Qdrant Patterns

Quick Guide: Use @qdrant/js-client-rest (v1.17.x) for high-performance vector search. Collections define vector dimensions and distance metrics upfront -- mismatches cause silent failures. Use must/should/must_not filter clauses with payload conditions (not Pinecone-style $eq/$and). Payload indexes are optional but critical for filter performance at scale -- create them explicitly with createPayloadIndex(). Named vectors let you store multiple embeddings per point (e.g., title + content). Quantization (scalar/binary/product) trades accuracy for memory and speed. The query() method is the universal search endpoint -- prefer it over the older search() method.

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CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST create payload indexes with createPayloadIndex() for any field used in filters -- unindexed fields cause full scans that degrade linearly with collection size)

(You MUST use must/should/must_not filter syntax -- Qdrant does NOT use $eq/$and/$or operators like Pinecone)

(You MUST match vector dimensions exactly between embedding model output and collection config -- dimension mismatches cause silent upsert failures or corrupt search results)

(You MUST set wait: true on writes when subsequent reads depend on the data -- Qdrant writes are asynchronous by default and may not be immediately visible)