Embed v3

Cost & access

Embed v3 is priced per million embedding tokens (or per request), not like chat completion. Compare dimensions and batch APIs on Cohere's pricing page. With a 128K tokens context window, long PDFs or chat histories increase input tokens quickly—trim history or summarize older turns in production.

Functional understanding

• Embedding model (often paired with LLMs).
• Modalities: text · License: Proprietary · Released 2024-03-01.
• Best-fit workflows for this model:
• • Semantic search, deduplication, and RAG retrieval—Embed v3 outputs vectors, not chat prose.
• • Clustering support tickets, docs, or product catalogs by meaning.

Technical foundation

• Cohere reports Undisclosed parameters; training data: Undisclosed.
• Context: 128K tokens. Open weights: no.
• Embed v3 is positioned as a embedding model in the Cohere lineup.

First API call

Use Cohere's embeddings endpoint; pass Embed v3 as the model name and store vectors in your DB or vector index.

import cohere

co = cohere.Client("YOUR_API_KEY")
response = co.embed(
    texts=["Text to embed for Embed v3"],
    model="cohere-embed-v3",
    input_type="search_document",
)
print(response.embeddings[0][:5], "...")

Important technical topics

• Prompting Embed v3: be explicit about output format. Weak: "Analyze this." Better: "Return JSON with fields id, total, date for Cohere billing data."
• Temperature: use 0–0.3 for extraction and compliance on Embed v3; 0.7–1.0 for brainstorming.
• Tokens: Embed v3 bills by tokens (~¾ word each). Undisclosed parameters affect capability; your bill is driven by context length and call volume.
• Context window (128K tokens): everything in one request—system prompt, tools, RAG chunks, and history—must fit. Truncate or summarize when approaching the limit for Embed v3.

Real enterprise patterns

• Index docs with Embed v3, then retrieve top-k chunks before calling a chat model.
• Hybrid search: combine keyword (BM25) + embeddings for better recall.
• Version embedding indexes when you change models—dimensions may differ.
• Monitor drift: re-embed when Cohere ships a new embedding revision.

Production & security

• Secrets: never commit keys for Embed v3; use vault + per-environment rotation.
• PII: mask before inference; log redacted prompts only.
• Observability: trace id per request; log model=cohere-embed-v3, tokens in/out, latency.
• Rate limits: handle Cohere 429/5xx with exponential backoff and circuit breakers.
• Guardrails: schema-validate JSON; block disallowed topics; cross-check numbers against source docs.

Mini projects with this model

• Semantic FAQ: embed help-center articles with Embed v3, answer from nearest neighbors.
• Duplicate ticket detector for support queues.
• Product recommendation by description similarity.
• Eval: NDCG@k on labeled query–doc pairs.

Suggested stack

• Language: Python 3.11+
• Embeddings: Embed v3 (Cohere)
• Vector DB: Pinecone, Chroma, pgvector, or Weaviate
• Orchestration: LangChain or LlamaIndex for chunking
• UI: Streamlit or Next.js for internal tools
• APIs: FastAPI

Learning path

• Python basics
• HTTP/REST and environment variables
• Cohere embeddings API for Embed v3
• Vector database fundamentals
• Chunking strategies
• RAG retrieval evaluation
• Deploy index refresh pipeline