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>
2627 lines
78 KiB
Plaintext
2627 lines
78 KiB
Plaintext
{
|
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"cells": [
|
||
{
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||
"cell_type": "markdown",
|
||
"metadata": {
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"id": "OzsRXoCSLC14"
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||
},
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"source": [
|
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"# RouterQuery Engine\n",
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||
"\n",
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||
"In this notebook we will look into `RouterQueryEngine` to route the user queries to one of the available query engine tools. These tools can be different indices/ query engine on same documents/ different documents."
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]
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||
},
|
||
{
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||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "SxWKA6fCK0mw"
|
||
},
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"source": [
|
||
"### Installation"
|
||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": null,
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||
"metadata": {
|
||
"id": "csEUaulS0EQ_"
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||
},
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||
"outputs": [],
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"source": [
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"!pip install llama-index\n",
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"!pip install llama-index-llms-anthropic\n",
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"!pip install llama-index-embeddings-huggingface"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {
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||
"id": "Z2_M_q-pK2-1"
|
||
},
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||
"source": [
|
||
"### Set Logging"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {
|
||
"id": "1Ry4vjMv0V7E"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
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"# NOTE: This is ONLY necessary in jupyter notebook.\n",
|
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"# Details: Jupyter runs an event-loop behind the scenes.\n",
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"# This results in nested event-loops when we start an event-loop to make async queries.\n",
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"# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
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"import nest_asyncio\n",
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"\n",
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"nest_asyncio.apply()\n",
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"\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",
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"\n",
|
||
"# Set up the StreamHandler to output to sys.stdout (Colab's output)\n",
|
||
"handler = logging.StreamHandler(sys.stdout)\n",
|
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"handler.setLevel(logging.INFO) # Set handler level to INFO\n",
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"\n",
|
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"# Add the handler to the logger\n",
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"logger.addHandler(handler)\n",
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"\n",
|
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"from IPython.display import display, HTML"
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]
|
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},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "No_1L4P4K5J2"
|
||
},
|
||
"source": [
|
||
"### Set Claude API Key"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"id": "6JXQosS80PNK"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"os.environ['ANTHROPIC_API_KEY'] = 'YOUR Claude API KEY'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
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"id": "4pEemHzPK7as"
|
||
},
|
||
"source": [
|
||
"### Set LLM and Embedding model\n",
|
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"\n",
|
||
"We will use anthropic latest released `Claude-3 Opus` LLM."
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"id": "Euj43bfS0Rqf"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.llms.anthropic import Anthropic\n",
|
||
"from llama_index.embeddings.huggingface import HuggingFaceEmbedding"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 313,
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"referenced_widgets": [
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"id": "wqwlOVH00TbB",
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"outputId": "1803fa26-e51e-4b9b-8f0d-5ac0d322f2e9"
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},
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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": 5,
|
||
"metadata": {
|
||
"id": "aIkLOdRz0Unp"
|
||
},
|
||
"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": "JP-m752eKRWW"
|
||
},
|
||
"source": [
|
||
"### Download Document"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "MozCygK32Zy0",
|
||
"outputId": "0f8d3446-3445-419c-a976-4405c0d93759"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"--2024-03-08 07:04:27-- https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\n",
|
||
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\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: 75042 (73K) [text/plain]\n",
|
||
"Saving to: ‘data/paul_graham/paul_graham_essay.txt’\n",
|
||
"\n",
|
||
"data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.002s \n",
|
||
"\n",
|
||
"2024-03-08 07:04:27 (28.6 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!mkdir -p 'data/paul_graham/'\n",
|
||
"!wget 'https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "G4nEvK7CKTbh"
|
||
},
|
||
"source": [
|
||
"### Load Document"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"id": "n4AGgEvf0dpo"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# load documents\n",
|
||
"from llama_index.core import SimpleDirectoryReader\n",
|
||
"documents = SimpleDirectoryReader(\"data/paul_graham\").load_data()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "rAlVBGBOKYmX"
|
||
},
|
||
"source": [
|
||
"### Create Indices and Query Engines."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"id": "yabW13VV2Vus"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core import SummaryIndex, VectorStoreIndex\n",
|
||
"# Summary Index for summarization questions\n",
|
||
"summary_index = SummaryIndex.from_documents(documents)\n",
|
||
"\n",
|
||
"# Vector Index for answering specific context questions\n",
|
||
"vector_index = VectorStoreIndex.from_documents(documents)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"id": "GQCnJnOl2i1M"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Summary Index Query Engine\n",
|
||
"summary_query_engine = summary_index.as_query_engine(\n",
|
||
" response_mode=\"tree_summarize\",\n",
|
||
" use_async=True,\n",
|
||
")\n",
|
||
"\n",
|
||
"# Vector Index Query Engine\n",
|
||
"vector_query_engine = vector_index.as_query_engine()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "9gxEcFxLKeSN"
|
||
},
|
||
"source": [
|
||
"### Create tools for summary and vector query engines."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"id": "qM0eVXy62ohZ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core.tools.query_engine import QueryEngineTool\n",
|
||
"\n",
|
||
"# Summary Index tool\n",
|
||
"summary_tool = QueryEngineTool.from_defaults(\n",
|
||
" query_engine=summary_query_engine,\n",
|
||
" description=\"Useful for summarization questions related to Paul Graham eassy on What I Worked On.\",\n",
|
||
")\n",
|
||
"\n",
|
||
"# Vector Index tool\n",
|
||
"vector_tool = QueryEngineTool.from_defaults(\n",
|
||
" query_engine=vector_query_engine,\n",
|
||
" description=\"Useful for retrieving specific context from Paul Graham essay on What I Worked On.\",\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "psgZak7mKkn3"
|
||
},
|
||
"source": [
|
||
"### Create Router Query Engine"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"id": "atzdlxci2rPk"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from llama_index.core.query_engine.router_query_engine import RouterQueryEngine\n",
|
||
"from llama_index.core.selectors.llm_selectors import LLMSingleSelector, LLMMultiSelector"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"id": "AX7laiwj2ut8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Create Router Query Engine\n",
|
||
"query_engine = RouterQueryEngine(\n",
|
||
" selector=LLMSingleSelector.from_defaults(),\n",
|
||
" query_engine_tools=[\n",
|
||
" summary_tool,\n",
|
||
" vector_tool,\n",
|
||
" ],\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "r_nIEGuNKor4"
|
||
},
|
||
"source": [
|
||
"### Test Queries"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "RCeKGLxG2wZ-",
|
||
"outputId": "86b24d46-31c2-4370-9323-95b2573c966a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n",
|
||
"Selecting query engine 0: The question is asking for a summary of the document. Choice 1 specifically mentions that it is useful for summarization questions related to Paul Graham's essay on What I Worked On, making it the most relevant choice for answering the given question..\n",
|
||
"HTTP Request: POST https://api.anthropic.com/v1/messages \"HTTP/1.1 200 OK\"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"response = query_engine.query(\"What is the summary of the document?\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 128
|
||
},
|
||
"id": "9e74o31q3kok",
|
||
"outputId": "49db0722-b863-495f-b6b3-8067e09555d1"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
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