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Baseten integration (#5862)
This PR adds a Baseten integration. I've done my best to follow the contributor's guidelines and add docs, an example notebook, and an integration test modeled after similar integrations' test. Please let me know if there is anything I can do to improve the PR. When it is merged, please tag https://twitter.com/basetenco and https://twitter.com/philip_kiely as contributors (the note on the PR template said to include Twitter accounts)
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Philip Kiely - Baseten
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docs/ecosystem/baseten.md
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docs/ecosystem/baseten.md
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# Baseten
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Learn how to use LangChain with models deployed on Baseten.
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## Installation and setup
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- Create a [Baseten](https://baseten.co) account and [API key](https://docs.baseten.co/settings/api-keys).
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- Install the Baseten Python client with `pip install baseten`
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- Use your API key to authenticate with `baseten login`
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## Invoking a model
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Baseten integrates with LangChain through the LLM module, which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace.
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You can deploy foundation models like WizardLM and Alpaca with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with this tutorial](https://docs.baseten.co/deploying-models/deploy).
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In this example, we'll work with WizardLM. [Deploy WizardLM here](https://app.baseten.co/explore/wizardlm) and follow along with the deployed [model's version ID](https://docs.baseten.co/managing-models/manage).
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```python
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from langchain.llms import Baseten
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wizardlm = Baseten(model="MODEL_VERSION_ID", verbose=True)
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wizardlm("What is the difference between a Wizard and a Sorcerer?")
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```
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docs/modules/models/llms/integrations/baseten.ipynb
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docs/modules/models/llms/integrations/baseten.ipynb
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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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"# Baseten\n",
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"\n",
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"[Baseten](https://baseten.co) provides all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.\n",
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"\n",
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"This example demonstrates using Langchain with models deployed on Baseten."
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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 [Baseten account](https://baseten.co) and an [API key](https://docs.baseten.co/settings/api-keys).\n",
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"\n",
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"You'll also need to install the Baseten Python package:"
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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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"outputs": [],
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"source": [
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"!pip install baseten"
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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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"outputs": [],
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"source": [
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"import baseten\n",
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"\n",
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"baseten.login(\"YOUR_API_KEY\")"
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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\n",
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"\n",
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"First, you'll need to deploy a model to Baseten.\n",
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"\n",
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"You can deploy foundation models like WizardLM and Alpaca with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with this tutorial](https://docs.baseten.co/deploying-models/deploy).\n",
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"\n",
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"In this example, we'll work with WizardLM. [Deploy WizardLM here](https://app.baseten.co/explore/llama) and follow along with the deployed [model's version ID](https://docs.baseten.co/managing-models/manage)."
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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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"outputs": [],
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"source": [
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"from langchain.llms import Baseten"
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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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"outputs": [],
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"source": [
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"# Load the model\n",
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"wizardlm = Baseten(model=\"MODEL_VERSION_ID\", verbose=True)"
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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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"outputs": [],
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"source": [
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"# Prompt the model\n",
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"\n",
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"wizardlm(\"What is the difference between a Wizard and a Sorcerer?\")"
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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\n",
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"\n",
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"We can chain together multiple calls to one or multiple models, which is the whole point of Langchain!\n",
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"\n",
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"This example uses WizardLM to plan a meal with an entree, three sides, and an alcoholic and non-alcoholic beverage pairing."
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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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"outputs": [],
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"source": [
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"from langchain.chains import SimpleSequentialChain\n",
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"from langchain import PromptTemplate, LLMChain"
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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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"outputs": [],
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"source": [
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"# Build the first link in the chain\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"cuisine\"],\n",
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" template=\"Name a complex entree for a {cuisine} dinner. Respond with just the name of a single dish.\",\n",
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")\n",
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"\n",
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"link_one = LLMChain(llm=wizardlm, prompt=prompt)"
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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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"outputs": [],
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"source": [
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"# Build the second link in the chain\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"entree\"],\n",
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" template=\"What are three sides that would go with {entree}. Respond with only a list of the sides.\",\n",
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")\n",
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"\n",
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"link_two = LLMChain(llm=wizardlm, prompt=prompt)"
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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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"outputs": [],
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"source": [
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"# Build the third link in the chain\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" input_variables=[\"sides\"],\n",
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" template=\"What is one alcoholic and one non-alcoholic beverage that would go well with this list of sides: {sides}. Respond with only the names of the beverages.\",\n",
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")\n",
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"\n",
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"link_three = LLMChain(llm=wizardlm, prompt=prompt)"
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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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"outputs": [],
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"source": [
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"# Run the full chain!\n",
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"\n",
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"menu_maker = SimpleSequentialChain(chains=[link_one, link_two, link_three], verbose=True)\n",
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"menu_maker.run(\"South Indian\")"
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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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