semantic-router

semantic-router

https://www.aurelio.ai/semantic-router

DeterministicDecision Makingfor AI.

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning.

https://github.com/aurelio-labs/semantic-router

Superfast AI decision making and intelligent processing of multi-modal data.

 

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — routing our requests using semantic meaning.

 

Semantic Router: Steer local LLMs for decision-making and more

https://buduroiu.com/blog/semantic-router-superfast-local-llm-decisions/

适配本地encoder

Encoders

Now that we’ve defined our routes and provided our utterances, we need a way of encoding these utterances and future inputs into a high-dimensional vector space for comparison.

Semantic Router includes a variety of encoders, including popular ones such as Cohere and OpenAI, but also support for open source models via the HuggingFaceEncoder and even non-LLM based encoding via TF-IDF.

For more information on encoders, refer to the aurelio-labs/semantic-router repository

For our use case, we want to be able to run our pipeline using consumer hardware, so we will use the HuggingFaceEncoder together with a multilingual embedding model, intfloat/multilingual-e5-base which has support for Chinese.

encoder = HuggingFaceEncoder(
  name="intfloat/multilingual-e5-base",
  device="mps"  # making use of Apple Metal hardware acceleration
)

 

调用本地encoder和LLM

https://github.com/aurelio-labs/semantic-router/blob/main/docs/05-local-execution.ipynb

 

 

如果没有匹配的路由反馈None

https://github.com/aurelio-labs/semantic-router/blob/main/docs/09-route-filter.ipynb

 

 

5G核心网

Semantic Routing for Enhanced Performance of
LLM-Assisted Intent-Based 5G Core Network
Management and Orchestratio

https://arxiv.org/pdf/2404.15869

 

Semantic Router superfast decision layer for LLMs and AI agents

https://www.geeky-gadgets.com/semantic-router-superfast-decision-layer-for-llms-and-ai-agents/#google_vignette

In the rapidly evolving world of artificial intelligence, a new framework is enhancing the way we create and interact with chatbots and AI assistants. This innovative tool, known as the Semantic Router, is reshaping our expectations of digital conversations by offering a level of understanding and response accuracy that was previously unattainable. James Briggs explains a more about the Semantic Router system

“Semantic Router is a superfast decision layer for your LLMs and agents that integrates with LangChain, improves RAG, and supports OpenAI and Cohere. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — routing our requests using semantic meaning. This approach unlock incredibly fast agentic decision making, the ability to use literally millions of tools, and provide much greater steerability and AI safety using semantics.”

At its core, the Semantic Router serves as a sophisticated decision-making layer that works in tandem with language models. Its primary function is to guide chatbots in delivering prompt and pertinent answers to user inquiries. By navigating through a semantic vector space, the router is able to align user questions with the most fitting predefined responses. This process significantly improves the reliability of the chatbot’s answers, ensuring that users receive the information they need without unnecessary delays or confusion.

The benefits of this technology are particularly evident in its ability to provide consistent and rapid responses. This is crucial for creating a smooth user experience, especially in environments where the performance of AI is under close scrutiny. Whether it’s for customer service, information retrieval, or casual conversation, the Semantic Router’s efficiency is a key factor in its success.

 

视频语义分帧

https://buduroiu.com/blog/semantic-router-gpt4o-video-chunking/

We can represent these splits visually by grabbing the middle frame from each of the splits:

 

Frames of the video as semantic splits, represented by the middle frame of each split.