https://github.com/deadbits/prompt-serve
Store and serve language model prompts
https://github.com/deadbits/prompt-serve
generative-ai large-language-models llm prompt-engineering prompt-server
Last synced: 12 months ago
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Store and serve language model prompts
- Host: GitHub
- URL: https://github.com/deadbits/prompt-serve
- Owner: deadbits
- Created: 2023-05-27T17:45:38.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-07-26T02:29:40.000Z (almost 3 years ago)
- Last Synced: 2025-03-31T07:01:59.642Z (about 1 year ago)
- Topics: generative-ai, large-language-models, llm, prompt-engineering, prompt-server
- Language: Python
- Homepage:
- Size: 479 KB
- Stars: 27
- Watchers: 4
- Forks: 1
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
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README
# 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](https://deadbits.substack.com/p/the-prompt-serve-schema)
## 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](tools/contentctl.py) for common tasks
* initializing new Git repository
* creating prompt files
* viewing repo statistics
* convert prompts to [LangChain](https://github.com/hwcase17/langchain) [Prompt Templates](https://python.langchain.com/docs/modules/model_io/prompts/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](schema.yml).
Check out the [prompts](prompts/) repository to see it in action.
```yaml
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](/tools/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](/tools/contentctl.py) 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](/tools/validate.py).
**Example output**
## Use in LangChain ⛓️
prompt-serve files can be easily converted to LangChain Prompt Templates.
The [content control tool](/tools/contentctl.py) can convert individual prompt-serve files to langchain format.
**Example output**
**Python**
```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](/tools/contentctl.py) 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
```