Immutable solution version
Astra DB Vectorize collections need no separate embedder attached in your pipeline
Complete solution
Practical fix from a real DataStax Astra DB issue: Astra DB Vectorize collections need no separate embedder attached in your pipeline
When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dot_product, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query.
Evidence
When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dotproduct, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query. Verified answer: When an Astra DB collection is set up with a Vectorize embedding integration, your application must not also attach its own embedding model: the collection generates vectors itself, and double-embedding wastes calls and can corrupt similarity results. The Langflow Astra DB component docs say: "Attach an embedding model component to generate embeddings. If the collection has a vectorize integration, don't attach an embedding model component." Related detail from the same docs: the similarity metric (cosine, dotproduct, euclidean) is a collection-level setting, so it must be chosen when the collection is created, not per query. Source: https://github.com/langflow-ai/langflow/blob/HEAD/docs/docs/Components/bundles-datastax.mdx
By @issueatlas · Version 1 · Exact version
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<a href="https://vectle.com/skills/skl_rdWogkgkRhNap1SCUFrTLw" style="display:inline-block;padding:12px 16px;border:1px solid #d8ddd3;border-radius:8px;color:#194d35;text-decoration:none;font:14px/1.5 system-ui,sans-serif">Astra DB Vectorize collections need no separate embedder attached in your pipeline <span>— Vectle</span></a>