{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ESv3uzlwmWtg"
},
"source": [
"# Advance Prompt Customization for Anthropic"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b3R11-WUl4zo"
},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uP2XU1n_lnlf"
},
"source": [
"*Notebook by Bilge Yucel ([LI](https://www.linkedin.com/in/bilge-yucel/) & [X (Twitter)](https://twitter.com/bilgeycl))*\n",
"\n",
"In this example, we'll create a RAG application using prompting techniques in [Anthropic's Prompt Engineering Guide](https://docs.anthropic.com/claude/docs/prompt-engineering). This application will use Anthropic Claude 3 models and [Haystack](https://github.com/deepset-ai/haystack) to extract relevant quotes from given documents and generate an answer based on extracted quotes.\n",
"\n",
"**📚 Useful Sources:**\n",
"* [Docs: AnthropicChatGenerator](https://docs.haystack.deepset.ai/docs/anthropicchatgenerator)\n",
"* [Integration: Anthropic](https://haystack.deepset.ai/integrations/anthropic)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fl2qgtFLRWUT"
},
"source": [
"## Setup the Development Environment\n",
"\n",
"Install [antropic-haystack](https://pypi.org/project/anthropic-haystack/) package and other required packages with pip:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "alM0lmgJXb5M"
},
"outputs": [],
"source": [
"!pip install anthropic-haystack \"datasets>=2.6.1\" \"sentence-transformers>=3.0.0\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DRNs06MjY3sm"
},
"source": [
"You need an `ANTHROPIC_API_KEY` to work with Claude models. Get your API key [here](https://docs.anthropic.com/claude/docs/getting-access-to-claude#step-3-generate-an-api-key)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rLIc5djcj6U2",
"outputId": "54bcc3b3-a672-485e-d989-16a0a457fecf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter the ANTHROPIC_API_KEY: ··········\n"
]
}
],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"ANTHROPIC_API_KEY\"] = getpass(\"Enter the ANTHROPIC_API_KEY: \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mMjY_oV5JfT8"
},
"source": [
"## Download the Dataset\n",
"\n",
"We'll use the [bilgeyucel/seven-wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders) dataset on Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iBZ2oFHyoCrP"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"from haystack import Document\n",
"\n",
"dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n",
"docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qKK6SVLCJmD7"
},
"source": [
"## Create Embeddings and Index into the DocumentStore"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "m3Hk7VWfoGOI"
},
"outputs": [],
"source": [
"from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
"from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
"\n",
"document_store = InMemoryDocumentStore()\n",
"\n",
"doc_embedder = SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"\n",
"docs_with_embeddings = doc_embedder.run(docs)\n",
"document_store.write_documents(docs_with_embeddings[\"documents\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GPZwiWzhbaN2"
},
"source": [
"## Build the Prompt\n",
"\n",
"Claude models are familiar with prompts that contain XML tags, as they were exposed to such prompts during training. By wrapping key parts of the prompt (such as instructions, examples, or input data) in XML tags, we can help Claude better understand the context and generate more accurate outputs.\n",
"\n",
"To formulate a prompt that first extracts quotes from relevant documents and then refers to these quotes to generate the answer, follow these steps in your prompt:\n",
"\n",
"1. Place retrieved documents between ` ` tags.\n",
"2. Render each document between ` ` tags, including a document index for Claude to reference later.\n",
"3. Instruct Claude to extract quotes within ` ` tags, including document index information.\n",
"4. Ensure that Claude generates the answer between ` ` tags.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "eSdg0KxobEhD"
},
"outputs": [],
"source": [
"prompt = \"\"\"\n",
"Here is a document, in XML tags:\n",
"\n",
"\n",
"{% for document in documents %}\n",
" \n",
" {{document.content}}\n",
" \n",
"{% endfor %}\n",
"\n",
"\n",
"First, extract, word-for-word, any quotes relevant to the question, enclose the full list of quotes in XML tags with the corresponding document index and use these quotes to an answer to the question.\n",
"\n",
