Retrieve
beta.retrieval.retrieve(RetrievalRetrieveParams**kwargs) -> RetrievalRetrieveResponse
POST/api/v1/retrieval/retrieve
Retrieve relevant chunks via hybrid search (vector + full-text), with filtering on built-in or user-defined metadata.
Retrieve
import os
from llama_cloud import LlamaCloud
client = LlamaCloud(
api_key=os.environ.get("LLAMA_CLOUD_API_KEY"), # This is the default and can be omitted
)
retrieval = client.beta.retrieval.retrieve(
index_id="idx-abc123",
query="What are the key findings?",
)
print(retrieval.results){
"results": [
{
"content": "content",
"metadata": {
"foo": "string"
},
"rerank_score": 0,
"score": 0,
"static_fields": {
"attachments": [
{
"attachment_name": "attachment_name",
"source_id": "source_id",
"type": "type"
}
],
"chunk_end_char": 0,
"chunk_index": 0,
"chunk_start_char": 0,
"chunk_token_count": 0,
"page_range_end": 0,
"page_range_start": 0,
"parsed_directory_file_id": "parsed_directory_file_id"
}
}
]
}Returns Examples
{
"results": [
{
"content": "content",
"metadata": {
"foo": "string"
},
"rerank_score": 0,
"score": 0,
"static_fields": {
"attachments": [
{
"attachment_name": "attachment_name",
"source_id": "source_id",
"type": "type"
}
],
"chunk_end_char": 0,
"chunk_index": 0,
"chunk_start_char": 0,
"chunk_token_count": 0,
"page_range_end": 0,
"page_range_start": 0,
"parsed_directory_file_id": "parsed_directory_file_id"
}
}
]
}