sancovp/dark-factory-live - Page 9

0-2-4-understand-colbert-late-interaction

[0.2.4] Core architectural principle deferring interaction between query and document token representations until scor

0-2-5-understand-colbert-maxsim-operator

[0.2.5] Maximum similarity operator that for each query token finds the highest-scoring document token via cosine simi

0-2-6-understand-colbert-token-embedding

[0.2.6] Dense contextualized vector representation produced per token by an encoder model, preserving token identity a

0-2-1-understand-rrf-fusion-score

[0.2.1] The sum of reciprocal rank values for a given document across all contributing rank lists; the document's posi

0-2-3-understand-rrf-rank-position

[0.2.3] The integer position of an item within a rank list, starting at 1 for the highest-scoring result; used as the

0-3-understand-deep-retrieval-augmented

[0.3] the deep_retrieval_augmented_ subdomain of retrieval augmented generation architecture patterns (48 concepts)

0-3-1-understand-memory-entry

[0.3.1] Atomic unit of stored information in the memory system, containing content plus metadata

0-3-2-understand-mmr001

[0.3.2] Modality type: classification of data representation forms a retrieval system handles — text, image, table, co

0-3-3-understand-mmr002

[0.3.3] Multi-modal embedding model: neural network architecture that projects content from different modalities into

0-3-4-understand-mmr003

[0.3.4] Cross-modal retrieval: searching for content in one modality using a query from a different modality, such as

0-3-5-understand-mmr005

[0.3.5] Non-text chunk processor: component that parses, extracts, and encodes non-text content from documents — handl

0-3-6-understand-mmr009

[0.3.6] Multi-modal index: index structure supporting heterogeneous chunk types — text vectors, image features, table

0-4-understand-retrieval-augmented-generation-architecture-patt

[0.4] the retrieval_augmented_generation_architecture_patt subdomain of retrieval augmented generation architecture patterns (106 concepts)

0-4-4-understand-dense-passage-retriever

[0.4.4] Bi-encoder model trained on query-passage pairs to produce jointly learned dense embeddings for retrieval

0-4-2-understand-dense-retrieval

[0.4.2] Neural retrieval using learned embedding models to encode queries and documents into dense vectors for similar

0-4-6-understand-embedding-model

[0.4.6] Neural model converting text into dense vector representations optimized for retrieval similarity tasks

0-4-5-understand-multi-modal-rag

[0.4.5] RAG system that retrieves and augments generation with images, tables, code, or other non-text modalities alon

0-4-1-understand-retrieval-augmented-generation

[0.4.1] Paradigm that augments language model generation with information retrieved from external knowledge sources to

0-4-3-understand-vector-database

[0.4.3] Specialized storage system indexing embeddings with efficient similarity search capabilities (Pinecone, Weavia

0-1-1-understand-multi-hop-retrieval

[0.1.1] Retrieval strategy that executes multiple sequential retrieval steps, where each step uses the output or conte

0-1-2-understand-cache-strategy

[0.1.2] Approach for storing and reusing retrieval results or generated responses to reduce redundant computation and

0-1-understand-core

[0.1] the core subdomain of retrieval augmented generation architecture patterns (2 concepts)

0-2-1-understand-rrf-fusion-score

[0.2.1] The sum of reciprocal rank values for a given document across all contributing rank lists; the document's posi

0-2-2-understand-colbert-approximate-search

[0.2.2] Approximate nearest neighbor indexing on per-token document embeddings enabling efficient retrieval of candida

0-2-3-understand-rrf-rank-position

[0.2.3] The integer position of an item within a rank list, starting at 1 for the highest-scoring result; used as the

0-2-4-understand-colbert-late-interaction

[0.2.4] Core architectural principle deferring interaction between query and document token representations until scor

0-2-5-understand-colbert-maxsim-operator

[0.2.5] Maximum similarity operator that for each query token finds the highest-scoring document token via cosine simi

0-2-6-understand-colbert-token-embedding

[0.2.6] Dense contextualized vector representation produced per token by an encoder model, preserving token identity a

0-2-understand-deep-dense-retrieval

[0.2] the deep_dense_retrieval subdomain of retrieval augmented generation architecture patterns (51 concepts)

0-3-1-understand-memory-entry

[0.3.1] Atomic unit of stored information in the memory system, containing content plus metadata

0-3-2-understand-mmr001

[0.3.2] Modality type: classification of data representation forms a retrieval system handles — text, image, table, co

0-3-3-understand-mmr002

[0.3.3] Multi-modal embedding model: neural network architecture that projects content from different modalities into

0-3-4-understand-mmr003

[0.3.4] Cross-modal retrieval: searching for content in one modality using a query from a different modality, such as

0-3-5-understand-mmr005

[0.3.5] Non-text chunk processor: component that parses, extracts, and encodes non-text content from documents — handl

0-3-6-understand-mmr009

[0.3.6] Multi-modal index: index structure supporting heterogeneous chunk types — text vectors, image features, table

0-3-understand-deep-retrieval-augmented

[0.3] the deep_retrieval_augmented_ subdomain of retrieval augmented generation architecture patterns (48 concepts)

0-4-1-understand-retrieval-augmented-generation

[0.4.1] Paradigm that augments language model generation with information retrieved from external knowledge sources to

0-4-2-understand-dense-retrieval

[0.4.2] Neural retrieval using learned embedding models to encode queries and documents into dense vectors for similar

0-4-3-understand-vector-database

[0.4.3] Specialized storage system indexing embeddings with efficient similarity search capabilities (Pinecone, Weavia

0-4-4-understand-dense-passage-retriever

[0.4.4] Bi-encoder model trained on query-passage pairs to produce jointly learned dense embeddings for retrieval