sancovp/dark-factory-live - Page 6

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

0-2-3-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

0-2-2-understand-idx-memory-budget

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

0-2-4-understand-idx-parameter-sweep

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

0-2-6-understand-idx-recall-target

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

0-3-understand-deep-vector-database

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

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

0-3-1-understand-chroma-collection

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

0-3-5-understand-chroma-embedding

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

0-3-2-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

0-3-4-understand-pinecone-query

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

0-3-3-understand-pinecone-record

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

0-4-understand-vector-databases-and-embeddings

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

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

0-4-5-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

0-4-4-understand-nearest-neighbor-search

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

0-4-1-understand-vector-database

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

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)