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679 lines
51 KiB
Plaintext
679 lines
51 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "a016701c",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Anthropic\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# ChatAnthropic\n",
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"\n",
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"This notebook covers how to get started with Anthropic chat models.\n",
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"\n",
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"## Setup\n",
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"\n",
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"For setup instructions, please see the Installation and Environment Setup sections of the [Anthropic Platform page](/docs/integrations/platforms/anthropic.mdx)."
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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": "91be2e12",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU langchain-anthropic"
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]
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},
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{
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"cell_type": "markdown",
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"id": "584ed5ec",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"\n",
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"We'll need to get an [Anthropic](https://console.anthropic.com/settings/keys) API key and set the `ANTHROPIC_API_KEY` environment variable:"
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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": 2,
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"id": "01578ae3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from getpass import getpass\n",
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"\n",
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"os.environ[\"ANTHROPIC_API_KEY\"] = getpass()"
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]
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},
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{
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"cell_type": "markdown",
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|
"id": "d1f9df276476f0bc",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"source": [
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"The code provided assumes that your ANTHROPIC_API_KEY is set in your environment variables. If you would like to manually specify your API key and also choose a different model, you can use the following code:\n",
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"```python\n",
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"chat = ChatAnthropic(temperature=0, api_key=\"YOUR_API_KEY\", model_name=\"claude-3-opus-20240229\")\n",
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"\n",
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"```\n",
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"\n",
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"In these demos, we will use the Claude 3 Opus model, and you can also use the launch version of the Sonnet model with `claude-3-sonnet-20240229`.\n",
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"\n",
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"You can check the model comparison doc [here](https://docs.anthropic.com/claude/docs/models-overview#model-comparison)."
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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": 2,
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"id": "238bdbaa-526a-4130-89e9-523aa44bb196",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_anthropic import ChatAnthropic\n",
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"from langchain_core.prompts import ChatPromptTemplate"
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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": 5,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:07.274418Z",
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"start_time": "2024-01-19T11:25:05.898031Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='저는 파이썬을 사랑합니다.\\n\\nTranslation:\\nI love Python.')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = ChatAnthropic(temperature=0, model_name=\"claude-3-opus-20240229\")\n",
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"\n",
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"system = (\n",
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" \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
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")\n",
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"human = \"{text}\"\n",
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"prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
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"\n",
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"chain = prompt | chat\n",
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"chain.invoke(\n",
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" {\n",
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" \"input_language\": \"English\",\n",
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" \"output_language\": \"Korean\",\n",
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" \"text\": \"I love Python\",\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": "markdown",
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"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
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"metadata": {},
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"source": [
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"## `ChatAnthropic` also supports async and streaming functionality:"
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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": 5,
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"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:10.448733Z",
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"start_time": "2024-01-19T11:25:08.866277Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='Sure, here\\'s a joke about a bear:\\n\\nA bear walks into a bar and says to the bartender, \"I\\'ll have a pint of beer and a.......... packet of peanuts.\"\\n\\nThe bartender asks, \"Why the big pause?\"\\n\\nThe bear replies, \"I don\\'t know, I\\'ve always had them!\"')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = ChatAnthropic(temperature=0, model_name=\"claude-3-opus-20240229\")\n",
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"prompt = ChatPromptTemplate.from_messages([(\"human\", \"Tell me a joke about {topic}\")])\n",
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"chain = prompt | chat\n",
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"await chain.ainvoke({\"topic\": \"bear\"})"
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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": 6,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-01-19T11:25:24.438696Z",
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"start_time": "2024-01-19T11:25:14.687480Z"
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},
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"tags": []
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},
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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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"Here is a list of famous tourist attractions in Japan:\n",
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"\n",
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"1. Tokyo Skytree (Tokyo)\n",
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"2. Senso-ji Temple (Tokyo)\n",
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"3. Meiji Shrine (Tokyo)\n",
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"4. Tokyo DisneySea (Urayasu, Chiba)\n",
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"5. Fushimi Inari Taisha (Kyoto)\n",
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"6. Kinkaku-ji (Golden Pavilion) (Kyoto)\n",
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"7. Kiyomizu-dera (Kyoto)\n",
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"8. Nijo Castle (Kyoto)\n",
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"9. Osaka Castle (Osaka)\n",
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"10. Dotonbori (Osaka)\n",
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"11. Hiroshima Peace Memorial Park (Hiroshima)\n",
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"12. Itsukushima Shrine (Miyajima Island, Hiroshima)\n",
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"13. Himeji Castle (Himeji)\n",
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"14. Todai-ji Temple (Nara)\n",
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"15. Nara Park (Nara)\n",
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"16. Mount Fuji (Shizuoka and Yamanashi Prefectures)\n",
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"17."
