Retrieve
client.retrievers.retriever.search(stringretrieverID, RetrieverSearchParams { query, organization_id, project_id, 3 more } params, RequestOptionsoptions?): CompositeRetrievalResult { image_nodes, nodes, page_figure_nodes }
POST/api/v1/retrievers/{retriever_id}/retrieve
Retrieve
import LlamaCloud from '@llamaindex/llama-cloud';
const client = new LlamaCloud({
apiKey: process.env['LLAMA_CLOUD_API_KEY'], // This is the default and can be omitted
});
const compositeRetrievalResult = await client.retrievers.retriever.search(
'182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e',
{ query: 'x' },
);
console.log(compositeRetrievalResult.image_nodes);{
"image_nodes": [
{
"node": {
"file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"image_size": 0,
"page_index": 0,
"metadata": {
"foo": "bar"
}
},
"score": 0,
"class_name": "class_name"
}
],
"nodes": [
{
"node": {
"id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"end_char_idx": 0,
"pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"retriever_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"retriever_pipeline_name": "retriever_pipeline_name",
"start_char_idx": 0,
"text": "text",
"metadata": {
"foo": "bar"
}
},
"class_name": "class_name",
"score": 0
}
],
"page_figure_nodes": [
{
"node": {
"confidence": 0,
"figure_name": "figure_name",
"figure_size": 0,
"file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"page_index": 0,
"is_likely_noise": true,
"metadata": {
"foo": "bar"
}
},
"score": 0,
"class_name": "class_name"
}
]
}Returns Examples
{
"image_nodes": [
{
"node": {
"file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"image_size": 0,
"page_index": 0,
"metadata": {
"foo": "bar"
}
},
"score": 0,
"class_name": "class_name"
}
],
"nodes": [
{
"node": {
"id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"end_char_idx": 0,
"pipeline_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"retriever_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"retriever_pipeline_name": "retriever_pipeline_name",
"start_char_idx": 0,
"text": "text",
"metadata": {
"foo": "bar"
}
},
"class_name": "class_name",
"score": 0
}
],
"page_figure_nodes": [
{
"node": {
"confidence": 0,
"figure_name": "figure_name",
"figure_size": 0,
"file_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
"page_index": 0,
"is_likely_noise": true,
"metadata": {
"foo": "bar"
}
},
"score": 0,
"class_name": "class_name"
}
]
}Note for AI agents: this documentation is built for programmatic access.
- Overview of all docs: https://developers.llamaindex.ai/llms.txt
- Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md
- Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters.
- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/