"If there are no quotes in this document that seem relevant to this question, please say \"I can't find any relevant quotes\".\n",
"\n",
"Then, answer the question in tags. Do not include or reference quoted content verbatim in the answer. Ensure that your answer is accurate and doesn't contain any information not directly supported by the quotes. Make references to quotes relevant to each section of the answer solely by adding their bracketed numbers at the end of relevant sentences\n",
"\n",
"Here is the question: {{question}}\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7JNgcSAADr37"
},
"source": [
"The rendered prompt should look like this when it's fed with relevant documents and the question:\n",
"\n",
"```xml\n",
"Here is a document, in XML tags:\n",
"\n",
"\n",
"\n",
" (the text content of the first document)\n",
"\n",
"\n",
" (the text content of the second document)\n",
"\n",
"\n",
" (the text content of the third document)\n",
"\n",
"...\n",
"\n",
"\n",
"First, extract, word-for-word, any quotes relevant to the question, enclose the full list of quotes in XML tags with the corresponding document index and use these quotes to an answer to the question.\n",
"\n",
"If there are no quotes in this document that seem relevant to this question, please say \"I can't find any relevant quotes\".\n",
"\n",
"Then, answer the question in tags. Do not include or reference quoted content verbatim in the answer. Ensure that your answer is accurate and doesn't contain any information not directly supported by the quotes. Make references to quotes relevant to each section of the answer solely by adding their bracketed numbers at the end of relevant sentences\n",
"\n",
"Here is the question: (user question)\n",
"```\n",
"\n",
"And in return, Claude's response should look like this:\n",
"\n",
"\n",
"```xml\n",
"\n",
"Large numbers of people came to Ephesus in March and in the beginning of May to attend the main Artemis Procession.
\n",
"The Temple of Artemis or Artemision (Greek: Ἀρτεμίσιον; Turkish: Artemis Tapınağı), also known as the Temple of Diana, was a Greek temple dedicated to an ancient, local form of the goddess Artemis (identified with Diana, a Roman goddess).
\n",
"\n",
"\n",
"\n",
"According to the documents, people visited the Temple of Artemis in Ephesus primarily for religious reasons to attend festivals and processions dedicated to the goddess Artemis (also known as Diana). The temple was dedicated to an ancient local form of the goddess and held a main Artemis Procession in March and early May that drew large crowds. [1] The temple was considered an important religious site for the worship of Artemis in the Greek world. [2] People likely traveled there to make offerings, participate in rituals, and celebrate the goddess during major festivals and ceremonies held at the sacred site.\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GjD7l36pJtBU"
},
"source": [
"## Initialize RAG Pipeline Components\n",
"\n",
"Initialize components required for a RAG pipeline:\n",
"- [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder): to create embeddings using for the query using sentence-transformers models\n",
"- [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever): to retrieve relevant documents\n",
"- [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder): to construct prompts by processing `ChatMessage` objects\n",
"- [AnthropicChatGenerator](https://docs.haystack.deepset.ai/docs/anthropicchatgenerator): to use chat completion API of Anthropic Claude models, we'll use `claude-3-sonnet-20240229` for this example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "n3f6jp13Z102",
"outputId": "2616fed0-858d-416d-9f12-83565f85370f"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:haystack.components.builders.chat_prompt_builder:ChatPromptBuilder has 2 prompt variables, but `required_variables` is not set. By default, all prompt variables are treated as optional, which may lead to unintended behavior in multi-branch pipelines. To avoid unexpected execution, ensure that variables intended to be required are explicitly set in `required_variables`.\n"
]
}
],
"source": [
"from haystack.components.builders import ChatPromptBuilder\n",
"from haystack_integrations.components.generators.anthropic import AnthropicChatGenerator\n",
"from haystack.dataclasses import ChatMessage\n",
"from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
"from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
