feat: Add vector_db_id to chunk metadata #3304
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varshaprasad96
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/lgtm
This change looks good. Just to add - this is for vector_db, if you'd like to use vector_stores instead (which is based on top of open ai APIs) we also have filename from where the chunk was retrieved (
llama-stack/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
Lines 449 to 450 in ef02b9e
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rubber stamp based on @franciscojavierarceo's and @varshaprasad96's reviews.
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# What does this PR do?
When running RAG in a multi vector DB setting, it can be difficult to
trace where retrieved chunks originate from. This PR adds the
`vector_db_id` into each chunk’s metadata, making it easier to
understand which database a given chunk came from. This is helpful for
debugging and for analyzing retrieval behavior of multiple DBs.
Relevant code:
```python
for vector_db_id, result in zip(vector_db_ids, results):
for chunk, score in zip(result.chunks, result.scores):
if not hasattr(chunk, "metadata") or chunk.metadata is None:
chunk.metadata = {}
chunk.metadata["vector_db_id"] = vector_db_id
chunks.append(chunk)
scores.append(score)
```
## Test Plan
* Ran Llama Stack in debug mode.
* Verified that `vector_db_id` was added to each chunk’s metadata.
* Confirmed that the metadata was printed in the console when using the
RAG tool.
---------
Co-authored-by: are-ces <[email protected]>
Co-authored-by: Francisco Arceo <[email protected]>
iamemilio
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Sep 24, 2025
# What does this PR do?
When running RAG in a multi vector DB setting, it can be difficult to
trace where retrieved chunks originate from. This PR adds the
`vector_db_id` into each chunk’s metadata, making it easier to
understand which database a given chunk came from. This is helpful for
debugging and for analyzing retrieval behavior of multiple DBs.
Relevant code:
```python
for vector_db_id, result in zip(vector_db_ids, results):
for chunk, score in zip(result.chunks, result.scores):
if not hasattr(chunk, "metadata") or chunk.metadata is None:
chunk.metadata = {}
chunk.metadata["vector_db_id"] = vector_db_id
chunks.append(chunk)
scores.append(score)
```
## Test Plan
* Ran Llama Stack in debug mode.
* Verified that `vector_db_id` was added to each chunk’s metadata.
* Confirmed that the metadata was printed in the console when using the
RAG tool.
---------
Co-authored-by: are-ces <[email protected]>
Co-authored-by: Francisco Arceo <[email protected]>
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What does this PR do?
When running RAG in a multi vector DB setting, it can be difficult to trace where retrieved chunks originate from. This PR adds the
vector_db_idinto each chunk’s metadata, making it easier to understand which database a given chunk came from. This is helpful for debugging and for analyzing retrieval behavior of multiple DBs.Relevant code:
Test Plan
vector_db_idwas added to each chunk’s metadata.