mirror of
https://github.com/anthropics/claude-cookbooks.git
synced 2025-10-06 01:00:28 +03:00
8d1c93365b
Reverted all instances of CLAUDE_API_KEY back to ANTHROPIC_API_KEY to maintain compatibility with existing infrastructure and GitHub secrets. This affects: - Environment variable examples (.env.example files) - Python scripts and notebooks - Documentation and README files - Evaluation scripts and test files Other naming changes (Claude API, Claude Console, Claude Docs, Claude Cookbook) remain intact. 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
2786 lines
85 KiB
Plaintext
2786 lines
85 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "XvksyJE6Onz_"
|
||
},
|
||
"source": [
|
||
"# SubQuestionQueryEngine\n",
|
||
"\n",
|
||
"Often, we encounter scenarios where our queries span across multiple documents. \n",
|
||
"\n",
|
||
"In this notebook, we delve into addressing complex queries that extend over various documents by breaking them down into simpler sub-queries and generate answers using the `SubQuestionQueryEngine`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "jrGtIJogMsC5"
|
||
},
|
||
"source": [
|
||
"### Installation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "s2vK4JUD4Yxg"
|
||
},
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||
"outputs": [],
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||
"source": [
|
||
"!pip install llama-index\n",
|
||
"!pip install llama-index-llms-anthropic\n",
|
||
"!pip install llama-index-embeddings-huggingface"
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]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "BK17UTVoMuns"
|
||
},
|
||
"source": [
|
||
"### Setup API Key"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"id": "2XlJDsnB4v2_"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"os.environ['ANTHROPIC_API_KEY'] = 'YOUR Claude API KEY'"
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]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "toeP1CYRMxuf"
|
||
},
|
||
"source": [
|
||
"### Setup LLM and Embedding model\n",
|
||
"\n",
|
||
"We will use anthropic latest released `Claude-3 Opus` LLM."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"id": "E6xhHINq4ynN"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.llms.anthropic import Anthropic\n",
|
||
"from llama_index.embeddings.huggingface import HuggingFaceEmbedding"
|
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]
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},
|
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{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 313,
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"referenced_widgets": [
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"76c0154ec5fe4be9b1fc9cb50c4ce7cb",
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"id": "GIsJJBFo40Jq",
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||
"outputId": "ed4d7267-327a-4ead-a61f-801ae9a69481"
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"llm = Anthropic(temperature=0.0, model='claude-3-opus-20240229')\n",
|
||
"embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-base-en-v1.5\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"id": "2Hqi93hb41nV"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core import Settings\n",
|
||
"Settings.llm = llm\n",
|
||
"Settings.embed_model = embed_model\n",
|
||
"Settings.chunk_size = 512"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ZM0Ksh5QM1yc"
|
||
},
|
||
"source": [
|
||
"### Setup logging"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"id": "pY6CL0_e429M"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
|
||
"# Details: Jupyter runs an event-loop behind the scenes.\n",
|
||
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
|
||
"# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
|
||
"import nest_asyncio\n",
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||
"\n",
|
||
"nest_asyncio.apply()\n",
|
||
"\n",
|
||
"import logging\n",
|
||
"import sys\n",
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||
"\n",
|
||
"# Set up the root logger\n",
|
||
"logger = logging.getLogger()\n",
|
||
"logger.setLevel(logging.INFO) # Set logger level to INFO\n",
|
||
"\n",
|
||
"# Clear out any existing handlers\n",
|
||
"logger.handlers = []\n",
|
||
"\n",
|
||
"# Set up the StreamHandler to output to sys.stdout (Colab's output)\n",
|
||
"handler = logging.StreamHandler(sys.stdout)\n",
|
||
"handler.setLevel(logging.INFO) # Set handler level to INFO\n",
|
||
"\n",
|
||
"# Add the handler to the logger\n",
|
||
"logger.addHandler(handler)\n",
|
||
"\n",
|
||
"from IPython.display import display, HTML"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "MhFt3P-IM4ai"
|
||
},
|
