# What does this PR do?
Adding `ChunkMetadata` so we can properly delete embeddings later.
More specifically, this PR refactors and extends the chunk metadata
handling in the vector database and introduces a distinction between
metadata used for model context and backend-only metadata required for
chunk management, storage, and retrieval. It also improves chunk ID
generation and propagation throughout the stack, enhances test coverage,
and adds new utility modules.
```python
class ChunkMetadata(BaseModel):
"""
`ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that
will NOT be inserted into the context during inference, but is required for backend functionality.
Use `metadata` in `Chunk` for metadata that will be used during inference.
"""
document_id: str | None = None
chunk_id: str | None = None
source: str | None = None
created_timestamp: int | None = None
updated_timestamp: int | None = None
chunk_window: str | None = None
chunk_tokenizer: str | None = None
chunk_embedding_model: str | None = None
chunk_embedding_dimension: int | None = None
content_token_count: int | None = None
metadata_token_count: int | None = None
```
Eventually we can migrate the document_id out of the `metadata` field.
I've introduced the changes so that `ChunkMetadata` is backwards
compatible with `metadata`.
<!-- If resolving an issue, uncomment and update the line below -->
Closes https://github.com/meta-llama/llama-stack/issues/2501
## Test Plan
Added unit tests
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Add search_mode parameter (vector/keyword/hybrid) to
openai_search_vector_store method. Fixes OpenAPI
code generation by using str instead of Literal type.
Closes: #2459
## 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.* -->
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
This adds the ability to list, retrieve, update, and delete Vector Store
Files. It implements these new APIs for the faiss and sqlite-vec
providers, since those are the two that also have the rest of the vector
store files implementation.
Closes#2445
## Test Plan
### test_openai_vector_stores Integration Tests
There are a number of new integration tests added, which I ran for each
provider as outlined below.
faiss (from ollama distro):
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \
--embedding-model=all-MiniLM-L6-v2
```
sqlite-vec (from starter distro):
```
llama stack run llama_stack/templates/starter/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \
--embedding-model=all-MiniLM-L6-v2
```
### file_search verification tests
I also ensured the file_search verification tests continue to work, both
for faiss and sqlite-vec.
faiss (ollama distro):
```
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run llama_stack/templates/ollama/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=meta-llama/Llama-3.2-3B-Instruct
```
sqlite-vec (starter distro):
```
llama stack run llama_stack/templates/starter/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo
```
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Move to use vector_stores.search for file search tool in Responses,
which supports filters.
closes#2435
## Test Plan
Added e2e test with fitlers.
myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search and filters' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=meta-llama/Llama-3.3-70B-Instruct
# What does this PR do?
Add support for hybrid search mode in SQLite-vec provider, which
combines
keyword and vector search for better results. The implementation:
- Adds hybrid search mode as a new option alongside vector and keyword
search
- Implements query_hybrid method in SQLiteVecIndex that:
- First performs keyword search to get candidate matches
- Then applies vector similarity search on those candidates
- Updates documentation to reflect the new search mode
This change improves search quality by leveraging both semantic
similarity
and keyword matching, while maintaining backward compatibility with
existing
vector and keyword search modes.
## Test Plan
```
pytest tests/unit/providers/vector_io/test_sqlite_vec.py -v -s --tb=short
/Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:217: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"
warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
=============================================================================================== test session starts ===============================================================================================
platform darwin -- Python 3.10.16, pytest-8.3.5, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '8.3.5', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'asyncio': '0.26.0', 'nbval': '0.11.0', 'cov': '6.1.1'}}
rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, anyio-4.8.0, asyncio-0.26.0, nbval-0.11.0, cov-6.1.1
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function
collected 10 items
tests/unit/providers/vector_io/test_sqlite_vec.py::test_add_chunks PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search_k_greater_than_results PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_no_keyword_matches PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_score_threshold PASSED
tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_different_embedding PASSED
```
---------
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
This is an initial working prototype of wiring up the `file_search`
builtin tool for the Responses API to our existing rag knowledge search
tool.
This is me seeing what I could pull together on top of the bits we
already have merged. This may not be the ideal way to implement this,
and things like how I shuffle the vector store ids from the original
response API tool request to the actual tool execution feel a bit hacky
(grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see
what I mean).
## Test Plan
I stubbed in some new tests to exercise this using text and pdf
documents.
Note that this is currently under tests/verification only because it
sometimes flakes with tool calling of the small Llama-3.2-3B model we
run in CI (and that I use as an example below). We'd want to make the
test a bit more robust in some way if we moved this over to
tests/integration and ran it in CI.
### OpenAI SaaS (to verify test correctness)
```
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search' \
--base-url=https://api.openai.com/v1 \
--model=gpt-4o
```
### Fireworks with faiss vector store
```
llama stack run llama_stack/templates/fireworks/run.yaml
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=meta-llama/Llama-3.3-70B-Instruct
```
### Ollama with faiss vector store
This sometimes flakes on Ollama because the quantized small model
doesn't always choose to call the tool to answer the user's question.
