mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
177 lines
3.7 KiB
Plaintext
177 lines
3.7 KiB
Plaintext
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Minimax\n",
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"\n",
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"[Minimax](https://api.minimax.chat) is a Chinese startup that provides natural language processing models for companies and individuals.\n",
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"\n",
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"This example demonstrates using Langchain to interact with Minimax."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup\n",
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"\n",
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"To run this notebook, you'll need a [Minimax account](https://api.minimax.chat), an [API key](https://api.minimax.chat/user-center/basic-information/interface-key), and a [Group ID](https://api.minimax.chat/user-center/basic-information)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Single model call"
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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": 33,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import Minimax"
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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": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the model\n",
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"minimax = Minimax(minimax_api_key=\"YOUR_API_KEY\", minimax_group_id=\"YOUR_GROUP_ID\")"
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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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"metadata": {
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"pycharm": {
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"is_executing": true
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}
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},
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"outputs": [],
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"source": [
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"# Prompt the model\n",
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"minimax(\"What is the difference between panda and bear?\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Chained model calls"
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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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"outputs": [],
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"source": [
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"# get api_key and group_id: https://api.minimax.chat/user-center/basic-information\n",
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"# We need `MINIMAX_API_KEY` and `MINIMAX_GROUP_ID`\n",
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"\n",
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"import os\n",
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"\n",
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"os.environ[\"MINIMAX_API_KEY\"] = \"YOUR_API_KEY\"\n",
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"os.environ[\"MINIMAX_GROUP_ID\"] = \"YOUR_GROUP_ID\""
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"from langchain.llms import Minimax\n",
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"from langchain import PromptTemplate, LLMChain"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"llm = Minimax()"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [
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"question = \"What NBA team won the Championship in the year Jay Zhou was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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],
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"metadata": {
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"collapsed": false
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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": ".venv",
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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": "3.10.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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