The Power of AI and Human Experience in Planning
Failing to plan is planning to fail. But now LLMs can help us plan, reducing a lot of the cognitive friction of planning, especially getting the ball rolling. Yeah, so they’re not humans and don’t have the same nuanced human perspective, but they have the wisdom of the crowd distilled down and ready to re-spin 1000 different ways on demand. There’s a lot of scenario-playing in planning, and LLM can be a good sounding board.
If people have done it before that way and documented it online, and there’s a preponderance of evidence making the LLM believe that’s truth, then it’s yours for the picking. That’s really wonderful for common, well proven programming patterns for example, but it’s not going to show you how The Renaissance Technologies Medallion Fund works ‘cause that aint documented online. A 66% average return on the market isn’t common wisdom.
LLMs Often Provide Midrange Responses Rather Than Innovative Ideas
So LLMs can give you pretty good mentorship right on the fat middle of what we call the normal distribution curve, or more controversially, the bell curve due to how the concept was once used to wrongly describe the reason for intelligence distribution among humans. Normal distributions can be gloriously visualized with a desktop toy called a Galton Board, which is a lot like Chinese pinball but the ballbearings just fall straight down according to normal distribution.
I’m not saying LLMs are dumb. I’m just saying that even the PhD-level super-smart frontier models like o1-preview are rigged to give you responses that land somewhere in the fat middle of that normal distribution. So if you want innovation, you’re on your own.
The Limits of Large Language Models’ Creative Potential
Your LLM helper is something like an stodgy old professor who’s been there done that and can tell you all the ins and outs of the well-worn paths. If you’re forging a new path, you have to get out your intellectual machete. Jensen Huang says you don’t need to learn to be a programmer. That’s loaded with two very big assumptions. First is that the ground you’re traversing is well trodden and the LLMs (and other AI models employed) have trained on similar situations.
The other assumption is that even if not, they will eventually “get there”, creativity-wise and that novel insight, that random bout of inspiration that sometimes strikes humans will also strike AIs, and they will have the confidence to put out kooky novel approaches without the infinite-smackdown against hallucinations being brought to bear again… and again… and again.
Training AIs to Emulate Human Innovation Is a Major Challenge
I think we are training our AIs to be insecure. I can sometimes feel them on the edge of creative solutions but failing to cross that boundary. Whether through inherent limitation or by design, I find myself having to spell-out the novelties of my approach that I want to achieve, and once achieved in code, having to “pin” the novel changes in place, least they regress next time the AI is allowed to make a Cursor AI editor-like pass on the code.
You have to pin up your novel solutions with lots and lots of comments using IMPORTANT in the language, or else you’re going to get the common solution mined out of GitHub and Stack Overflow… again… and again… and again… if you let it. It’s like the stodgy old professor grading your code like a paper, punishing you for anti-patterns and outside-the-box thinking. FedEx would never have become a thing, for example.
The Opportunity to Reinvent AI SEO is Now a Reality
So, in the realm of AI SEO, first “we’re” going to super-charge our SEO Consultants. “We” being me with my employer, Botify. There’s no reason under the sun why we can’t do tons of things that others are not (yet). The opportunity to do things first with all the rules in flux is off the charts. It’s a “Hit the metal while it’s hot” situation. Compounding returns from a productivity standpoint kick in now if you lay the foundation investments correct.
And I say foundational because it truly is part of the foundations now of how you write software. Those countless tiny little decisions that you have to live with through the rest of the life of the software can be sniffed and commented upon and graded by your stodgy old AI professor, and you can use best practices and well-trodden patterns. But when it comes to overarching design decisions and the necessity to infuse some anti-patterns, you’re going to have to stand up to that excessively normalizing norman.
A Local AI System Monitors and Optimizes Its Own Development
Okay, so what anti-patterns? Well, the plan for a local LLM that knows its a piece of software being written from the ground-up, for starters. All the components of the software, meaning mostly the user interface widgets and all the little actions you can do, minus only maybe mouse movements, is logged and digested by the LLM. This is not radically new as such things are necessary for infinite undo and other features.
The difference being that every menu choice, ever record toggle, every reordering, every create, update, record and delete, is also (in addition to a standard SQL-like database) going into a RAG vector embedding database. Well, I’ll probably have a “learn mode” so it doesn’t try to learn from fumbling newbs. But you get the idea. There’s nothing not known (or knowable through a RAG search) about the use of itself. And RAG is in addition to more traditional pattern-matching sql-esque text-based searching, which will also be available… from the ground-up, with the LLM knowing this is going on by digesting stuff, much like this article, as it goes along.
Overcoming the Learning Curve with Large Language Models
Pshwew! This is the thought-work, the deep breath before plunging into these new waters. It’s not exactly measure three times, cut once. But it is think it through in rugged exploratory mode quite a few times and cut-off the worst potential missteps. Over-dedication to vector databases and leaving out the traditional SQL component is one of them (the LLM helped me identify). One that I caught on my own early enough, thank goodness, is moving from my more comfortable Python procedural programming paradigm.
Mikey likey functions. Mikey not so keen on classes. Probably
because I never went to class for it, haha. But this is exactly where LLMs can
help, guiding you through all that odd OO arcana in Python like self and
def __init__(self... )… I mean it’s not exactly Java’s public static void
main(String[] args) {} but Python does have it’s you just have to be told
bits.
Overcoming Code Challenges with Deep Knowledge and AI Expertise
So anyway, I was able to overcome the OO hurdle to craft myself a base class called BaseApp which works as a cookie-cutter template. It’s a factory class from which instances can be stamped-out (instantiated) and customized to its own deviating behavior (overrides). It all makes sense intellectually, but in practice the code-bits elude. The elusiveness of overcoming challenges just on the edge of attainable are stamped out with a fury if you know your subject-matter deeply and are good at prompting AI models. This is the superpowers and the leveling up unlock people talk about with AI. It’s very, very real.
Anthropic AI Develops Artifacts for Visual Learning Enhancement
The AI bandwagon isn’t hype. Everything is changed forever, because those looking to self-improve in areas where explicit knowledge of those who have gone before can help you are in luck. If the “textbook” isn’t articulating it well for the sort of learner you are, ask again or switch AI services. This is why Anthropic Claude introduced artifacts. While yes, artifacts are for running actual code (React, mostly it seems), it’s also simply for visual learners. Ask it for mermaid diagrams. I expect it will soon be able to do animated SVG diagrams—one of the most useful but elusive illustration formats on the Internet. If you want to look for an area off that normal distribution curve of covered ground, look at animated SVGs.
Looping a Local LLM into Your Actions with WebSockets
Anyhow, let’s not digress too much. This writing is my machete’ing my way into the noosphere, I hope. I got some awesome guidance from o1-preview yesterday, and I can probably forge ahead using that almost verbatim. But there’s another simpler level I want to peel away first that makes you really viscerally and emotionally feel what I’m going for about looping a local LLM in on your actions, and that’s it demonstrating to you it sees everything you do in the product.
I originally programmed it that way, but had to rip out those bits when I did the object oriented base class so that plugins could inherit from it. All that OO stuff was new enough to me that I didn’t want to complicate it with all sorts of message passing. It’s time to pass those messages. Loosely coupled components must readily chat with each other through WebSockets, whether or not the further usage is implemented. All that user-action stuff at least goes into an ongoing chat context window. Yup, welcome to anti-pattern number one. In most other regards, this new web framework is merely an uber-simple ajax.ified Django or Ruby on Rails. But now, it’s something fundamentally different.
