Prerequisites

To complete this Quick Guide, you’ll need:

  1. The Pieces Desktop App installed and actively running on your device.

  2. Long-Term Memory enabled in the Pieces Desktop App.

  3. Optional—Pieces connected in a Python IDE via MCP, such as Visual Studio Code or JetBrains IDEs (including PyCharm).

In This Quick Guide

This Quick Guide shows how to combine Long-Term Memory with Conversational Search—including scoping a chat to a specific captured memory using Chat on a Timeline summary—so you can get AI help implementing a feature in a Python app.

As a developer, a common daily task is reviewing a ticket in a tool like GitHub Issues or Jira and then implementing it in a codebase.

This often involves switching back and forth between the code and the ticket, which can affect your productivity due to constant context switching.

With Pieces, Long-Term Memory captures the ticket as you read it. After you work in your project, Pieces can use those captured memories—and you can open Conversational Search scoped to the right summary from Timeline using the Chat action on the three-dots menu.

Review a GitHub Issue

The first step is to review the issue by letting Pieces capture it, and then ask Conversational Search about it.

Open the following GitHub issue in your browser. Slowly scroll through the comments on the issue, taking maybe 30 seconds or so to scroll through it.\ \ You can use this issue as an example. In Conversational Search, start a new chat, ensuring the Long-Term Memory (LTM) context is enabled, and use the following prompt: 
```plaintext
Summarize the create a sign up page issue I was just reading
```

Pieces will respond with a summary of the issue:

Conversational Search summarizing a GitHub issue using LTM context

Clone the Project

This issue refers to a sci-fi store—a small web application written in Python and Flask for an upcoming retail store that sells themed sci-fi toys.

Clone this GitHub repository. Inside the repo is a folder called `scifi_store`. 
If you use an IDE like VS Code or JetBrains PyCharm, open this folder in that IDE.

Tie the project to Conversational Search (Timeline Chat)

You cannot attach a project folder or individual files directly to Conversational Search. Instead, LTM must capture your work in scifi_store, then you scope a chat to the summary or event that holds that context.

With **Long-Term Memory** enabled, spend a few minutes in your IDE inside `scifi_store`—open files, scroll relevant modules, or run the app—so Pieces records useful workflow context. In the Pieces Desktop App, open [Pieces Timeline](/products/desktop/timeline). Look for a recent **roll-up** or **summary** that reflects that coding session (for example activity involving your editor and that repo). Open that item in the main panel. Click the **three-dots menu** (⋮) on the event header and select **`Chat`**. Conversational Search opens with that memory in scope. 
For more detail, see [Chat from a summary](/products/desktop/timeline/event-actions#chat-from-a-summary).

Prompt Conversational Search

With the GitHub issue in LTM and a scoped chat tied to a summary that includes your project activity, you can ask Pieces how to implement the issue.

In the same Conversational Search chat, use this prompt (or one like it): 
```plaintext
How can I implement this issue in this project?
```
The Conversational Search will use **Long-Term Memory**—including the issue you read and the workflow captured from `scifi_store` inside the scoped summary—to propose implementation steps. 
The response may include concrete suggestions such as code for an endpoint, a new page using existing templates, and so on.

Review these code changes along with the original codebase.

<Image src="https://storage.googleapis.com/hashnode_product_documentation_assets/quick_guides/using_pieces_copilot_with_memory_context/new_media/asking_to_fix.png" alt="Conversational Search reasoning over the project and GitHub issue" align="center" fullwidth="true" />
🎉 Congratulations, you’ve completed the *Using Conversational Search with Context* Quick Guide! 🎉

Bonus—Try One Prompt

This Quick Guide showed two prompts: one to get the details about the issue and another to learn how to implement it.

This was done in 2 stages to illustrate the information from the Pieces Long-Term Memory, but is unnecessary. You can do this in a single prompt!

Start from home or your IDE, or open **`Chat`** from a Timeline summary that already contains both the issue and recent work in `scifi_store`. If you need one focused memory in scope, use **`Chat`** on the relevant summary’s three-dots menu—see [Chat from a summary](/products/desktop/timeline/event-actions#chat-from-a-summary). Use this single prompt when interacting with Conversational Search; 
```plaintext
How can I implement the create a sign up page issue I was just reading in this Python project?
```
The assistant uses your **Long-Term Memory** (issue + captured IDE activity) to suggest how to implement the ticket—especially if you used **Chat** on a summary that already bundles that context. 
<Image src="https://storage.googleapis.com/hashnode_product_documentation_assets/quick_guides/using_pieces_copilot_with_memory_context/new_media/extra_example.png" alt="Conversational Search implementing an issue with a single prompt" align="center" fullwidth="true" />