Conversational Applications

Synalinks is designed to handle conversational applications as well as query-based systems. In the case of a conversational applications, the input data model is a list of chat messages, and the output an individual chat message. The Program is in that case responsible of handling a single conversation turn.

Now we can program our application like you would do with any Program. For this example, we are going to make a very simple chatbot.

By default, if no data_model/schema is provided to the Generator it will output a ChatMessage like output. If the data model is None, then you can enable streaming.

Note: Streaming is disabled during training and should only be used in the last Generator of your pipeline.

import synalinks
import asyncio

from synalinks.backend import ChatMessage
from synalinks.backend import ChatRole
from synalinks.backend import ChatMessages

language_model = synalinks.LanguageModel(
    model="ollama/mistral",
)

async def main():
    inputs = synalinks.Input(data_model=ChatMessages)
    outputs = await synalinks.Generator(
        language_model=language_model,
        prompt_template=synalinks.chat_prompt_template(),
        streaming=False,  # Marimo chat don't handle streaming yet
    )(inputs)

    program = synalinks.Program(
        inputs=inputs,
        outputs=outputs,
        name="simple_chatbot",
        description="A simple conversation application",
    )

    # Plot this program to understand it
    synalinks.utils.plot_program(
        program,
        show_module_names=True,
        show_trainable=True,
        show_schemas=True,
    )

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

In this notebook, we explored how Synalinks handle conversational applications. You have now a solid understanding to create chatbots and conversational agents.

Key Takeaways

• Conversational Flow Management: Synalinks effectively manages conversational applications by handling inputs as a list of chat messages and generating individual chat messages as outputs. This structure allows for efficient processing of conversation turns.

• Streaming and Real-Time Interaction: Synalinks supports streaming for real-time interactions, enhancing user engagement. However, streaming is disabled during training and should be used only in the final Generator.

• Customizable Prompt Templates: The prompt templates can be tailored to fit conversational contexts, guiding the language model to produce coherent and relevant responses.