warpspeed 0.3.0

Python framework for AI workflows and pipelines.

Warpspeed

Warpspeed is a Python framework for creating AI workflow DAGs and pipelines. It augments transformer models with tools for accessing external APIs, such as searches, calculators, spreadsheets, docs, email, and many others. Our initial focus is on supporting large language models (LLMs) but we plan to expand framework's capabilities to cover text-to-anything functionality soon.

With Warpspeed, you can accomplish the following:

1. 🚰 Build sequential AI pipelines and sprawling DAG workflows for complex use cases.
2. 🧰️ Augment LLMs with chain of thought capabilities and external tools, such as calculators, web search, spreadsheet editors, and API connectors.
3. 💾 Add memory to AI pipelines for context preservation.

Please note that Warpspeed is in early development. Its APIs and documentation are subject to change. For now, this README file is the most accurate source of documentation and examples.

Getting Started

First, install Warpspeed with pip:

pip install warpspeed

Second, configure an OpenAI client by getting an API key and adding it to your environment as OPENAI_API_KEY. Warpspeed uses OpenAI Completions API to execute LLM prompts and to work with LlamaIndex data structures.

With Warpspeed, you can create structures, such as Pipelines and Workflows, that are composed of different types of steps. You can also define structures as JSON objects and load them into Warpspeed dynamically. Let's start with defining a simple pipeline.

🚰 AI Pipelines and Workflows

Pipelines are lists of steps that are executed sequentially. Pipelines can have Memory, which makes them ideal for storing LLM conversations.

pipeline = Pipeline(
    memory=Memory()
)

pipeline.add_steps(
    PromptStep("Hi, my name is Scotty. Who are you?"),
    PromptStep("What is my name?")
)

utils.Conversation(pipeline).to_string()

Boom! Our first conversation, à la ChatGPT, is here:

Q: Hi, my name is Scotty. Who are you?
A: Hi Scotty, my name is Assistant. I'm here to help answer your questions.
Q: What is my name?
A: Your name is Scotty.

You can dynamically pass arguments to the prompt by using Jinja templates:

PromptStep("tell me about {{ topic }}", context={"topic": "the hobbit novel"})

In addition to user-defined fields, the context object contains the following:

• inputs: inputs into the current step referencable by parent step IDs.
• structure: the structure that the step belongs to.
• parents: parent steps referencable by IDs.
• children: child steps referencable by IDs.

Now, let's build a simple workflow. Let's say, we want to write a story in a fantasy world with some unique characters. We could setup a workflow that generates a world based on some keywords. Then we pass the world description to any number of child steps that create characters. Finally, the last step pulls in information from all parent steps and writes up a short story.

def character_step(step_id, character_name) -> PromptStep:
    return PromptStep(
        "Based on the following world description create a character named {{ name }}:\n{{ inputs['world'] }}",
        context={
            "name": character_name
        },
        id=step_id
    )

world_step = PromptStep(
    "Create a fictional world based on the following key words {{ keywords|join(', ') }}",
    context={
        "keywords": ["fantasy", "ocean", "tidal lock"]
    },
    id="world"
)

character_step_1 = character_step("scotty", "Scotty")
character_step_2 = character_step("annie", "Annie")

story_step = PromptStep(
    "Based on the following description of the world and characters, write a short story:\n{{ inputs['world'] }}\n{{ inputs['scotty'] }}\n{{ inputs['annie'] }}",
    id="story"
)

workflow = Workflow()

workflow.add_step(world_step)

world_step.add_child(character_step_1)
world_step.add_child(character_step_2)
world_step.add_child(story_step)

character_step_1.add_child(story_step)
character_step_2.add_child(story_step)

workflow.run()

And here is the beginning of our story:

Scotty and Annie had been friends since childhood, and their bond had only grown stronger over the years. Scotty had always been fascinated by the ocean and its secrets, and Annie had always been drawn to its magical creatures. [...]