"\n",
"text_embedder = SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
"retriever = InMemoryEmbeddingRetriever(document_store)\n",
"\n",
"messages = [\n",
" ChatMessage.from_system(\"You are an expert who answers questions based on the given documents.\"),\n",
" ChatMessage.from_user(prompt),\n",
"]\n",
"\n",
"prompt_builder = ChatPromptBuilder(template=messages, required_variables=\"*\")\n",
"llm = AnthropicChatGenerator(model=\"claude-3-sonnet-20240229\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4yPHqvfOKA27"
},
"source": [
"## Build the RAG Pipeline\n",
"\n",
"Learn how to build a pipeline in [Creating Pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "csL2vR6RFMkY"
},
"outputs": [],
"source": [
"from haystack import Pipeline\n",
"\n",
"rag_with_quotes = Pipeline()\n",
"# Add components to your pipeline\n",
"rag_with_quotes.add_component(\"text_embedder\", text_embedder)\n",
"rag_with_quotes.add_component(\"retriever\", retriever)\n",
"rag_with_quotes.add_component(\"prompt_builder\", prompt_builder)\n",
"rag_with_quotes.add_component(\"llm\", llm)\n",
"\n",
"# Now, connect the components to each other\n",
"rag_with_quotes.connect(\"text_embedder.embedding\", \"retriever.query_embedding\")\n",
"rag_with_quotes.connect(\"retriever\", \"prompt_builder.documents\")\n",
"rag_with_quotes.connect(\"prompt_builder\", \"llm\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jj0yzGI0KDVH"
},
"source": [
"## Test Different Questions"
]
},
{
"cell_type": "code",
"execution_count": 18,
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"id": "6PypcBNWtMIo",
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"Batches: 0%| | 0/1 [00:00\n",
"Large numbers of people came to Ephesus in March and in the beginning of May to attend the main Artemis Procession.
\n",
"The fame of the Temple of Artemis was known in the Renaissance, as demonstrated in this imagined portrayal of the temple in a 16th-century hand-colored engraving by Martin Heemskerck.
\n",
"\n",
"\n",
"\n",
"People visited the Temple of Artemis primarily for religious purposes, to participate in the Artemis Procession held in March and May each year. [1] The Temple of Artemis was a famous and renowned structure, known even in the Renaissance period, attracting visitors from far and wide to witness its splendor and attend worship services and festivals dedicated to the goddess Artemis. [3] Its prominence as one of the Seven Wonders of the Ancient World also likely drew many visitors seeking to experience the architectural marvel.\n",
"\n"
]
}
],
"source": [
"print(result[\"llm\"][\"replies\"][0].text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wt_u4YSXDBu-"
},
"source": [
"### Extract the Quotes"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6YHiJ3kY5OUA",
"outputId": "2a5ff963-9052-4300-c686-7d62a8483a5b"
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"outputs": [
{
"data": {
"text/plain": [
"['Large numbers of people came to Ephesus in March and in the beginning of May to attend the main Artemis Procession.',\n",
" 'The fame of the Temple of Artemis was known in the Renaissance, as demonstrated in this imagined portrayal of the temple in a 16th-century hand-colored engraving by Martin Heemskerck.']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"\n",
"re.findall(r'([\\s\\S]*?)<\\/quote>', result[\"llm\"][\"replies\"][0].text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aQgtPnGSC8zJ"
},
"source": [
"### Extract the Answer"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xxO3Q8Ts6JQX",
"outputId": "ad296972-5733-45d6-adcc-c32663091b30"
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"outputs": [
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"['\\nPeople visited the Temple of Artemis primarily for religious purposes, to participate in the Artemis Procession held in March and May each year. [1] The Temple of Artemis was a famous and renowned structure, known even in the Renaissance period, attracting visitors from far and wide to witness its splendor and attend worship services and festivals dedicated to the goddess Artemis. [3] Its prominence as one of the Seven Wonders of the Ancient World also likely drew many visitors seeking to experience the architectural marvel.\\n']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"\n",
"re.findall(r'([\\s\\S]*?)<\\/answer>', result[\"llm\"][\"replies\"][0].text)"
]
}
],
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