notdiamond 0.2.5b0

Not Diamond Python Library

Getting started with Not Diamond

Not Diamond automatically determines which model is best-suited to respond to any query, drastically improving LLM output quality while reducing costs and latency and avoiding vendor lock-in. Unlike any other model router, Not Diamond is 100% privacy-preserving and continuously adapts to your preferences (demo video).

Installation

Requires Python 3.9+

pip install notdiamond

If your application isn't in Python, you can directly call our REST API endpoint.

Key features

• Maintain privacy: All inputs are fuzzy hashed before being sent to the Not Diamond API. We return a label for the recommended model and LLM calls go out client-side. This means we never see your raw query strings or your response outputs.
• Maximize performance: Not Diamond outperforms Claude 3 Opus on major evaluation benchmarks. Our cold-start recommendations are based on hundreds of thousands of data points from rigorous evaluation benchmarks and real-world data.
• Continuously improve: By providing feedback on routing decisions, Not Diamond continuously learns a hyper-personalized routing algorithm optimized to your preferences and your application's requirements.
• Reduce cost and latency: Define explicit quality, cost, and latency tradeoffs to cut down your inference costs and achieve blazing fast speeds. Not Diamond determines which model to call in under 40ms—less than the time it takes an LLM to stream a single token.

Not Diamond vs. Claude 3 Opus

These are preliminary results and require further validation, but initial evaluations show that Not Diamond outperforms Claude 3 Opus on major benchmarks:

Dataset	Claude 3 Opus	Not Diamond
MMLU	85.21	88.25
BIG-Bench-Hard	81.06	81.24
ARC-Challenge	93.56	95.59
WinoGrande	76.80	79.60
MBPP	47.40	69.05

As can be seen with the MBPP benchmark, the biggest gains emerge when Claude 3 Opus scores particularly low on a benchmark. More testing is required however and we will be releasing a full technical report soon.

🚧 Beta testing ahead

Not Diamond is still in beta! Please let us know if you have any feedback or ideas on how we can improve. Tomás is at [e-mail] or 917 725 2192.

👍 Free to use!

Not Diamond is 100% free to use during beta ♡

API keys

👍 Sign up and get a Not Diamond API key.

Create a .env file with your Not Diamond API key, and the API keys of the models you want to route between.

OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
ANTHROPIC_API_KEY="YOUR_ANTHROPIC_API_KEY"
MISTRAL_API_KEY="YOUR_MISTRAL_API_KEY"
COHERE_API_KEY="YOUR_COHERE_API_KEY"
NOTDIAMOND_API_KEY="YOUR_NOTDIAMOND_API_KEY"

Alternatively, you can also set API keys programmatically as described further below.

📘 API keys

The notdiamond library uses your API keys to call the highest-quality LLM client-side. We never pass your keys to our servers. This means notdiamond will only call models you have access to. You can also use our router to determine the best model to call regardless of whether you have access or not (example). Our router supports most of the popular open and proprietary models (full list).

Drop me a line if you have a specific model requirement and we're happy to work with you to support it.

Example

If you already have existing projects in LangChain, check out our Langchain integration guide. An integration for OpenAI is also coming soon.

Create a main.py file in the same folder as the .env file you created earlier, or try it in Colab.

from notdiamond.llms.llm import NDLLM
from notdiamond.prompts.prompt import NDPromptTemplate

# Define the template object -> the string that will be routed to the best LLM
prompt_template = NDPromptTemplate("Write a merge sort in Python.")

# Define the available LLMs you'd like to route between
llm_providers = ['openai/gpt-3.5-turbo', 'openai/gpt-4','openai/gpt-4-1106-preview', 'openai/gpt-4-turbo-preview',
                 'anthropic/claude-3-haiku-20240307', 'anthropic/claude-3-sonnet-20240229', 'anthropic/claude-3-opus-20240229']

# Create the NDLLM object -> like a 'meta-LLM' combining all of the specified models
nd_llm = NDLLM(llm_providers=llm_providers)

# After fuzzy hashing the inputs, the best LLM is determined by the ND API and the LLM is called client-side
result, session_id, provider = nd_llm.invoke(prompt_template=prompt_template)

print("ND session ID: ", session_id)  # A unique ID of the invoke. Important for personalizing ND to your use-case
print("LLM called: ", provider.model)  # The LLM routed to
print("LLM output: ", result.content)  # The LLM response

👍 Run it!

python main.py