vector-databases-and-embeddings-module

Plugin vector-databases-and-embeddings-module in sancovp/dark-factory-live. Commands read from the repo's manifest — 1 month ago.

Skills (30)

• 0.1.1-understand-extends_pinecone_index

  [0.1.1] The inheritance relationship where pod-based and serverless deployment models specialize the base Pinecone ind

• 0.1.2-understand-is_component_of_pinecone_record

  [0.1.2] The partitive relationship where a dense or sparse vector constitutes one of the mandatory or optional fields

• 0.1.3-understand-reads_from_pinecone_index

  [0.1.3] The data access relationship where fetch or query operations retrieve stored records from a Pinecone index by

• 0.1.4-understand-writes_to_pinecone_index

  [0.1.4] The data mutation relationship where upsert or delete operations modify the record set stored within a Pinecon

• 0.1.5-understand-authenticates_to_pinecone_project

  [0.1.5] The relationship where a scoped API key grants authenticated access to a specific Pinecone project and its res

• 0.1.6-understand-belongs_to_pinecone_index

  [0.1.6] The containment relationship indicating a record resides within a specific Pinecone index for storage and retr

• 0.1-understand-core

  [0.1] the core subdomain of vector databases and embeddings (30 concepts)

• 0.2.1-understand-idx_algorithm_selection

  [0.2.1] Choice among ANN index types HNSW IVF ANNOY LSH determined by data scale query patterns and accuracy requireme

• 0.2.2-understand-idx_memory_budget

  [0.2.2] Available RAM or VRAM for index and raw vectors constraining quantization choices and index density

• 0.2.3-understand-idx_hybrid_composition

  [0.2.3] Strategy combining multiple index types such as HNSW over PQ or IVF with scalar quantization to balance speed

• 0.2.4-understand-idx_parameter_sweep

  [0.2.4] Systematic exploration of parameter space to find optimal recall speed trade off for given constraints

• 0.2.5-understand-asymmetric_quantization

  [0.2.5] A quantization scheme using both scale_factor and zero_point to map an arbitrary [min, max] float range to the

• 0.2.6-understand-idx_recall_target

  [0.2.6] Desired fraction of true nearest neighbors returned governing aggressiveness of search pruning and parameter t

• 0.2-understand-deep_nearest_neighbor_sea

  [0.2] the deep_nearest_neighbor_sea subdomain of vector databases and embeddings (47 concepts)

• 0.3.1-understand-chroma_collection

  [0.3.1] A named namespace within Chroma that stores embeddings, associated documents, metadata, and IDs as an isolated

• 0.3.2-understand-chroma_query

  [0.3.2] A search request containing a query vector (or text to embed) plus optional top-k and metadata filter paramete

• 0.3.3-understand-pinecone_record

  [0.3.3] A data unit in Pinecone containing id, dense vector values, optional sparse values, and key-value metadata.

• 0.3.4-understand-pinecone_query

  [0.3.4] A read operation that returns approximate nearest neighbors to a query vector with optional metadata filtering

• 0.3.5-understand-chroma_embedding

  [0.3.5] A dense vector of floating-point numbers produced by an embedding function, representing the semantic position

• 0.3.6-understand-chroma_client

  [0.3.6] The primary Python API client for Chroma; wraps HTTP or in-memory transport to a Chroma server or persistence

• 0.3-understand-deep_vector_database

  [0.3] the deep_vector_database subdomain of vector databases and embeddings (45 concepts)

• 0.4.1-understand-vector_database

  [0.4.1] A specialized database system optimized for storing, indexing, and searching high-dimensional vector embedding

• 0.4.2-understand-approximate_nearest_neighbor

  [0.4.2] An ANN algorithm that trades exact recall for sub-linear query time by probabilistically organizing the embedd

• 0.4.3-understand-faiss

  [0.4.3] Meta's open-source C++/Python library for dense vector similarity search; implements IVF, PQ, HNSW, ONNG, and

• 0.4.4-understand-nearest_neighbor_search

  [0.4.4] Query operation returning the k closest vectors by a distance or similarity metric in embedding space.

• 0.4.5-understand-hnsw_index

  [0.4.5] Hierarchical Navigable Small World graph; layered skip-list + NN graph enabling logarithmic-layer traversal th

• 0.4.6-understand-ivf_index

  [0.4.6] Inverted File Index; clusters vectors into Voronoi cells via k-means; search prunes to nearest centroids then

• 0.4-understand-vector_databases_and_embeddings

  [0.4] the vector_databases_and_embeddings subdomain of vector databases and embeddings (140 concepts)

• 0-understand-vector databases and embeddings

  [0] the vector databases and embeddings library — every skill a certified region of the KB; call number = home : facets

• using-vector-databases-and-embeddings

  Use the vector databases and embeddings neurosymbolic module: RAG library, agent brain, growable KB — proof-checked

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