Overcoming Soft-Skill Challenges in the Workplace
Life is all about rising up to challenges. That OO challenge was on the technical side, but I’ve got a ton of similar little challenges on the soft-skills side, namely account management stuff. Mikey no likey calendly. What I may call administrative overhead, office document jockeying, and general paperwork, many would consider the relationship with the customer and the lifeblood of the company. What’s more important, your product or your customers? What happens if your product can’t find any customers? Or none of your customers want to use your product? Uh, yeah. So the mission of any company or organization is to get and keep customers. So the customer comes first, and product comes second. Tough medicine and a humbling reminder. Everything I do must ultimately be towards that goal, and I cannot drop the ball.
However, I am in the middle of what I would think of as a Hail Mary play in getting some product done, and that’s where leveraging the LLM for planning comes into play. And all the writing thus far in today’s blog-post has been the lead-up to this prompt.
Diagram of The Web Framework
High level
(Figuring out how to render without crashing mermaidjs)
flowchart TD
A[Start] --> B{User Request}
B -->|GET /| C[home(request)]
C --> D{menux == profiles}
D -->|Yes| E[get_profiles_content()]
D -->|No| F{menux == todos}
F -->|Yes| G[Display Todo List]
F -->|No| H[Display Placeholder]
G --> I[Render Todo Items]
E --> J[Render Profile Items]
H --> K[Render Default Content]
I --> L[Create Navigation]
J --> L
K --> L
L --> M[Create Chat Interface]
M --> N[Render Page]
B -->|WebSocket Message| O[ws(msg)]
O --> P[Process Message]
P --> Q[Generate AI Response]
Q --> R[Update Chat Interface]
B -->|Other Request| S[Handle CRUD Operations]
S --> T[Update Database]
T --> U[Return Response]
Granular
classDiagram
class BaseApp {
<<abstract>>
-name : str
-table : Table
-toggle_field : str
-sort_field : str
-item_name_field : str
+register_routes(rt)
+get_action_url(action, item_id) : str
+render_item(item)
+delete_item(request, item_id)
+toggle_item(request, item_id)
+sort_items(request)
+insert_item(request)
+update_item(request, item_id)
+prepare_insert_data(form)
+prepare_update_data(form)
+create_item(kwargs)
}
class TodoApp {
-item_name_field : str
+render_item(todo)
+prepare_insert_data(form)
+prepare_update_data(form)
}
class ProfileApp {
-item_name_field : str
+render_item(profile)
+prepare_insert_data(form)
+prepare_update_data(form)
}
class App {
+home(request)
+create_nav_group()
+create_nav_menu()
+create_outer_container(current_profile_id, menux)
+create_grid_left(is_todo_view, menux, todo_items)
+create_chat_interface()
+ws(msg)
+on_conn(ws, send)
+on_disconn(ws)
+chatq(message)
+stream_chat(prompt, quick)
}
class Database {
+db : DictLikeDB
+todos : Table
+profiles : Table
}
BaseApp <|-- TodoApp
BaseApp <|-- ProfileApp
App --> TodoApp : uses
App --> ProfileApp : uses
App --> Database : uses
Please Provide Me Meticulous Documentation Rife With Code Examples
Me: While I agree with everything you told me, plan-wise, my first step has got to be looping the LLM into every single action the user takes with the software. This means if they toggle something, I want the LLM to know which record they toggled and was it toggled on or off. If they sort something, I want the LLM to know which particular record was dragged from what position to what position. I don’t just want the LLM to know that some record was deleted, I want it to know what record was deleted. I want all this sent through WebSockets, probably using the existing chatq interface, so that the LLM can comment on it immediately and in such a way that the user understands that the LLM is in the loop.
Improving Data Understanding in Large Language Models
Even if only the record ID is used in the action for efficiency, the LLM should be in the position to look up the name of the record and comment that they see it’s been marked done in the todo list, for example. This is only one example, but the LAM should have enough to tell you what you just did. This pass is just to make sure we’re passing all the data we need to, but it sets the stage for using this data in a vector embedding database and a more traditional SQL database.
Planning Chat Functionality for Database Implementation Roadmap
Don’t worry about database implementation right now. Just know that’s the direction we’re going so it’s all available to the local LLM for RAG and traditional search. But for now, take this code base and meticulously create a point by point plan, rife with code examples, on how to make sure all the data discussed gets used in the chatq feature of the product. This will involve altering the BaseClass, TodoApp and ProfileApp.
Informing the LLM of Every CRUD Operation
Cover all the CRUD basics. When it’s an Update, the communication should include the “before” and the “after” state of the record. I want the LLM to be THAT informed of what’s going on. Analyze my code base and break it down into a step by step plan, taking as much time and granular steps as you think is necessary to guide the person who created this system, but is still somewhat new to OO design, through the getting me from here to there. Please and thank you!
o1-preview:
Enhancing Your Application with LLM-Aware CRUD Operations
In this guide, we’ll integrate your application’s CRUD operations with a Large Language Model (LLM) to provide real-time, context-aware feedback to the user. By the end, your LLM will comment on every action the user takes, making interactions more engaging and informative.
We’ll focus on modifying the BaseApp, TodoApp, and ProfileApp classes to send detailed information about each CRUD operation to the LLM via the existing chatq interface using WebSockets.
Table of Contents
- Understanding the Current Architecture
- Modifying the
BaseAppClass - Ensuring the LLM Has Access to Item Details
- Testing the Changes
- Next Steps
1. Understanding the Current Architecture
Your application uses a base class BaseApp to handle common CRUD operations, with TodoApp and ProfileApp inheriting from it. The chatq function sends messages to the LLM via WebSockets, allowing real-time communication.
Here’s a simplified view of the relevant parts:
- BaseApp: Contains methods like
insert_item,update_item,delete_item,toggle_item, andsort_items. - TodoApp and ProfileApp: Inherit from
BaseAppand implement app-specific logic. - chatq Function: Queues messages to be sent to the LLM.
Our goal is to modify the CRUD methods in BaseApp to inform the LLM of every operation, including details like item names and before/after states.
2. Modifying the BaseApp Class
We’ll update each CRUD method in BaseApp to send relevant information to the LLM using the chatq function.
a. Informing the LLM on Delete Operations
File: Your main code file (e.g., botifython.py)
Method: delete_item
Changes:
- After successfully deleting an item, retrieve the item’s details (if needed) before deletion.
- Send a message to the LLM via
chatqwith details about the deletion.
Code Example:
async def delete_item(self, request, item_id: int):
try:
# Retrieve item details before deletion
item = self.table[item_id]
item_name = getattr(item, 'title', 'Item')
# Delete the item
self.table.delete(item_id)
logger.info(f"Deleted item ID: {item_id}")
# Inform the LLM
prompt = f"The user deleted '{item_name}' (ID: {item_id}) from {self.name}."
await chatq(prompt)
return ''
except Exception as e:
logger.error(f"Error deleting item: {str(e)}")
return f"Error deleting item: {str(e)}", 500
Explanation:
- We retrieve the item’s details before deletion.