Workflows and pipelines can also be defined in JSON files and loaded dynamically in Python:

{
  "prompt_driver": {
    "temperature": 0.5,
    "type": "OpenAiPromptDriver"
  },
  "steps": [
    {
      "id": "world",
      "type": "PromptStep",
      "parent_ids": [],
      "child_ids": [
        "scotty",
        "annie"
      ],
      "prompt_template": "Create a fictional world based on the following key words {{ keywords|join(', ') }}",
      "context": {
        "keywords": [
          "fantasy",
          "ocean",
          "tidal lock"
        ]
      }
    },
    ...
  ]
}

Here is how you can load and run it:

with open("workflow.json", "r") as file:
    workflow = Workflow.from_json(file.read())

    workflow.run()

🧰️ Tools

The most powerful feature of Warpspeed is the ability of workflow and pipeline prompt steps to generate chains of thought and use tools that can interact with the outside world. We use the ReAct technique to implement reasoning and acting in the underlying LLMs without using any fine-tuning. There are two types of tool steps that Warpspeed supports:

• ToolStep takes one tool as a parameter and passes it to the LLM that decides if it should use it to respond to the prompt.
• ToolkitStep takes multiple tools as a parameter, so that the underlying LLM can decide which tool to use for every chain of thought step.

Here is how to use tools:

pipeline = Pipeline()

pipeline.add_steps(
    ToolStep(
        "Research and summarize the most important events of February 2023",
        tool=WikiTool(),
        id="research"
    ),
    ToolkitStep(
        "Calculate 3^12 and send an email with the answer and the following text to [e-mail]:\n{{ inputs['research'] }}",
        tools=[
            CalculatorTool(),
            EmailTool(
                host="localhost",
                port=1025,
                from_email="[e-mail]",
                use_ssl=False
            )
        ],
        id="calc_email"
    )
)

pipeline.run()

ToolStep instructs an LLM to use a WikiTool that provides a JSON schema and few-shot learning examples that the LLM is automatically "trained" on to interact with Warpspeed. The LLM can then decide to use a tool to provide a better prompt response by adding substeps that follow the Thought/Action/Observation ReAct routine. For this prompt, it can obviously use a Wiki tool to obtain new information.

ToolkitStep works the same way, but it provides multiple tools for the LLM to choose from depending on the task. In our example, the LLM uses CalculatorTool to calculate 3^12 and EmailTool to send an email.

Warpspeed supports multiple tools and allows you to implement your own.

CalculatorTool

This tool enables LLMs to make simple calculations. Here's how to use it:

ToolStep(
    "what's 123^321?",
    tool=CalculatorTool()
)

The LLM will be prompted to reason via the Thought/Action/Observation loop to use the calculator and respond with an answer that the calculator provided.

Warning By default, this tool uses PythonRunner, which executes code locally with sanitized exec. This is not ideal for production environments, where you generally want to execute arbitrary code in a container. We are working on adding more code runner options soon.

DataScientist

This tool enables LLMs to run more complex calculations in Python. The user can notify the LLM which libraries are available by specifying them in the constructor. By default, only math is available.

ToolStep(
    "what's 123^321?",
    tool=DataScientistTool(
        libs={"numpy": "np", "math": "math"}
    )
)

This will make numpy available as np via import numpy as np and math as math via import math. Before injecting libraries in the constructor, make sure they are installed in your current environment.

Warning By default, this tool uses PythonRunner, which executes code locally with sanitized exec. This is not ideal for production environments, where you generally want to execute arbitrary code in a container. We are working on adding more code runner options soon.

GoogleSheetsWriterTool and GoogleSheetsReaderTool

These tools enable LLMs to read from and write to Google Sheets worksheets. Before using those tools, make sure to download the service account credentials JSON file and share your spreadsheet with the service account. For more information refer to the gspread auth docs.

