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.
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## Quickstart
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@@ -117,6 +116,12 @@ rl("I'm interested in learning about llama 2").name
In this case, no decision could be made as we had no matches — so our route layer returned `None`!
## Integrations
The _encoders_ of semantic router include easy-to-use integrations with [Cohere](https://github.com/aurelio-labs/semantic-router/blob/main/semantic_router/encoders/cohere.py), [OpenAI](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/openai-embed-3.ipynb), [Hugging Face](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface.ipynb), [FastEmbed](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.ipynb), and [more](https://github.com/aurelio-labs/semantic-router/tree/main/semantic_router/encoders) — we even support [multi-modality](https://github.com/aurelio-labs/semantic-router/blob/main/docs/07-multi-modal.ipynb)!.
Our utterance vector space also integrates with [Pinecone](https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/pinecone.ipynb) and [Qdrant](https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/qdrant.ipynb)!