||
"source": [
|
||
"### Download Data\n",
|
||
"\n",
|
||
"We will use Uber and Lyft 2021 10K SEC Filings"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "ukHLfRJC44fG",
|
||
"outputId": "2f5f2a1b-645d-4fa4-f6ab-b8f65373ba07"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"--2024-03-08 07:07:32-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf\n",
|
||
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ...\n",
|
||
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
|
||
"HTTP request sent, awaiting response... 200 OK\n",
|
||
"Length: 1880483 (1.8M) [application/octet-stream]\n",
|
||
"Saving to: ‘./uber_2021.pdf’\n",
|
||
"\n",
|
||
"./uber_2021.pdf 100%[===================>] 1.79M --.-KB/s in 0.02s \n",
|
||
"\n",
|
||
"2024-03-08 07:07:32 (87.4 MB/s) - ‘./uber_2021.pdf’ saved [1880483/1880483]\n",
|
||
"\n",
|
||
"--2024-03-08 07:07:33-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf\n",
|
||
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\n",
|
||
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
|
||
"HTTP request sent, awaiting response... 200 OK\n",
|
||
"Length: 1440303 (1.4M) [application/octet-stream]\n",
|
||
"Saving to: ‘./lyft_2021.pdf’\n",
|
||
"\n",
|
||
"./lyft_2021.pdf 100%[===================>] 1.37M --.-KB/s in 0.02s \n",
|
||
"\n",
|
||
"2024-03-08 07:07:33 (74.9 MB/s) - ‘./lyft_2021.pdf’ saved [1440303/1440303]\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O './uber_2021.pdf'\n",
|
||
"!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O './lyft_2021.pdf'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ItM1L4OmM-Wb"
|
||
},
|
||
"source": [
|
||
"### Load Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"id": "pltY8vjU59mQ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core import SimpleDirectoryReader\n",
|
||
"lyft_docs = SimpleDirectoryReader(input_files=[\"lyft_2021.pdf\"]).load_data()\n",
|
||
"uber_docs = SimpleDirectoryReader(input_files=[\"uber_2021.pdf\"]).load_data()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "7dTY-clm5_k_",
|
||
"outputId": "7ef4e12e-6348-476b-cab0-a3e38b2c5c98"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loaded lyft 10-K with 238 pages\n",
|
||
"Loaded Uber 10-K with 307 pages\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f'Loaded lyft 10-K with {len(lyft_docs)} pages')\n",
|
||
"print(f'Loaded Uber 10-K with {len(uber_docs)} pages')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "xSDF_QEsNBci"
|
||
},
|
||
"source": [
|
||
"### Index Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"background_save": true
|
||
},
|
||
"id": "LRAZWegi6Dyi"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core import VectorStoreIndex\n",
|
||
"lyft_index = VectorStoreIndex.from_documents(lyft_docs[:100])\n",
|
||
"uber_index = VectorStoreIndex.from_documents(uber_docs[:100])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "p6hwyc5DNGMk"
|
||
},
|
||
"source": [
|
||
"### Create Query Engines"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"id": "EHmUyRcC6F5f"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"lyft_engine = lyft_index.as_query_engine(similarity_top_k=5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"id": "15hf7apf6HWF"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"uber_engine = uber_index.as_query_engine(similarity_top_k=5)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ebyg3tfNNIzM"
|
||
},
|
||
"source": [
|
||
"### Querying"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"colab": {
|
||
"background_save": true
|
||
},
|
||
"id": "_6-FAxif6InL",
|
||
"outputId": "4d1adf30-cc28-4f79-a906-aabf82a03e8d"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<p style=\"font-size:20px\">According to the consolidated statements of operations on page 79, Lyft's total revenue for the year ended December 31, 2021 was $3,208.3 million.</p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"response = await lyft_engine.aquery('What is the revenue of Lyft in 2021? Answer in millions with page reference')\n",
|
||
"display(HTML(f'<p style=\"font-size:20px\">{response.response}</p>'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"background_save": true
|
||
},
|
||
"id": "2VSINSWB6SjI",
|
||
"outputId": "9b5e0403-b613-464a-835a-e15689bc86b5"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<p style=\"font-size:20px\">According to the Consolidated Statements of Operations on page 77, Uber's revenue for the year ended December 31, 2021 was $17,455 million.</p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"response = await uber_engine.aquery('What is the revenue of Uber in 2021? Answer in millions, with page reference')\n",
|
||
