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186 lines
4.9 KiB
Plaintext
186 lines
4.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "87854970",
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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/customization/streaming/SimpleIndexDemo-streaming.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": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
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"metadata": {},
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"source": [
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"# Streaming"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8a660477",
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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": "d3dc5ada",
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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": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
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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:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
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"Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.7) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"import logging\n",
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"import sys\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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"from llama_index.core import VectorStoreIndex, SimpleDirectoryReader"
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]
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},
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{
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"cell_type": "markdown",
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"id": "738c7284",
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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": "5eb5ecc0",
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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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"attachments": {},
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"cell_type": "markdown",
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"id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
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"metadata": {},
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"source": [
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"#### Load documents, build the VectorStoreIndex"
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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": "03d1691e-544b-454f-825b-5ee12f7faa8a",
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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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"cell_type": "code",
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"execution_count": null,
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"id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
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"metadata": {},
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"outputs": [],
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"source": [
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"index = VectorStoreIndex.from_documents(documents)"
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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": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
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"metadata": {},
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"source": [
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"#### Query 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": "85466fdf-93f3-4cb1-a5f9-0056a8245a6f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# set Logging to DEBUG for more detailed outputs\n",
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"query_engine = index.as_query_engine(streaming=True, similarity_top_k=1)\n",
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"response_stream = query_engine.query(\n",
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" \"What did the author do growing up?\",\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": "29ed26f2",
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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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"\n",
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"The author grew up writing short stories and programming on an IBM 1401. He also nagged his father to buy him a TRS-80 microcomputer, on which he wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He eventually went to college to study philosophy, but found it boring and switched to AI."
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]
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}
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],
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"source": [
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"response_stream.print_response_stream()"
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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": "llama",
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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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