langchain/docs/extras/integrations/chat/anthropic_functions.ipynb
Harrison Chase 8f14ddefdf
add anthropic functions wrapper (#8475)
a cheeky wrapper around claude that adds in function calling support
(kind of, hence it going in experimental)
2023-07-30 07:23:46 -07:00

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6.5 KiB
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{
"cells": [
{
"cell_type": "markdown",
"id": "5125a1e3",
"metadata": {},
"source": [
"# Anthropic Functions\n",
"\n",
"This notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "378be79b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
" warnings.warn(\n"
]
}
],
"source": [
"from langchain_experimental.llms.anthropic_functions import AnthropicFunctions"
]
},
{
"cell_type": "markdown",
"id": "65499965",
"metadata": {},
"source": [
"## Initialize Model\n",
"\n",
"You can initialize this wrapper the same way you'd initialize ChatAnthropic"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e1d535f6",
"metadata": {},
"outputs": [],
"source": [
"model = AnthropicFunctions(model='claude-2')"
]
},
{
"cell_type": "markdown",
"id": "fcc9eaf4",
"metadata": {},
"source": [
"## Passing in functions\n",
"\n",
"You can now pass in functions in a similar way"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0779c320",
"metadata": {},
"outputs": [],
"source": [
"functions=[\n",
" {\n",
" \"name\": \"get_current_weather\",\n",
" \"description\": \"Get the current weather in a given location\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city and state, e.g. San Francisco, CA\"\n",
" },\n",
" \"unit\": {\n",
" \"type\": \"string\",\n",
" \"enum\": [\"celsius\", \"fahrenheit\"]\n",
" }\n",
" },\n",
" \"required\": [\"location\"]\n",
" }\n",
" }\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ad75a933",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fc703085",
"metadata": {},
"outputs": [],
"source": [
"response = model.predict_messages(\n",
" [HumanMessage(content=\"whats the weater in boston?\")], \n",
" functions=functions\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "04d7936a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=' ', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\", \"unit\": \"fahrenheit\"}'}}, example=False)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"response"
]
},
{
"cell_type": "markdown",
"id": "0072fdba",
"metadata": {},
"source": [
"## Using for extraction\n",
"\n",
"You can now use this for extraction."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7af5c567",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_extraction_chain\n",
"schema = {\n",
" \"properties\": {\n",
" \"name\": {\"type\": \"string\"},\n",
" \"height\": {\"type\": \"integer\"},\n",
" \"hair_color\": {\"type\": \"string\"},\n",
" },\n",
" \"required\": [\"name\", \"height\"],\n",
"}\n",
"inp = \"\"\"\n",
"Alex is 5 feet tall. Claudia is 1 feet taller Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bd01082a",
"metadata": {},
"outputs": [],
"source": [
"chain = create_extraction_chain(schema, model)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b5a23e9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
" {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(inp)"
]
},
{
"cell_type": "markdown",
"id": "90ec959e",
"metadata": {},
"source": [
"## Using for tagging\n",
"\n",
"You can now use this for tagging"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "03c1eb0d",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import create_tagging_chain"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "581c0ece",
"metadata": {},
"outputs": [],
"source": [
"schema = {\n",
" \"properties\": {\n",
" \"sentiment\": {\"type\": \"string\"},\n",
" \"aggressiveness\": {\"type\": \"integer\"},\n",
" \"language\": {\"type\": \"string\"},\n",
" }\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d9a8570e",
"metadata": {},
"outputs": [],
"source": [
"chain = create_tagging_chain(schema, model)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "cf37d679",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'sentiment': 'positive', 'aggressiveness': '0', 'language': 'english'}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.run(\"this is really cool\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}