"display(HTML(f'<p style=\"font-size:20px\">{response.response}</p>'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "przIf6LINKuI"
|
||
},
|
||
"source": [
|
||
"### Create Tools"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"id": "kgK60jJq6VEz"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core.tools import QueryEngineTool, ToolMetadata\n",
|
||
"from llama_index.core.query_engine import SubQuestionQueryEngine\n",
|
||
"\n",
|
||
"query_engine_tools = [\n",
|
||
" QueryEngineTool(\n",
|
||
" query_engine=lyft_engine,\n",
|
||
" metadata=ToolMetadata(name='lyft_10k', description='Provides information about Lyft financials for year 2021')\n",
|
||
" ),\n",
|
||
" QueryEngineTool(\n",
|
||
" query_engine=uber_engine,\n",
|
||
" metadata=ToolMetadata(name='uber_10k', description='Provides information about Uber financials for year 2021')\n",
|
||
" ),\n",
|
||
"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "kL1xN3SnNNr_"
|
||
},
|
||
"source": [
|
||
"### Create `SubQuestionQueryEngine`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {
|
||
"id": "d-4zLgq8NTaR"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"sub_question_query_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "umqoF0qNNZL8"
|
||
},
|
||
"source": [
|
||
"### Querying"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 409
|
||
},
|
||
"id": "k5wLiAiD6Xsy",
|
||
"outputId": "2a7b85df-0be9-4f9e-be45-3413d9838395"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"Generated 4 sub questions.\n",
|
||
"\u001b[1;3;38;2;237;90;200m[uber_10k] Q: What was Uber's revenue in 2020?\n",
|
||
"\u001b[0m\u001b[1;3;38;2;90;149;237m[uber_10k] Q: What was Uber's revenue in 2021?\n",
|
||
"\u001b[0m\u001b[1;3;38;2;11;159;203m[lyft_10k] Q: What was Lyft's revenue in 2020?\n",
|
||
"\u001b[0m\u001b[1;3;38;2;155;135;227m[lyft_10k] Q: What was Lyft's revenue in 2021?\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;11;159;203m[lyft_10k] A: According to Lyft's consolidated statements of operations data, Lyft's total revenue in 2020 was $2,364,681,000.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;90;149;237m[uber_10k] A: According to Uber's consolidated statements of operations, Uber's revenue in 2021 was $17,455 million.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;237;90;200m[uber_10k] A: According to Uber's consolidated statements of operations, Uber's revenue in 2020 was $11,139 million.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;155;135;227m[lyft_10k] A: According to Lyft's consolidated statements of operations, Lyft's total revenue in 2021 was $3,208,323,000. This consisted of:\n",
|
||
"\n",
|
||
"- Revenue from contracts with customers (under ASC 606) of $2,957,979,000\n",
|
||
"- Rental revenue (under ASC 842) of $250,344,000\n",
|
||
"\n",
|
||
"So in total, Lyft generated revenue of $3,208,323,000 in the year ended December 31, 2021.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"response = await sub_question_query_engine.aquery('Compare revenue growth of Uber and Lyft from 2020 to 2021')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"id": "PgVULcF6I0bH"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<p style=\"font-size:20px\">Uber and Lyft both saw significant revenue growth from 2020 to 2021:\n",
|
||
"\n",
|
||
"Uber's revenue increased from $11,139 million in 2020 to $17,455 million in 2021. This represents year-over-year growth of about 56.7%.\n",
|
||
"\n",
|
||
"Lyft's total revenue grew from $2,364,681,000 in 2020 to $3,208,323,000 in 2021, an increase of approximately 35.7%. \n",
|
||
"\n",
|
||
"So while Lyft started from a lower revenue base, Uber achieved higher revenue growth in percentage terms between 2020 and 2021 (56.7% vs 35.7%).\n",
|
||
"\n",
|
||
"Both companies rebounded strongly in 2021 after seeing major impacts to their ride-sharing businesses in 2020 due to the COVID-19 pandemic. But Uber was able to grow its revenue more rapidly to reach $17.5 billion, compared to $3.2 billion for Lyft.</p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(HTML(f'<p style=\"font-size:20px\">{response.response}</p>'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"Generated 4 sub questions.\n",
|
||
"\u001b[1;3;38;2;237;90;200m[uber_10k] Q: What investments did Uber make in 2021\n",
|
||
"\u001b[0m\u001b[1;3;38;2;90;149;237m[uber_10k] Q: What was the total amount invested by Uber in 2021\n",
|
||
"\u001b[0m\u001b[1;3;38;2;11;159;203m[lyft_10k] Q: What investments did Lyft make in 2021\n",
|
||
"\u001b[0m\u001b[1;3;38;2;155;135;227m[lyft_10k] Q: What was the total amount invested by Lyft in 2021\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;90;149;237m[uber_10k] A: Based on the context provided, in 2021 Uber invested:\n",
|
||
"\n",
|
||
