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community: refactor Baseten integration with new API endpoints & docs #15017

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community: refactor Baseten integration with new API endpoints & docs #15017

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7edd5a9
Refactor Baseten <> LangChain integration
philipkiely-baseten Dec 13, 2023
97798f5
llm kwargs
philipkiely-baseten Dec 15, 2023
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review feedback
philipkiely-baseten Dec 21, 2023
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120 changes: 56 additions & 64 deletions docs/docs/integrations/llms/baseten.ipynb
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Expand Up @@ -7,9 +7,9 @@
"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",
"[Baseten](https://baseten.co) is a [Provider](https://python.langchain.com/docs/integrations/providers/baseten) in the LangChain ecosystem that implements the LLMs component.\n",
"\n",
"This example demonstrates using Langchain with models deployed on Baseten."
"This example demonstrates using an LLM — Mistral 7B hosted on Baseten — with LangChain."
]
},
{
Expand All @@ -19,29 +19,16 @@
"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",
"To run this example, you'll need:\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",
"* A [Baseten account](https://baseten.co)\n",
"* An [API key](https://docs.baseten.co/observability/api-keys)\n",
"\n",
"Export your API key to your as an environment variable called `BASETEN_API_KEY`.\n",
"\n",
"baseten.login(\"YOUR_API_KEY\")"
"```sh\n",
"export BASETEN_API_KEY=\"paste_your_api_key_here\"\n",
"```"
]
},
{
Expand All @@ -53,9 +40,9 @@
"\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",
"You can deploy foundation models like Mistral and Llama 2 with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with Truss](https://truss.baseten.co/welcome).\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)."
"In this example, we'll work with Mistral 7B. [Deploy Mistral 7B here](https://app.baseten.co/explore/mistral_7b_instruct) and follow along with the deployed model's ID, found in the model dashboard."
]
},
{
Expand All @@ -64,7 +51,7 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import Baseten"
"from langchain_community.llms import Baseten"
]
},
{
Expand All @@ -74,7 +61,7 @@
"outputs": [],
"source": [
"# Load the model\n",
"wizardlm = Baseten(model=\"MODEL_VERSION_ID\", verbose=True)"
"mistral = Baseten(model=\"MODEL_ID\", deployment=\"production\")"
]
},
{
Expand All @@ -84,8 +71,7 @@
"outputs": [],
"source": [
"# Prompt the model\n",
"\n",
"wizardlm(\"What is the difference between a Wizard and a Sorcerer?\")"
"mistral(\"What is the Mistral wind?\")"
]
},
{
Expand All @@ -97,7 +83,7 @@
"\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."
"For example, we can replace GPT with Mistral in this [demo of terminal emulation](https://python.langchain.com/docs/modules/agents/how_to/chatgpt_clone)."
]
},
{
Expand All @@ -106,8 +92,37 @@
"metadata": {},
"outputs": [],
"source": [
"from langchain.chains import LLMChain, SimpleSequentialChain\n",
"from langchain.prompts import PromptTemplate"
"from langchain.chains import LLMChain\n",
"from langchain.memory import ConversationBufferWindowMemory\n",
"from langchain.prompts import PromptTemplate\n",
"\n",
"template = \"\"\"Assistant is a large language model trained by OpenAI.\n",
"\n",
"Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.\n",
"\n",
"Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.\n",
"\n",
"Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.\n",
"\n",
"{history}\n",
"Human: {human_input}\n",
"Assistant:\"\"\"\n",
"\n",
"prompt = PromptTemplate(input_variables=[\"history\", \"human_input\"], template=template)\n",
"\n",
"\n",
"chatgpt_chain = LLMChain(\n",
" llm=mistral,\n",
" llm_kwargs={\"max_length\": 4096},\n",
" prompt=prompt,\n",
" verbose=True,\n",
" memory=ConversationBufferWindowMemory(k=2),\n",
")\n",
"\n",
"output = chatgpt_chain.predict(\n",
" human_input=\"I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd.\"\n",
")\n",
"print(output)"
]
},
{
Expand All @@ -116,14 +131,8 @@
"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)"
"output = chatgpt_chain.predict(human_input=\"ls ~\")\n",
"print(output)"
]
},
{
Expand All @@ -132,14 +141,8 @@
"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)"
"output = chatgpt_chain.predict(human_input=\"cd ~\")\n",
"print(output)"
]
},
{
Expand All @@ -148,28 +151,17 @@
"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",
"output = chatgpt_chain.predict(\n",
" human_input=\"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\"\n",
")\n",
"\n",
"link_three = LLMChain(llm=wizardlm, prompt=prompt)"
"print(output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"# Run the full chain!\n",
"\n",
"menu_maker = SimpleSequentialChain(\n",
" chains=[link_one, link_two, link_three], verbose=True\n",
")\n",
"menu_maker.run(\"South Indian\")"
"As we can see from the final example, which outputs a number that may or may not be correct, the model is only approximating likely terminal output, not actually executing provided commands. Still, the example demonstrates Mistral's ample context window, code generation capabilities, and ability to stay on-topic even in conversational sequences."
]
}
],
Expand Down
70 changes: 58 additions & 12 deletions docs/docs/integrations/providers/baseten.md
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@@ -1,25 +1,71 @@
# Baseten

