived from the Chain class?\",\n",
" \"What kind of retrievers does LangChain have?\",\n",
"]\n",
"chat_history = []\n",
"qa_dict = {}\n",
"\n",
"for question in questions:\n",
" result = qa({\"question\": question, \"chat_history\": chat_history})\n",
" chat_history.append((question, result[\"answer\"]))\n",
" qa_dict[question] = result[\"answer\"]\n",
" print(f\"-> **Question**: {question} \\n\")\n",
" print(f\"**Answer**: {result['answer']} \\n\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'question': 'LangChain possesses a variety of retrievers including:\\n\\n1. ArxivRetriever\\n2. AzureCognitiveSearchRetriever\\n3. BM25Retriever\\n4. ChaindeskRetriever\\n5. ChatGPTPluginRetriever\\n6. ContextualCompressionRetriever\\n7. DocArrayRetriever\\n8. ElasticSearchBM25Retriever\\n9. EnsembleRetriever\\n10. GoogleVertexAISearchRetriever\\n11. AmazonKendraRetriever\\n12. KNNRetriever\\n13. LlamaIndexGraphRetriever\\n14. LlamaIndexRetriever\\n15. MergerRetriever\\n16. MetalRetriever\\n17. MilvusRetriever\\n18. MultiQueryRetriever\\n19. ParentDocumentRetriever\\n20. PineconeHybridSearchRetriever\\n21. PubMedRetriever\\n22. RePhraseQueryRetriever\\n23. RemoteLangChainRetriever\\n24. SelfQueryRetriever\\n25. SVMRetriever\\n26. TFIDFRetriever\\n27. TimeWeightedVectorStoreRetriever\\n28. VespaRetriever\\n29. WeaviateHybridSearchRetriever\\n30. WebResearchRetriever\\n31. WikipediaRetriever\\n32. ZepRetriever\\n33. ZillizRetriever\\n\\nIt also includes self query translators like:\\n\\n1. ChromaTranslator\\n2. DeepLakeTranslator\\n3. MyScaleTranslator\\n4. PineconeTranslator\\n5. QdrantTranslator\\n6. WeaviateTranslator\\n\\nAnd remote retrievers like:\\n\\n1. RemoteLangChainRetriever'}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qa_dict"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The class hierarchy for Memory is as follows:\n",
"\n",
" BaseMemory --> BaseChatMemory --> <name>Memory # Examples: ZepMemory, MotorheadMemory\n",
"\n",
"The class hierarchy for ChatMessageHistory is as follows:\n",
"\n",
" BaseChatMessageHistory --> <name>ChatMessageHistory # Example: ZepChatMessageHistory\n",
"\n",
"The class hierarchy for Prompt is as follows:\n",
"\n",
" BasePromptTemplate --> PipelinePromptTemplate\n",
" StringPromptTemplate --> PromptTemplate\n",
" FewShotPromptTemplate\n",
" FewShotPromptWithTemplates\n",
" BaseChatPromptTemplate --> AutoGPTPrompt\n",
" ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What is the class hierarchy?\"])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The classes derived from the Chain class are:\n",
"\n",
"- APIChain\n",
"- OpenAPIEndpointChain\n",
"- AnalyzeDocumentChain\n",
"- MapReduceDocumentsChain\n",
"- MapRerankDocumentsChain\n",
"- ReduceDocumentsChain\n",
"- RefineDocumentsChain\n",
"- StuffDocumentsChain\n",
"- ConstitutionalChain\n",
"- ConversationChain\n",
"- ChatVectorDBChain\n",
"- ConversationalRetrievalChain\n",
"- FlareChain\n",
"- ArangoGraphQAChain\n",
"- GraphQAChain\n",
"- GraphCypherQAChain\n",
"- HugeGraphQAChain\n",
"- KuzuQAChain\n",
"- NebulaGraphQAChain\n",
"- NeptuneOpenCypherQAChain\n",
"- GraphSparqlQAChain\n",
"- HypotheticalDocumentEmbedder\n",
"- LLMChain\n",
"- LLMBashChain\n",
"- LLMCheckerChain\n",
"- LLMMathChain\n",
"- LLMRequestsChain\n",
"- LLMSummarizationCheckerChain\n",
"- MapReduceChain\n",
"- OpenAIModerationChain\n",
"- NatBotChain\n",
"- QAGenerationChain\n",
"- QAWithSourcesChain\n",
"- RetrievalQAWithSourcesChain\n",
"- VectorDBQAWithSourcesChain\n",
"- RetrievalQA\n",
"- VectorDBQA\n",
"- LLMRouterChain\n",
"- MultiPromptChain\n",
"- MultiRetrievalQAChain\n",
"- MultiRouteChain\n",
"- RouterChain\n",
"- SequentialChain\n",
"- SimpleSequentialChain\n",
"- TransformChain\n",
"- TaskPlaningChain\n",
"- QueryChain\n",
"- CPALChain\n",
"\n"
]
}
],
"source": [
"print(qa_dict[\"What classes are derived from the Chain class?\"])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The LangChain class includes various types of retrievers such as:\n",
"\n",
"- ArxivRetriever\n",
"- AzureCognitiveSearchRetriever\n",
"- BM25Retriever\n",
"- ChaindeskRetriever\n",
"- ChatGPTPluginRetriever\n",
"- ContextualCompressionRetriever\n",
"- DocArrayRetriever\n",
"- ElasticSearchBM25Retriever\n",
"- EnsembleRetriever\n",
"- GoogleVertexAISearchRetriever\n",
"- AmazonKendraRetriever\n",
"- KNNRetriever\n",
"- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
"- MergerRetriever\n",
"- MetalRetriever\n",
"- MilvusRetriever\n",
"- MultiQueryRetriever\n",
"- ParentDocumentRetriever\n",
"- PineconeHybridSearchRetriever\n",
"- PubMedRetriever\n",
"- RePhraseQueryRetriever\n",
"- RemoteLangChainRetriever\n",
"- SelfQueryRetriever\n",
"- SVMRetriever\n",
"- TFIDFRetriever\n",
"- TimeWeightedVectorStoreRetriever\n",
"- VespaRetriever\n",
"- WeaviateHybridSearchRetriever\n",
"- WebResearchRetriever\n",
"- WikipediaRetriever\n",
"- ZepRetriever\n",
"- ZillizRetriever\n"
]
}
],
"source": [
"print(qa_dict[\"What kind of retrievers does LangChain have?\"])"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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