This page explains each field of the output from Vertex AI RAG Engine.
retrieveContexts
This section describes each field defined in the retrieveContexts
API and
uses the fields in sample code.
Fields
| Field name | Description |
|---|---|
source_uri
|
The original source file before it's imported into RAG. If the file
is imported from Cloud Storage or Google Drive, source_uri
is the original
file URI in Cloud Storage or Drive. If the file is
uploaded, source_uri
is the file's display name. |
source_display_name
|
The file's display name. |
text
|
The text chunk that is relevant to the query. |
score
|
The similarity or distance between the query and the text chunk.
The similarity or distance depends on the vectorDB
that you choose. For ragManagedDB
, the score is the COSINE_DISTANCE
. |
Sample output
This code sample demonstrates the use of the fields to produce sample output.
contexts
{
source_uri:
"gs://sample_folder/hello_world.txt"
source_display_name:
"hello_world.txt"
text:
"Hello World!"
score:
0
.60545359030757784
}
generateContent
Most of the fields defined for the generateContent
API can found in the Response body
.
Fields
This section describes each field defined in the grounding_metadata
part of
the generateContent
API and uses the fields in sample code.
Field name
Description
text
The response generated by Gemini.
grounding_chunks
The chunks returned by Vertex AI RAG Engine.
retrieved_context
A repeated field that can have zero or more chunks used to ground the generated content.
-
uri
- The
source_urispecifies where the data is originally stored.
-
title
- The
source_display_nameis the filename or display name of the original file.
-
text
- The text chunk is used to ground the Gemini response.
grounding_supports
The relationship between the generated content and the grounding chunks. This is a repeated field. Each
grounding_supports
field shows the relationship between one text segment of the generated context and one or more text chunks that are RAG retrieved.segment
The grounded text segment of the generated text.
-
start_index
- The first index of the grounded text. If the
start_indexis missing, then thestart_indexis0.
-
end_index
- The last index of the grounded text.
-
text
- The grounded text.
grounding_chunk_indices
The chunk that's used to ground the text segment. There can be more than one chunk used to ground the text. The index starts from
0
, which represents the first chunk in the grounding_chunks
field. The ground is on the entire chunk. The part of the chunk that grounds the response isn't specified.confidence_scores
The score that's used to ground the text on a given chunk. The highest score possible is
1
and the higher the score, the higher the confidence level. Each score matches each grounding_chunk_indices
. Only the chunks with a confidence score of at least 0.6
are included in the output.Sample output
This code sample demonstrates the use of the fields to produce sample output.
candidates
{
content
{
role:
"model"
parts
{
text:
"The rectangle is red and the background is white. The rectangle appears to be on some type of document editing software. \n"
}
}
grounding_metadata
{
grounding_chunks
{
retrieved_context
{
uri:
"a.txt"
title:
"a.txt"
text:
"Okay , I see a red rectangle on a white background . It looks like it\'s on some sort of document editing software. It has those small squares and circles around it, indicating that it\'s a selected object ."
}
}
grounding_chunks
{
retrieved_context
{
uri:
"b.txt"
title:
"b.txt"
text:
"The video is identical to the last time I described it . It shows a blue rectangle on a white background."
}
}
grounding_chunks
{
retrieved_context
{
uri:
"c.txt"
title:
"c.txt"
text:
"Okay , I remember the rectangle was blue in the past session . Now it is red.\n The red rectangle is still there . It \' s still in the same position on the white background, with the same handles around it. Nothing new is visible since last time.\n You \' re welcome . The red rectangle is still the only thing visible."
}
}
grounding_supports
{
segment
{
end_index:
49
text:
"The rectangle is red and the background is white."
}
grounding_chunk_indices:
2
grounding_chunk_indices:
0
confidence_scores:
0
.958192229
confidence_scores:
0
.992316723
}
grounding_supports
{
segment
{
start_index:
50
end_index:
120
text:
"The rectangle appears to be on some type of document editing software."
}
grounding_chunk_indices:
0
confidence_scores:
0
.98374176
}
}
}
What's next
- To learn more about RAG context in the API reference, see Context .
- To learn more about RAG, see Vertex AI RAG Engine overview .
