These automations and macros enhance mindmaps created by Mindjet Mindmanager.
- Azure OpenAI w/ GPT4 Turbo (use your key) -> best tested
- OpenAI w/ GPT-4o (use your key) -> best results
- Anthropic w/ Claude 3 (use your key)
- Groq (platform) w/ LLama3 (use your key)
- Perplexity (platform) w/ LLama3 (use your key)
- Google Gemini w/ Pro and Flash (use your key)
- Google Vertex AI w/ Gemini Pro and Gemini Flash (use your access token)
- Ollama (local) w/ any LLM (use LLama3, Zephyr or Mixtral model for best results)
- MLX (local w/ Apple Silicon) w/ any LLM (use LLama3 model for best results)
- Windows compatible (run macro/context menu or call the Python script directly)
- macOS compatible (run Automator workflow (Quick Action) or call the Python script directly)
- Just native LLM requests via API calls - no middleware needed
- Map format can be radial map or orgchart
- Using map templates on MAC
- Map styles on Windows are persistent, automatic collapsing of nodes
- Refinement of the map or topic.
- Refinement of the map or topic from a development perspective.
- Create examples for one, more (selected) or all topics.
- Clustering topics from scratch.
- Clustering by one or more criterias eg. Organization/Process/Project/Expertise, Capex-Opex perspective.
- Complex cases (multiple calls): eg. refinement + clustering + examples.
Python has to be installed first (eg. using Chocolatey).
Run install.bat or
pip install --upgrade requests
pip install --upgrade pywin32
Macros can be registered directly by merging the macro_registration.reg to the registry. Hint: view order in Mindmanager is sorted by the GUIDs. All macros can then be executed using the context menu of topics (right mouse button click).
Macros can also be executed by the Macro Editor. The macros are similar but the action parameter.
Python has to be installed first (eg. using Homebrew).
pip install --upgrade requests
pip install --upgrade appscript
Automator workflows can be copied to the Services folder by executing the copy_to_services.sh shell script. To make the script executable:
chmod +x ./copy_to_services.sh
All Automator workflow settings are similar but the action parameter:
The workflows are then available at the "MindManager" main menu -> Services
The solution ist best tested with Azure OpenAI. Results are perfect but slow for every use case. Execution time is far better with OpenAI / GPT-4o.
Gemini Pro results are good. Gemini Flash does (most of the time) only generate up to 3 levels at max, so a refinement does currently not work.
Ollama results are dependent on the used model. LLama3, Zephyr and Mixtral are working well.
Anthropic Claude 3 results are ogood. The OPUS model is a little bit expensive.
groq is sure the fastest LLM platform by now. Payment for API usage is still unclear because there is no way to set a payment method (as of 2024-05-05).
Perplexity works perfekt as an univeral LLM platform.
MLX results are dependent on the used model. LLama3 works well.
LLM Api relevant information should be stored in environment variables and mapped to the corresponding variables in the config.py file. Not every parameter is used at the moment (token count, levels deep etc.).
# GPT4(o), best in class
# CLOUD_TYPE = 'AZURE' # best, uncensored(?)
# CLOUD_TYPE = 'OPENAI' # best, uncensored(?)
# Ollama (local models), best results
# CLOUD_TYPE = 'OLLAMA+mixtral' # best, censored
# CLOUD_TYPE = 'OLLAMA+solar' # best, uncensored
# CLOUD_TYPE = 'OLLAMA+mistral' # best, uncensored
# CLOUD_TYPE = 'OLLAMA+openchat' # very good, uncensored
# CLOUD_TYPE = 'OLLAMA+zephyr' # very good, uncensored *
# CLOUD_TYPE = 'OLLAMA+neural-chat' # good, uncensored
# CLOUD_TYPE = 'OLLAMA+wizardlm2' # very good, uncensored (with warnings)
# CLOUD_TYPE = 'OLLAMA+llama3' # good, uncensored
# CLOUD_TYPE = 'OLLAMA+llama3:70b' # good, censored, slow
# CLOUD_TYPE = 'OLLAMA+phi3' # good, censored
# Ollama (local models), not working
# CLOUD_TYPE = 'OLLAMA+mistral-openorca' # bad, uncensored
# CLOUD_TYPE = 'OLLAMA+phi' # not working
# CLOUD_TYPE = 'OLLAMA+llama2' # not working
# CLOUD_TYPE = 'OLLAMA+llama2-uncensored' # not working
# CLOUD_TYPE = 'OLLAMA+wizard-vicuna-uncensored' # not working
# CLOUD_TYPE = 'OLLAMA+yi' # not working
# Google Gemini (use with VPN)
CLOUD_TYPE = 'GEMINI_PRO' # good
