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PipelineAI allows you to run your ML models at scale in the cloud. It also provides API access to several LLM models.
This notebook goes over how to use LangChain with PipelineAI.

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PipelineAI example

This example shows how PipelineAI integrated with LangChain and it is created by PipelineAI.

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Setup

The pipeline-ai library is required to use the PipelineAI API, AKA Pipeline Cloud. Install pipeline-ai using pip install pipeline-ai. 
# Install the package
pip install -qU  pipeline-ai

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Example

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Imports

import os

from langchain_community.llms import PipelineAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate

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Set the environment API Key

Make sure to get your API key from PipelineAI. Check out the cloud quickstart guide. You’ll be given a 30 day free trial with 10 hours of serverless GPU compute to test different models. 
os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"

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Create the PipelineAI instance

When instantiating PipelineAI, you need to specify the id or tag of the pipeline you want to use, e.g. pipeline_key = "public/gpt-j:base". You then have the option of passing additional pipeline-specific keyword arguments: 
llm = PipelineAI(pipeline_key="YOUR_PIPELINE_KEY", pipeline_kwargs={...})

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Create a Prompt template

We will create a prompt template for Question and Answer. 
template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

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Initiate the LLMChain

llm_chain = prompt | llm | StrOutputParser()

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Run the LLMChain

Provide a question and run the LLMChain. 
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.invoke(question)
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