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]
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}
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],
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"source": [
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"chat = ChatAnthropic(temperature=0.3, model_name=\"claude-3-opus-20240229\")\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [(\"human\", \"Give me a list of famous tourist attractions in Japan\")]\n",
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")\n",
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"chain = prompt | chat\n",
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"for chunk in chain.stream({}):\n",
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" print(chunk.content, end=\"\", flush=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ab0174d8-7140-413c-80a9-7cf3a8b81bb4",
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"metadata": {},
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"source": [
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"## [Beta] Tool-calling\n",
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"\n",
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"With Anthropic's [tool-calling, or tool-use, API](https://docs.anthropic.com/claude/docs/functions-external-tools), you can define tools for the model to invoke. This is extremely useful for building tool-using chains and agents, as well as for getting structured outputs from a model.\n",
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"\n",
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":::note\n",
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"\n",
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"Anthropic's tool-calling functionality is still in beta.\n",
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"\n",
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":::\n",
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"\n",
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"### bind_tools()\n",
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"\n",
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"With `ChatAnthropic.bind_tools`, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. Under the hood these are converted to an Anthropic tool schemas, which looks like:\n",
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"```\n",
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"{\n",
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" \"name\": \"...\",\n",
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" \"description\": \"...\",\n",
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" \"input_schema\": {...} # JSONSchema\n",
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"}\n",
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"```\n",
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"and passed in every model invocation."
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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": 3,
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|
"id": "42f87466-cb8e-490d-a9f8-aa0f8e9b4217",
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"metadata": {},
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"outputs": [
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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/bagatur/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: The function `bind_tools` is in beta. It is actively being worked on, so the API may change.\n",
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" warn_beta(\n"
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]
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}
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],
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"source": [
|
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"from langchain_core.pydantic_v1 import BaseModel, Field\n",
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"\n",
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"llm = ChatAnthropic(model=\"claude-3-opus-20240229\", temperature=0)\n",
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"\n",
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"\n",
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"class GetWeather(BaseModel):\n",
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" \"\"\"Get the current weather in a given location\"\"\"\n",
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"\n",
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" location: str = Field(..., description=\"The city and state, e.g. San Francisco, CA\")\n",
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"\n",
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"\n",
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"llm_with_tools = llm.bind_tools([GetWeather])"
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]
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},
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{
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"cell_type": "code",
|
|
"execution_count": 4,
|
|
"id": "997be6ff-3fd3-4b1c-b7e3-2e5fed4ac964",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=[{'text': '<thinking>\\nThe user is asking about the current weather in a specific location, San Francisco. The relevant tool to answer this is the GetWeather function.\\n\\nLooking at the parameters for GetWeather:\\n- location (required): The user directly provided the location in the query - \"San Francisco\"\\n\\nSince the required \"location\" parameter is present, we can proceed with calling the GetWeather function.\\n</thinking>', 'type': 'text'}, {'id': 'toolu_01StzxdWQSZhAMbR1CCchQV9', 'input': {'location': 'San Francisco, CA'}, 'name': 'GetWeather', 'type': 'tool_use'}], response_metadata={'id': 'msg_01HepCTzqXJed5iNuLgV1VCZ', 'model': 'claude-3-opus-20240229', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 487, 'output_tokens': 143}}, id='run-1a1b3289-ba2c-47ae-8be1-8929d7cc547e-0', tool_calls=[{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'toolu_01StzxdWQSZhAMbR1CCchQV9'}])"
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]
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},
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"execution_count": 4,
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"metadata": {},
|
|
"output_type": "execute_result"
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}
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],
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"source": [
|
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"ai_msg = llm_with_tools.invoke(\n",
|
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" \"what is the weather like in San Francisco\",\n",
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")\n",
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"ai_msg"