But, it often works.
```
ollama run llama3.2:3b
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
--image-type venv \
--env OLLAMA_URL="http://0.0.0.0:11434"
pytest -sv tests/verifications/openai_api/test_responses.py \
-k'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=meta-llama/Llama-3.2-3B-Instruct
```
### OpenAI provider with sqlite-vec vector store
```
llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv
pytest -sv tests/verifications/openai_api/test_responses.py \
-k 'file_search' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model=openai/gpt-4o-mini
```
### Ensure existing vector store integration tests still pass
```
ollama run llama3.2:3b
INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
--image-type venv \
--env OLLAMA_URL="http://0.0.0.0:11434"
LLAMA_STACK_CONFIG=http://localhost:8321 \
pytest -sv tests/integration/vector_io \
--text-model "meta-llama/Llama-3.2-3B-Instruct" \
--embedding-model=all-MiniLM-L6-v2
```
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Updated the `search` functionality return response to match openai.
## Test Plan
```
pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
Extracts common OpenAI vector-store code into its own mixin so that all
providers can share the same core logic.
This also makes it easy for Llama Stack to support both vector-stores
and Llama Stack APIs in the interim so that both share the same
underlying vector-dbs.
Each provider contains storage specific logic to `create / edit / delete
/ list` vector dbs while the plumbing logic is standardized in the
common code.
Ensured that this works well with both faiss and sqllite-vec.
### Test Plan
```
llama stack run starter
pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
Adding OpenAI compat `/v1/vector-store` apis.
This PR implements the `faiss` provider with followup PRs coming up for
other providers.
Added routes to create, update, delete, list vector stores.
Also added route to search a vector store
Inserting into vector stores is missing and will be a follow up diff.
### Test Plan
- Added new integration test for testing the faiss provider
```
pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
# What does this PR do?
This PR introduces support for keyword based FTS5 search with BM25
relevance scoring. It makes changes to the existing EmbeddingIndex base
class in order to support a search_mode and query_str parameter, that
can be used for keyword based search implementations.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
run
```
pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
```
Output:
```
pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
/Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"
warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
====================================================== test session starts =======================================================
platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python
cachedir: .pytest_cache
metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.4-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0'}}
rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack
configfile: pyproject.toml
plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0
asyncio: mode=auto, asyncio_default_fixture_loop_scope=None
collected 7 items
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_fts PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED
llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED
```
For reference, with the implementation, the fts table looks like below:
```
Chunk ID: 9fbc39ce-c729-64a2-260f-c5ec9bb2a33e, Content: Sentence 0 from document 0
Chunk ID: 94062914-3e23-44cf-1e50-9e25821ba882, Content: Sentence 1 from document 0
Chunk ID: e6cfd559-4641-33ba-6ce1-7038226495eb, Content: Sentence 2 from document 0
Chunk ID: 1383af9b-f1f0-f417-4de5-65fe9456cc20, Content: Sentence 3 from document 0
Chunk ID: 2db19b1a-de14-353b-f4e1-085e8463361c, Content: Sentence 4 from document 0
Chunk ID: 9faf986a-f028-7714-068a-1c795e8f2598, Content: Sentence 5 from document 0
Chunk ID: ef593ead-5a4a-392f-7ad8-471a50f033e8, Content: Sentence 6 from document 0
Chunk ID: e161950f-021f-7300-4d05-3166738b94cf, Content: Sentence 7 from document 0
Chunk ID: 90610fc4-67c1-e740-f043-709c5978867a, Content: Sentence 8 from document 0
Chunk ID: 97712879-6fff-98ad-0558-e9f42e6b81d3, Content: Sentence 9 from document 0
Chunk ID: aea70411-51df-61ba-d2f0-cb2b5972c210, Content: Sentence 0 from document 1
Chunk ID: b678a463-7b84-92b8-abb2-27e9a1977e3c, Content: Sentence 1 from document 1
Chunk ID: 27bd63da-909c-1606-a109-75bdb9479882, Content: Sentence 2 from document 1
Chunk ID: a2ad49ad-f9be-5372-e0c7-7b0221d0b53e, Content: Sentence 3 from document 1
Chunk ID: cac53bcd-1965-082a-c0f4-ceee7323fc70, Content: Sentence 4 from document 1
```
Query results:
Result 1: Sentence 5 from document 0
Result 2: Sentence 5 from document 1
Result 3: Sentence 5 from document 2
[//]: # (## Documentation)
---------
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
The goal of this PR is code base modernization.
Schema reflection code needed a minor adjustment to handle UnionTypes
and collections.abc.AsyncIterator. (Both are preferred for latest Python
releases.)
