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Graph RAG
text-embedding-3 / Voyage / Cohere
Best-in-class embedding models for retrieval quality
What it is
OpenAI text-embedding-3, Voyage AI, Cohere embed-v3 — the current top-of-class for English and multilingual semantic encoding. The single biggest lever on RAG quality.
How Vaaani uses it
- Encoding documents into a vector DB
- Multilingual semantic search across mixed languages
- Code embeddings (Voyage code) for codebase search
- Domain-tuned variants for legal / medical / financial corpora
Why it makes the cut
Most RAG quality issues are embedding quality issues. Switching from a free model to text-embedding-3-large often beats spending a week on prompt engineering.
Sample code
from openai import OpenAI client = OpenAI() emb = client.embeddings.create( model="text-embedding-3-large", input=["Vaaani builds AI workers"]) vec = emb.data[0].embedding # 3072-dim
Related in the Vaaani stack
Have a project that needs text-embedding-3?
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