apollo-sdk 0.2.11

A radically simple framework for ML/AI model management

apollo-sdk: model management tool

Apollo is an open-source validation framework to deploy guardrails for improved user experience and responsiveness of your AI systems..

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Why Apollo API?

Before Apollo, testing model quality and automating workloads was time-consuming, with Apollo, you can simplify, accelerate and backtest the entire process. This makes it easier to train classifiers, handle real-time changes and make data driven decisions.

🚀 Interesting, how can I try it?

Lets install the SDK first...

pip install apollo-sdk

Regression tests vs Automated pipelines

apollo-sdk helps you tune LLM prompts systematically across many relevant test cases. By evaluating and comparing LLM outputs to build decision making workflows. Users can test prompt quality and catch regressions faster.

Evaluating prompt quality

With Apollo python library and CLI toolkit, you can:

• Detecting real-time changes in your data
• Automating tasks against image, video, audio or text
• Simplifying the process of back-testing quality for your AI models
• Making sure your integration is robust, so you never again have to worry about stuck/stale data or false-positives
• Test multiple prompts against predefined test cases
• Evaluate quality and catch regressions by comparing LLM outputs side-by-side
• Speed up evaluations with caching and concurrent tests
• Flag bad outputs automatically by setting "expectations"
• Use as a command line tool, or integrate into your workflow with our library
• Use any AI provider, API or database under one API

apollo-sdk produces table views that allow you to quickly review prompt outputs across many inputs. The goal: tune prompts systematically across all relevant test cases, instead of testing prompts by trial and error.

Usage (command line)

View full documentation »

Support for user interface coming soon

It works on the command line, you can output to [json, csv, yaml]:

To get started, run the following command:

apollo-sdk init

This will create some templates in your current directory: prompts.txt, vars.csv, and config.json.

After editing the prompts and variables to your desired state, apollo-sdk command to kick off an prompt evaluation test:

apollo-sdk -p ./prompts.txt -v ./vars.csv -r openai:completion

If you're looking to customize your usage, you have a wide set of parameters at your disposal. See the Configuration docs for more detail:

Option	Description
-p, --prompts <paths...>	Paths to prompt files, directory, or glob
-r, --providers <name or path...>	One of: openai:chat, openai:completion, openai:model-name, hive:hate, google:safety, etc. See AI Providers
-o, --output <path>	Path to output file (csv, json, yaml, html)
-v, --vars <path>	Path to file with prompt variables (csv, json, yaml)
-c, --config <path>	Path to configuration file. config.json is automatically loaded if present
-j, --max-concurrency <number> coming soon	Maximum number of concurrent API calls
--table-cell-max-length <number> coming soon	Truncate console table cells to this length
--grader coming soon	Provider that will grade outputs, if you are using

Examples

View full documentation »

Prompt quality

In this example, we evaluate whether adding adjectives to the personality of an chat bot affects the responses:

apollo-sdk -p prompts.txt -v vars.csv -r openai:gpt-3.5-turbo

This command will evaluate the prompts in prompts.txt, substituing the variable values from vars.csv, and output results in your terminal.

Have a look at the setup and full output in another format:

apollo-sdk -p prompts.txt -v vars.csv -r openai:gpt-3.5-turbo -o output.json

You can also output a nice spreadsheet, JSON, or YAML file:

{
  "results": [
    {
      "prompt": {
        "raw": "Rephrase this in French: Hello world",
        "display": "Rephrase this in French: {{body}}"
      },
      "vars": {
        "body": "Hello world"
      },
      "response": {
        "output": "Bonjour le monde",
        "tokenUsage": {
          "total": 19,
          "prompt": 16,
          "completion": 3
        }
      }
    }
    // ...
  ],
  "stats": {
    "successes": 4,
    "failures": 0,
    "tokenUsage": {
      "total": 120,
      "prompt": 72,
      "completion": 48
    }
  }
}

Here's an example of a side-by-side comparison of multiple prompts and inputs:

Model quality

You can evaluate the difference between safety outputs for a specific context:

Model quality tests & python package for model testing is a beta feature at the moment, open an issue and tag us to setup

apollo-sdk -p prompts.txt -r hiveai:hate google:safety -o output.json

Configuration

• Setting up an model test: Learn more about how to set up prompt files, vars file, output, etc.

