mirror of https://github.com/hwchase17/langchain
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)pull/5708/head
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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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{
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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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"""Wrapper around Baseten deployed model API."""
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import logging
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from typing import Any, Dict, List, Mapping, Optional
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from pydantic import Field
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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logger = logging.getLogger(__name__)
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class Baseten(LLM):
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"""Use your Baseten models in Langchain
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To use, you should have the ``baseten`` python package installed,
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and run ``baseten.login()`` with your Baseten API key.
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The required ``model`` param can be either a model id or model
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version id. Using a model version ID will result in
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slightly faster invocation.
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Any other model parameters can also
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be passed in with the format input={model_param: value, ...}
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The Baseten model must accept a dictionary of input with the key
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"prompt" and return a dictionary with a key "data" which maps
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to a list of response strings.
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Example:
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.. code-block:: python
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from langchain.llms import Baseten
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my_model = Baseten(model="MODEL_ID")
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output = my_model("prompt")
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"""
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model: str
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input: Dict[str, Any] = Field(default_factory=dict)
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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**{"model_kwargs": self.model_kwargs},
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of model."""
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return "baseten"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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) -> str:
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"""Call to Baseten deployed model endpoint."""
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try:
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import baseten
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except ImportError as exc:
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raise ValueError(
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"Could not import Baseten Python package. "
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"Please install it with `pip install baseten`."
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) from exc
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# get the model and version
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try:
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model = baseten.deployed_model_version_id(self.model)
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response = model.predict({"prompt": prompt})
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except baseten.common.core.ApiError:
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model = baseten.deployed_model_id(self.model)
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response = model.predict({"prompt": prompt})
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return "".join(response)
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"""Test Baseten API wrapper."""
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import os
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import baseten
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import pytest
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from langchain.llms.baseten import Baseten
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@pytest.mark.requires(baseten)
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def test_baseten_call() -> None:
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"""Test valid call to Baseten."""
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baseten.login(os.environ["BASETEN_API_KEY"])
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llm = Baseten(model=os.environ["BASETEN_MODEL_ID"])
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output = llm("Say foo:")
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assert isinstance(output, str)
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