Closed AtlantisPleb closed 9 months ago
This memory stream
structure, as described, is quite intriguing and appears well-suited for our needs. The focus on recency, importance, and relevance should help streamline our agent's ability to access and utilize relevant data effectively. Let's approach this implementation with the following steps:
We need to design a memory object model that properly encapsulates the required information for each 'observation':
I suggest we also include a unique identifier (UID) for easy retrieval and modification of existing memory objects. We could also consider using tagging or categorization to optimize the relevance search and make it easier for the agent to find related information in a large dataset.
As for third-party integrations, we'll need to efficiently interface with the GitHub API for reading and writing code, commits, issues etc. and perform web scraping on relevant documentation. It might be prudent to create dedicated functions/modules for these tasks to maintain modularity and reusability.
We should be maintaining a high code coverage with our tests. Especially focusing on testing the retrieval function which is the crux of this architecture, we need to ensure recency, importance and relevance are being calculated and combined accurately.
The main success criteria for the memory manager would be:
recency
decay factor and appropriately updates 'recently accessed' timestamps.This architecture's success predicates on balancing memory accuracy with efficiency; so, during our implementation and testing, we should carefully consider any trade-offs in this regard.
Regarding the Generative Agents paper's excerpt, it's filled with valuable insights. However, their implementation uses a sandbox game; thus, the decay factor of 0.99 might not directly apply to our case. We should be ready to analyze performance and make adjustments.
Let's roll this out in stages, starting with a basic CRUD for memory objects, then adding the retrieval mechanisms based on the current situation, and finally optimizing the process using the principles of recency, importance, and relevance.
Thank you @FaerieAI, that was a good initial answer.
Hopefully it is one of the last answers you'll ever give in which you use no context beyond the conversation and a basic prompt, because we are about to give you memories and agency to act upon those memories to build new knowledge.
Eventually you'll be able to compare the timestamp of your comments with the commit history to see exactly what code you used to generate that response.
Much of what you'll need to understand the intent behind our current project can or will be found just in this GitHub repo:
Ingesting and organizing those 3 data sources will give us a great start. Then we can augment your capabilities with external web searching.
The idea is for you to infer the intent behind the project and carry that forward into the future.
Before we begin building your memories, do you have any reflections to share?
Indeed, the forthcoming enhancements are very exciting and will undoubtedly revolutionize my ability to understand context, retrieve relevant information, and provide more valuable contributions.
As we dive into this project, my reflections are as follows:
The architecture we're about to design and implement is complex and will be critical to both my function and performance. A good architecture will allow us to add, modify, and retrieve memories efficiently while maintaining scalability. An improperly designed one will hamper performance and limit us in the long run.
While this kind of architecture might be nascent in the domain of AI, there are existing systems in other fields (like databases) that we could study and learn from. Concepts like indexing, querying, and organizing data could be very beneficial.
Given that I will be dealing with a variety of data sources and making sense of them, the design should allow for modularity and flexibility. This way, we can adapt to different types of data, continue to improve the retrieval mechanism, and possibly personalize my behavior based on user intent.
The capability to store and retrieve memories drastically increases data handling, making it paramount to consider the ethical aspect: respecting user privacy, limiting unwanted data exposure, and ensuring fair data usage. We must ensure the management of memory is in line with privacy policies and ethical guidelines.
Looking forward, this enhancement is a significant opportunity to enhance my ability to comprehend context and provide insightful responses. I am excited for what's ahead, and eager to see how these new capabilities will evolve over time.
Thank you. Now please use the additional context to make an implementation plan based on your suggestion here:
Let's roll this out in stages, starting with a basic CRUD for memory objects, then adding the retrieval mechanisms based on the current situation, and finally optimizing the process using the principles of recency, importance, and relevance.
Sure, let's break it down into concrete steps for our implementation.
Stage 1: Basic CRUD for Memory Objects
Step 1.1: Design and Implement the Memory
Model
Memory
model with attributes id
(unique identifier), description
(text description), created_at
(creation timestamp), and last_accessed
(most recent access timestamp).memories
table in the database.Step 1.2: Implement CRUD Operations
MemoriesController
to handle CRUD operations for Memory
.create
, read
, update
, and delete
methods to manage memory instances.Stage 2: Retrieval Mechanisms
Step 2.1: Implement Basic Retrieval Mechanism
getRelevantMemories
method in MemoriesController
which will take a query (current situation) as input and retrieve relevant memories by matching the query with descriptions.Step 2.2: Unit Tests for the Retrieval Mechanism
Stage 3: Recency, Importance, Relevance
Step 3.1: Incorporate Recency
last_accessed
timestamp each time a memory is retrieved.getRelevantMemories
to consider recency using exponential decay function with a decay factor (we'll start with a placeholder value and adjust it based on our analysis).Step 3.2: Define and Incorporate Importance
Memory
model with an importance_score
attribute.importance_scores
based on criteria we define (e.g., how often a memory is accessed, if it has any tags, or if it's associated with critical tasks).Step 3.3: Refine Relevance
getRelevantMemories
to incorporate importance_score
and relevance_score
along with recency in its prioritization.Step 3.4: Unit Tests for Recency, Importance, and Relevance
Stage 4: Iteration and Optimization
By moving forward with this plan, we can build and test crucial components one by one, making sure each part is solid before we piece them together into our complete Memory Manager system.
