All projects on Base matching “text embeddings”

Use when you need image embeddings or text embeddings in one 2048-dimension space for retrieval, RAG, semantic search, clustering, or dedup (Qwen3-VL-Embedding-2B, OpenAI-compatible). Batch up to 256 items per call, served 32 at a time. An x402 call is $0.002 when the request includes images, $0.001 for text-only. USDC on Base. 400 is a bad body, 413 is over 48 MiB, 429 means busy (Retry-After).

Turn text into a numeric embedding vector for semantic search, RAG and similarity. Multilingual.

Search functions or classes by meaning and by keyword. ``mode=bm25`` ranks by lexical match, ``mode=vector`` by embedding similarity to the query text, and ``mode=hybrid`` (default) fuses both rankings with reciprocal rank fusion. Returns up to ``top_k`` matches with their repository, file path and score; the ``domain``, ``ontology_concept_uri``, ``confidence_min`` and ``has_tests`` filters apply

Encode text to HTML entities so it can be safely embedded in markup. By default uses hex numeric references; set useNamedReferences for named entities (&, ©) or encodeEverything to encode every character.

Scores text against every category in all six taxonomies (one embedding, cosine similarity) and returns the top `per` rows per taxonomy. With `taxonomy` and `category` it also returns the target's rank, score, gap to #1, and a phrase bank split into phrases the text already uses and phrases it…

Research an ecommerce store, online business, or ecommerce market using current public sources. Returns store analysis, categories, positioning, pricing patterns, offers, policies, competitors, market research, and citations.