- Six vignettes added covering the full pipeline: web data, Wikipedia data, regex search, dictionary search, semantic search (RAG), and basic NLP processing.
- Basic NLP vignette walks through
nlp_split_sentences(),nlp_tokenize_text()(word and Biber methods), andnlp_cast_tokens()stepwise and as a single pipe. - README revamped: tighter intro, API map, RAG/agent positioning, vignette links.
util_fetch_embeddings()re-added for embedding generation via Hugging Face inference endpoints (reversed 1.1.0 removal; now calls the HF inference API rather than loading models locally).nlp_cast_tokens()documented and surfaced -- flattens the token list fromnlp_tokenize_text()into a long-format data frame with optional character spans.- Suggests trimmed:
ellmerand unused packages removed.
- Package is now organized around a four-stage pipeline: Fetch → Read → Process → Search. All functions use a consistent verb_noun pattern.
- Acquire:
fetch_urls()(from web search),fetch_wiki_urls(),fetch_wiki_refs()— return URLs or metadata, not full text. - Ingest:
read_urls()— read content from URLs into R (replacesweb_scrape_urls). - Process:
nlp_split_*,nlp_tokenize_text(),nlp_index_tokens()(andnlp_roll_chunks()for rolling windows). - Search: Four retrieval options —
search_regex()(regex/KWIC),search_index()(BM25),search_vector()(cosine over your own embeddings),search_dict()(dictionary match; replacesner_extract_entities). - Common parameters standardized:
corpus(replacestif),by(replacestext_hierarchy).
- In-package embedding generation (e.g. Hugging Face API). Use your own embedding pipeline and pass your embedding matrix as the \code{embeddings} argument to \code{search_vector()}.
- Legacy names:
web_search,wiki_search,wiki_find_references,web_scrape_urls,ner_extract_entities,sem_nearest_neighbors/sem_search_corpus(replaced bysearch_vectorandsearch_regex).
- README revamped around the API map and a single “golden path” workflow.
- DESCRIPTION and package help updated for the four-stage pipeline; version set to 1.1.0.
- Initial release: URL fetching, URL content reading, NLP processing (split, tokenize, index), and corpus/search utilities.