Quick start

To get started with Dingo, you can install the framework using pip:

pip install agent-dingo

Now we can create a simple pipeline that summarizes the text provided as input and translates it into French. In this particular example, we will use GPT-3.5 model from OpenAI, but Dingo supports other LLM providers as well.

Firstly, make sure to set the OPENAI_API_KEY environment variable to your OpenAI API key:

export OPENAI_API_KEY=your-api-key

Next, create a new Python script and import the necessary modules:

from agent_dingo.llm.openai import OpenAI
from agent_dingo.core.blocks import PromptBuilder
from agent_dingo.core.message import UserMessage
from agent_dingo.core.state import ChatPrompt

Then, define the pipeline by creating and chaining the building blocks together:

# Model
gpt = OpenAI("gpt-3.5-turbo")

# Summary prompt block
summary_pb = PromptBuilder(
    [UserMessage("Summarize the text in 10 words: ```{text}```.")]
)

# Translation prompt block
translation_pb = PromptBuilder(
    [UserMessage("Translate the text into {language}: ```{summarized_text}```.")],
    from_state=["summarized_text"],
)

# Pipeline
pipeline = summary_pb >> gpt >> translation_pb >> gpt

Finally, run the pipeline with the input text:

input_text = """
Dingo is an ancient lineage of dog found in Australia, exhibiting a lean and sturdy physique adapted for speed and endurance, dingoes feature a wedge-shaped skull and come in colorations like light ginger, black and tan, or creamy white. They share a close genetic relationship with the New Guinea singing dog, diverging early from the domestic dog lineage. Dingoes typically form packs composed of a mated pair and their offspring, indicating social structures that have persisted through their history, dating back approximately 3,500 years in Australia.
"""

output = pipeline.run(text = input_text, language = "french")
print(output)

To deploy the pipeline as a web service, you can use the following code:

#server.py
from agent_dingo.serve import serve_pipeline

serve_pipeline({"gpt-summary-translation": pipeline}, port=8000)

This will start a web server on port 8000, exposing the pipeline as a REST API. You can now send requests using any HTTP client, or even using the official OpenAI python client library:

# client.py
import openai

client = openai.OpenAI(base_url = "http://localhost:8000")

messages = [
    {"role": "context_text", "content": "<text to summarize>"},
    {"role": "context_language", "content": "french"},
]

out = client.chat.completions.create(messages = messages, model = "gpt-summary-translation")
print(out)

In this example, we have created a simple pipeline which is not designed for multi-turn conversations. To make it compatible with OpenAI Chat structure, Dingo defines special message roles like context_text and context_language which are used to pass the input arguments to the pipeline. Section Core goes into more details on differences between context and chat inputs and how to handle them in Dingo.