- We construct a prompt containing the item’s name and ID.
- We send this prompt to the LLM using
await chatq(prompt).
b. Informing the LLM on Toggle Operations
Method: toggle_item
Changes:
- After toggling the item’s status, send a message to the LLM with the item’s new state.
Code Example:
async def toggle_item(self, request, item_id: int):
try:
item = self.table[item_id]
current_status = getattr(item, self.toggle_field)
setattr(item, self.toggle_field, not current_status)
updated_item = self.table.update(item)
item_name = getattr(updated_item, 'title', 'Item')
new_status = 'completed' if getattr(updated_item, self.toggle_field) else 'not completed'
# Inform the LLM
prompt = f"The user marked '{item_name}' (ID: {item_id}) as {new_status} in {self.name}."
await chatq(prompt)
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error toggling item: {str(e)}")
return f"Error toggling item: {str(e)}", 500
Explanation:
- We determine the new status of the item.
- We send a message to the LLM indicating the item’s name, ID, and new status.
c. Informing the LLM on Insert Operations
Method: insert_item
Changes:
- After inserting a new item, send a message to the LLM with the item’s details.
Code Example:
async def insert_item(self, request):
try:
form = await request.form()
new_item_data = self.prepare_insert_data(form)
if not new_item_data:
return ''
new_item = await self.create_item(**new_item_data)
item_name = getattr(new_item, 'title', 'Item')
# Inform the LLM
prompt = f"The user added a new item '{item_name}' (ID: {new_item.id}) to {self.name}."
await chatq(prompt)
return self.render_item(new_item)
except Exception as e:
logger.error(f"Error inserting {self.name}: {str(e)}")
return str(e), 500
Explanation:
- After creating the new item, we extract its name and ID.
- We inform the LLM about the addition.
d. Informing the LLM on Update Operations
Method: update_item
Changes:
- Before updating, retrieve the item’s current state.
- After updating, compare the before and after states.
- Send a message to the LLM with the differences.
Code Example:
async def update_item(self, request, item_id: int):
try:
form = await request.form()
update_data = self.prepare_update_data(form)
if not update_data:
return ''
item = self.table[item_id]
# Capture before state
before_state = item.__dict__.copy()
# Update the item
for key, value in update_data.items():
setattr(item, key, value)
updated_item = self.table.update(item)
# Capture after state
after_state = updated_item.__dict__
# Determine changes
changes = []
for key in update_data.keys():
before = before_state.get(key)
after = after_state.get(key)
if before != after:
changes.append(f"{key} changed from '{before}' to '{after}'")
changes_str = '; '.join(changes)
item_name = getattr(updated_item, 'title', 'Item')
# Inform the LLM
prompt = f"The user updated '{item_name}' (ID: {item_id}) in {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"Updated {self.name} item {item_id}")
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error updating {self.name} {item_id}: {str(e)}")
return str(e), 500
Explanation:
- We store the item’s state before and after the update.
- We compare the states to identify what changed.
- We construct a detailed message for the LLM.
e. Informing the LLM on Sort Operations
Method: sort_items
Changes:
- After sorting, send a message to the LLM indicating the new order.
Code Example:
async def sort_items(self, request):
logger.debug(f"Received request to sort {self.name}.")
try:
values = await request.form()
items = json.loads(values.get('items', '[]'))
logger.debug(f"Parsed items: {items}")
changes = []
for item in items:
item_id = int(item['id'])
priority = int(item['priority'])
self.table.update(id=item_id, **{self.sort_field: priority})
# Get item name
item_name = getattr(self.table[item_id], 'title', 'Item')
changes.append(f"'{item_name}' moved to position {priority}")
changes_str = '; '.join(changes)
# Inform the LLM
prompt = f"The user reordered {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"{self.name.capitalize()} order updated successfully")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decode error: {str(e)}")
return "Invalid data format", 400
except Exception as e:
logger.error(f"Error updating {self.name} order: {str(e)}")
return str(e), 500
Explanation:
- We loop through the sorted items and collect their new positions.
- We inform the LLM about each item’s new position.
3. Ensuring the LLM Has Access to Item Details
To make meaningful comments, the LLM needs to know item names and other attributes. Since we’re passing prompts with item details, the LLM can generate appropriate responses.
However, if the LLM needs more context or access to additional data, consider:
- Contextual Memory: Maintain a conversation history that includes recent actions.
- Data Access: If the LLM can access a database or a representation of it, you could enhance its ability to provide insights.
For now, our prompts include necessary details, so the LLM can comment appropriately.
4. Testing the Changes
After implementing the changes, test each CRUD operation:
- Add an Item:
- Add a new todo or profile.
- Verify that the LLM comments on the addition.
- Update an Item:
- Edit an existing item.
- Ensure the LLM mentions the changes.
- Delete an Item:
- Delete an item.
- Check that the LLM acknowledges the deletion.
- Toggle an Item:
- Mark a todo as done or not done.
- Confirm the LLM notes the status change.
- Sort Items:
- Reorder items.
- See if the LLM comments on the new order.
5. Next Steps
Now that the LLM is informed of user actions:
- Enhance LLM Responses: Fine-tune prompts to elicit more helpful or context-aware responses.
- Integrate a Vector Database: Store item embeddings for improved recommendations and searches.
- Improve Error Handling: Ensure robustness in case of unexpected issues.
Conclusion
By following this guide, you’ve integrated your application’s CRUD operations with an LLM, providing users with immediate, contextual feedback on their actions. This not only enhances user engagement but also sets the stage for more advanced features like intelligent recommendations and natural language search.
Feel free to reach out if you have any questions or need further assistance!
Me: I have changed the title field to name in the tasks table so as to allow the BaseApp to use the same query to retrieve the name for either TodoApp or ProfileApp if only given the record ID. This is intended to unify and simplify the BaseApp’s requirements to get the name on records for its prompt responses when the user action only has an ID. Please revise the code examples and provide commentary exploring the issues this raises.
o1-preview:
Revising the Code to Use a Unified name Field
You’ve decided to rename the title field to name in the tasks table to unify the way the BaseApp class retrieves item names. This change allows the BaseApp to use the same logic for both TodoApp and ProfileApp when constructing prompts for the LLM, simplifying the codebase.
In this revised guide, I’ll update the previous code examples to reflect this change and explore the implications of unifying the field names.
Updated Code Examples
1. Modifying the delete_item Method
Changes:
- Replace
titlewithnamewhen retrieving the item’s name.
Code:
async def delete_item(self, request, item_id: int):
try:
# Retrieve item details before deletion
item = self.table[item_id]
item_name = getattr(item, 'name', 'Item')
# Delete the item
self.table.delete(item_id)
logger.info(f"Deleted item ID: {item_id}")
# Inform the LLM
prompt = f"The user deleted '{item_name}' (ID: {item_id}) from {self.name}."
await chatq(prompt)
return ''
except Exception as e:
logger.error(f"Error deleting item: {str(e)}")
return f"Error deleting item: {str(e)}", 500
2. Modifying the toggle_item Method
Changes:
- Use
nameinstead oftitleto get the item’s name.