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]
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},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "1e63ac67-8c42-4468-8178-e54f13c3c5c3",
|
|
"metadata": {},
|
|
"source": [
|
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"Notice that the output message content is a list that contains a text block and then a tool_use block:"
|
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]
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},
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{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "7c4cd4c4-1c78-4d6c-8607-759e32a8903b",
|
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"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[{'text': '<thinking>\\nThe user is asking about the current weather in a specific location, San Francisco. The relevant tool to answer this is the GetWeather function.\\n\\nLooking at the parameters for GetWeather:\\n- location (required): The user directly provided the location in the query - \"San Francisco\"\\n\\nSince the required \"location\" parameter is present, we can proceed with calling the GetWeather function.\\n</thinking>',\n",
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" 'type': 'text'},\n",
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" {'id': 'toolu_01StzxdWQSZhAMbR1CCchQV9',\n",
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" 'input': {'location': 'San Francisco, CA'},\n",
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" 'name': 'GetWeather',\n",
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" 'type': 'tool_use'}]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ai_msg.content"
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]
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},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "d446bd0f-06cc-4aa6-945d-74335d5a8780",
|
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"metadata": {},
|
|
"source": [
|
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"Crucially, the tool calls are also extracted into the `tool_calls` where they are in a standardized, model-agnostic format:"
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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": 7,
|
|
"id": "e36f254e-bb89-4978-9351-a463b13eb3c7",
|
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"metadata": {},
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"outputs": [
|
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{
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"data": {
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"text/plain": [
|
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"[{'name': 'GetWeather',\n",
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" 'args': {'location': 'San Francisco, CA'},\n",
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" 'id': 'toolu_01StzxdWQSZhAMbR1CCchQV9'}]"
|
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]
|
|
},
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
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"source": [
|
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"ai_msg.tool_calls"
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]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "90e015e0-c6e5-4ff5-8fb9-be0cd3c86395",
|
|
"metadata": {},
|
|
"source": [
|
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"::: {.callout-tip}\n",
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"\n",
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"ChatAnthropic model outputs are always a single AI message that can have either a single string or a list of content blocks. The content blocks can be text blocks or tool-duse blocks. There can be multiple of each and they can be interspersed.\n",
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"\n",
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":::"
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]
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},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "8652ee98-814c-4ed6-9def-275eeaa9651e",
|
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"metadata": {},
|
|
"source": [
|
|
"### Parsing tool calls\n",
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"\n",
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"The `langchain_anthropic.output_parsers.ToolsOutputParser` makes it easy to parse the tool calls from an Anthropic AI message into Pydantic objects if we'd like:"
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]
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},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
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|
"id": "59c175b1-0929-4ed4-a608-f0006031a3c2",
|
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"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
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"from langchain_anthropic.output_parsers import ToolsOutputParser"
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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": 16,
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|
"id": "08f6c62c-923b-400e-9bc8-8aff417466b2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[GetWeather(location='New York City, NY'),\n",
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" GetWeather(location='Los Angeles, CA'),\n",
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" GetWeather(location='San Francisco, CA'),\n",
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" GetWeather(location='Cleveland, OH')]"
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]
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|
},
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|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
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"parser = ToolsOutputParser(pydantic_schemas=[GetWeather])\n",
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"chain = llm_with_tools | parser\n",
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"chain.invoke(\"What is the weather like in nyc, la, sf and cleveland\")"
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]
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},
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|
{
|
|
"cell_type": "markdown",
|
|
"id": "ab05dd51-0a9e-4b7b-b182-65cec44941ac",
|
|
"metadata": {},
|
|
"source": [
|
|
"### with_structured_output()\n",
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"\n",
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"The [BaseChatModel.with_structured_output interface](/docs/modules/model_io/chat/structured_output) makes it easy to get structured output from chat models. You can use `ChatAnthropic.with_structured_output`, which uses tool-calling under the hood), to get the model to more reliably return an output in a specific format:"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "e047b831-2338-4c2d-9ee4-0763f74e80e1",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"GetWeather(location='San Francisco, CA')"
