# What does this PR do?
his PR allows users to customize the template used for chunks when
inserted into the context. Additionally, this enables metadata injection
into the context of an LLM for RAG. This makes a naive and crude
assumption that each chunk should include the metadata, this is
obviously redundant when multiple chunks are returned from the same
document. In order to remove any sort of duplication of chunks, we'd
have to make much more significant changes so this is a reasonable first
step that unblocks users requesting this enhancement in
https://github.com/meta-llama/llama-stack/issues/1767.
In the future, this can be extended to support citations.
List of Changes:
- `llama_stack/apis/tools/rag_tool.py`
- Added `chunk_template` field in `RAGQueryConfig`.
- Added `field_validator` to validate the `chunk_template` field in
`RAGQueryConfig`.
- Ensured the `chunk_template` field includes placeholders `{index}` and
`{chunk.content}`.
- Updated the `query` method to use the `chunk_template` for formatting
chunk text content.
- `llama_stack/providers/inline/tool_runtime/rag/memory.py`
- Modified the `insert` method to pass `doc.metadata` for chunk
creation.
- Enhanced the `query` method to format results using `chunk_template`
and exclude unnecessary metadata fields like `token_count`.
- `llama_stack/providers/utils/memory/vector_store.py`
- Updated `make_overlapped_chunks` to include metadata serialization and
token count for both content and metadata.
- Added error handling for metadata serialization issues.
- `pyproject.toml`
- Added `pydantic.field_validator` as a recognized `classmethod`
decorator in the linting configuration.
- `tests/integration/tool_runtime/test_rag_tool.py`
- Refactored test assertions to separate `assert_valid_chunk_response`
and `assert_valid_text_response`.
- Added integration tests to validate `chunk_template` functionality
with and without metadata inclusion.
- Included a test case to ensure `chunk_template` validation errors are
raised appropriately.
- `tests/unit/rag/test_vector_store.py`
- Added unit tests for `make_overlapped_chunks`, verifying chunk
creation with overlapping tokens and metadata integrity.
- Added tests to handle metadata serialization errors, ensuring proper
exception handling.
- `docs/_static/llama-stack-spec.html`
- Added a new `chunk_template` field of type `string` with a default
template for formatting retrieved chunks in RAGQueryConfig.
- Updated the `required` fields to include `chunk_template`.
- `docs/_static/llama-stack-spec.yaml`
- Introduced `chunk_template` field with a default value for
RAGQueryConfig.
- Updated the required configuration list to include `chunk_template`.
- `docs/source/building_applications/rag.md`
- Documented the `chunk_template` configuration, explaining how to
customize metadata formatting in RAG queries.
- Added examples demonstrating the usage of the `chunk_template` field
in RAG tool queries.
- Highlighted default values for `RAG` agent configurations.
# Resolves https://github.com/meta-llama/llama-stack/issues/1767
## Test Plan
Updated both `test_vector_store.py` and `test_rag_tool.py` and tested
end-to-end with a script.
I also tested the quickstart to enable this and specified this metadata:
```python
document = RAGDocument(
document_id="document_1",
content=source,
mime_type="text/html",
metadata={"author": "Paul Graham", "title": "How to do great work"},
)
```
Which produced the output below:
This highlights the usefulness of the additional metadata. Notice how
the metadata is redundant for different chunks of the same document. I
think we can update that in a subsequent PR.
# Documentation
I've added a brief comment about this in the documentation to outline
this to users and updated the API documentation.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Add examples for how to define RAGDocuments. Not sure if this is the
best place for these docs. @raghotham Please advise
## Test Plan
None, documentation
[//]: # (## Documentation)
Signed-off-by: Kevin <kpostlet@redhat.com>
# What does this PR do?
This PR modifies some of the docs to help them map to (1) the mental
model of software engineers building AI models starting with RAG and
then moving to Agents and (2) aligning the navbar somewhat closer to the
diagram on the home page.
## Test Plan
N/A Tested locally.
# Documentation
Take a look at the screen shot for below and after.
## Before
## After
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Another for https://github.com/meta-llama/llama-stack/issues/1815
This links the `CONTRIBUTING.md` file directly so that we don't have to
maintain two different files.
Also I updated the title for RAG under Building AI Applications.
## Changes
Look of what the Contributing page looks like, proof it sources directly
from the markdown file.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
# What does this PR do?
The agent API allows to query multiple DBs using the `vector_db_ids`
argument of the `rag` tool:
```py
toolgroups=[
{
"name": "builtin::rag",
"args": {"vector_db_ids": [vector_db_id]},
}
],
```
This means that multiple DBs can be used to compose an aggregated
context by executing the query on each of them.
When documents are passed to the next agent turn, there is no explicit
way to configure the vector DB where the embeddings will be ingested. In
such cases, we can assume that:
- if any `vector_db_ids` is given, we use the first one (it probably
makes sense to assume that it's the only one in the list, otherwise we
should loop on all the given DBs to have a consistent ingestion)
- if no `vector_db_ids` is given, we can use the current logic to
generate a default DB using the default provider. If multiple providers
are defined, the API will fail as expected: the user has to provide
details on where to ingest the documents.
(Closes#1270)
## Test Plan
The issue description details how to replicate the problem.
[//]: # (## Documentation)
---------
Signed-off-by: Daniele Martinoli <dmartino@redhat.com>
Summary:
- [new] Agent concepts (session, turn)
- [new] how to write custom tools
- [new] non-streaming API and how to get outputs
- [update] remaining `memory` -> `rag` rename
- [new] note importance of `instructions`
Test Plan:
read
# What does this PR do?
This PR improves the documentation in several ways:
- **Fixed incorrect link in `tools.md`** to ensure all references point
to the correct resources.
- **Added instructions for running the `code-interpreter` agent in a
Podman container**, helping users configure and execute the tool in
containerized environments.
- **Introduced an unregister command for single and multiple vector
databases**, making it easier to manage vector DBs.
- **Provided a simple example script for using the `code-interpreter`
agent**, giving users a practical reference for implementation.
These updates enhance the clarity, usability, and completeness of the
documentation.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
The following steps were performed to verify the accuracy of the
changes:
1. **Validated all fixed link** by checking their destinations to ensure
correctness.
2. **Ran the `code-interpreter` agent in a Podman container** following
the new instructions to confirm functionality.
3. **Executed the vector database unregister commands** and verified
that both single and multiple databases were correctly removed.
4. **Tested the new example script for `code-interpreter`**, ensuring it
runs without errors.
All changes were reviewed and tested successfully, improving the
documentation's accuracy and ease of use.
[//]: # (## Documentation)
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