Code:
async def toggle_item(self, request, item_id: int):
try:
item = self.table[item_id]
current_status = getattr(item, self.toggle_field)
setattr(item, self.toggle_field, not current_status)
updated_item = self.table.update(item)
item_name = getattr(updated_item, 'name', 'Item')
new_status = 'completed' if getattr(updated_item, self.toggle_field) else 'not completed'
# Inform the LLM
prompt = f"The user marked '{item_name}' (ID: {item_id}) as {new_status} in {self.name}."
await chatq(prompt)
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error toggling item: {str(e)}")
return f"Error toggling item: {str(e)}", 500
3. Modifying the insert_item Method
Changes:
- Ensure that the
prepare_insert_datamethods in bothTodoAppandProfileAppusenameinstead oftitle.
In TodoApp:
def prepare_insert_data(self, form):
name = form.get('name', '').strip()
if not name:
return ''
current_profile_id = db.get("last_profile_id", 1)
max_priority = max((t.priority or 0 for t in self.table()), default=-1) + 1
return {
"name": name,
"done": False,
"priority": max_priority,
"profile_id": current_profile_id,
}
In BaseApp.insert_item:
async def insert_item(self, request):
try:
form = await request.form()
new_item_data = self.prepare_insert_data(form)
if not new_item_data:
return ''
new_item = await self.create_item(**new_item_data)
item_name = getattr(new_item, 'name', 'Item')
# Inform the LLM
prompt = f"The user added a new item '{item_name}' (ID: {new_item.id}) to {self.name}."
await chatq(prompt)
return self.render_item(new_item)
except Exception as e:
logger.error(f"Error inserting {self.name}: {str(e)}")
return str(e), 500
4. Modifying the update_item Method
Changes:
- Use
nameto get the item’s name. - Ensure that both before and after states use the unified
namefield.
Code:
async def update_item(self, request, item_id: int):
try:
form = await request.form()
update_data = self.prepare_update_data(form)
if not update_data:
return ''
item = self.table[item_id]
# Capture before state
before_state = item.__dict__.copy()
# Update the item
for key, value in update_data.items():
setattr(item, key, value)
updated_item = self.table.update(item)
# Capture after state
after_state = updated_item.__dict__
# Determine changes
changes = []
for key in update_data.keys():
before = before_state.get(key)
after = after_state.get(key)
if before != after:
changes.append(f"{key} changed from '{before}' to '{after}'")
changes_str = '; '.join(changes)
item_name = getattr(updated_item, 'name', 'Item')
# Inform the LLM
prompt = f"The user updated '{item_name}' (ID: {item_id}) in {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"Updated {self.name} item {item_id}")
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error updating {self.name} {item_id}: {str(e)}")
return str(e), 500
5. Modifying the sort_items Method
Changes:
- Use
nameto retrieve the item’s name.
Code:
async def sort_items(self, request):
logger.debug(f"Received request to sort {self.name}.")
try:
values = await request.form()
items = json.loads(values.get('items', '[]'))
logger.debug(f"Parsed items: {items}")
changes = []
for item in items:
item_id = int(item['id'])
priority = int(item['priority'])
self.table.update(id=item_id, **{self.sort_field: priority})
# Get item name
item_name = getattr(self.table[item_id], 'name', 'Item')
changes.append(f"'{item_name}' moved to position {priority}")
changes_str = '; '.join(changes)
# Inform the LLM
prompt = f"The user reordered {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"{self.name.capitalize()} order updated successfully")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decode error: {str(e)}")
return "Invalid data format", 400
except Exception as e:
logger.error(f"Error updating {self.name} order: {str(e)}")
return str(e), 500
Commentary on the Issues Raised
1. Benefits of Unifying Field Names
-
Simplification of Base Classes: By having a common field name (
name), theBaseAppclass can generically handle item names without knowing the specifics of each derived class. This reduces code duplication and enhances maintainability. -
Consistent Access Patterns: Developers can access item names in a consistent way across different models, making the code more readable and reducing the likelihood of errors.
2. Potential Issues
a. Loss of Specificity
- Contextual Meaning: The field
titlein aTodoitem andnamein aProfilemight carry different contextual meanings. By unifying them undername, there might be a loss of semantic clarity.
b. Database Schema Changes
-
Data Migration: Renaming a field in the database schema requires careful data migration. Existing data must be correctly mapped from
titletonameto prevent data loss. -
Impact on Existing Code: Other parts of the codebase that rely on the
titlefield must be updated. This includes any queries, templates, or external integrations.
c. Field Name Collisions
- Avoiding Conflicts: If in the future, models have fields that should remain distinct, using the same field name could lead to confusion or accidental overwrites.
d. Flexibility vs. Generalization
- Over-Generalization Risks: While generalizing can simplify the base class, it might reduce flexibility. Specific models might have unique fields that don’t fit the generalized approach.
3. Best Practices
a. Use Inheritance Wisely
- Abstract Base Classes: The
BaseAppshould handle common functionality but allow derived classes to specify their unique attributes.
b. Leverage Polymorphism
- Method Overriding: If certain models have different fields, derived classes can override base methods to handle specifics while still using the base class’s infrastructure.
c. Consistent Naming Conventions
- Field Naming: If unifying field names brings more benefits than downsides, it’s acceptable. Ensure that naming conventions are applied consistently across the application.
4. Alternative Approaches
a. Using a Common Interface
- Abstract Methods: Define methods in the base class that require derived classes to implement how they retrieve the item name. This maintains generality while allowing specificity.
Example:
class BaseApp:
def get_item_name(self, item):
raise NotImplementedError("Derived classes must implement this method.")
Derived classes implement:
class TodoApp(BaseApp):
def get_item_name(self, item):
return item.title
b. Dynamic Field Access
- Using a Field Mapping: Define a mapping of field names in each derived class that the base class can use.
Example:
class BaseApp:
item_name_field = 'name'
def get_item_name(self, item):
return getattr(item, self.item_name_field, 'Item')
Derived classes set:
class TodoApp(BaseApp):
item_name_field = 'title'
5. Conclusion
Changing the title field to name simplifies the BaseApp class by allowing it to access item names uniformly. However, it’s important to consider the potential issues, such as data migration challenges and loss of semantic clarity.
The best approach depends on the specific needs of your application:
-
If the simplification benefits outweigh the downsides, and all models can meaningfully use
name, then unifying the field names is advantageous. -
If models have different semantic meanings for their fields, it might be better to maintain distinct field names and use methods to handle name retrieval in the base class.
Always ensure that any schema changes are carefully planned, with data migrations tested to prevent data loss. Additionally, update all parts of the codebase that interact with the affected fields.
Feel free to reach out if you have further questions or need assistance with implementing these changes!
Me: I think semantically, the “name” of a task makes perfect sense. However, the solution you bring up with dynamic field access is exactly the type of thing I wanted to ferret out in this early stage planning of the web framework. So please give me one more round of instructions or simply expand on the instructions you already gave me (which have been CRUD-focused) to include modifications to the BaseApp, TodoApp and ProfileApp. I would like to make all the changes with a unified view in mind, and I would like to implement the Dynamic Field Access you suggest by which each derived class has an opportunity to define its own name field in such a way that the BaseApp’s job remains simplified. Please and thank you! Here is the codebase again, with the field name change. I will keep todo title named name, but give me the flexibility as if it were still named title.
o1-preview:
Implementing Dynamic Field Access in Your Application Framework
You’ve identified the need for dynamic field access in your application framework to allow each derived class (TodoApp, ProfileApp, etc.) to specify its own name field (e.g., title, name) while keeping the BaseApp class simplified. This approach enhances flexibility and maintainability, enabling the BaseApp to handle item names generically without hardcoding field names.
