◆ Category · 18 assets

Vector Stores

Browse 18 Vector Stores modes for AI coding agents — production-grounded, cited, installable. Part of the VIBE library.

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binary-quantization-expert-mode

Deep expertise in embedding quantization — binary (1-bit), scalar (int8), product (PQ); rescore-after-quant pipeline, Hamming distance, when it's free vs lossy

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chroma-expert-mode

Deep expertise in Chroma 1.0+ — collections, multi-modal (OpenCLIP), distance metrics, persistence options, server vs embedded, Rust core performance

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code-embed-expert-mode

Deep expertise in code-specific embedding models — voyage-code-3, jina-embeddings-v2-base-code, jina-code-embeddings, CodeRankEmbed, GraphCodeBERT

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embedding-fine-tune-expert-mode

Deep expertise in fine-tuning embedding models with sentence-transformers v3+ — SentenceTransformerTrainer, MultipleNegativesRankingLoss, Matryoshka, hard negatives mining

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embedding-model-picker-expert-mode

Decision guide for picking embedding models in 2025-2026 — OpenAI, Cohere, Voyage, Jina, BGE, E5, Nomic, Stella, Snowflake Arctic; reading MTEB; trading dimension/cost/quality

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lancedb-expert-mode

Deep expertise in LanceDB — Lance columnar format, embedded + serverless modes, S3-backed tables, full-text search, versioning, and multimodal lakehouse

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marqo-expert-mode

Deep expertise in Marqo — end-to-end vector search with embedding inference baked in, ONNX/GPU acceleration, and ecommerce-specialized models

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milvus-expert-mode

Deep expertise in Milvus 2.5+ — index zoo (HNSW/DiskANN/IVF/SCANN/CAGRA), partitions, multi-vector hybrid search, GPU indexes, and Milvus Lite

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mongodb-atlas-vector-expert-mode

Deep expertise in MongoDB Atlas Vector Search — $vectorSearch aggregation stage, $rankFusion / $scoreFusion hybrid, and HNSW index management

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multilingual-embed-expert-mode

Deep expertise in multilingual embedding models — BGE-M3 (dense+sparse+ColBERT), multilingual-e5, Jina v3, Cohere multilingual, Nomic v2, Arctic-Embed

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opensearch-vector-expert-mode

Deep expertise in OpenSearch k-NN — Lucene/Faiss/NMSLIB engines, neural sparse, hybrid query DSL, and ML Commons inference

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pgvector-expert-mode

Deep expertise in pgvector 0.8+ for PostgreSQL — HNSW/IVFFlat tuning, halfvec/sparsevec, hybrid search with tsvector + RRF, and pgvectorscale (DiskANN)

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pinecone-expert-mode

Deep expertise in Pinecone serverless — namespaces, sparse-dense indexes, integrated inference (embed + rerank), and dedicated read nodes

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qdrant-expert-mode

Deep expertise in Qdrant — payload filtering, scalar/binary/PQ quantization, multi-vector, dense+sparse hybrid, and distributed mode

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redis-vector-expert-mode

Deep expertise in Redis Stack / RediSearch — vector index types (FLAT, HNSW, SVS-VAMANA), KNN + range queries, hybrid filter syntax

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turbopuffer-expert-mode

Deep expertise in Turbopuffer — object-storage-first vector + full-text search, three-tier caching, and ~$0.02/GB cold storage economics

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vespa-expert-mode

Deep expertise in Vespa — tensor framework, ranking expressions, ColBERT MaxSim, sparse + dense in one query, and multi-phase ranking

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weaviate-expert-mode

Deep expertise in Weaviate v1.27+ — collections, named vectors, vectorizer modules, hybrid search, and per-tenant shard isolation at million-tenant scale

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