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Harrison/predibase (#8046)
Co-authored-by: Abhay Malik <32989166+Abhay-765@users.noreply.github.com>
This commit is contained in:
Harrison Chase
GitHub
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24
docs/extras/ecosystem/integrations/predibase.md
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24
docs/extras/ecosystem/integrations/predibase.md
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# Predibase
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Learn how to use LangChain with models on Predibase.
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## Setup
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- Create a [Predibase](hhttps://predibase.com/) account and [API key](https://docs.predibase.com/sdk-guide/intro).
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- Install the Predibase Python client with `pip install predibase`
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- Use your API key to authenticate
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### LLM
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Predibase integrates with LangChain by implementing LLM module. You can see a short example below or a full notebook under LLM > Integrations > Predibase.
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```python
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import os
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os.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"
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from langchain.llms import Predibase
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model = Predibase(model = 'vicuna-13b', predibase_api_key=os.environ.get('PREDIBASE_API_TOKEN'))
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response = model("Can you recommend me a nice dry wine?")
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print(response)
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```
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@ -0,0 +1,214 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Predibase\n",
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"\n",
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"[Predibase](https://predibase.com/) allows you to train, finetune, and deploy any ML model—from linear regression to large language model. \n",
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"\n",
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"This example demonstrates using Langchain with models deployed on Predibase"
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]
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},
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{
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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 [Predibase account](https://predibase.com/free-trial/?utm_source=langchain) and an [API key](https://docs.predibase.com/sdk-guide/intro).\n",
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"\n",
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"You'll also need to install the Predibase 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 predibase\n",
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"import os\n",
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"\n",
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"os.environ[\"PREDIBASE_API_TOKEN\"] = \"{PREDIBASE_API_TOKEN}\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Initial 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import Predibase\n",
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"\n",
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"model = Predibase(\n",
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" model=\"vicuna-13b\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
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")"
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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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"response = model(\"Can you recommend me a nice dry wine?\")\n",
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"print(response)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Chain Call Setup"
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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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"llm = Predibase(\n",
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" model=\"vicuna-13b\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SequentialChain"
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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 LLMChain\n",
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"from langchain.prompts import PromptTemplate"
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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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"# This is an LLMChain to write a synopsis given a title of a play.\n",
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"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
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"\n",
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"Title: {title}\n",
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"Playwright: This is a synopsis for the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
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"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)"
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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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"# This is an LLMChain to write a review of a play given a synopsis.\n",
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"template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n",
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"\n",
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"Play Synopsis:\n",
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"{synopsis}\n",
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"Review from a New York Times play critic of the above play:\"\"\"\n",
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"prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)\n",
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"review_chain = LLMChain(llm=llm, prompt=prompt_template)"
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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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"# This is the overall chain where we run these two chains in sequence.\n",
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"from langchain.chains import SimpleSequentialChain\n",
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"\n",
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"overall_chain = SimpleSequentialChain(\n",
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" chains=[synopsis_chain, review_chain], verbose=True\n",
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")"
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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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"review = overall_chain.run(\"Tragedy at sunset on the beach\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Fine-tuned LLM (Use your own fine-tuned LLM from Predibase)"
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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 Predibase\n",
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"\n",
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"model = Predibase(\n",
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" model=\"my-finetuned-LLM\", predibase_api_key=os.environ.get(\"PREDIBASE_API_TOKEN\")\n",
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")\n",
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"# replace my-finetuned-LLM with the name of your model in Predibase"
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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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"# response = model(\"Can you help categorize the following emails into positive, negative, and neutral?\")"
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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": "Python 3.8.9 64-bit",
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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": "3.8.9"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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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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@ -43,6 +43,7 @@ from langchain.llms.openllm import OpenLLM
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from langchain.llms.openlm import OpenLM
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from langchain.llms.petals import Petals
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from langchain.llms.pipelineai import PipelineAI
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from langchain.llms.predibase import Predibase
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from langchain.llms.predictionguard import PredictionGuard
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from langchain.llms.promptlayer_openai import PromptLayerOpenAI, PromptLayerOpenAIChat
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from langchain.llms.replicate import Replicate
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@ -100,6 +101,7 @@ __all__ = [
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"OpenLM",
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"Petals",
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"PipelineAI",
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"Predibase",
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"PredictionGuard",
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"PromptLayerOpenAI",
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"PromptLayerOpenAIChat",
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@ -156,6 +158,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
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"openlm": OpenLM,
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"petals": Petals,
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"pipelineai": PipelineAI,
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"predibase": Predibase,
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"replicate": Replicate,
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"rwkv": RWKV,
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"sagemaker_endpoint": SagemakerEndpoint,
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51
langchain/llms/predibase.py
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51
langchain/llms/predibase.py
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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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class Predibase(LLM):
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"""Use your Predibase models with Langchain.
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To use, you should have the ``predibase`` python package installed,
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and have your Predibase API key.
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"""
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model: str
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predibase_api_key: str
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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@property
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def _llm_type(self) -> str:
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return "predibase"
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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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**kwargs: Any
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) -> str:
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try:
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from predibase import PredibaseClient
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pc = PredibaseClient(token=self.predibase_api_key)
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except ImportError as e:
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raise ImportError(
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"Could not import Predibase Python package. "
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"Please install it with `pip install predibase`."
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) from e
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except ValueError as e:
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raise ValueError("Your API key is not correct. Please try again") from e
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# load model and version
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results = pc.prompt(prompt, model_name=self.model)
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return results[0].response
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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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