Learn how to use LangChain with models deployed on Baseten.
[Baseten](https://baseten.co) provides all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.

## Installation and setup
As a model inference platform, Baseten is a `Provider` in the LangChain ecosystem. The Baseten integration currently implements a single `Component`, LLMs, but more are planned!

- 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`
Baseten lets you run both open source models like Llama 2 or Mistral and run proprietary or fine-tuned models on dedicated GPUs. If you're used to a provider like OpenAI, using Baseten has a few differences:

## Invoking a model
* Rather than paying per token, you pay per minute of GPU used.
* Every model on Baseten uses [Truss](https://truss.baseten.co/welcome), our open-source model packaging framework, for maximum customizability.
* While we have some [OpenAI ChatCompletions-compatible models](https://docs.baseten.co/api-reference/openai), you can define your own I/O spec with Truss.

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 learn more about Baseten in [our docs](https://docs.baseten.co/) or read on for LangChain-specific info.

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).
## Setup: LangChain + Baseten

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).
You'll need two things to use Baseten models with LangChain:

- A [Baseten account](https://baseten.co)
- An [API key](https://docs.baseten.co/observability/api-keys)

Export your API key to your as an environment variable called `BASETEN_API_KEY`.

```sh
export BASETEN_API_KEY="paste_your_api_key_here"
```

## Component guide: LLMs

Baseten integrates with LangChain through the [LLM component](https://python.langchain.com/docs/integrations/llms/baseten), which provides a standardized and interoperable interface for models that are deployed on your Baseten workspace.

You can deploy foundation models like Mistral and Llama 2 with one click from the [Baseten model library](https://app.baseten.co/explore/) or if you have your own model, [deploy it with Truss](https://truss.baseten.co/welcome).

In this example, we'll work with Mistral 7B. [Deploy Mistral 7B here](https://app.baseten.co/explore/mistral_7b_instruct) and follow along with the deployed model's ID, found in the model dashboard.

To use this module, you must:

* Export your Baseten API key as the environment variable BASETEN_API_KEY
* Get the model ID for your model from your Baseten dashboard
* Identify the model deployment ("production" for all model library models)

[Learn more](https://docs.baseten.co/deploy/lifecycle) about model IDs and deployments.

Production deployment (standard for model library models)

```python
from langchain.llms import Baseten
from langchain_community.llms import Baseten

mistral = Baseten(model="MODEL_ID", deployment="production")
mistral("What is the Mistral wind?")
```

wizardlm = Baseten(model="MODEL_VERSION_ID", verbose=True)
Development deployment

wizardlm("What is the difference between a Wizard and a Sorcerer?")
```python
from langchain_community.llms import Baseten

mistral = Baseten(model="MODEL_ID", deployment="development")
mistral("What is the Mistral wind?")
```

Other published deployment

```python
from langchain_community.llms import Baseten

mistral = Baseten(model="MODEL_ID", deployment="DEPLOYMENT_ID")
mistral("What is the Mistral wind?")
```

Streaming LLM output, chat completions, embeddings models, and more are all supported on the Baseten platform and coming soon to our LangChain integration. Contact us at [[email protected]](mailto:[email protected]) with any questions about using Baseten with LangChain.
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