-
Notifications
You must be signed in to change notification settings - Fork 13.5k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
17 changed files
with
646 additions
and
335 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| # Baseten | ||
|
|
||
| Learn how to use LangChain with models deployed on Baseten. | ||
|
|
||
| ## Installation and setup | ||
|
|
||
| - Create a [Baseten](https://baseten.co) account and [API key](https://docs.baseten.co/settings/api-keys). | ||
| - Install the Baseten Python client with `pip install baseten` | ||
| - Use your API key to authenticate with `baseten login` | ||
|
|
||
| ## Invoking a model | ||
|
|
||
| Baseten integrates with LangChain through the LLM module, which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace. | ||
|
|
||
| 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). | ||
|
|
||
| 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). | ||
|
|
||
| ```python | ||
| from langchain.llms import Baseten | ||
|
|
||
| wizardlm = Baseten(model="MODEL_VERSION_ID", verbose=True) | ||
|
|
||
| wizardlm("What is the difference between a Wizard and a Sorcerer?") | ||
| ``` |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,196 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Baseten\n", | ||
| "\n", | ||
| "[Baseten](https://baseten.co) provides all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.\n", | ||
| "\n", | ||
| "This example demonstrates using Langchain with models deployed on Baseten." | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Setup\n", | ||
| "\n", | ||
| "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", | ||
| "\n", | ||
| "You'll also need to install the Baseten Python package:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install baseten" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import baseten\n", | ||
| "\n", | ||
| "baseten.login(\"YOUR_API_KEY\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Single model call\n", | ||
| "\n", | ||
| "First, you'll need to deploy a model to Baseten.\n", | ||
| "\n", | ||
| "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", | ||
| "\n", | ||
| "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)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain.llms import Baseten" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Load the model\n", | ||
| "wizardlm = Baseten(model=\"MODEL_VERSION_ID\", verbose=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Prompt the model\n", | ||
| "\n", | ||
| "wizardlm(\"What is the difference between a Wizard and a Sorcerer?\")" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Chained model calls\n", | ||
| "\n", | ||
| "We can chain together multiple calls to one or multiple models, which is the whole point of Langchain!\n", | ||
| "\n", | ||
| "This example uses WizardLM to plan a meal with an entree, three sides, and an alcoholic and non-alcoholic beverage pairing." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from langchain.chains import SimpleSequentialChain\n", | ||
| "from langchain import PromptTemplate, LLMChain" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Build the first link in the chain\n", | ||
| "\n", | ||
| "prompt = PromptTemplate(\n", | ||
| " input_variables=[\"cuisine\"],\n", | ||
| " template=\"Name a complex entree for a {cuisine} dinner. Respond with just the name of a single dish.\",\n", | ||
| ")\n", | ||
| "\n", | ||
| "link_one = LLMChain(llm=wizardlm, prompt=prompt)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Build the second link in the chain\n", | ||
| "\n", | ||
| "prompt = PromptTemplate(\n", | ||
| " input_variables=[\"entree\"],\n", | ||
| " template=\"What are three sides that would go with {entree}. Respond with only a list of the sides.\",\n", | ||
| ")\n", | ||
| "\n", | ||
| "link_two = LLMChain(llm=wizardlm, prompt=prompt)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Build the third link in the chain\n", | ||
| "\n", | ||
| "prompt = PromptTemplate(\n", | ||
| " input_variables=[\"sides\"],\n", | ||
| " 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", | ||
| ")\n", | ||
| "\n", | ||
| "link_three = LLMChain(llm=wizardlm, prompt=prompt)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# Run the full chain!\n", | ||
| "\n", | ||
| "menu_maker = SimpleSequentialChain(chains=[link_one, link_two, link_three], verbose=True)\n", | ||
| "menu_maker.run(\"South Indian\")" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.10.4" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
Oops, something went wrong.