verdi-llm 0.1.3

An LLM tool for an even easier use of AiiDA.

verdi-llm

verdi-llm helps AiiDA users generate verdi commands using natural language queries. Instead of memorizing complex command syntax, simply describe what you want to do, and verdi-llm will suggest the appropriate command.

Installation

pip install verdi-llm

Quick Start

Interactive Setup Process:

1. Choose a backend (currently supports: groq)
2. Enter your API key for the selected backend
3. Wait for embeddings to be generated (one-time setup)

Example:

$ verdi-llm configure
This command will help you choose and configure an LLM backend for AiiDA.

Please follow the instructions below to set up your preferred LLM backend.
Step #1 Choose a backend:
      groq
Your choice: groq
Step #2 Enter your API key for groq:
Your API key: gsk_xxxxxxxxxxxxxxxxxxxx

Configuration saved to ~/.aiida/llm/config.json
You can now use the verdi smart command to interact with the LLM backend.

Non-Interactive Setup Process: verdi-llm configure --backend groq --api-key <API-KEY> -- to generate a key see here

2. Generate Commands

Use the cli command to get AI-powered command suggestions:

verdi-llm cli "your question here"

Example:

verdi-llm cli "how do I list all my calculations"

Output:

💡 Suggested command (in 1.2 seconds): 
verdi process list

[E]xecute, [M]odify, [C]ancel? 

3. Interactive Shell Mode

Loading embeddings takes a few seconds, which can become annoying if you plan to ask multiple questions. It's better to load them once with the interactive shell:

verdi-llm shell

User Workflows

Workflow 1: Quick Command Generation

Goal: Get a single command suggestion and execute it

# Step 1: Ask your question
verdi-llm cli "show me the status of my computer setup"

# Step 2: Review the suggestion
💡 Suggested command (in 0.8 seconds): 
verdi computer list --all

# Step 3: Choose action
[E]xecute, [M]odify, [C]ancel? e

# Step 4: Command executes automatically
$ verdi computer list --all
[Command output appears here]

Workflow 2: Interactive Shell Session

Goal: Work in an interactive environment with persistent AI assistance

# Launch shell
verdi-llm shell

# The shell loads AI models (one-time per session)
🚀 Initializing verdi-llm shell...
   Loading SentenceTransformer (this may take a few seconds)...
✅ SentenceTransformer loaded successfully!
🎯 verdi-llm shell ready!

# Use regular commands
verdi-llm:~/projects$ ls
project1/  project2/  data/

# Ask AI for help
verdi-llm:~/projects$ cli create a new calculation job
💡 Suggested command:
verdi process submit

[E]xecute, [M]odify, [C]ancel? m
Enter modified command: verdi process submit --help

# Regular shell features work
verdi-llm:~/projects$ cd project1
verdi-llm:~/projects/project1$ 

# Exit when done
verdi-llm:~/projects/project1$ exit
👋 Exiting verdi-llm shell

Command Examples

Basic Usage Examples

Natural language query	Typical suggested command
"is my computer configured correctly"	verdi computer configure show
"show details of process 123"	verdi process show 123
"get information about node 456"	verdi node show 456

Advanced Usage Examples

Complex queries	Suggested command patterns
"Delete the working directories of all finished calculation jobs since the last month on cluster1 and cluster2 without asking for confirmation"	verdi calcjob cleanworkdir --past-days 30 --computers cluster1,cluster2 --exit-status 0 --force
"Export band structure data node 98765 as a matplotlib PDF with y-axis range from -5 to 10, LaTeX-formatted labels, and saves it to bands.pdf"	verdi data core.bands export 98765 --format mpl_pdf --y-min-lim -5 --y-max-lim 10 --prettify-format latex --output bands.pdf

RAG System Insights

The system uses Retrieval-Augmented Generation (RAG) to find relevant commands:

1. Your query → Converted to embeddings
2. Similarity search → Finds top 3 most relevant commands
3. Context building → Includes command usage, descriptions, and options
4. AI generation → Creates specific command based on context

Troubleshooting

Common Issues and Solutions

Issue: "No configuration file found"

Solution: Run `verdi-llm configure` first

Issue: AI responses are slow

Possible causes:
- First-time embedding generation
- Network latency to LLM backend
- Large command database

Solutions:
- Use the shell mode for faster subsequent queries
- Check your internet connection
- Wait for initial setup to complete

Issue: Commands don't work as expected

Solutions:
- Use the [M]odify option to adjust commands
- Check if you have the required AiiDA setup
- Verify command parameters match your environment

API Backend Information

Groq Integration

Currently supports Groq's API with the following models:

• Default: llama3-8b-8192
• Features: Fast inference, good command understanding
• Rate limits: Subject to Groq's API limitations

Adding New Backends

The architecture supports additional backends. Future versions may include more backends. If you prefer another backend, please open an issue with your suggestion!

Support and Feedback

• Report issues with specific query examples
• Suggest improvements for command understanding
• Share successful query patterns