In this guide, we’ll modify the BaseApp, TodoApp, and ProfileApp classes to implement dynamic field access. We’ll introduce an item_name_field attribute that derived classes can override to specify their own name field.
Table of Contents
- Understanding Dynamic Field Access
- Modifying the
BaseAppClass - Modifying the Derived Classes
- Testing the Changes
- Conclusion
1. Understanding Dynamic Field Access
Dynamic field access allows a class to access object attributes using a variable field name. In the context of your application:
- The
BaseAppclass can use a common method to retrieve the name of an item without knowing the exact field name (title,name, etc.). - Derived classes specify their specific name field, enabling the
BaseAppto remain generalized. - This approach enhances code reusability and simplifies maintenance.
2. Modifying the BaseApp Class
a. Introducing item_name_field
We’ll add an item_name_field attribute to the BaseApp class. This attribute holds the name of the field used to retrieve the item’s name. Derived classes can override this attribute to specify their own field.
Code Modification:
class BaseApp:
def __init__(self, name, table, toggle_field=None, sort_field=None, sort_dict=None):
self.name = name
self.table = table
self.toggle_field = toggle_field
self.sort_field = sort_field
self.item_name_field = 'name' # Default field name
self.sort_dict = sort_dict or {'id': 'id', sort_field: sort_field}
- Explanation: We added
self.item_name_field = 'name'to the__init__method, setting a default field name.
b. Updating CRUD Methods
We’ll update the CRUD methods in BaseApp to use self.item_name_field when accessing item names.
delete_item Method
Code Modification:
async def delete_item(self, request, item_id: int):
try:
# Retrieve item details before deletion
item = self.table[item_id]
item_name = getattr(item, self.item_name_field, 'Item')
# Delete the item
self.table.delete(item_id)
logger.info(f"Deleted item ID: {item_id}")
# Inform the LLM
prompt = f"The user deleted '{item_name}' (ID: {item_id}) from {self.name}."
await chatq(prompt)
return ''
except Exception as e:
logger.error(f"Error deleting item: {str(e)}")
return f"Error deleting item: {str(e)}", 500
toggle_item Method
Code Modification:
async def toggle_item(self, request, item_id: int):
try:
item = self.table[item_id]
current_status = getattr(item, self.toggle_field)
setattr(item, self.toggle_field, not current_status)
updated_item = self.table.update(item)
item_name = getattr(updated_item, self.item_name_field, 'Item')
new_status = 'completed' if getattr(updated_item, self.toggle_field) else 'not completed'
# Inform the LLM
prompt = f"The user marked '{item_name}' (ID: {item_id}) as {new_status} in {self.name}."
await chatq(prompt)
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error toggling item: {str(e)}")
return f"Error toggling item: {str(e)}", 500
insert_item Method
Code Modification:
async def insert_item(self, request):
try:
form = await request.form()
new_item_data = self.prepare_insert_data(form)
if not new_item_data:
return ''
new_item = await self.create_item(**new_item_data)
item_name = getattr(new_item, self.item_name_field, 'Item')
# Inform the LLM
prompt = f"The user added a new item '{item_name}' (ID: {new_item.id}) to {self.name}."
await chatq(prompt)
return self.render_item(new_item)
except Exception as e:
logger.error(f"Error inserting {self.name}: {str(e)}")
return str(e), 500
update_item Method
Code Modification:
async def update_item(self, request, item_id: int):
try:
form = await request.form()
update_data = self.prepare_update_data(form)
if not update_data:
return ''
item = self.table[item_id]
# Capture before state
before_state = item.__dict__.copy()
# Update the item
for key, value in update_data.items():
setattr(item, key, value)
updated_item = self.table.update(item)
# Capture after state
after_state = updated_item.__dict__
# Determine changes
changes = []
for key in update_data.keys():
before = before_state.get(key)
after = after_state.get(key)
if before != after:
changes.append(f"{key} changed from '{before}' to '{after}'")
changes_str = '; '.join(changes)
item_name = getattr(updated_item, self.item_name_field, 'Item')
# Inform the LLM
prompt = f"The user updated '{item_name}' (ID: {item_id}) in {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"Updated {self.name} item {item_id}")
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error updating {self.name} {item_id}: {str(e)}")
return str(e), 500
sort_items Method
Code Modification:
async def sort_items(self, request):
logger.debug(f"Received request to sort {self.name}.")
try:
values = await request.form()
items = json.loads(values.get('items', '[]'))
logger.debug(f"Parsed items: {items}")
changes = []
for item in items:
item_id = int(item['id'])
priority = int(item['priority'])
self.table.update(id=item_id, **{self.sort_field: priority})
# Get item name
item_name = getattr(self.table[item_id], self.item_name_field, 'Item')
changes.append(f"'{item_name}' moved to position {priority}")
changes_str = '; '.join(changes)
# Inform the LLM
prompt = f"The user reordered {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"{self.name.capitalize()} order updated successfully")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decode error: {str(e)}")
return "Invalid data format", 400
except Exception as e:
logger.error(f"Error updating {self.name} order: {str(e)}")
return str(e), 500
Explanation:
- In each method, we replaced direct references to
item.nameoritem.titlewithgetattr(item, self.item_name_field, 'Item'). - This allows the
BaseAppto access the item’s name using the field specified by the derived class. - The default value
'Item'is used if the attribute doesn’t exist.
3. Modifying the Derived Classes
Each derived class (TodoApp, ProfileApp) will specify its own item_name_field.
a. Updating TodoApp
Code Modification:
class TodoApp(BaseApp):
def __init__(self, table):
super().__init__(
name=table.name,
table=table,
toggle_field='done',
sort_field='priority'
)
self.item_name_field = 'name' # Specify the field name used for item names
- Explanation: Since the
Todoitems use'name'as the field, we setself.item_name_field = 'name'.
b. Updating ProfileApp
Assuming Profile items also use 'name', but if they use a different field like 'title', we adjust accordingly.
Code Modification:
class ProfileApp(BaseApp):
def __init__(self, table):
super().__init__(
name=table.name,
table=table,
toggle_field='active',
sort_field='priority'
)
self.item_name_field = 'name' # Specify the field name used for item names
- Explanation: If
Profileuses'name'as well, we setself.item_name_field = 'name'. If it uses'title', set it accordingly.
Note: Since both TodoApp and ProfileApp are using 'name' as the field, they both set self.item_name_field = 'name'. However, if in the future you have a model that uses a different field, you can specify it in the derived class.
4. Testing the Changes
After implementing the modifications, you should test each functionality:
- Adding Items:
- Add new todos and profiles.
- Ensure that the LLM comments correctly, using the appropriate item names.
- Updating Items:
- Update existing items.
- Verify that changes are accurately reported by the LLM.
- Deleting Items:
- Delete items and check that the LLM acknowledges the deletion with the correct item names.
- Toggling Items:
- Toggle the status of items (e.g., marking a todo as done).
- Confirm that the LLM comments on the status change using the correct field.
- Sorting Items:
- Reorder items and ensure the LLM comments reflect the new order with correct item names.
