mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
288 lines
6.5 KiB
Plaintext
288 lines
6.5 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5125a1e3",
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"metadata": {},
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"source": [
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"# Anthropic Functions\n",
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"\n",
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"This notebook shows how to use an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions."
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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": 1,
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"id": "378be79b",
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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/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",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain_experimental.llms.anthropic_functions import AnthropicFunctions"
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]
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},
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{
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"cell_type": "markdown",
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"id": "65499965",
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"metadata": {},
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"source": [
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"## Initialize Model\n",
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"\n",
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"You can initialize this wrapper the same way you'd initialize ChatAnthropic"
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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": "e1d535f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = AnthropicFunctions(model='claude-2')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fcc9eaf4",
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"metadata": {},
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"source": [
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"## Passing in functions\n",
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"\n",
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"You can now pass in functions in a similar way"
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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": "0779c320",
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"metadata": {},
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"outputs": [],
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"source": [
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"functions=[\n",
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" {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather in a given location\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city and state, e.g. San Francisco, CA\"\n",
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" },\n",
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" \"unit\": {\n",
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" \"type\": \"string\",\n",
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" \"enum\": [\"celsius\", \"fahrenheit\"]\n",
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" }\n",
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" },\n",
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" \"required\": [\"location\"]\n",
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" }\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": "code",
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"execution_count": 5,
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"id": "ad75a933",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.schema import HumanMessage"
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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": "fc703085",
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"metadata": {},
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"outputs": [],
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"source": [
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"response = model.predict_messages(\n",
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" [HumanMessage(content=\"whats the weater in boston?\")], \n",
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" functions=functions\n",
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")"
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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,
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"id": "04d7936a",
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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=' ', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\", \"unit\": \"fahrenheit\"}'}}, example=False)"
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]
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},
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"execution_count": 7,
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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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"response"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0072fdba",
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"metadata": {},
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"source": [
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"## Using for extraction\n",
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"\n",
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"You can now use this for extraction."
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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": 8,
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"id": "7af5c567",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import create_extraction_chain\n",
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"schema = {\n",
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" \"properties\": {\n",
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" \"name\": {\"type\": \"string\"},\n",
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" \"height\": {\"type\": \"integer\"},\n",
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" \"hair_color\": {\"type\": \"string\"},\n",
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" },\n",
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" \"required\": [\"name\", \"height\"],\n",
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"}\n",
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"inp = \"\"\"\n",
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"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",
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" \"\"\""
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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": 9,
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"id": "bd01082a",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_extraction_chain(schema, model)"
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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": 10,
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"id": "b5a23e9f",
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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': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
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" {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
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]
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},
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"execution_count": 10,
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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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"chain.run(inp)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "90ec959e",
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"metadata": {},
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"source": [
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"## Using for tagging\n",
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"\n",
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"You can now use this for tagging"
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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": 11,
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"id": "03c1eb0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import create_tagging_chain"
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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": 12,
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"id": "581c0ece",
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"metadata": {},
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"outputs": [],
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"source": [
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"schema = {\n",
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" \"properties\": {\n",
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" \"sentiment\": {\"type\": \"string\"},\n",
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" \"aggressiveness\": {\"type\": \"integer\"},\n",
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" \"language\": {\"type\": \"string\"},\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": "code",
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"execution_count": 14,
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"id": "d9a8570e",
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"metadata": {},
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"outputs": [],
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"source": [
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"chain = create_tagging_chain(schema, model)"
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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": 15,
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"id": "cf37d679",
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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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"{'sentiment': 'positive', 'aggressiveness': '0', 'language': 'english'}"
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]
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},
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"execution_count": 15,
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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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"chain.run(\"this is really cool\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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