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374 lines
12 KiB
Plaintext
374 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "38b4bd73",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/ZepIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "2f0a7faf6e43d4c1",
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"metadata": {},
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"source": [
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"# Zep Vector Store\n",
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"\n",
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"## A long-term memory store for LLM applications\n",
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"\n",
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"This notebook demonstrates how to use the Zep Vector Store with LlamaIndex.\n",
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"\n",
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"## About Zep\n",
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"\n",
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"Zep makes it easy for developers to add relevant documents, chat history memory & rich user data to their LLM app's prompts.\n",
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"\n",
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"## Note\n",
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"\n",
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"Zep can automatically embed your documents. The LlamaIndex implementation of the Zep Vector Store utilizes LlamaIndex's embedders to do so.\n",
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"\n",
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"## Getting Started\n",
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"\n",
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"**Quick Start Guide:** https://docs.getzep.com/deployment/quickstart/\n",
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"**GitHub:** https://github.com/getzep/zep\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bca41fe0",
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"metadata": {},
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"source": [
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"If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
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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": "aa418deb",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install llama-index-vector-stores-zep"
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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": "7b4a2b47",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install llama-index"
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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": "1bb0627e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# !pip install zep-python"
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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": "b6547bd146426a4a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"import sys\n",
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"from uuid import uuid4\n",
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"\n",
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"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
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"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
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"\n",
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"import os\n",
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"import openai\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()\n",
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"\n",
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"# os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
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"openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
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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": "e69fa431d972ca88",
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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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"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
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"NumExpr defaulting to 8 threads.\n"
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]
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}
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],
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"source": [
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"from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n",
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"from llama_index.vector_stores.zep import ZepVectorStore"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "0a797bf8",
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"metadata": {},
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"source": [
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"Download Data"
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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": "31416430",
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"metadata": {},
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"outputs": [],
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"source": [
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"!mkdir -p 'data/paul_graham/'\n",
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"!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
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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": "43aeb87a167a31e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# load documents\n",
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"documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "5285bd54baf41918",
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"metadata": {},
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"source": [
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"## Create a Zep Vector Store and Index\n",
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"\n",
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"You can use an existing Zep Collection, or create a new one.\n"
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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": "c8d4cea738a5e867",
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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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"INFO:httpx:HTTP Request: GET http://localhost:8000/healthz \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: GET http://localhost:8000/healthz \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 404 Not Found\"\n",
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"HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 404 Not Found\"\n",
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"INFO:llama_index.vector_stores.zep:Collection grahamfbf0c456a2ad46c2887a707ccc7bb5df does not exist, will try creating one with dimensions=1536\n",
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"Collection grahamfbf0c456a2ad46c2887a707ccc7bb5df does not exist, will try creating one with dimensions=1536\n",
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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: GET http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/document \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/document \"HTTP/1.1 200 OK\"\n"
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]
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}
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],
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"source": [
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"from llama_index.core import StorageContext\n",
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"\n",
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"zep_api_url = \"http://localhost:8000\"\n",
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"collection_name = f\"graham{uuid4().hex}\"\n",
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"\n",
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"vector_store = ZepVectorStore(\n",
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" api_url=zep_api_url,\n",
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" collection_name=collection_name,\n",
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" embedding_dimensions=1536,\n",
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")\n",
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"\n",
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"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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"\n",
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"index = VectorStoreIndex.from_documents(\n",
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" documents, storage_context=storage_context\n",
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")"
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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": "c72041211dcbdedc",
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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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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/search?limit=2 \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/grahamfbf0c456a2ad46c2887a707ccc7bb5df/search?limit=2 \"HTTP/1.1 200 OK\"\n",
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"The author worked on writing and programming outside of school before college. They wrote short stories and tried writing programs on an IBM 1401 computer using an early version of Fortran. They later got a microcomputer and started programming more extensively, writing simple games, a program to predict rocket heights, and a word processor. They initially planned to study philosophy in college but switched to AI. They also started publishing essays online and realized the potential of the web as a medium for publishing.\n"
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]
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}
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],
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"source": [
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"query_engine = index.as_query_engine()\n",
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"response = query_engine.query(\"What did the author do growing up?\")\n",
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"\n",
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"print(str(response))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "e0732f58bfe12bfd",
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"metadata": {},
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"source": [
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"## Querying with Metadata filters\n"
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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": "356e0ec08af5c035",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core.schema import TextNode\n",
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"\n",
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"nodes = [\n",
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" TextNode(\n",
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" text=\"The Shawshank Redemption\",\n",
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" metadata={\n",
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" \"author\": \"Stephen King\",\n",
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" \"theme\": \"Friendship\",\n",
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" },\n",
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" ),\n",
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" TextNode(\n",
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" text=\"The Godfather\",\n",
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" metadata={\n",
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" \"director\": \"Francis Ford Coppola\",\n",
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" \"theme\": \"Mafia\",\n",
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" },\n",
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" ),\n",
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" TextNode(\n",
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" text=\"Inception\",\n",
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" metadata={\n",
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" \"director\": \"Christopher Nolan\",\n",
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" },\n",
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" ),\n",
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"]"
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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": "18afce2b836a8dd",
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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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"INFO:httpx:HTTP Request: GET http://localhost:8000/healthz \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: GET http://localhost:8000/healthz \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 404 Not Found\"\n",
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"HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 404 Not Found\"\n",
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"INFO:llama_index.vector_stores.zep:Collection movies40ffd4f8a68c4822ae1680bb752c07e1 does not exist, will try creating one with dimensions=1536\n",
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"Collection movies40ffd4f8a68c4822ae1680bb752c07e1 does not exist, will try creating one with dimensions=1536\n",
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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: GET http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1 \"HTTP/1.1 200 OK\"\n",
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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/document \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/document \"HTTP/1.1 200 OK\"\n"
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]
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}
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],
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"source": [
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"collection_name = f\"movies{uuid4().hex}\"\n",
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"\n",
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"vector_store = ZepVectorStore(\n",
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" api_url=zep_api_url,\n",
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" collection_name=collection_name,\n",
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" embedding_dimensions=1536,\n",
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")\n",
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"\n",
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"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
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"index = VectorStoreIndex(nodes, storage_context=storage_context)"
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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": "32138298211dddee",
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"metadata": {},
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"outputs": [],
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"source": [
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"from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters\n",
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"\n",
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"filters = MetadataFilters(\n",
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" filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n",
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")"
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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": "4fa27e386c42f215",
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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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"INFO:httpx:HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/search?limit=2 \"HTTP/1.1 200 OK\"\n",
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"HTTP Request: POST http://localhost:8000/api/v1/collection/movies40ffd4f8a68c4822ae1680bb752c07e1/search?limit=2 \"HTTP/1.1 200 OK\"\n",
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"\n",
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" Node ID: 2b5ad50a-8ec0-40fa-b401-6e6b7ac3d304\n",
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"Text: The Godfather\n",
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"Score: 0.8841066656525941\n"
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]
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}
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],
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"source": [
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"retriever = index.as_retriever(filters=filters)\n",
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"result = retriever.retrieve(\"What is inception about?\")\n",
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"\n",
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"for r in result:\n",
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" print(\"\\n\", r.node)\n",
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" print(\"Score:\", r.score)"
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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",
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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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}
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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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