Embeddings generation
We set up embeddings generation — turning your texts (products, articles, documents, queries) into numeric 'meaning vectors' by which a machine understands semantic proximity. This is the technical foundation of semantic search (874), RAG (893) and recommendations. Honestly upfront: embeddings are a technical FOUNDATION, not a ready feature: they bring value to the user only together with a vector DB (892) and an application; quality depends on the chosen embedding model and the quality of your data; and their generation must be paid for (by volume/tokens).
Embeddings generation — overview
Embeddings generation is the process of converting your data (texts, sometimes images) into vectors — sets of numbers that reflect meaning such that semantically close objects end up 'near' each other in the vector space. Semantic search, RAG, clustering (885), recommendations are built on this. We pick an embedding model for the language, task, privacy and budget, set up the vector generation and update pipeline, store them in the vector DB (892). Honestly about the role, this is key: embeddings are a technical foundation, not a ready user feature. The 'meaning numbers' by themselves give no value — they are needed as a foundation for search (874), RAG (893), clustering (885). Value appears together with storage and the application on top. We honestly build exactly the foundation. Honestly about quality and model dependence: semantic quality depends on the chosen embedding model and the data. Different models are stronger in different languages/domains; a 'universally best' one does not exist. On bad or heterogeneous data embeddings will be weaker ('garbage in — garbage out'). We honestly pick a model for your language and task. Honestly about updating: when data changes embeddings must be regenerated (otherwise search becomes outdated); and when the embedding model changes — everything must be recomputed (incompatibility). This is part of maintenance, not a one-off action. Honestly about cost and privacy: generation is a paid resource (by text volume/tokens); data goes to the model — for sensitive cases we discuss local embedding models. Honestly about the effect: it gives a quality semantic foundation for AI features, but the result by itself is not visible to the user. Honestly about access: data is needed. An important boundary: this is embeddings; vector DB (storage) — 892; semantic search — 874; RAG — 893; clustering — 885. Picture this: instead of 'the machine does not understand the meaning of text' — meaning vectors as a foundation for smart features. The base price starts from 35,000 ₽ (depends on volume and the model).
Problems we solve
- You need semantic search/RAG but have no vector representation of data.
- The machine does not understand semantic proximity of texts.
- It is unclear which embedding model to choose for the language/task.
- Data changes, but vectors become outdated and search lies.
What's included in the Embeddings generation service
- Choosing an embedding model for language, task, privacy, budget
- A vector-generation pipeline from your data
- Setting up update/regeneration on data change
- Storage in the vector DB (892)
- Honest boundaries (a foundation, not a ready feature; depends on model/data)
- Local embedding options for privacy
- A link with search (874), RAG (893), clustering (885)
- Handover and review with you
What you get
- Quality embeddings as a foundation for AI features
- Correct vector updating on data change
- A deliberate model choice for your language/task
- Honest boundaries (a foundation; value in the link with the application)
How the work goes: steps
- We define data, language, task, privacy; pick the model
- We set up the vector generation and update pipeline
- We store in the vector DB, honestly set boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Do embeddings give anything to the user by themselves?
No, honestly: they are a technical foundation, not a ready feature. The 'meaning numbers' by themselves bring no value — they are a foundation for semantic search (874), RAG (893), clustering (885), recommendations. Value appears together with a vector DB (892) and the application on top. We honestly build exactly the foundation rather than pass off embeddings as a 'smart feature'.
Which embedding model is the best?
A 'universally best' one does not exist, and that is honest. Different models are stronger in different languages and domains, differ in price and privacy (cloud vs local). Quality also depends on your data. We pick a model for your language, task and budget and honestly note the trade-offs rather than take 'the trendiest'.
Set up embeddings — and forever?
No, honestly: when data changes vectors must be regenerated, otherwise search becomes outdated. And when the embedding model changes everything must be recomputed — old and new vectors are incompatible. This is part of maintenance. We set up the update process and honestly factor it in rather than promise 'once and forgotten'.
About the provider
The «Embeddings generation» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.