# CLOUD_TYPE = 'GEMINI_FLASH' # one-shot ok, generates maps only 3 levels deep
# CLOUD_TYPE = 'GEMINIPROJECT_PRO' # good
# CLOUD_TYPE = 'GEMINIPROJECT_FLASH' # one-shot ok, generates maps only 3 levels deep
# Claude3
# CLOUD_TYPE = 'CLAUDE3_OPUS' # good
# CLOUD_TYPE = 'CLAUDE3_SONNET' # good
# CLOUD_TYPE = 'CLAUDE3_HAIKU' # good
# groq
# CLOUD_TYPE = 'GROQ+mixtral-8x7b-32768' # good
# CLOUD_TYPE = 'GROQ+llama3-8b-8192' # good
# CLOUD_TYPE = 'GROQ+llama3-70b-8192' # good
# CLOUD_TYPE = 'GROQ+gemma-7b-it' # good
# Perplexity
# CLOUD_TYPE = 'PERPLEXITY+mistral-7b-instruct' # deprecated
# CLOUD_TYPE = 'PERPLEXITY+mixtral-8x7b-instruct' # deprecated
# CLOUD_TYPE = 'PERPLEXITY+llama-3-8b-instruct' # ok
# CLOUD_TYPE = 'PERPLEXITY+llama-3-70b-instruct' # good
# CLOUD_TYPE = 'PERPLEXITY+llama-3-sonar-small-32k-chat' # ok
# CLOUD_TYPE = 'PERPLEXITY+llama-3-sonar-large-32k-chat' # good
# CLOUD_TYPE = 'PERPLEXITY+llama-3-sonar-small-32k-online' # reduced usability
# CLOUD_TYPE = 'PERPLEXITY+llama-3-sonar-large-32k-online' # good
# MLX server, MACOS only (pip install mlx-lm)
# python -m mlx_lm.server --model mlx-community/Meta-Llama-3-8B-Instruct-4bit --port 8080 --log-level DEBUG
# CLOUD_TYPE = 'MLX+llama3-8b' # good
LLM_TEMPERATURE = float('0.1')
MAX_TOKENS = int('4000')
MAX_RETRIES = int('3')
TOP_MOST_RESULTS = int('5')
MAX_RETURN_WORDS = int('5')
LEVELS_DEEP = int('5')
INDENT_SIZE = int('2')
LINE_SEPARATOR = "\n"
OPENAI_COMPATIBILITY = False
if CLOUD_TYPE == "OPENAI":
OPENAI_COMPATIBILITY = True
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY_NATIVE')
OPENAI_API_URL = "https://api.openai.com/v1/chat/completions"
OPENAI_DEPLOYMENT = ""
OPENAI_API_VERSION = ""
OPENAI_MODEL = "gpt-4o" # only for OPENAI relevant
API_URL = OPENAI_API_URL
KEY_HEADER_TEXT = "Authorization"
KEY_HEADER_VALUE = "Bearer " + OPENAI_API_KEY
elif CLOUD_TYPE == "AZURE":
OPENAI_COMPATIBILITY = True
OPENAI_API_KEY = os.getenv('OPENAI2_API_KEY')
OPENAI_API_URL = os.getenv('OPENAI2_API_BASE')
OPENAI_DEPLOYMENT = os.getenv('OPENAI2_DEPLOYMENT')
OPENAI_API_VERSION = os.getenv('OPENAI2_API_VERSION')
OPENAI_MODEL = ""
API_URL = f"{OPENAI_API_URL}openai/deployments/{OPENAI_DEPLOYMENT}/chat/completions?api-version={OPENAI_API_VERSION}"
KEY_HEADER_TEXT = "api-key"
KEY_HEADER_VALUE = OPENAI_API_KEY
elif "GEMINI" in CLOUD_TYPE:
system = CLOUD_TYPE.split("_")[0]
if system == "GEMINI":
model = CLOUD_TYPE.split("_")[-1]
if model == "PRO":
MODEL_ID = "gemini-1.5-pro-latest"
elif model == "FLASH":
MODEL_ID = "gemini-1.5-flash-latest"
else:
raise Exception("Error: Unknown GEMINI model")
GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY_AI')
API_URL = f"https://generativelanguage.googleapis.com/v1beta/models/{MODEL_ID}:generateContent?key={GOOGLE_API_KEY}"
KEY_HEADER_TEXT = ""
KEY_HEADER_VALUE = ""
elif system == "GEMINIPROJECT":
# cloud.google.com/vertex-ai/docs/generative-ai/start/quickstarts/quickstart-multimodal
# Service Account / Key -> Create new key -> JSON
# gcloud auth activate-service-account --key-file=<path/to/your/keyfile.json>
# gcloud auth print-access-token
model = CLOUD_TYPE.split("_")[-1]
if model == "PRO":
MODEL_ID = "gemini-1.5-pro-preview-0514"
elif model == "FLASH":
MODEL_ID = "gemini-1.5-flash-preview-0514"