To read from a spreadsheet:

ToolStep(
    "read all spreadsheet values from the 2nd and 3rd columns",
    tool=GoogleSheetsReaderTool(
        auth_key_path=os.path.expanduser("~/Desktop/service_account.json"),
        spreadsheet_key="<Google Sheets spreadsheet ID>",
        worksheet_name="<optional worksheet name, defaults to the first worksheet>"
    )
)

To write to a spreadsheet:

ToolStep(
    "Create a spreadsheet with columns for 2022 months in the MM/YYYY format, last column for totals, and rows for profit, revenue, and loss",
    tool=GoogleSheetsWriterTool(
        auth_key_path=os.path.expanduser("~/Desktop/service_account.json"),
        spreadsheet_key="<Google Sheets spreadsheet ID>",
        worksheet_name="<optional worksheet name, defaults to the first worksheet>"
    )
)

EmailTool

This tool enables LLMs to send emails.

ToolStep(
    "send an email with a haiku to [e-mail]",
    EmailTool(
        host="localhost",
        port=1025,
        from_email="[e-mail]",
        use_ssl=False
    )
)

For debugging purposes, you can run a local SMTP server that the LLM will send emails to:

python -m smtpd -c DebuggingServer -n localhost:1025

WikiTool

This tool enables LLMs to search and query Wikipedia articles:

ToolStep(
    "Research and summarize biggest world news stories in February of 2023",
    tool=WikiTool()
)

Building Your Own Tool

Building your own tools is easy with Warpspeed! All you need is a Python class, JSON schema do describe tool actions to the LLM, and a set of examples. Let's build a simple random number generator.

First, create a Python class in a separate directory that generates a random float and optionally truncates it:

import random
from typing import Optional
from warpspeed.tools import Tool


class RandomGenTool(Tool):
    def run(self, num_of_decimals: Optional[int]) -> float:
        if num_of_decimals is None:
            return random.random()
        else:
            return round(random.random(), num_of_decimals)

Add a schema.json file describing the tool:

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "random_gen",
  "description": "This tool can be used to generate random numbers",
  "type": "object",
  "properties": {
    "tool": {
      "type": "string",
      "enum": ["random_gen"]
    },
    "input": {
      "type": "int",
      "description": "The number of decimals to be considered while rounding. Default to null."
    }
  },
  "required": ["tool", "input"]
}

Finally, add an examples.j2 Jinja file with a couple of few-shot learning examples:

Q: generate a random number
Thought: I need to use the random_gen tool to answer this question.
Action: {"tool": "random_gen", "input": null}{{ stop_sequence }}
Observation: 0.8444218515250481
Thought: I have enough information to answer the original question
Action: {"tool": "exit", "input": "0.8444218515250481"}{{ stop_sequence }}

Q: generate a random number and round it to 2 decimal places
Thought: I need to use the random_gen tool to answer this question.
Action: {"tool": "random_gen", "input": 2}{{ stop_sequence }}
Observation: 0.14
Thought: I have enough information to answer the original question
Action: {"tool": "exit", "input": "0.14"}{{ stop_sequence }}

To use the tool:

ToolStep(
    "generate a random number and round it to 3 decimal places",
    tool=RandomGenTool()
)

Check out other Warpspeed tools to learn more about tools' implementation details.

💾 Memory

Warpspeed supports different types of memory for pipelines. Due to the non-linear nature of workflows you can't use memory with them yet, but we are currently investigating other possibilities.

By default, pipelines don't initialize memory, so you have to explicitly pass it to them:

Pipeline(
    memory=Memory()
)

There are two other types of memory: BufferMemory and SummaryMemory. BufferMemory will keep a sliding window of steps that are used to construct a prompt:

Pipeline(
    memory=BufferMemory(buffer_size=3)
)

This works great for shorter pipelines but fails if the whole workflow context needs to be present. You can use SummaryMemory to address that:

Pipeline(
    memory=SummaryMemory(
        summarizer=CompletionDriverSummarizer(
            driver=OpenAiPromptDriver()
        ),
        offset=2
    )
)

This will progressively summarize the whole pipeline except for the last two steps.

Contributing

Contributions in the form of bug reports, feature ideas, or pull requests are super welcome! Take a look at the current issues and if you'd like to help please submit a pull request with some tests.

License

Warpspeed is available under the Apache 2.0 License.