Building Automated Pipelines in the User Interface or Programmatically

View full documentation »

Let's setup your first Integration!

It will pull from your local database (and keep it in sync).

# import the package
from apollo.client import Apollo

# sync data from your database instance
# (we support supabase at the current moment or postgresql via uri format)
Apollo.connect("postgres://username:password@hostname:port/database_name")

# If you want to test out operation on your external connection
Apollo.fetch_tables()
Apollo.query("desc", "table", "column")

...and create a workflow with a simple command:

Note: you can use our sandbox api and skip providing a token or obtain a Auth token here, sign up today on our Site

# import the package
from apollo.client import Apollo

# Use any provider
Apollo.use("google_perspective:<model name>", secret="YOUR_API_TOKEN_HERE")

# Lets check to see if a phrase contains threats
Apollo.detectText(prompt="Phrase1", content_id="content-id", community_id="user-id")

Example response:

{
  "attributeScores": {
    "THREAT": {
      "spanScores": [
        {
          "begin": 0,
          "end": 12,
          "score": { "value": 0.008090926, "type": "PROBABILITY" }
        }
      ],
      "summaryScore": { "value": 0.008090926, "type": "PROBABILITY" }
    },
    "INSULT": {
      "spanScores": [
        {
          "begin": 0,
          "end": 12,
          "score": { "value": 0.008804884, "type": "PROBABILITY" }
        }
      ],
      "summaryScore": { "value": 0.008804884, "type": "PROBABILITY" }
    },
    "SPAM" // ...
  },
  "languages": ["en"],
  "clientToken": "content_123",
  "detectedLanguages": ["en", "fil"]
}

Experimental inputs:

# Create custom rules which creates a task!
Apollo.rule('Phrase1', '>=', '0.8')

# https://docs.apolloapi.io/docs/features
Apollo.detectImage('Image1', 'contains', 'VERY_LIKELY') # Image Analysis/OCR
Apollo.detectSpeech('Audio1', 'contains', 'UNLIKELY') # Audio Processing
Apollo.detectVideo('Video1', 'contains', 'POSSIBLE') # Video Analysis
Apollo.detectText('Phrase1', 'contains', 'UNKNOWN') # Text Analysis
Apollo.test('prompt', 'expected_output') # ML Validation

That's all it takes!

In practice, you probably want to use one of our native SDKs to interact with Apollo's API or use our custom browser client so you dont have to write code. If so, sign up at Apollo API!

Cool, what can I build with it?

• Apollo can help you quickly automate tasks for model management, performance, labeling, object detection and more.
• Teams can use Apollo to build native in-app connections related to active response, content moderation, risk management, fraud detection, etc.
• Some automate their personal lives with Apollo by integrating against discord communities or their personal lives

Development

Contributions are welcome! Please feel free to submit a pull request or open an issue.

📦 pre-commit config

As an open source project, Apollo welcomes contributions from the community at large. This isn’t an exhaustive reference and is a living document subject to change as needed when the project formalizes any practice or pattern.

Clone the repo and start Apollo locally...

git clone https://github.com/apolloapi/apolloapi.git
cd apolloapi && python3 -m venv env && source env/bin/activate && pip install -r requirements.txt

• After installing system dependencies be sure to install pre-commit for lint checks

pip install pre-commit

pre-commit install

pre-commit run --all-files

Apollo uses commit messages for automated generation of project changelog. For every pull request we request contributors to be compliant with the following commit message notation.

<type>: <summary>

<body>

Accepted <type> values:

• new = newly implemented user-facing features
• chg = changes in existing user-facing features
• fix = user-facing bugfixes
• oth = other changes which users should know about
• dev = any developer-facing changes, regardless of new/chg/fix status

Summary (The first line)

The first line should not be longer than 75 characters, the second line is always blank and other lines should be wrapped at 80 characters.

🔍 Neat, I would like to learn more

⭐ Follow our development by starring us here on GitHub ⭐

• Share feedback or ask questions on the Discord community
• Chat with a member of the team 👋
• Check our blog on Trust & Safety
• Look at our docs on how to get started here!

AI Providers

We support OpenAI as well as a number of models. It's also possible to set up your own custom AI provider. See Provider documentation for more details.