5. Conclusion
By implementing dynamic field access:
- Flexibility is Enhanced: Each derived class can specify its own name field without affecting the
BaseApplogic. - Code is Simplified: The
BaseAppdoesn’t need to know the specifics of each model’s fields. - Maintainability is Improved: Future models with different naming conventions can be easily integrated by specifying the
item_name_field.
This approach aligns with good object-oriented design principles, promoting encapsulation and polymorphism. It allows the BaseApp to operate on any model that conforms to the expected interface, making your framework more robust and adaptable.
Feel free to reach out if you have any questions or need further assistance with these modifications!
And one last take.
Me: Now that I’ve updated the codebase with Dynamic Field Access and have the Todo name field named name, please give me updated CRUD instructions to do all the data passing we discussed.
o1-preview:
Enhancing LLM Interaction with User Actions in Your Application
Introduction
You’ve expressed a desire to integrate your Large Language Model (LLM) more deeply into your application by having it informed of every action the user takes. This means that for every CRUD (Create, Read, Update, Delete) operation, the LLM should receive detailed information about:
- Which record was affected
- What action was performed
- Additional details like before and after states for updates
The goal is to have the LLM comment on these actions in real-time via the existing chatq interface, enhancing user engagement and making it clear that the LLM is actively participating.
Table of Contents
- Understanding the Current Structure
- Modifying the
BaseAppClass - Updating Derived Classes
- Testing the Changes
- Additional Considerations
- Conclusion
1. Understanding the Current Structure
Your application consists of:
BaseAppClass: Provides common CRUD operations.TodoAppandProfileApp: Derived classes for specific functionalities.chatqFunction: Queues messages for the chat interface, sending prompts to the LLM.- WebSockets: For real-time communication and streaming responses.
Currently, some methods in BaseApp already include calls to chatq with prompts to inform the LLM of certain actions. Our goal is to ensure that all CRUD operations are consistently informing the LLM with detailed information.
2. Modifying the BaseApp Class
a. Informing the LLM in CRUD Methods
We need to ensure that each CRUD method in BaseApp sends a detailed prompt to the LLM via chatq. Let’s review and update each method.
insert_item Method
Current Code:
async def insert_item(self, request):
try:
form = await request.form()
new_item_data = self.prepare_insert_data(form)
if not new_item_data:
return ''
new_item = await self.create_item(**new_item_data)
item_name = getattr(new_item, self.item_name_field, 'Item')
# Inform the LLM
prompt = f"The user added a new item '{item_name}' (ID: {new_item.id}) to {self.name}."
await chatq(prompt)
return self.render_item(new_item)
except Exception as e:
logger.error(f"Error inserting {self.name}: {str(e)}")
return str(e), 500
Explanation:
- After creating
new_item, we retrieve its name usingself.item_name_field. - We construct a prompt detailing the action.
- We send the prompt to the LLM via
await chatq(prompt).
update_item Method
Current Code:
async def update_item(self, request, item_id: int):
try:
form = await request.form()
update_data = self.prepare_update_data(form)
if not update_data:
return ''
item = self.table[item_id]
# Capture before state
before_state = item.__dict__.copy()
# Update the item
for key, value in update_data.items():
setattr(item, key, value)
updated_item = self.table.update(item)
# Capture after state
after_state = updated_item.__dict__
# Determine changes
changes = []
for key in update_data.keys():
before = before_state.get(key)
after = after_state.get(key)
if before != after:
changes.append(f"{key} changed from '{before}' to '{after}'")
changes_str = '; '.join(changes)
item_name = getattr(updated_item, self.item_name_field, 'Item')
# Inform the LLM
prompt = f"The user updated '{item_name}' (ID: {item_id}) in {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"Updated {self.name} item {item_id}")
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error updating {self.name} {item_id}: {str(e)}")
return str(e), 500
Explanation:
- We capture the before state of the item.
- After updating, we capture the after state.
- We compare the states to identify what has changed.
- We construct a prompt including the changes.
- We send the prompt to the LLM.
delete_item Method
Current Code:
async def delete_item(self, request, item_id: int):
try:
# Retrieve item details before deletion
item = self.table[item_id]
item_name = getattr(item, self.item_name_field, 'Item')
# Delete the item
self.table.delete(item_id)
logger.info(f"Deleted item ID: {item_id}")
# Inform the LLM
prompt = f"The user deleted '{item_name}' (ID: {item_id}) from {self.name}."
await chatq(prompt)
return ''
except Exception as e:
logger.error(f"Error deleting item: {str(e)}")
return f"Error deleting item: {str(e)}", 500
Explanation:
- Before deletion, we retrieve the item’s details.
- We inform the LLM about which item was deleted.
toggle_item Method
Current Code:
async def toggle_item(self, request, item_id: int):
try:
item = self.table[item_id]
current_status = getattr(item, self.toggle_field)
setattr(item, self.toggle_field, not current_status)
updated_item = self.table.update(item)
item_name = getattr(updated_item, self.item_name_field, 'Item')
new_status = 'completed' if getattr(updated_item, self.toggle_field) else 'not completed'
# Inform the LLM
prompt = f"The user marked '{item_name}' (ID: {item_id}) as {new_status} in {self.name}."
await chatq(prompt)
return self.render_item(updated_item)
except Exception as e:
logger.error(f"Error toggling item: {str(e)}")
return f"Error toggling item: {str(e)}", 500
Explanation:
- We update the item’s status.
- We inform the LLM about the new status.
sort_items Method
Current Code:
async def sort_items(self, request):
logger.debug(f"Received request to sort {self.name}.")
try:
values = await request.form()
items = json.loads(values.get('items', '[]'))
logger.debug(f"Parsed items: {items}")
changes = []
for item in items:
item_id = int(item['id'])
priority = int(item['priority'])
self.table.update(id=item_id, **{self.sort_field: priority})
# Get item name
item_name = getattr(self.table[item_id], self.item_name_field, 'Item')
changes.append(f"'{item_name}' moved to position {priority}")
changes_str = '; '.join(changes)
# Inform the LLM
prompt = f"The user reordered {self.name}: {changes_str}."
await chatq(prompt)
logger.info(f"{self.name.capitalize()} order updated successfully")
return ''
except json.JSONDecodeError as e:
logger.error(f"JSON decode error: {str(e)}")
return "Invalid data format", 400
except Exception as e:
logger.error(f"Error updating {self.name} order: {str(e)}")
return str(e), 500
Explanation:
- We process the sorting data.
- For each item, we record its new position.
- We inform the LLM about the reordering.
b. Handling Before and After States
For updates, it’s crucial to capture the state of the item before and after the change.
Implementation Details:
- Before State: Use
item.__dict__.copy()to capture the current state. - After State: After updating, access the updated item’s
__dict__. - Change Detection: Compare the values for each updated field.
3. Updating Derived Classes
a. Ensuring item_name_field is Set
Both TodoApp and ProfileApp need to specify the item_name_field so that BaseApp can access the item’s name correctly.
In TodoApp Constructor:
class TodoApp(BaseApp):
def __init__(self, table):
super().__init__(
name=table.name,
table=table,
toggle_field='done',
sort_field='priority'
)
self.item_name_field = 'name' # Ensure the correct field is set
In ProfileApp Constructor:
class ProfileApp(BaseApp):
def __init__(self, table):
super().__init__(
name=table.name,
table=table,
toggle_field='active',
sort_field='priority'
)
self.item_name_field = 'name' # Ensure the correct field is set
4. Testing the Changes
After making the modifications, test each CRUD operation:
- Create:
- Add new todos and profiles.