else:
raise Exception("Error: Unknown GEMINI model")
PROJECT_ID = os.getenv('GOOGLE_PROJECT_ID_AI')
API_ENDPOINT="us-central1-aiplatform.googleapis.com"
LOCATION_ID="us-central1"
GOOGLE_ACCESS_TOKEN = os.getenv('GOOGLE_ACCESS_TOKEN_AI') # limited time use
API_URL = f"https://{API_ENDPOINT}/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION_ID}/publishers/google/models/{MODEL_ID}:generateContent"
KEY_HEADER_TEXT = "Authorization"
KEY_HEADER_VALUE = "Bearer " + GOOGLE_ACCESS_TOKEN
else:
raise Exception("Error: Unknown GEMINI system")
elif "OLLAMA" in CLOUD_TYPE:
OPENAI_COMPATIBILITY = True
MODEL_ID = CLOUD_TYPE.split("+")[-1]
if OPENAI_COMPATIBILITY:
API_URL="http://localhost:11434/v1/chat/completions"
else:
API_URL="http://localhost:11434/api/generate"
elif "CLAUDE3" in CLOUD_TYPE:
OPENAI_COMPATIBILITY = True
model = CLOUD_TYPE.split("_")[-1]
if model == "HAIKU":
MODEL_ID = "claude-3-haiku-20240307"
elif model == "SONNET":
MODEL_ID = "claude-3-sonnet-20240229"
elif model == "OPUS":
MODEL_ID = "claude-3-opus-20240229"
else:
raise Exception("Error: Unknown CLAUDE3 model")
ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY')
ANTHROPIC_VERSION="2023-06-01"
KEY_HEADER_TEXT = "x-api-key"
KEY_HEADER_VALUE = ANTHROPIC_API_KEY
API_URL="https://api.anthropic.com/v1/messages"
elif "GROQ" in CLOUD_TYPE:
OPENAI_COMPATIBILITY = True
MODEL_ID = CLOUD_TYPE.split("+")[-1]
GROQ_API_KEY = os.getenv('GROQ_API_KEY')
KEY_HEADER_TEXT = "Authorization"
KEY_HEADER_VALUE = "Bearer " + GROQ_API_KEY
API_URL="https://api.groq.com/openai/v1/chat/completions"
elif "PERPLEXITY" in CLOUD_TYPE:
OPENAI_COMPATIBILITY = True
MODEL_ID = CLOUD_TYPE.split("+")[-1]
PERPLEXITY_API_KEY = os.getenv('PERPLEXITY_API_KEY')
KEY_HEADER_TEXT = "Authorization"
KEY_HEADER_VALUE = "Bearer " + PERPLEXITY_API_KEY
API_URL="https://api.perplexity.ai/chat/completions"
elif "MLX" in CLOUD_TYPE:
OPENAI_COMPATIBILITY = True
MODEL_ID = CLOUD_TYPE.split("+")[-1] # not used, depends on how the server was started
API_URL="http://localhost:8080/v1/chat/completions"Prompt crafting is lightly implemented using the following strategy:
MindManager COM objects are addressed by using the PyWin32 library:
MindManager objects are addressed by using the AppScript library:
The Mermaid mindmap syntax is used when talking to the OpenAI LLM as an intermediate "language". Log file contents for input, output, prompt can be used in other use cases eg. mindmap visualizations in GitHub markdown files.
Log files content:
Example using a Mermaid mindmap in a (this!) GitHub markdown file.
Code:
```mermaid
mindmap
Creating an AI Startup
Market Research
Identify Target Audience
Analyze Competitors
Understand Market Trends
Assess Market Needs
Evaluate Market Size
Business Model
Define Value Proposition
Choose Revenue Streams
Plan Monetization Strategy
Identify Cost Structure
Determine Key Partnerships```
Github rendering of the (this!) map:
mindmap
Creating an AI Startup
Market Research
Identify Target Audience
Analyze Competitors
Understand Market Trends
Assess Market Needs
Evaluate Market Size
Business Model
Define Value Proposition
Choose Revenue Streams
Plan Monetization Strategy
Identify Cost Structure
Determine Key Partnerships
You can also use the content inside the Mermaid online editor (https://mermaid.live/edit):
The API execution time can be somewhat lengthy, typically around 20 seconds or more per call. For more intricate use cases, this duration can extend to 2 minutes or more, with execution time also varying based on token count.
Currently, this project is in the early development phase, and generated outputs may include errors. Automated testing has not yet been implemented.
Should a runtime error occur, please attempt at least a second call to the process ;-)