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
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
0.4.6-understand-embedding_model[0.4.6] Neural model converting text into dense vector representations optimized for retrieval similarity tasks
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-understand-retrieval-augmented-generation-architecture-patterns[0] the retrieval augmented generation architecture patterns library — every skill a certified region of the KB; call number = home : facets
using-retrieval-augmented-generation-architectUse the retrieval augmented generation architecture patterns neurosymbolic module: RAG library, agent brain, growable KB — proof-checked