Using Chat Participants' Info In Later Messages

Hey guys! Let's dive into a cool question about how to use information you get from chat participants in your future messages. Specifically, we're looking at how to make this work in the context of something like Microsoft's VS Code and similar tools. Imagine you're chatting with a bot or a tool that fetches info, and you want to use that info later on in your conversation. Pretty neat, right?

The Scenario: Getting GitHub Issue Data

So, the scenario is this: you're using a chat participant, like one in VS Code, and you ask it to do something. For example, you might use a command like @githubpr issue 1234. This command reaches out to GitHub, grabs the details of issue number 1234, and brings them back into your chat. Awesome!

Now, here’s the twist. You have another command or a prompt file – let’s call it /analyze-github-issue. This prompt is designed to analyze the GitHub issue you just fetched. It’s supposed to look at the issue details and tell you if it’s a bug, a feature request, or something already handled. But... it's not working as expected.

The Problem: Lost in Translation

The problem? The /analyze-github-issue prompt doesn’t seem to know about the GitHub issue data you just got. The chat agent says something like, “I need more info!” It's like the agent is forgetting everything you've told it in the previous step. This is a common issue, and understanding it can help you build much more useful and powerful tools.

Making the Connection: Passing Information Between Steps

The core of the problem lies in how information is passed between different parts of a chat interaction. When you run @githubpr issue 1234, the output (the issue details) needs to be accessible to the /analyze-github-issue command. Here's how to solve it:

1. Context is King: Maintaining State

Think of the chat as having a memory, or context. This context is where the information from the first step (getting the GitHub issue) needs to be stored. Most modern chat systems or tools will have a way to maintain this context. This might involve storing the issue details in a temporary variable, a session object, or a dedicated memory space for the chat.

2. Prompt Engineering: Referencing the Context

Your /analyze-github-issue prompt needs to know where to find the issue details. You'll have to modify the prompt to tell it how to access the information stored in the context. For example, if the issue details are stored in a variable called github_issue_data, your prompt might look like this:

Using the information stored in the variable `github_issue_data`, determine if this is a bug, a feature, or if this is already implemented/supported.

3. System Design: Connecting the Dots

The system itself needs to be set up to pass the information. When the @githubpr issue 1234 command runs, it must not only fetch the issue details but also store them in the context. Then, when /analyze-github-issue is called, the system must retrieve the information from the context and provide it to the prompt. This often involves some form of scripting or automation to link the two steps together.

Practical Steps and Solutions

Let's break down some practical steps and possible solutions to make this work.

A. Check Your Tool's Documentation

• Find out how your chat tool manages context. Does it have a way to store variables? Does it use sessions? Read the documentation thoroughly to understand how to save and access information between messages.
• Look for built-in features. Many tools provide features specifically for chaining commands together. This might involve using a special syntax or a configuration option to pass data from one command to another.

B. Modify Your Prompts (Carefully!)

• Be specific. Instead of just saying “Using the information returned by the chat participant,” be explicit. Tell the prompt exactly what information it should look for and where to find it (e.g., “Look for the issue title in the variable issue_title”).
• Test and Iterate. Write your prompt, test it, and refine it. Chatbots and AI models often require a bit of trial and error to get them working just right.

C. Automate the Process

• Scripting. Use scripting to automate the steps. For example, after @githubpr issue 1234 runs, your script could extract the issue details and store them in a variable, which the /analyze-github-issue prompt then accesses.
• Integrations. Look for integrations that connect your tools. If you're using VS Code, explore extensions or integrations that let you link GitHub and other services, allowing data to flow easily between them.

Advanced Techniques

Let's level up with some more advanced ways to achieve this. These might be overkill for simple scenarios, but they're valuable if you're building complex workflows.

1. Custom Chatbots and APIs

• Build a custom chatbot. Develop your own chatbot to control the conversation flow. This gives you complete control over data storage, retrieval, and prompt processing. You can use APIs to connect with various services (like GitHub).
• Use a dedicated API. Create an API that fetches issue details, stores them in a database, and then provides them to your analysis prompt. This gives you robust data management and allows for more complex analysis.

2. Semantic Understanding

• Natural Language Processing (NLP). Use NLP techniques to allow the system to understand what you mean, even if you don’t explicitly name variables. Train an NLP model to identify keywords and relationships between different parts of the conversation.
• Vector Databases. Store information in vector databases. Then, you can search for similar issues and relevant data. Vector databases allow for more sophisticated and nuanced data retrieval.

3. Orchestration Tools

• Workflow Engines. Use tools like Apache Airflow, or similar workflow engines to create complex data processing pipelines. These tools can automate the entire process and manage dependencies between different steps.
• Low-Code/No-Code Platforms. Consider low-code/no-code platforms that offer pre-built integrations and simplified workflows. These can make it easier to connect your chat tool with various services.

Example Implementation (Conceptual)

Let's outline a simplified conceptual implementation using Python and a hypothetical chat tool. This is a simplified example, but it shows the main concepts.

# Assume you're using a library to interact with your chat tool and GitHub
import chat_tool_api
import github_api

# 1. Get the GitHub issue details
def get_github_issue(issue_number):
    issue_details = github_api.get_issue(issue_number)
    return issue_details

# 2. Store the issue details in chat context
def store_issue_in_context(chat_session, issue_details):
    chat_tool_api.set_context_variable(chat_session, "github_issue_data", issue_details)

# 3. Analyze the issue
def analyze_issue(chat_session, prompt_file):
    issue_details = chat_tool_api.get_context_variable(chat_session, "github_issue_data")
    # Read the prompt file
    with open(prompt_file, "r") as f:
        prompt = f.read()

    # Combine the prompt with the issue details
    full_prompt = f"{prompt}\nIssue Details: {issue_details}"

    # Send the prompt to the analysis tool
    analysis_result = chat_tool_api.send_prompt(full_prompt)
    return analysis_result

# Example Usage
chat_session = chat_tool_api.start_session()
issue_number = 1234

# Get the issue
issue_details = get_github_issue(issue_number)

# Store in chat context
store_issue_in_context(chat_session, issue_details)

# Analyze the issue using a prompt file
analysis_result = analyze_issue(chat_session, "analyze-github-issue.prompt.md")

print(analysis_result)
chat_tool_api.end_session(chat_session)

In this example:

• We get the GitHub issue using github_api.get_issue(). We then store it using chat_tool_api.set_context_variable(). The example assumes a chat tool API.
• The analyze_issue() function retrieves the issue from context using chat_tool_api.get_context_variable(). It then combines the issue details with the prompt, sending everything to a tool.
• The full_prompt combines your prompt with the issue details. This is crucial! Make sure you understand where data is stored in the context, and how to get it into the full_prompt.

Conclusion

Getting information from chat participants in subsequent messages is a core skill for building powerful and useful tools. By understanding how to manage context, design prompts, and integrate different components, you can create seamless and efficient workflows. The key is making sure information flows from one step to the next. The more you practice, the better you'll get at making these connections.

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For more in-depth information about GitHub issues and API use, you can check out the GitHub Developer documentation. It's got everything you need to know to work with GitHub’s data.