External Rerank via API / Litellm #9542
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Hi, is there a way how to re-rank remotely, for example via reranker deployed in vLLM or via Litellm? As far as I know there is SentenceTransformersSimilarityRanker for local reranking, there is Cohere/Jina external rerankers... but not for Rerank API like https://docs.litellm.ai/docs/rerank Thanks! |
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Replies: 2 comments
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Now I see that Litellm: "follows the cohere api request / response for the rerank api". And "api_base_url" in CohereRanker can be changed, so it should work in theory.. 🤔 |
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Hi @vaclcer — interesting question, and one I’ve seen pop up more frequently as people shift toward modular and remote RAG setups. We actually tackled this in our own framework when trying to decouple ranking from generation, especially in long-chain workflows. You’re spot on: since CohereRanker allows you to change api_base_url, you can indeed redirect to a compatible endpoint (e.g. LiteLLM or even your own proxy layer). But most tools don’t go far enough in managing semantic drift between retrieved candidates and re-ranked output — especially if done remotely. If it helps, here’s the core problem we mapped: We ended up designing a lightweight bridge that lets us inject external reranker logic (via API or native wrapper) into the pipeline with controlled variance and explainability. Happy to share more if your use case requires semantic traceability or multi-model fallback. Hope this helps clarify things — your direction’s totally valid. |
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Hi @vaclcer — interesting question, and one I’ve seen pop up more frequently as people shift toward modular and remote RAG setups.
We actually tackled this in our own framework when trying to decouple ranking from generation, especially in long-chain workflows. You’re spot on: since CohereRanker allows you to change api_base_url, you can indeed redirect to a compatible endpoint (e.g. LiteLLM or even your own proxy layer). But most tools don’t go far enough in managing semantic drift between retrieved candidates and re-ranked output — especially if done remotely.
If it helps, here’s the core problem we mapped:
🔍 Problem #5 — Semantic ≠ Embedding
We ended up designing a lightweight bridge t…