Classify Logistics Review Text Data Using an Embeddings API in Node.js: 3 Steps A developer outlined a three-step approach for classifying logistics review text in Node.js using an embeddings API: embed a small set of labeled reference examples, compare each new review item via cosine similarity, and assign a label only when the top match clears a tested threshold, otherwise returning an explicit 'unknown'. The design keeps the embedding provider behind a single adapter configured through environment variables so the backend can be swapped without rewriting the review queue, and stores the winning score and reference example alongside each finding for auditing. Short answer: use embeddings to classify logistics text by comparing a new review item with pre-labeled examples, then keep the provider behind a Node.js adapter so the API can change without changing your queue. To classify logistics review text data using embeddings, create vectors for a small labeled set and compare each new change with cosine similarity. The least complex design is one embedding per text, a provider adapter, and an explicit threshold. Keep the embedding API behind one function. That gives you a useful classifier now and a provider switch later without rewriting the review queue. Start small. The label boundary is harder than the API call. The score is evidence, not a verdict. Here is the decision matrix I would use: | Option | Good fit | Main cost | |---|---|---| | Embeddings plus nearest labeled examples | Stable labels such as routing , warehouse , or carrier API | You must curate representative examples | | K-means or another clustering tool | Many tags, weak labels, and discovery work | Clusters need human names and review | | LLM classification with structured output | Ambiguous policy decisions and labels that need explanations | More prompt, model, and output validation work | My default is the first row for code-change review. It is cheap to reason about operationally, easy to test with fixtures, and does not lock the application to a prompt format. I would add clustering when the label set is genuinely large, and use an LLM when similarity alone cannot express the policy. Do not embed only the pull request title. For a logistics system, concatenate the changed service, the short change summary, and the affected operational area. A title like “refactor scanner client” is underspecified; “scanner client retries carrier-label requests” carries the signal that should land near a carrier-integration label. The labeled examples are your reference set. They are not a training model in the usual supervised-learning sense. Each example is a piece of text paired with a tag. At runtime, embed the new review item, compute its similarity to every reference vector, and choose the strongest match only when it clears a threshold you have tested on held-out examples. That threshold is a product decision. A false warehouse tag can route a review to the wrong owner. An explicit unknown result is often better than a confident-looking guess. Store the winning score and the reference example with the finding so a reviewer can audit why the tag was returned. This is the part that tends to get skipped. A reference set with three polished examples can look convincing while missing the vocabulary used by dispatchers, warehouse operators, and carrier integrations. Add examples from each operational area, include near-misses, and test the threshold on examples the classifier never saw. The resulting review is less glamorous than choosing a model, but it tells you whether “carrier API” and “warehouse scanner” are actually separable in your data. If they are not, change the labels before changing providers. The openai npm package is a practical client for an OpenAI-compatible embeddings surface, but the package should not leak into the rest of the review pipeline. Put the client construction and request in one adapter. Configure the base URL, key, and model through environment variables. That keeps the classification logic independent from the backend and makes provider comparison an integration test instead of a rewrite. The example below classifies one item against labeled examples. It uses the Infrai embeddings route through an environment-provided base URL, with no hard-coded credential or model identifier, and returns the score for inspection. The same adapter shape can point at another compatible provider. type LabeledExample = { text: string; label: string; vector?: number }; const baseUrl = process.env.INFRAI BASE URL; const apiKey = process.env.INFRAI API KEY; const model = process.env.EMBEDDINGS MODEL; if baseUrl || apiKey || model { throw new Error "INFRAI BASE URL, INFRAI API KEY, and EMBEDDINGS MODEL are required" ; } function cosineSimilarity a: number , b: number : number { let dot = 0; let normA = 0; let normB = 0; for let i = 0; i < a.length; i += 1 { dot += a i b i ; normA += a i 2; normB += b i 2; } if normA === 0 || normB === 0 return 0; return dot / Math.sqrt normA Math.sqrt normB ; } async function embed text: string : Promise