Retrieval-Augmented Generation and RAG Evaluation Tools


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Documentation for package ‘ragR’ version 0.1.0

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ragR-package ragR: Retrieval-Augmented Generation and Evaluation Tools
api_chat_handler API handler: chat with the RAG pipeline
api_clear_all_handler API handler: clear the entire vector store
api_clear_handler API handler: clear a vector-store collection
api_ragas_clear_handler API handler: clear RAG evaluation files
api_ragas_handler API handler: compute RAG evaluation metrics
api_ragas_report_handler API handler: generate a RAG evaluation performance report
chunk_text_sentence Chunk text into sentences (strict)
clear_qa_log Clear the QA log on disk
clear_qa_metrics Clear QA metrics on disk
compute_ragas_metrics Compute RAGAS metrics
compute_ragas_metrics_llm Compute RAGAS-style metrics (LLM scored)
generate_openai_chat Generate a chat completion from OpenAI
generate_ragas_report Generate a RAG evaluation performance report
generate_ragas_report_llm Generate a RAG evaluation report using large language model scoring
get_openai_embeddings Get embeddings from OpenAI
ingest_documents Ingest documents into the vector store
load_qa_log Load a QA log from disk
load_qa_metrics Load QA metrics from disk
load_rag_config Load RAG configuration
log_rag_interaction Append one RAG interaction to the QA log
plot_ragas_means Plot mean RAG evaluation metrics to a PNG
qa_log_empty Create an empty QA log tibble
qa_metrics_empty Create an empty QA metrics tibble
query_rag Query the RAG pipeline
ragR ragR: Retrieval-Augmented Generation and Evaluation Tools
save_qa_log Save a QA log to disk
save_qa_metrics Save QA metrics to disk
summarize_ragas Summarize RAGAS metrics across QA pairs
vectorstore_clear_all Delete all collections from the R-native vector store
vectorstore_delete_collection Delete a collection from the R-native vector store
vectorstore_load Load the R-native vector store
vectorstore_query Query the R-native vector store for nearest neighbors
vectorstore_upsert Upsert embeddings into the R-native vector store