Note to reviewers: almost all changes here are automatically generated
by pyupgrade. Some additional unused imports were cleaned up. The only
change worth of note can be found under `docs/openapi_generator` and
`llama_stack/strong_typing/schema.py` where reflection code was updated
to deal with "newer" types.
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
# What does this PR do?
Removed local execution option from the remote Qdrant provider and
introduced an explicit inline provider for the embedded execution.
Updated the ollama template to include this option: this part can be
reverted in case we don't want to have two default `vector_io`
providers.
(Closes#1082)
## Test Plan
Build and run an ollama distro:
```bash
llama stack build --template ollama --image-type conda
llama stack run --image-type conda ollama
```
Run one of the sample ingestionapplicatinos like
[rag_with_vector_db.py](https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py),
but replace this line:
```py
selected_vector_provider = vector_providers[0]
```
with the following, to use the `qdrant` provider:
```py
selected_vector_provider = vector_providers[1]
```
After running the test code, verify the timestamp of the Qdrant store:
```bash
% ls -ltr ~/.llama/distributions/ollama/qdrant.db/collection/test_vector_db_*
total 784
-rw-r--r--@ 1 dmartino staff 401408 Feb 26 10:07 storage.sqlite
```
[//]: # (## Documentation)
---------
Signed-off-by: Daniele Martinoli <dmartino@redhat.com>
Co-authored-by: Francisco Arceo <farceo@redhat.com>
llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.
This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279
## Test Plan
Ensure all `llama` CLI `model` sub-commands work:
```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```
Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```
Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs
Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.
```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```
# What does this PR do?
- Remove hardcoded configurations from pre-commit.
- Allow configuration to be set via pyproject.toml.
- Merge .ruff.toml settings into pyproject.toml.
- Ensure the linter and formatter use the defined configuration instead
of being overridden by pre-commit.
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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: Sébastien Han <seb@redhat.com>
# What does this PR do?
This changes all VectorIO providers classes to follow the pattern
`<ProviderName>VectorIOConfig` and `<ProviderName>VectorIOAdapter`. All
API endpoints for VectorIOs are currently consistent with `/vector-io`.
Note that API endpoint for VectorDB stay unchanged as `/vector-dbs`.
## Test Plan
I don't have a way to test all providers. This is a simple renaming so
things should work as expected.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (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)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
I tried running the Qdrant provider and found some bugs. See #1021 for
details. @terrytangyuan wrote there:
> Please feel free to submit your changes in a PR. I fixed similar
issues for pgvector provider. This might be an issue introduced from a
refactoring.
So I am submitting this PR.
Closes#1021
## Test Plan
Here are the highlights for what I did to test this:
References:
-
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html
-
https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py
-
https://github.com/meta-llama/llama-stack/blob/main/docs/zero_to_hero_guide/README.md#build-configure-and-run-llama-stack
Install and run Qdrant server:
```
podman pull qdrant/qdrant
mkdir qdrant-data
podman run -p 6333:6333 -v $(pwd)/qdrant-data:/qdrant/storage qdrant/qdrant
```
Install and run Llama Stack from the venv-support PR (mainly because I
didn't want to install conda):
```
brew install cmake # Should just need this once
git clone https://github.com/meta-llama/llama-models.git
gh repo clone cdoern/llama-stack
cd llama-stack
gh pr checkout 1018 # This is the checkout that introduces venv support for build/run. Otherwise you have to use conda. Eventually this wil be part of main, hopefully.
uv sync --extra dev
uv pip install -e .
source .venv/bin/activate
uv pip install qdrant_client
LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack build --template ollama --image-type venv
```
```
edit llama_stack/templates/ollama/run.yaml
```
in that editor under:
```
vector_io:
```
add:
```
- provider_id: qdrant
provider_type: remote::qdrant
config: {}
```
see
https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/vector_io/qdrant/config.py#L14
for config options (but I didn't need any)
```
LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack run ollama --image-type venv \
--port $LLAMA_STACK_PORT \
--env INFERENCE_MODEL=$INFERENCE_MODEL \
--env SAFETY_MODEL=$SAFETY_MODEL \
--env OLLAMA_URL=$OLLAMA_URL
```
Then I tested it out in a notebook. Key highlights included:
```
qdrant_provider = None
for provider in client.providers.list():
if provider.api == "vector_io" and provider.provider_id == "qdrant":
qdrant_provider = provider
qdrant_provider
assert qdrant_provider is not None, "QDrant is not a provider. You need to edit the run yaml file you use in your `llama stack run` call"
vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
embedding_dimension=384,
provider_id=qdrant_provider.provider_id,
)
```
Other than that, I just followed what was in
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html
It would be good to have automated tests for this in the future, but
that would be a big undertaking.
Signed-off-by: Bill Murdock <bmurdock@redhat.com>
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>
See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.
This is the first part:
- delete other kinds of memory banks (keyvalue, keyword, graph) for now;
we will introduce a keyvalue store API as part of this design but not
use it in the RAG tool yet.
- renaming of the APIs
