let's think step by step in depth on how to create from scratch a langchain framework called ATBmemoryAGI that will be an intelliggent memory management system to empower ai assistants so they become self improving , learn and evolve from past interactions with users , here is the logic of the framweork "Case Study: Contextual Memory in Action

To better understand the Contextual Memory module and its practical application, let's consider a hypothetical scenario where CrewAI is used to manage a software development project.

1. Scenario

Imagine you are the project manager of a software development team working on a new mobile application. You have decided to use CrewAI to help manage the project, assign tasks, and provide contextual information to team members.

2. Initial Setup

You set up CrewAI with the necessary memory modules: Short-Term Memory, Long-Term Memory, and Entity Memory. You also configure the Contextual Memory module to interact with these memory modules.

3. Task Assignment

One of the tasks in your project is to implement a new feature in the mobile application. You assign this task to a team member, Alice, using CrewAI. The task description is as follows:

```
Task: Implement the new 'Share Location' feature in the mobile application.
```

4. Contextual Memory in Action

When Alice receives the task, she wants to understand the context and any relevant information related to the task. She uses the Contextual Memory module to build the context for the task.

a. `build_context_for_task`: Alice calls the `build_context_for_task` method with the task description and an empty context string. The method creates a query by combining the task description and the context string:

```python
query = f"{task.description} {context}".strip()
# query = "Implement the new 'Share Location' feature in the mobile application"
```

b. `_fetch_ltm_context`: The method calls `_fetch_ltm_context` to retrieve historical data or insights from Long-Term Memory related to the task. The Long-Term Memory module searches for relevant information and returns the following results:

```
Historical Data:
- The 'Share Location' feature was discussed in the project kickoff meeting.
- The feature should use the device's GPS to share the user's location with their contacts.
```

c. `_fetch_stm_context`: The method then calls `_fetch_stm_context` to retrieve recent relevant insights from Short-Term Memory. The Short-Term Memory module searches for relevant information and returns the following results:

```
Recent Insights:
- The 'Share Location' feature should be implemented using the device's native location services.
- The feature should include a privacy setting to allow users to control who can see their location.
```

d. `_fetch_entity_context`: Finally, the method calls `_fetch_entity_context` to retrieve relevant entity information from Entity Memory. The Entity Memory module searches for relevant information and returns the following results:

```
Entities:
- The 'Share Location' feature is related to the 'User' entity, as it involves sharing the user's location.
- The 'Share Location' feature is also related to the 'Contact' entity, as it involves sharing the user's location with their contacts.
```

e. Formatting and Returning Context: The Contextual Memory module formats the retrieved context as a string and returns it to Alice:

```
Historical Data:
- The 'Share Location' feature was discussed in the project kickoff meeting.
- The feature should use the device's GPS to share the user's location with their contacts.

Recent Insights:
- The 'Share Location' feature should be implemented using the device's native location services.
- The feature should include a privacy setting to allow users to control who can see their location.

Entities:
- The 'Share Location' feature is related to the 'User' entity, as it involves sharing the user's location.
- The 'Share Location' feature is also related to the 'Contact' entity, as it involves sharing the user's location with their contacts.
```

5. Task Execution

With the contextual information provided by the Contextual Memory module, Alice now has a clear understanding of the task, its background, and any relevant insights or entities. She can use this information to efficiently implement the 'Share Location' feature in the mobile application.

6. Conclusion

This case study demonstrates how the Contextual Memory module can be used in a practical scenario to provide relevant contextual information for a task. By combining insights from Short-Term Memory, Long-Term Memory, and Entity Memory, the Contextual Memory module ensures that team members have access to the most pertinent information when executing tasks, leading to improved performance and decision-making."