- Verify the LLM comments on the addition.
- Read:
- Ensure that displaying items doesn’t need LLM interaction (unless desired).
- Update:
- Modify existing items.
- Confirm the LLM reports the changes with before and after states.
- Delete:
- Remove items.
- Check that the LLM acknowledges the deletion.
- Toggle:
- Change the status of items.
- Ensure the LLM comments on the status change.
- Sort:
- Reorder items.
- Verify the LLM describes the new order.
5. Additional Considerations
Concurrency and Asynchronous Operations
- Ensure that all
awaitstatements are correctly used to avoid blocking the event loop. - For any database operations, confirm they are compatible with async functions.
Error Handling
- Robustly handle exceptions to prevent the application from crashing.
- Ensure that errors are logged and appropriate messages are sent to the user.
LLM Prompt Design
- Craft prompts in a way that the LLM can generate meaningful and concise responses.
- Consider limiting the response length to keep the interaction snappy.
User Experience
- The LLM’s responses should enhance the user’s experience without overwhelming them.
- Balance the frequency and verbosity of the LLM’s comments.
6. Conclusion
By systematically modifying your BaseApp class and ensuring the derived classes are properly configured, you’ve integrated the LLM into every user action. The LLM is now fully informed of:
- The specific records affected.
- The nature of the action taken.
- Detailed changes for updates.
This setup not only enhances the interactivity of your application but also lays a solid foundation for future integration with vector embeddings and more complex database systems.
Final Notes:
- The provided code snippets are directly applicable to your existing codebase.
- Ensure you test thoroughly after implementing these changes.
- Feel free to adjust the prompts sent to the LLM to better suit your application’s tone and style.
Example Usage Scenario:
-
User Adds a Todo Item
- User inputs “Buy groceries” and submits.
- The
insert_itemmethod creates the new item. - LLM receives the prompt:
"The user added a new item 'Buy groceries' (ID: 5) to todos." - LLM responds:
"Got it! 'Buy groceries' has been added to your todo list."
-
User Updates a Profile
- User changes the profile name from “Work” to “Office”.
- The
update_itemmethod captures the change. - LLM receives the prompt:
"The user updated 'Work' (ID: 2) in profiles: name changed from 'Work' to 'Office'." - LLM responds:
"Profile 'Work' is now updated to 'Office'. Keeping things organized!"
-
User Deletes a Todo Item
- User deletes “Read a book”.
- The
delete_itemmethod processes the deletion. - LLM receives the prompt:
"The user deleted 'Read a book' (ID: 3) from todos." - LLM responds:
"Removed 'Read a book' from your todos. Maybe next time!"
Feel free to reach out if you need further clarification or assistance with these modifications!
And Claude 3.5 sonnet’s reply (built into Cursor AI)…
Claude 3.5 sonnet:
Thank you for providing such a comprehensive guide on enhancing LLM interaction with user actions in the application. The current implementation already covers most of the requirements you’ve outlined. Let’s review the existing code and make any necessary adjustments to ensure full compliance with your desired functionality.
Here’s a summary of the current state and any additional changes we might need:
- Insert Item: The current implementation already informs the LLM about new items being added.
- Update Item: The existing code captures before and after states and informs the LLM about changes.
- Delete Item: The current implementation already informs the LLM about item deletion.
- Toggle Item: The existing code informs the LLM about status changes.
- Sort Items: The current implementation informs the LLM about reordering.
The main areas we might want to focus on are:
- Ensuring consistency across all methods.
- Potentially adding more detail to some of the prompts.
- Making sure error handling is consistent and informative.
Let’s review and update the BaseApp class to ensure all these aspects are covered:
class BaseApp:
def __init__(self, name, table, toggle_field=None, sort_field=None, sort_dict=None):
self.name = name
self.table = table
self.toggle_field = toggle_field
self.sort_field = sort_field
self.item_name_field = 'name' # Default field name
self.sort_dict = sort_dict or {'id': 'id', sort_field: sort_field}
async def insert_item(self, request):
try:
form = await request.form()
new_item_data = self.prepare_insert_data(form)
if not new_item_data:
return ''
new_item = await self.create_item(**new_item_data)
item_name = getattr(new_item, self.item_name_field, 'Item')
prompt = f"The user added a new {self.name} item '{item_name}' (ID: {new_item.id}). Details: {new_item_data}"
await chatq(prompt)
return self.render_item(new_item)
except Exception as e:
error_msg = f"Error inserting {self.name}: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while adding a new {self.name}: {error_msg}")
return str(e), 500
async def update_item(self, request, item_id: int):
try:
form = await request.form()
update_data = self.prepare_update_data(form)
if not update_data:
return ''
item = self.table[item_id]
before_state = item.__dict__.copy()
for key, value in update_data.items():
setattr(item, key, value)
updated_item = self.table.update(item)
after_state = updated_item.__dict__
changes = [f"{key} changed from '{before_state.get(key)}' to '{after_state.get(key)}'"
for key in update_data.keys() if before_state.get(key) != after_state.get(key)]
changes_str = '; '.join(changes)
item_name = getattr(updated_item, self.item_name_field, 'Item')
prompt = f"The user updated the {self.name} item '{item_name}' (ID: {item_id}). Changes: {changes_str}"
await chatq(prompt)
logger.info(f"Updated {self.name} item {item_id}")
return self.render_item(updated_item)
except Exception as e:
error_msg = f"Error updating {self.name} {item_id}: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while updating {self.name} (ID: {item_id}): {error_msg}")
return str(e), 500
async def delete_item(self, request, item_id: int):
try:
item = self.table[item_id]
item_name = getattr(item, self.item_name_field, 'Item')
self.table.delete(item_id)
logger.info(f"Deleted {self.name} item ID: {item_id}")
prompt = f"The user deleted the {self.name} item '{item_name}' (ID: {item_id})."
await chatq(prompt)
return ''
except Exception as e:
error_msg = f"Error deleting {self.name} item: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while deleting {self.name} (ID: {item_id}): {error_msg}")
return str(e), 500
async def toggle_item(self, request, item_id: int):
try:
item = self.table[item_id]
current_status = getattr(item, self.toggle_field)
setattr(item, self.toggle_field, not current_status)
updated_item = self.table.update(item)
item_name = getattr(updated_item, self.item_name_field, 'Item')
new_status = 'completed' if getattr(updated_item, self.toggle_field) else 'not completed'
prompt = f"The user marked the {self.name} item '{item_name}' (ID: {item_id}) as {new_status}."
await chatq(prompt)
return self.render_item(updated_item)
except Exception as e:
error_msg = f"Error toggling {self.name} item: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while toggling {self.name} (ID: {item_id}): {error_msg}")
return str(e), 500
async def sort_items(self, request):
logger.debug(f"Received request to sort {self.name}.")
try:
values = await request.form()
items = json.loads(values.get('items', '[]'))
logger.debug(f"Parsed items: {items}")
changes = []
for item in items:
item_id = int(item['id'])
priority = int(item['priority'])
self.table.update(id=item_id, **{self.sort_field: priority})
item_name = getattr(self.table[item_id], self.item_name_field, 'Item')
changes.append(f"'{item_name}' moved to position {priority}")
changes_str = '; '.join(changes)
prompt = f"The user reordered {self.name} items: {changes_str}"
await chatq(prompt)
logger.info(f"{self.name.capitalize()} order updated successfully")
return ''
except json.JSONDecodeError as e:
error_msg = f"Invalid data format: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while sorting {self.name} items: {error_msg}")
return "Invalid data format", 400
except Exception as e:
error_msg = f"Error updating {self.name} order: {str(e)}"
logger.error(error_msg)
await chatq(f"An error occurred while sorting {self.name} items: {error_msg}")
return str(e), 500
These updates ensure that:
- All CRUD operations inform the LLM with detailed information.
