Notes on finding the right paragraph

Suppose you have a folder of product documentation, and someone asks, “Can I get my money back?” The relevant paragraph is titled “Refund policy.” A search for the exact words might miss it. An embeddings endpoint can help you find text with a similar meaning, even when the wording differs.

This is one of the most useful reasons to learn about embeddings. You do not need to start with the mathematics. Start with the job: finding the right material before showing it to a person or passing it to an AI assistant.

§ What an embeddings endpoint returns

An embeddings endpoint takes input, often text, and returns a vector: a list of numbers representing aspects of that input’s meaning. It does not normally return a written answer.

An embedding model is trained so that related inputs tend to produce nearby vectors under a suitable similarity measure. Your application compares those vectors to find likely matches. Similarity is useful evidence, not proof that two passages say the same thing.

A chat endpoint writes a response. An embeddings endpoint produces a representation you can search or compare. These are different jobs, and a model available for chat is not automatically available for embeddings.

§ Where embeddings become useful

• Semantic search: find a refund policy when someone searches for getting their money back.
• Retrieval for an assistant: select relevant documentation to include with a user’s question.
• Grouping and recommendations: identify related support tickets, articles, or product descriptions.
• Duplicate detection: flag entries that express similar ideas despite different wording.

Embeddings are less helpful when you need an exact identifier, a date range, or a strict permission check. Use ordinary database filters for those requirements. Many practical search systems combine keyword matching, vector similarity, and structured filters.

§ The smallest useful workflow

First, split your documents into manageable passages. Keep enough context in each passage to make it understandable on its own. Store metadata such as its document title, source identifier, and access permissions.

Next, send those passages to an embedding model and save the returned vectors alongside the text and metadata. A vector database can help at scale, but a small prototype may only need a library that compares vectors.

When someone searches, embed their question with the same embedding model. Compare that vector with your stored vectors and retrieve the closest passages. Apply access controls before returning content or sending it to a generation model.

If you want a written answer, send the retrieved passages and the question to a chat endpoint. This pattern is commonly called retrieval-augmented generation, or RAG. The embeddings locate context; the generation model uses that context to compose an answer.

§ Check the endpoint before building around it

In the OpenAI API format, embedding requests conventionally use the /v1/embeddings route with a model identifier and input. However, OpenAI compatibility does not by itself guarantee that every route or embedding model is available.

Vana API provides an OpenAI-compatible endpoint at https://api.vanaapi.com and also supports Anthropic Messages and Gemini native protocols. Before planning an embeddings workflow through it, check the current documentation for embeddings support, accepted model identifiers, input limits, and pricing. Do not substitute a chat model name and assume it will work.

Create a Vana API account to explore its available models and supported endpoints. Its token-based billing has no monthly fee; confirm the applicable rates for the specific model and operation you intend to use.

§ A few decisions worth recording

Keep track of the embedding model used for every stored collection. Vectors from different models generally should not be compared, even when their lengths match. Changing models usually means re-embedding your documents.

Also update embeddings when source text changes, and treat vectors as sensitive derived data rather than anonymized text. Finally, test with real questions. If a basic keyword search already finds the right paragraph reliably, you may not need embeddings yet.