wehub-resource-sync
62 lines
5.8 KiB
Markdown
62 lines
5.8 KiB
Markdown
# Guided Conversations
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This sample highlights a framework for a pattern of use cases we refer to as guided conversations.
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These are scenarios where an agent with a goal and constraints leads a conversation. There are many of these scenarios where we hold conversations that are driven by an objective and constraints. For example:
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- a teacher guiding a student through a lesson
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- a call center representative collecting information about a customer's issue
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- a sales representative helping a customer find the right product for their specific needs
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- an interviewer asking candidate a series of questions to assess their fit for a role
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- a nurse asking a series of questions to triage the severity of a patient's symptoms
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- a meeting where participants go around sharing their updates and discussing next steps
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The common thread between all these scenarios is that they are between a **creator** leading the conversation and a **user(s)** who are participating.
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The creator defines the goals, a plan for how the conversation should flow, and often collects key information through a form throughout the conversation.
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They must exercise judgment to navigate and adapt the conversation towards achieving the set goal all while writing down key information and planning in advance.
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The goal of this framework is to show how we can build a common framework to create AI agents that can assist a creator in running conversational scenarios semi-autonomously and generating **artifacts** like notes, forms, and plans that can be used to track progress and outcomes. A key tenant of this framework is the following principal: *think with the model, plan with the code*. This means that the model is used to understand user inputs and make complex decisions, but code is used to apply constraints and provide structure to make the system **reliable**. To better understand this concept, start with the [notebooks](./notebooks/).
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## Features
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We were motivated to create this sample while noticing some common challenges with using agents for conversation scenarios:
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| Common Challenges | Guided Conversations |
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| --------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| Focus - Drift from their original goals | Define the agent's goal in terms of completing an ["artifact"](./guided_conversation/plugins/artifact.py), which is a precise representation of what the agent needs to do in the conversation |
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| Pacing - Rushing through conversations, being overly verbose, and struggle to understand time | Encourage the agent to regularly update an [agenda](./guided_conversation/plugins/agenda.py) where each agenda item is allocated an estimated number of times, time limits are programmatically validated, and programmatically convert time-based units (e.g. seconds, minutes) to turns using [resource constraints](./guided_conversation/utils/resources.py) |
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| Downstream Use Cases - Difficult to use chat logs for further processing or analysis | The [artifact](./guided_conversation/plugins/artifact.py) serves as (1) a structured record of the conversation that can be more easily analyzed afterward, (2) a way to monitor the agent's progress in real-time |
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## Installation
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This sample uses the same tooling as the [Semantic Kernel](https://github.com/microsoft/semantic-kernel/blob/main/python/pyproject.toml) Python source which uses [poetry](https://python-poetry.org/docs/) to install dependencies for development.
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1. `poetry install`
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1. Activate `.venv` that was created by poetry
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1. Set up the environment variables or a `.env` file for the LLM service you want to use.
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1. If you add new dependencies to the `pyproject.toml` file; run `poetry update`.
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### Quickstart
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1. Fork the repository.
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1. Install dependencies (see Installation) & set up environment variables
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1. Try the [01_guided_conversation_teaching.ipynb](./notebooks/01_guided_conversation_teaching.ipynb) as an example.
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1. For best quality and reliability, we recommend using the `gpt-4-1106-preview` or `gpt-4o` models since this sample requires complex reasoning and function calling abilities.
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## How You Can Use This Framework
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### Add a new scenario
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Create a new file and and define the following inputs:
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- An artifact
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- Rules
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- Conversation flow (optional)
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- Context (optional)
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- Resource constraint (optional)
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See the [interactive script](./interactive_guided_conversation.py) for an example.
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### Editing Existing Plugins
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Edit plugins at [plugins](./guided_conversation/plugins/)
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### Editing the Orchestrator
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Go to [guided_conversation_agent.py](./guided_conversation/plugins/guided_conversation_agent.py).
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### Reusing Plugins
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We also encourage the open source community to pull in the artifact and agenda plugins to accelerate existing work. We believe that these plugins alone can improve goal-following in other agents.
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