You'll need your own API key, which you can generate at online.nimbleway.com/settings/api-keys. Set it in your shell as NIMBLE_API_KEY and the examples work as written.
As discussed, we're starting with the US; Japan will follow once some routine infrastructure work is done.
AIO Extract Template (google_sge_new). One call with the search query. Returns the raw HTML in data.html and the AI Overview at data.parsing.entities.AIOverview.
SERP endpoint (/v2/serp). One call with the search query and country. Returns the raw HTML of the results page in data.html; AIO availability depends on whether Google shows one for the query.
1. AIO Extract Template google_sge_new
curl -X POST https://sdk.nimbleway.com/v1/agents/run \
-H "Authorization: Bearer $NIMBLE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"agent":"google_sge_new","params":{"query":"what is retrieval augmented generation"}}'The raw HTML is in data.html and the AI Overview is at data.parsing.entities.AIOverview.
Response (shortened)
{
"url": "https://www.google.com/search?hl=en&gl=us&q=what+is+retrieval+augmented+generation",
"status": "success",
"status_code": 200,
"data": {
"html": "<!DOCTYPE html><html itemscope=\"\" itemtype=\"http://schema.org/SearchResultsPage\" lang=\"en\">…",
"parsing": {
"entities": {
"AIOverview": [{
"entity_type": "AIOverview",
"blocks": [
{"content": "Primary Meaning: RAG combines information retrieval (searching external databases or documents) with text generation (an LLM)."},
{"content": "Indexing & Chunking: External documents are broken into smaller text pieces (chunks) and converted into mathematical number-based representations called embeddings. YouTube·KodeKloud +1"},
{"content": "Retrieval: When a user asks a question, the system searches a vector database or search index for chunks that closely match the meaning of the query. Reddit·r/LanguageTechnology +1"},
{"content": "Augmentation: The retrieved text chunks are injected into the prompt as background context alongside the user's original question. YouTube·Oxylabs +1"},
…
],
"content": "Retrieval-Augmented Generation (RAG) is an AI framework that improves Large Language Model (LLM) responses by searching and retrieving relevant facts from external data sources before generating an answer. …"
}]
}
}
}
}
2. SERP endpoint POST /v2/serp
curl -X POST https://sdk.nimbleway.com/v2/serp \
-H "Authorization: Bearer $NIMBLE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"search_engine":"google_search","query":"what is retrieval augmented generation","country":"US","parse":false}'The raw HTML is in data.html. Set "parse": true to also get the parsed entities, with the AI Overview at data.parsing.entities.AIOverview.
Response (shortened)
{
"url": "https://www.google.com/search?hl=en&gl=us&q=what+is+retrieval+augmented+generation",
"status": "success",
"status_code": 200,
"data": {
"html": "<!doctype html><html itemscope=\"\" itemtype=\"http://schema.org/SearchResultsPage\" lang=\"en\">…
…<div role=\"heading\" aria-level=\"2\"><strong class=\"WJFise\">AI Overview</strong></div>…"
}
}
The AI Overview block is inside the HTML (0.97 MB for this query).