Embedding / RAG
#1
92
text-embedding-4-large
OpenAIOpenAI’s latest large embedding model tuned for retrieval, deduping, and RAG backends.
Best for
Enterprise RAG, semantic search, and hybrid vector indexes.
Embedding / RAGEditorial profile
Gemini embedding 004
Shipping Gemini-class embedding endpoint for Vertex AI and Gemini API retrieval stacks.
90
SkillRank score
Strong
#2
Rank
Embedding
Editorial
Source mode
No public repository mapped.
Source Confidence
Editorial source profile
62
Source match
Not repo-backed
Recorded history
7 snapshots
Official link
Attached
Freshness
46 days
Fit Meter
Decision readiness signals
Product fit
90/100
Based on the current SkillRank score for this model profile.
Source confidence
62/100
Editorial profile without accepted repo verification.
Adoption signal
48/100
No verified public repository signal is available.
Freshness
62/100
Last profile or source update is 46 days old.
Overview
What this profile is for
Shipping Gemini-class embedding endpoint for Vertex AI and Gemini API retrieval stacks.
Fit matrix
Where it fits and where it struggles
Best for
GCP-native RAG, multimodal-ish retrieval stacks, and batch embedding.
Not ideal for
Gemini embedding 004 is directional for embeddings and retrieval—not a turnkey substitute for policy review, legal clearance, or offline evaluation on your private corpus.
Strengths
Why teams shortlist it
- Shipping Gemini-class embedding endpoint for Vertex AI and Gemini API retrieval stacks Editors weigh practical packaging—documentation clarity, integration ergonomics, and how teams describe day-two operations—not lab trivia alone.
Weaknesses
What to test carefully
- Automated signals lag reality when vendors ship quietly or repos pivot.
- Gemini embedding 004 may look “fresh” or “stale” before marketing updates catch up.
- Treat SkillRank scores as conversation starters, especially across regulated industries or sealed-source releases.
Commercial notes
Pricing and rollout considerations
Listed as “Paid / API” on SkillRank for quick triage. Enterprise tiers, inference bundles, and regional tax often diverge from headline pricing—budget owners should validate quotes with Gemini embedding 004 directly before committing spend.
Listed tier: Paid / API
Setup
Getting started
Baseline retrieval quality with a fixed evaluation slice (questions + golden answers) before scaling ingestion. Document chunking strategies, metadata filters, and rerankers—you will iterate faster with instrumentation than with bigger prompts alone.
Evaluation
Checklist before production use
Gemini embedding 004 should be evaluated with a labeled retrieval set, not only with demo queries. Track recall at k, answer groundedness, citation accuracy, latency, index size, and how quality changes when documents are stale, duplicated, multilingual, or full of tables.
Rollout plan
Pilot path
Start Gemini embedding 004 with a small corpus and a frozen evaluation set. Add observability for retrieval misses, stale chunks, and low-confidence answers before broadening to private documents or customer-facing search.
Risk controls
Guardrails
For Gemini embedding 004, validate privacy boundaries before indexing documents. Use access-control-aware retrieval, remove stale or revoked content, and test prompt-injection attempts against retrieved passages.
Capabilities
Signals and tags
embeddingsRAGmultimodal
Data sources
How this profile stays current
SkillRank separates editorial model profiles from GitHub-verified repository telemetry. Public repository rows are checked against the GitHub API during the daily crawler. Vendor positioning statements are summarized from official pages. Always verify SLAs, regions, pricing, and availability on the provider site before procurement.
Last updated
Snapshot policy
Editorial snapshot 2026-05-06. Recorded snapshots appear when available; GitHub stars appear only for verified public repositories. Automated signals may lag vendor-only releases or private forks.
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