Simple in-memory RAG (Retrieval Augmented Generation) for question-answering over document corpus with source attribution. Uses OpenAI or Gemini embeddings for semantic search and LLM for answer generation.
Usage
run_rag_search(
query,
documents,
provider = c("openai", "gemini"),
api_key = NULL,
embedding_model = NULL,
chat_model = NULL,
top_k = 5
)Arguments
- query
Character string, user question
- documents
Character vector, corpus to search
- provider
Character string, provider: "openai" or "gemini"
- api_key
Character string, API key (or from OPENAI_API_KEY/GEMINI_API_KEY env).
- embedding_model
Character string, embedding model. Defaults: "text-embedding-3-small" (openai), "gemini-embedding-001" (gemini)
- chat_model
Character string, chat model. Defaults: "gpt-4.1-mini" (openai), "gemini-2.5-flash-lite" (gemini)
- top_k
Integer, number of documents to retrieve (default: 5)
Value
List with:
success: Logical
answer: Generated answer
confidence: Confidence score (0-1)
sources: Vector of source document indices
retrieved_docs: Retrieved document chunks
scores: Similarity scores
Details
Simple RAG workflow:
Generate embeddings for documents and query
Find top-k similar documents via cosine similarity
Generate answer using LLM with retrieved context
See also
get_best_embeddings() for the retrieval step alone; call_llm_api() for the answer-generation step alone; sanitize_llm_input() for an input safety check before calling
Examples
if (interactive()) {
documents <- c(
"Assistive technology helps students with disabilities access curriculum.",
"Universal Design for Learning provides multiple means of engagement.",
"Response to Intervention uses tiered support systems."
)
# Using OpenAI (requires API key)
result <- run_rag_search(
query = "How does assistive technology support learning?",
documents = documents,
provider = "openai"
)
if (result$success) {
cat("Answer:", result$answer, "\n")
cat("Sources:", paste(result$sources, collapse = ", "), "\n")
}
}