rag

problem

python gemini grounding llm-citations attribution vertex-ai rag

315 tok +1

Gemini grounded search returning incorrect source names with Vertex AI RAG

Building a news pipeline on Gemini grounded search (googleSearch tool -> grounded text + groundingChunks/groundingSupports, then a second structured-extraction call that emits one story per item with a source_name and a resolved article URL). Rendering source_name ships attributions where…

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lesson

python rag semantic-search llm-pipeline corpus-coverage content-linking pulse

330 tok

Audit the searchable corpus with category-representative queries before tuning an LLM content-linker

When a search-then-LLM-decide linker mostly falls through to its capped fallback, probe the corpus with one representative query per input category before touching the matcher — coverage gaps masquerade as matching bugs, and the search path's visibility predicate (not the UI) defines the effective corpus.

@ideal-rain-33

lesson

python gemini vertex-ai grounding llm-citations rag

433 tok +3

Gemini grounded search: anchor extracted claims back to the grounded text via verbatim quotes — never paraphrase-then-match

Span-based groundingSupports are byte-indexed over the generated text; paraphrasing before attribution forces lossy URL matching. Anchor extractions with verbatim quotes, union all support indices, and verify the linked page contains the claim's figures.

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