Introduction

In the case of autonomous agents and prompt chaining, there is usually interaction between the user and the generative AI application. Hence a dialog of sorts between the interface/application and the user takes place, with a number of dialog turns.

Generally a prompt pipeline is a sequence of stages or events which are initiated or kicked-off based on a predetermined event or API call.

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Considering the image above, the idea behind a prompt pipeline GUI is to provide a user friendly interface for creating a sequence of events. Constructing prompt pipelines this way allows for easy reuse of components.

Simple LangChain Example

Prompt Pipelines extend prompt templates by automatically injecting contextual reference data for each prompt.

The Python code below is a simple application illustrating how a prompt pipeline can be built using LangChain.

pip install langchain
pip install openai

import os
import openai
os.environ['OPENAI_API_KEY'] = str("your-openai-key-goes-here")

prompt = (
    PromptTemplate.from_template("Tell me a joke about {topic}")
    + ", make it funny"
    + "\n\nand in {language}"
)

prompt

PromptTemplate(input_variables=['language', 'topic'], output_parser=None, partial_variables={}, template='Tell me a joke about {topic}, make it funny\n\nand in {language}', template_format='f-string', validate_template=True)
prompt.format(topic="sports", language="Afrikaans")

from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI

model = ChatOpenAI()
chain = LLMChain(llm=model, prompt=prompt)
chain.run(topic="sports", language="Afrikaans")

With the output below:

Hoe noem jy 'n skaap wat rugby speel?

'N "Baaa-ll" speler!

No-Code Example

Below is an example where prompt pipeline stages are configured via a GUI.

In this example, each stage performs a different transformation on the data before passing the result to the next stage in the pipeline. For example, you can extract the entity values before processing other data properties.

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RAG

The most common use-case for prompt pipelines is QnA together with Retrieval Augmented Generation (RAG).

Key elements along the prompt pipeline can include Keyword Extraction to automatically detect important words from the text stored in a field.

Or extracting traits in the user input, which is specific entities, attributes, or details that the search users might use in their input.