RouteLLM - Smart Routing

The rapid evolution of artificial intelligence has led to the development of increasingly sophisticated language models.

Large Language Models (LLMs) are at the forefront of this transformation, demonstrating unprecedented capabilities in understanding and generating human language. Among the innovative solutions leveraging LLMs, RouteLLM stands out as a groundbreaking framework designed to enhance the utility, efficiency, and accessibility of these models.

This paper explores the fundamentals of LLMs, introduces RouteLLM, and delves into its advantages, installation procedures, practical use cases, and code samples.

What is LLM ?

• Definition: A Large Language Model (LLM) is a type of artificial intelligence that is trained on extensive datasets to understand and generate human language.
• Architecture: LLMs are primarily built on transformer architectures, which enable them to process and analyze large amounts of text data efficiently.
• Capabilities: LLMs can perform a variety of natural language processing tasks, including text generation, translation, summarization, and question answering.
• Generative AI: As generative models, LLMs can produce coherent and contextually relevant text in response to prompts, making them useful for applications like chatbots and content creation.
• Versatility: They can be fine-tuned or prompt-tuned for specific tasks, adapting to various applications across industries, from customer service to creative writing.
• Examples: Notable LLMs include OpenAI's GPT series (e.g., GPT-3, GPT-4), Google's BERT and Gemini, Meta's LLaMA, and Anthropic's Claude.
• Limitations: LLMs can produce inaccurate or biased information, known as "hallucinations," depending on the quality of the training data. They also require careful management to avoid security and ethical issues.

What is RouteLLM

RouteLLM is an open-source framework designed to optimize the routing of queries between different large language models (LLMs) based on their performance and cost. It aims to address the challenge of selecting the most suitable model for a given query while balancing the trade-off between response quality and operational costs.

https://github.com/lm-sys/RouteLLM

• Intelligent Routing: RouteLLM employs router models trained on preference data to determine the best LLM for each incoming query based on its complexity.
• Cost Efficiency: By directing simpler queries to less expensive models and reserving complex queries for more powerful models, RouteLLM can achieve significant cost reductions while maintaining high performance. Evaluations show RouteLLM can reduce costs by over 85% in some scenarios compared to using only the most powerful LLM, while still achieving 95% of that model's performance.
• Scalability: The framework is designed to be scalable, allowing organizations to adapt their LLM deployments as their needs evolve. The routers can be retrained with new data to improve performance over time.
• Flexibility: RouteLLM supports a wide range of LLMs, allowing users to integrate different models based on their specific needs. This flexibility ensures that the most appropriate model is used for each task, enhancing overall effectiveness.

How Does it Work ?

The framework employs several machine learning models to predict which LLM is likely to provide the best response. Key routing models include:

• Similarity-Weighted Ranking Router: Uses a weighted Elo calculation based on similarity to determine the best model.
• Matrix Factorization Model: Learns a scoring function to evaluate how well a model can answer a given prompt.
• BERT Classifier: Utilizes BERT to predict which model will yield a better response.
• Causal LLM Classifier: Similar to the BERT classifier, this model also predicts the optimal LLM for a query.

Installation

PyPI

pip install "routellm[serve,eval]"        

From Source

git clone https://github.com/lm-sys/RouteLLM.git
cd RouteLLM
pip install -e .[serve,eval]        

QuickStart

Routing to Public Models

import sys import os from routellm.controller import Controller
from rich import print 

os.environ["OPENAI_API_KEY"] = "sk-XXXXXX" 

# Replace with your model provider, we use Anyscale's Mixtral here. os.environ["ANYSCALE_API_KEY"] = "esecret_XXXXXX" 

client = Controller (     
             routers=["mf"],    
             strong_model="gpt-4-1106-preview",                      
              weak_model="anyscale/mistralai/Mixtral-8x7B-Instruct-v0.1",     
 ) 

response = client.chat.completions.create(     
# This tells RouteLLM to use the MF router with a cost threshold of 0.11593        
              model="router-mf-0.11593",      
              messages= [  {       
                       "role": "user", 
                       "content": "Hello!"
               }        ]   
)
print(response)        

Routing to Local Models with Ollama

import sysfrom routellm.controller 
import Controller
from rich import print
os.environ[”GROQ_API_KEY"] = "sk-XXXXXX”

self.client = Controller(            
       routers=["mf"],            
       strong_model="groq/llama3-8b-8192",            
       weak_model="ollama_chat/llama3”
)

response = self.client.chat.completions.create(            
# This tells RouteLLM to use the MF router with a cost threshold of 0.11593            
# For 50.0% strong model calls for mf, threshold = 0.11593            
# For 40.0% strong model calls for mf, threshold = 0.1339            
         model="router-mf-0.1339",            
         messages=[  {
               "role": "user", 
               "content": prompt
         } ]
)

print(response)        

Use Cases - Customer Support Chatbot

Imagine a customer support chatbot that utilizes RouteLLM to manage inquiries.

The application employs two LLMs: a high-performance model (e.g., GPT-4) and a cost-effective model (e.g., anyscale).

• Complex Inquiry: A customer asks, "Can you explain your return policy in detail?”

Routing Decision: The RouteLLM router evaluates the complexity and routes the inquiry to GPT-4.

• Simple Inquiry: Another user asks, "What are your store hours?”

Efficient Routing: The router recognizes this straightforward question and routes it to anyscale.

This approach allows the chatbot to efficiently handle a high volume of inquiries while minimizing costs and maximizing response quality.

#llm #routellm #smartrouting #ai