"- $2.3 billion in acquisition of businesses, net of cash acquired\n",
|
||
"- $1.1 billion in purchases of marketable securities \n",
|
||
"- $982 million in purchases of non-marketable equity securities\n",
|
||
"- $297 million in purchases of notes receivable\n",
|
||
"- $298 million in purchases of property and equipment\n",
|
||
"\n",
|
||
"So in total, Uber invested approximately $5.0 billion in 2021 across business acquisitions, marketable and non-marketable securities, notes receivable, and property and equipment purchases.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;11;159;203m[lyft_10k] A: Based on the context provided, in 2021 Lyft invested in marketable securities and term deposits:\n",
|
||
"\n",
|
||
"- Lyft purchased marketable securities of $3.8 billion in 2021. The marketable securities consisted of investment grade available-for-sale debt securities. \n",
|
||
"\n",
|
||
"- Lyft also invested in term deposits of $0.5 billion in 2021. The term deposits were at cost, which approximated fair value.\n",
|
||
"\n",
|
||
"As of December 31, 2021, Lyft's investment portfolio had a weighted-average remaining maturity of less than one year. Lyft's investment policy is designed to minimize exposure to credit losses.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;237;90;200m[uber_10k] A: Based on the provided context, in 2021 Uber made investments to:\n",
|
||
"\n",
|
||
"- Increase the number of Drivers, consumers, merchants, shippers, and carriers using their platform through incentives, discounts, and promotions\n",
|
||
"- Expand within existing markets and into new markets \n",
|
||
"- Increase research and development expenses\n",
|
||
"- Expand marketing channels and operations\n",
|
||
"- Hire additional employees\n",
|
||
"- Add new products and offerings to their platform\n",
|
||
"\n",
|
||
"The context indicates Uber expected to incur losses in the near term as a result of substantial increases in operating expenses from continuing to make these types of investments.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"\u001b[1;3;38;2;155;135;227m[lyft_10k] A: Based on the financial information provided in the context, the cash flow statement shows that Lyft had net cash provided by investing activities of $267.0 million for the year ended December 31, 2021. This primarily consisted of:\n",
|
||
"\n",
|
||
"- Proceeds from sales and maturities of marketable securities of $3.8 billion \n",
|
||
"- Maturities of term deposits of $675.5 million\n",
|
||
"- Partially offset by purchases of marketable securities of $3.8 billion and term deposits of $0.5 billion\n",
|
||
"\n",
|
||
"So while the total proceeds from sales/maturities was around $4.5 billion, Lyft reinvested most of that, with net new investments of approximately $267 million in 2021.\n",
|
||
"\u001b[0mHTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"response = await sub_question_query_engine.aquery('Compare the investments made by Uber and Lyft')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<p style=\"font-size:20px\">In 2021, Uber and Lyft made different types of investments:\n",
|
||
"\n",
|
||
"Uber invested a total of approximately $5.0 billion, which included:\n",
|
||
"- $2.3 billion in acquisition of businesses \n",
|
||
"- $1.1 billion in marketable securities\n",
|
||
"- $982 million in non-marketable equity securities\n",
|
||
"- $297 million in notes receivable \n",
|
||
"- $298 million in property and equipment\n",
|
||
"\n",
|
||
"Lyft had net investments of around $267 million, primarily in:\n",
|
||
"- Marketable securities, with purchases of $3.8 billion, offset by sales and maturities of $3.8 billion\n",
|
||
"- Term deposits of $0.5 billion, offset by maturities of $675.5 million\n",
|
||
"\n",
|
||
"So while Uber invested heavily in acquiring businesses, securities, notes, and property, Lyft focused its investments on shorter-term marketable securities and term deposits, with most proceeds being reinvested. Uber's investments were substantially larger in total dollar amount compared to Lyft's net investments in 2021.</p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(HTML(f'<p style=\"font-size:20px\">{response.response}</p>'))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
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|
||
"display_name": "Python 3 (ipykernel)",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
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|
||
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|
||
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|
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|
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|
||
}
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|