open-webui-embeddings

# Open WebUI embeddings + reranking — operator reference Target: operators wiring Open WebUI's RAG pipeline to HuggingFace Text Embeddings Inference (TEI) via LiteLLM. Three hops, each with its own wire-shape quirks. Most failure modes silently degrade to "answer quality dropped" rather than visible errors — this skill is a triage for catching them at config-time. **Siblings in the `open-webui` plugin.** Setting these values *through the REST API* rather than the settings UI — and the knowledge/RAG endpoints that ingest documents — is the **`open-webui-api`** skill. Running more than one Open WebUI replica, where RAG requests and their WebSocket streams must survive hitting a different pod, is **`open-webui-valkey-websocket`**. ## The architecture in 30 seconds ``` Open WebUI → LiteLLM proxy → TEI (GPU) └ embed: openai-driver → /v1/embeddings └ rerank: huggingface-driver → /rerank (Cohere↔TEI translation) ``` Why proxy through LiteLLM rather than point Open WebUI at TEI directly? - **Embed:** TEI exposes `/v1/embeddings` natively (OpenAI-compat) — direct path works. LiteLLM adds: virtual-key auth, per-model rate limits, request logging, optional caching. - **Rerank:** TEI's native `/rerank` is `{query, texts}` → `[{index, score}]`. Open WebUI's `ExternalReranker` sends Cohere shape `{query, documents, top_n}` → `{results: [{index, relevance_score}]}`. **Direct path fails with HTTP 422** — wire shapes do not match. LiteLLM's HuggingFace rerank han...