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 handler translates between the two.

Skipping LiteLLM is therefore feasible only for embed; rerank requires either LiteLLM (or another Cohere↔TEI shim) unless Open WebUI itself is patched.

Wire shapes (exact)

Embed — Open WebUI code path

backend/open_webui/retrieval/utils.py:677 (generate_openai_batch_embeddings):

POST {RAG_OPENAI_API_BASE_URL}/embeddings        ← URL is auto-appended
Authorization: Bearer {RAG_OPENAI_API_KEY}
Content-Type: application/json

{"input": ["text1", "text2", ...], "model": "{RAG_EMBEDDING_MODEL}"}

Response parsed as data["data"][i]["embedding"] (OpenAI shape).

Async fan-out (utils.py:905, get_embedding_function → asyncio.gather at utils.py:963): chunks bundled into batches of RAG_EMBEDDING_BATCH_SIZE (default 1); all batches dispatched concurrently via asyncio.gather with optional semaphore from RAG_EMBEDDING_CONCURRENT_REQUESTS (default 0 = unlimited). A 100-chunk file at default config fires 100 concurrent single-chunk requests.

Rerank — Open WebUI code path

backend/open_webui/retrieval/models/external.py:14 (ExternalReranker, predict at line 27):

POST {RAG_EXTERNAL_RERANKER_URL}                 ← URL is exact, NOT appended
Authorization: Bearer {RAG_EXTERNAL_RERANKER_API_KEY}
Content-Type: application/json

{"model": "{RAG_RERANKING_MODEL}", "query": "...",
 "documents": ["doc1", "doc2", ...], "top_n": N}

Response parsed: data["results"] sorted by index, extracts relevance_score. Cohere shape, strict.

Failure handling: requests.post() exception or non-2xx → predict() returns None → retrieval silently downgrades to un-reranked hybrid order. No user-visible error in Open WebUI. Always alert on rerank-side 4xx in TEI/LiteLLM logs.

Open WebUI environment variables

Variable	Mode	Notes
RAG_EMBEDDING_ENGINE	embed	Set to openai. Works for OpenAI, LiteLLM, TEI direct, vLLM direct — anything OpenAI-compat.
RAG_OPENAI_API_BASE_URL	embed	Open WebUI appends /embeddings. Set to http://litellm:4000/v1 (proxy) or http://tei:8080/v1 (direct).
RAG_OPENAI_API_KEY	embed	Bearer token. TEI ignores; LiteLLM enforces virtual key.
RAG_EMBEDDING_MODEL	embed	Sent in payload as model. Must match LiteLLM's model_name exactly (case-sensitive, full HF path).
RAG_EMBEDDING_BATCH_SIZE	embed	Texts per HTTP request. Default 1. Bumping to 32 reduces per-request overhead during indexing.
RAG_EMBEDDING_CONCURRENT_REQUESTS	embed	Concurrency cap. Default 0 = unlimited (asyncio.gather without semaphore). Set to a bounded number (4-8) to avoid bursting TEI.
RAG_EMBEDDING_PREFIX_FIELD_NAME	embed	Extra field name for prefix-needing models (e.g. prompt for EmbeddingGemma). Leave unset for BGE-M3 — its query/passage symmetry is built into the model.
RAG_EMBEDDING_QUERY_PREFIX / RAG_EMBEDDING_CONTENT_PREFIX	embed	Prefix strings (paired with the field name above). Unused for BGE-M3.
RAG_RERANKING_ENGINE	rerank	Set to external for Cohere-shape endpoints.
RAG_EXTERNAL_RERANKER_URL	rerank	Full URL including path (no auto-append). E.g. http://litellm:4000/v1/rerank.
RAG_EXTERNAL_RERANKER_API_KEY	rerank	Bearer token.
RAG_RERANKING_MODEL	rerank	Sent in payload as model. Match LiteLLM's model_name.
RAG_EXTERNAL_RERANKER_TIMEOUT	rerank	Seconds. Bump for very large Top_K × Hybrid Search candidate pools.

Triage table

Symptom	First check	Where
Embed returns 400 with encoding_format: expected value	Add encoding_format: float to the LiteLLM litellm_params	references/gotchas.md §1
Embed returns 422 with inputs: data did not match...	Switch to openai driver — HF driver's task_type detection failed	references/gotchas.md §2
Rerank returns 422 with batch size N > maximum allowed batch size M	Bump TEI --max-client-batch-size	references/gotchas.md §3
Rerank returns 404 on POST /v1	Open WebUI rerank URL needs full path including /v1/rerank	references/gotchas.md §7
Open WebUI "Retrieved 1 source" but answer quality dropped	Rerank is silently 4xx — check TEI/LiteLLM logs	references/gotchas.md §3
TEI pod hangs at "Starting FlashBert model"	Wrong arch image — match GPU compute capability	references/gotchas.md §5
TEI returns 429 during knowledge-base upload	Open WebUI concurrency too high; cap RAG_EMBEDDING_CONCURRENT_REQUESTS	references/gotchas.md §6
Reranker quality degraded since recent config change	--max-batch-tokens past trained ceiling lets long inputs through	references/gotchas.md §4
vector_db directory growing fast	ChromaDB is fine to ~1 GB; past that switch to pgvector halfvec	references/gotchas.md §8

Reference index

• references/gotchas.md — nine gotchas with HTTP error strings, root causes, and fixes. Load when triage table points here.
• references/end-to-end-config.md — full working LiteLLM + Open WebUI + TEI config (BGE-M3 + BGE-Reranker-v2-m3 worked example). Load when bootstrapping a new deployment.
• references/performance.md — quality verification (cross-engine numerical-identity check) + throughput baseline. Load for sizing or post-deployment health checks.
• references/sources.md — authoritative source files and PR/issue URLs underlying every claim. Load to verify a specific claim or run freshen mode.