prompt-serve

store and serve language model prompts

Overview 📖

prompt-serve helps you manage all of your large language model (LLM) prompts and associated settings/metadata in a straightforward, version controlled manner.

This project provides a YAML schema for storing prompts in a structured manner and a small API server that handles interactions with a Git repository, so you can treat prompts more like re-usable code.

• Release blog post

Highlights ✨

• YAML schema for prompts and associated metadata
• Associate prompts to one another to represent chains
• Create "packs" of multiple prompts or chains to represent categories of tasks or workflows
• Store any kind of prompt text or template
• Store LLM provider, model, and settings
• Command-line utility for common tasks
  • initializing new Git repository
  • creating prompt files
  • viewing repo statistics
  • convert prompts to LangChain Prompt Templates
• Command-line utility for validating single prompt or directory against schema
• Version controlled via Git
• API server to upload or retrieve prompts (proof of concept / work in progress)

Schema 🗺️

Prompts follow the schema provided in schema.yml.

Check out the prompts repository to see it in action.

title: prompt-title-or-name
uuid: prompt-uuid
description: prompt-description
category: prompt-category
provider: model-provider
model: model-name
model_settings:
  temperature: 0.8
  top_k: 40
  top_p: 0.9
prompt: prompt-text
input_variables:
  - var1
  - var2
references:
  - https://example.com
  - https://example.com
associations:
  - prompt_uuid
  - prompt_uuid
packs:
  - pack-uuid
  - pack-uuid
tags:
  - tag
  - tag

Validation ✅

You can use the validate.py utility to verify prompts meet the schema and have unique UUIDs.

By specifying the --create argument, a new UUID will be provided if a given prompt isn't unique for your scanned set. You can also gather statistics on the types of prompts in your collection by passing --gen-stats (see the next section for example stats output).

usage: validate.py [-h] [-s SCHEMA] [-f FILE] [-d DIRECTORY] [-c] [-g]

Validate YAML files against the prompt-serve schema.

options:
  -h, --help            show this help message and exit
  -s SCHEMA, --schema SCHEMA
                        schema file to validate against
  -f FILE, --file FILE  single file to validate
  -d DIRECTORY, --directory DIRECTORY
                        directory to validate
  -c, --create          create new uuids if validation fails
  -g, --gen-stats       generate statistics from directory

Example output

Statistics utility 📊

The content control tool can be used to scan a directory of prompt-serve repository and display statistics about all the prompts in the collection, including information on the category, provider, model, and tags.

Stats can also be optionally collected when running validate.py.

Example output

Use in LangChain ⛓️

prompt-serve files can be easily converted to LangChain Prompt Templates.

The content control tool can convert individual prompt-serve files to langchain format.

Example output

Python

import yaml
from langhain import PromptTemplate

def convert(path_to_ps_prompt):    
    with open(path_to_ps_prompt, 'r') as fp:
        data = yaml.safe_load(fp)
        prompt = data['prompt']
            
        if 'input_vars' in data.keys():
            input_vars = data['input_vars']
            langchain_template = PromptTemplate(template=prompt, input_variables=input_vars)
        else:
            langchain_template = PromptTemplate(template=prompt, input_variables=[])
  
        return langchain_template

Prompt creation utility ✍️

The content control tool can be used to interactively create a prompt with the prompt-serve schema.

🪲 This is just a proof of concept and has a few known bugs. You would be better served creating these on your own for now.

• multi-line input for "prompt" field not handled correctly
• no defaults are set for optional fields

$ python create.py -n summary.yml
creating prompt file summary.yml ...
title (str): Summarize blog posts
description (str): Summarize a blog post with key takeaways
category (str): summarization
provider (str) : openai
model (str) : gpt-3.5-turbo
temperature (float) : 0.8
top_k (int) : 
top_p (float) : 0.9
max_tokens (int) : 512
stream (bool) : false
presence_penalty (float) : 
frequency_penalty (float) : 
prompt (str): Summarize the blog post provided below with 3-5 key takeaways as bullet points: {blog_content}
references (seq) : https://github.com/deadbits/prompt-serve
associations (seq) : 
packs (seq) : 
tags (seq) : 
 successfully wrote file summary.yml