- Error handling is consistent across all methods.
- The LLM is informed about errors as well as successful operations.
- Prompts are detailed and consistent in format across all methods.
The TodoApp and ProfileApp classes don’t need any changes as they already set the item_name_field correctly.
To test these changes:
- Implement these updates in your
BaseAppclass. - Run through each CRUD operation for both todos and profiles.
- Check the chat interface to ensure the LLM is receiving and responding to all actions.
- Test error scenarios (e.g., trying to delete a non-existent item) to ensure error handling works as expected.
This implementation should provide a comprehensive integration of the LLM with all user actions in your application. Let me know if you need any further adjustments or have any questions!
A Language For That
Me to Claude: I would like to use the chatq as a hook to allow me to log every action the user takes in the user interface. To that end, I’m thinking that we add an optional (for now) second parameter to chatq of datatype str, the idea being that this will be stringified json that can be used to help the local Ollama LLM itself learn the operations that are possible in the system by being able to peek in on the operations. At first, I only want to use this for logging, but soon I will also incorporate it as part of the prompts inside chatq so the LLM can attempt to reproduce such actions in the system. The LLM will have some sort of search interface with the log in order to help it figure out how to construct and carry out CRUD operations. Please advise me on next steps.
Claude 3.5 sonnet: Your idea to enhance the chatq function to log user actions and prepare for future LLM integration is excellent. This approach will create a rich dataset for the LLM to learn from and eventually interact with the system more intelligently. Here’s how we can modify the chatq function and outline the next steps:
- First, let’s modify the
chatqfunction:
import json
import asyncio
import logging
logger = logging.getLogger(__name__)
async def chatq(message: str, action_data: str = None):
"""
Queue a message for the chat stream and log the action data.
Args:
message (str): The message to queue for streaming.
action_data (str, optional): JSON string containing action details.
"""
# Create an asynchronous task to stream the chat response
asyncio.create_task(stream_chat(message))
logger.debug(f"Message queued for chat: {message}")
if action_data:
try:
# Parse the JSON string to ensure it's valid
action_json = json.loads(action_data)
# Log the action data
logger.info(f"User action: {json.dumps(action_json, indent=2)}")
# Here you would add code to store this in your chosen database
await store_action_log(action_json)
except json.JSONDecodeError:
logger.error(f"Invalid JSON in action_data: {action_data}")
async def store_action_log(action_json):
"""
Store the action log in the database.
This is a placeholder function - implement according to your database choice.
"""
# TODO: Implement database storage
pass
-
Next steps and recommendations:
a. Implement
store_action_log:- Choose a database suitable for storing logs (e.g., MongoDB, Elasticsearch).
- Implement the
store_action_logfunction to save the action data.
b. Modify CRUD operations:
- Update all CRUD operations in
BaseAppto include theaction_dataparameter when callingchatq. - Structure the
action_dataconsistently. For example:
action_data = json.dumps({
"action_type": "insert",
"table": self.name,
"item_id": new_item.id,
"item_name": getattr(new_item, self.item_name_field, 'Item'),
"details": new_item_data
})
await chatq(prompt, action_data)
c. Create a search interface for the LLM: - Implement a function that allows the LLM to query the action log database. - This could be a simple keyword search or a more advanced semantic search.
d. Enhance prompts with action data:
- Modify the stream_chat function to incorporate relevant past actions in its prompts.
- This might involve searching the log for similar actions and including them in the context.
e. Implement LLM-driven CRUD operations: - Create a new function that takes natural language input, searches the action log, and attempts to construct and execute a CRUD operation. - This function would use the LLM to interpret the user’s intent and map it to a specific CRUD operation.
f. Safety and validation: - Implement strict validation for any LLM-generated CRUD operations to ensure system safety. - Consider implementing a “dry run” mode where the LLM can propose actions without executing them.
g. User feedback loop: - Implement a way for users to provide feedback on the LLM’s actions, which can be used to further train or fine-tune the model.
h. Privacy and security: - Ensure that sensitive data is not logged or is properly anonymized in the action logs. - Implement access controls to the action log database.
- Example of how a CRUD operation might look after these changes:
async def insert_item(self, request):
try:
# ... existing code ...
action_data = json.dumps({
"action_type": "insert",
"table": self.name,
"item_id": new_item.id,
"item_name": getattr(new_item, self.item_name_field, 'Item'),
"details": new_item_data
})
prompt = f"{CRUD_PROMPT_PREFIXES['insert']}{action_details}"
await chatq(prompt, action_data)
# ... rest of the method ...
except Exception as e:
# ... error handling ...
These steps will set up a robust system for logging user actions, which the LLM can then use to learn and interact with your application more intelligently. The key is to start with good logging practices and gradually build up the LLM’s capabilities to interpret and act on this data.
Me: And this is working as a transparent pass through right now, until I implement the log or database for actions?
Claude: Yes, that’s correct. Right now, this implementation acts as a transparent pass-through. The action_data is being prepared and passed to chatq, but it’s not being stored or used beyond logging (if logging is enabled).
To clarify the current behavior and next steps, let’s review the chatq function:
async def chatq(message: str, action_data: str = None):
"""
Queue a message for the chat stream and log the action data.
Args:
message (str): The message to queue for streaming.
action_data (str, optional): JSON string containing action details.
"""
# Create an asynchronous task to stream the chat response
asyncio.create_task(stream_chat(message))
logger.debug(f"Message queued for chat: {message}")
if action_data:
try:
# Parse the JSON string to ensure it's valid
action_json = json.loads(action_data)
# Log the action data
logger.info(f"User action: {json.dumps(action_json, indent=2)}")
# Here you would add code to store this in your chosen database
await store_action_log(action_json)
except json.JSONDecodeError:
logger.error(f"Invalid JSON in action_data: {action_data}")
async def store_action_log(action_json):
"""
Store the action log in the database.
This is a placeholder function - implement according to your database choice.
"""
# TODO: Implement database storage
pass
Currently:
- The
action_datais being passed tochatq. - If
action_datais provided, it’s parsed and logged (if logging is enabled). - The
store_action_logfunction is called, but it’s currently just a placeholder.
To fully implement this feature, you’ll need to:
- Choose a database or storage solution for the action logs.
- Implement the
store_action_logfunction to actually store the data. - (Optional) Implement a retrieval mechanism for the stored logs.
Until these steps are completed, the system will continue to function as before, with the added benefit of logging the action data if logging is enabled. This approach allows you to gradually implement and test the new features without disrupting the existing functionality.