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|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
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"structured_llm = llm.with_structured_output(GetWeather)\n",
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"structured_llm.invoke(\n",
|
|
" \"what is the weather like in San Francisco\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2d74b83e-bcd3-47e6-911e-82b5dcfbd20e",
|
|
"metadata": {},
|
|
"source": [
|
|
"The main difference between using \n",
|
|
"```python\n",
|
|
"llm.with_structured_output(GetWeather)\n",
|
|
"``` \n",
|
|
"vs \n",
|
|
"\n",
|
|
"```python\n",
|
|
"llm.bind_tools([GetWeather]) | ToolsOutputParser(pydantic_schemas=[GetWeather])\n",
|
|
"``` \n",
|
|
"is that it will return only the first GetWeather call, whereas the second approach will return a list."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5b61884e-3e4e-4145-b10d-188987ae1eb6",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Passing tool results to model\n",
|
|
"\n",
|
|
"We can use `ToolMessage`s with the appropriate `tool_call_id`s to pass tool results back to the model:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "9d07a1c1-4542-440e-a1fb-392542267fb8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"AIMessage(content='Based on calling the GetWeather function, the weather in San Francisco, CA is:\\nRain with a high temperature of 54°F and winds from the southwest at 15-25 mph. There is a 100% chance of rain.', response_metadata={'id': 'msg_01J7nWVRPPTgae4eDpf9yR3M', 'model': 'claude-3-opus-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 670, 'output_tokens': 56}}, id='run-44fcd34f-9c24-464f-94dd-63bd0d22870d-0')"
|
|
]
|
|
},
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain_core.messages import AIMessage, HumanMessage, ToolMessage\n",
|
|
"\n",
|
|
"messages = [\n",
|
|
" HumanMessage(\"What is the weather like in San Francisco\"),\n",
|
|
" AIMessage(\n",
|
|
" content=[\n",
|
|
" {\n",
|
|
" \"text\": '<thinking>\\nBased on the user\\'s question, the relevant function to call is GetWeather, which requires the \"location\" parameter.\\n\\nThe user has directly specified the location as \"San Francisco\". Since San Francisco is a well known city, I can reasonably infer they mean San Francisco, CA without needing the state specified.\\n\\nAll the required parameters are provided, so I can proceed with the API call.\\n</thinking>',\n",
|
|
" \"type\": \"text\",\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"type\": \"tool_use\",\n",
|
|
" \"id\": \"toolu_01SCgExKzQ7eqSkMHfygvYuu\",\n",
|
|
" \"name\": \"GetWeather\",\n",
|
|
" \"input\": {\"location\": \"San Francisco, CA\"},\n",
|
|
" \"text\": None,\n",
|
|
" },\n",
|
|
" ],\n",
|
|
" ),\n",
|
|
" ToolMessage(\n",
|
|
" \"Rain. High 54F. Winds SW at 15 to 25 mph. Chance of rain 100%.\",\n",
|
|
" tool_call_id=\"toolu_01SCgExKzQ7eqSkMHfygvYuu\",\n",
|
|
" ),\n",
|
|
"]\n",
|
|
"llm_with_tools.invoke(messages)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1c82d198-77ce-4d5a-a65b-a98fd3c10740",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Streaming\n",
|
|
"\n",
|
|
"::: {.callout-warning}\n",
|
|
"\n",
|
|
"Anthropic does not currently support streaming tool calls. Attempting to stream will yield a single final message.\n",
|
|
"\n",
|
|
":::"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "d1284ddc-eb82-44be-b034-5046809536de",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/Users/bagatur/langchain/libs/partners/anthropic/langchain_anthropic/chat_models.py:328: UserWarning: stream: Tool use is not yet supported in streaming mode.\n",
|
|
" warnings.warn(\"stream: Tool use is not yet supported in streaming mode.\")\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[AIMessage(content=[{'text': '<thinking>\\nThe user is asking for the current weather in a specific location, San Francisco. The GetWeather function is the relevant tool to answer this request, as it returns the current weather for a given location.\\n\\nThe GetWeather function has one required parameter:\\nlocation: The city and state, e.g. San Francisco, CA\\n\\nThe user provided the city San Francisco in their request. They did not specify the state, but it can be reasonably inferred that they are referring to San Francisco, California since that is the most well known city with that name.\\n\\nSince the required location parameter has been provided by the user, we can proceed with calling the GetWeather function.\\n</thinking>', 'type': 'text'}, {'text': None, 'type': 'tool_use', 'id': 'toolu_01V9ZripoQzuY8HubspJy6fP', 'name': 'GetWeather', 'input': {'location': 'San Francisco, CA'}}], id='run-b825206b-5b6b-48bc-ad8d-802dee310c7f')]"
|
|
]
|
|
},
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"list(llm_with_tools.stream(\"What's the weather in san francisco\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "70d5e0fb",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Multimodal\n",
|
|
"\n",
|
|
"Anthropic's Claude-3 models are compatible with both image and text inputs. You can use this as follows:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "3e9d1ab5",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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\"/>"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.HTML object>"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# open ../../../static/img/brand/wordmark.png as base64 str\n",
|
|
"import base64\n",
|
|
"from pathlib import Path\n",
|
|
"\n",
|
|
"from IPython.display import HTML\n",
|
|
"\n",
|
|
"img_path = Path(\"../../../static/img/brand/wordmark.png\")\n",
|
|
"img_base64 = base64.b64encode(img_path.read_bytes()).decode(\"utf-8\")\n",
|
|
"\n",
|
|
"# display b64 image in notebook\n",
|
|
"HTML(f'<img src=\"data:image/png;base64,{img_base64}\"/>')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "b6bb2aa2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"AIMessage(content='This logo is for LangChain, which appears to be some kind of software or technology platform based on the name and minimalist design style of the logo featuring a silhouette of a bird (likely an eagle or hawk) and the company name in a simple, modern font.')"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from langchain_core.messages import HumanMessage\n",
|
|
"\n",
|
|
"chat = ChatAnthropic(model=\"claude-3-opus-20240229\")\n",
|
|
"messages = [\n",
|
|
" HumanMessage(\n",
|
|
" content=[\n",
|
|
" {\n",
|
|
" \"type\": \"image_url\",\n",
|
|
" \"image_url\": {\n",
|
|
" # langchain logo\n",
|
|
" \"url\": f\"data:image/png;base64,{img_base64}\", # noqa: E501\n",
|
|
" },\n",
|
|
" },\n",
|
|
" {\"type\": \"text\", \"text\": \"What is this logo for?\"},\n",
|
|
" ]\n",
|
|
" )\n",
|
|
"]\n",
|
|
"chat.invoke(messages)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|