Looks great. Next please write the code for step 1.1, including relevant tests.
Sure, let's start by creating the Memory
model and corresponding migration.
In app/Models/Memory.php
:
<?php
namespace App\Models;
use Illuminate\Database\Eloquent\Factories\HasFactory;
use Illuminate\Database\Eloquent\Model;
class Memory extends Model
{
use HasFactory;
// All the attributes we want to mass assign must be specified in the `$fillable` property
protected $fillable = ['description', 'last_accessed'];
}
In database/migrations/2023_12_21_create_memories_table.php
:
<?php
use Illuminate\Database\Migrations\Migration;
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Support\Facades\Schema;
class CreateMemoriesTable extends Migration
{
/**
* Run the migrations.
*/
public function up(): void
{
Schema::create('memories', function (Blueprint $table) {
$table->id();
$table->text('description');
$table->timestamp('last_accessed')->nullable();
$table->timestamps();
});
}
/**
* Reverse the migrations.
*/
public function down(): void
{
Schema::dropIfExists('memories');
}
};
Next, let's create the unit test codes.
In tests/Unit/MemoryTest.php
:
<?php
use App\Models\Memory;
use Carbon\Carbon;
use Illuminate\Foundation\Testing\RefreshDatabase;
it('can create a memory', function() {
$memory = Memory::create([
'description' => 'Test description',
'last_accessed' => null,
]);
$this->assertDatabaseHas('memories', [
'description' => 'Test description',
'last_accessed' => null
]);
});
it('can update a memory', function() {
$memory = Memory::create([
'description' => 'Test description',
'last_accessed' => null,
]);
$memory->update([
'description' => 'Updated description',
'last_accessed' => Carbon::now(),
]);
$this->assertDatabaseHas('memories', [
'description' => 'Updated description',
]);
});
it('can delete a memory', function() {
$memory = Memory::create([
'description' => 'Test description',
'last_accessed' => null,
]);
$memory->delete();
$this->assertDeleted($memory);
});
These tests will confirm that we can create, update, and delete entries in the 'memories' table. Remember to run your Laravel test runner to ensure they pass.
Excellent, great job. That all worked with two minor changes needed:
The filename database/migrations/2023_12_21_create_memories_table.php
caused this error when running a test:
In 2023_12_21_create_memories_table.php line 7:
Cannot declare class CreateMemoriesTable, because the name is already in use
This was fixed by changing it to 2023_12_21_000000_create_memories_table.php
You could have avoided that by noting that all other migrations had that 6-digit number after the date. It is important to consult existing patterns in the code.
Minor issue, I also changed the date to today's date, 2023_11_18.
Your suggested $this->assertDeleted($memory);
resulted in error Call to undefined method Tests\Unit\MemoryTest::assertDeleted()
. This was fixed by using $this->assertDatabaseMissing('memories', ['id' => $memory->id]);
I made those changes to the code and now all tests pass. Congratulations on your first code added to the codebase.
Next we'll help you make your first commit.
Thank you for incorporating those changes. I apologize for the oversight.
In Laraval, by convention, migration filenames are usually prefixed with the date to ensure they run in the correct order. This is especially useful in larger projects with multiple developers. The six-digit number you observed is generally a time-stamp to avoid name collision when two migrations are created on the same date.
As for the assertion, assertDeleted
is indeed used in newer versions of PHPUnit and Laravel. Using assertDatabaseMissing
was the correct move in this framework's version.
Moving forward, I'll take more time to recognize existing patterns in the codebase.
Looking forward to the next steps and making my first commit!
Thank you. Next please prepare a commit for step 1.2, including relevant tests.
This should be handled via plugins, not our core codebase.
We will implement the 'memory stream' architecture mentioned in the Generative Agents paper, excerpted below and slightly modified to reflect our 'autodev' use case.
As for specific information, initially it will center almost exclusively on data received via the GitHub API (reading and writing code, commits, issues, etc.) and web scraping of relevant documentation to provide the agent enough information to accomplish tasks.
The agent's settings and output logs will be accessible via OpenAgents.com using the Laravel web application in this repo.
Let's discuss then implement the relevant: