# What does this PR do?
This PR fixes a bug in LlamaStack 0.3.0 where vector stores created via
the OpenAI-compatible API (`POST /v1/vector_stores`) would fail with
`VectorStoreNotFoundError` after server restart when attempting
operations like `vector_io.insert()` or `vector_io.query()`.
The bug affected **6 vector IO providers**: `pgvector`, `sqlite_vec`,
`chroma`, `milvus`, `qdrant`, and `weaviate`.
Created with the assistance of: claude-4.5-sonnet
## Root Cause
All affected providers had a broken
`_get_and_cache_vector_store_index()` method that:
1. Did not load existing vector stores from persistent storage during
initialization
2. Attempted to use `vector_store_table` (which was either `None` or a
`KVStore` without the required `get_vector_store()` method)
3. Could not reload vector stores after server restart or cache miss
## Solution
This PR implements a consistent pattern across all 6 providers:
1. **Load vector stores during initialization** - Pre-populate the cache
from KV store on startup
2. **Fix lazy loading** - Modified `_get_and_cache_vector_store_index()`
to load directly from KV store instead of relying on
`vector_store_table`
3. **Remove broken dependency** - Eliminated reliance on the
`vector_store_table` pattern
## Testing steps
### 1.1 Configure the stack
Create or use an existing configuration with a vector IO provider.
**Example `run.yaml`:**
```yaml
vector_io_store:
- provider_id: pgvector
provider_type: remote::pgvector
config:
host: localhost
port: 5432
db: llamastack
user: llamastack
password: llamastack
inference:
- provider_id: sentence-transformers
provider_type: inline::sentence-transformers
config:
model: sentence-transformers/all-MiniLM-L6-v2
```
### 1.2 Start the server
```bash
llama stack run run.yaml --port 5000
```
Wait for the server to fully start. You should see:
```
INFO: Started server process
INFO: Application startup complete
```
---
## Step 2: Create a Vector Store
### 2.1 Create via API
```bash
curl -X POST http://localhost:5000/v1/vector_stores \
-H "Content-Type: application/json" \
-d '{
"name": "test-persistence-store",
"extra_body": {
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_dimension": 384,
"provider_id": "pgvector"
}
}' | jq
```
### 2.2 Expected Response
```json
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"object": "vector_store",
"name": "test-persistence-store",
"status": "completed",
"created_at": 1730304000,
"file_counts": {
"total": 0,
"completed": 0,
"in_progress": 0,
"failed": 0,
"cancelled": 0
},
"usage_bytes": 0
}
```
**Save the `id` field** (e.g.,
`vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d`) — you’ll need it for the next
steps.
---
## Step 3: Insert Data (Before Restart)
### 3.1 Insert chunks into the vector store
```bash
export VS_ID="vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d"
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"Python is a high-level programming language known for its readability.\",
\"metadata\": {\"source\": \"doc1\", \"page\": 1}
},
{
\"content\": \"Machine learning enables computers to learn from data without explicit programming.\",
\"metadata\": {\"source\": \"doc2\", \"page\": 1}
},
{
\"content\": \"Neural networks are inspired by biological neurons in the brain.\",
\"metadata\": {\"source\": \"doc3\", \"page\": 1}
}
]
}"
```
### 3.2 Expected Response
Status: **200 OK**
Response: *Empty or success confirmation*
---
## Step 4: Query Data (Before Restart – Baseline)
### 4.1 Query the vector store
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"What is machine learning?\"
}" | jq
```
### 4.2 Expected Response
```json
{
"chunks": [
{
"content": "Machine learning enables computers to learn from data without explicit programming.",
"metadata": {"source": "doc2", "page": 1}
},
{
"content": "Neural networks are inspired by biological neurons in the brain.",
"metadata": {"source": "doc3", "page": 1}
}
],
"scores": [0.85, 0.72]
}
```
**Checkpoint:** Works correctly before restart.
---
## Step 5: Restart the Server (Critical Test)
### 5.1 Stop the server
In the terminal where it’s running:
```
Ctrl + C
```
Wait for:
```
Shutting down...
```
### 5.2 Restart the server
```bash
llama stack run run.yaml --port 5000
```
Wait for:
```
INFO: Started server process
INFO: Application startup complete
```
The vector store cache is now empty, but data should persist.
---
## Step 6: Verify Vector Store Exists (After Restart)
### 6.1 List vector stores
```bash
curl http://localhost:5000/v1/vector_stores | jq
```
### 6.2 Expected Response
```json
{
"object": "list",
"data": [
{
"id": "vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d",
"name": "test-persistence-store",
"status": "completed"
}
]
}
```
**Checkpoint:** Vector store should be listed.
---
## Step 7: Insert Data (After Restart – THE BUG TEST)
### 7.1 Insert new chunks
```bash
curl -X POST http://localhost:5000/vector-io/insert \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"chunks\": [
{
\"content\": \"This chunk was inserted AFTER the server restart.\",
\"metadata\": {\"source\": \"post-restart\", \"test\": true}
}
]
}"
```
### 7.2 Expected Results
**With Fix (Correct):**
```
Status: 200 OK
Response: Success
```
**Without Fix (Bug):**
```json
{
"detail": "VectorStoreNotFoundError: Vector Store 'vs_a1b2c3d4-e5f6-4a7b-8c9d-0e1f2a3b4c5d' not found."
}
```
**Critical Test:** If insertion succeeds, the fix works.
---
## Step 8: Query Data (After Restart – Verification)
### 8.1 Query all data
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"restart\"
}" | jq
```
### 8.2 Expected Response
```json
{
"chunks": [
{
"content": "This chunk was inserted AFTER the server restart.",
"metadata": {"source": "post-restart", "test": true}
}
],
"scores": [0.95]
}
```
**Checkpoint:** Both old and new data are queryable.
---
## Step 9: Multiple Restart Test (Extra Verification)
### 9.1 Restart again
```bash
Ctrl + C
llama stack run run.yaml --port 5000
```
### 9.2 Query after restart
```bash
curl -X POST http://localhost:5000/vector-io/query \
-H "Content-Type: application/json" \
-d "{
\"vector_store_id\": \"$VS_ID\",
\"query\": \"programming\"
}" | jq
```
**Expected:** Works correctly across multiple restarts.
<hr>This is an automatic backport of pull request #3977 done by
[Mergify](https://mergify.com).
Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Juan Pérez de Algaba <124347725+jperezdealgaba@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
Refactor setting default vector store provider and embedding model to
use an optional `vector_stores` config in the `StackRunConfig` and clean
up code to do so (had to add back in some pieces of VectorDB). Also
added remote Qdrant and Weaviate to starter distro (based on other PR
where inference providers were added for UX).
New config is simply (default for Starter distro):
```yaml
vector_stores:
default_provider_id: faiss
default_embedding_model:
provider_id: sentence-transformers
model_id: nomic-ai/nomic-embed-text-v1.5
```
## Test Plan
CI and Unit tests.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
**This PR changes configurations in a backward incompatible way.**
Run configs today repeat full SQLite/Postgres snippets everywhere a
store is needed, which means duplicated credentials, extra connection
pools, and lots of drift between files. This PR introduces named storage
backends so the stack and providers can share a single catalog and
reference those backends by name.
## Key Changes
- Add `storage.backends` to `StackRunConfig`, register each KV/SQL
backend once at startup, and validate that references point to the right
family.
- Move server stores under `storage.stores` with lightweight references
(backend + namespace/table) instead of full configs.
- Update every provider/config/doc to use the new reference style;
docs/codegen now surface the simplified YAML.
## Migration
Before:
```yaml
metadata_store:
type: sqlite
db_path: ~/.llama/distributions/foo/registry.db
inference_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
conversations_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
```
After:
```yaml
storage:
backends:
kv_default:
type: kv_sqlite
db_path: ~/.llama/distributions/foo/kvstore.db
sql_default:
type: sql_postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
stores:
metadata:
backend: kv_default
namespace: registry
inference:
backend: sql_default
table_name: inference_store
max_write_queue_size: 10000
num_writers: 4
conversations:
backend: sql_default
table_name: openai_conversations
```
Provider configs follow the same pattern—for example, a Chroma vector
adapter switches from:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
kvstore:
type: sqlite
db_path: ~/.llama/distributions/foo/chroma.db
```
to:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
persistence:
backend: kv_default
namespace: vector_io::chroma_remote
```
Once the backends are declared, everything else just points at them, so
rotating credentials or swapping to Postgres happens in one place and
the stack reuses a single connection pool.
# What does this PR do?
Enables automatic embedding model detection for vector stores and by
using a `default_configured` boolean that can be defined in the
`run.yaml`.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
- Unit tests
- Integration tests
- Simple example below:
Spin up the stack:
```bash
uv run llama stack build --distro starter --image-type venv --run
```
Then test with OpenAI's client:
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8321/v1/", api_key="none")
vs = client.vector_stores.create()
```
Previously you needed:
```python
vs = client.vector_stores.create(
extra_body={
"embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
"embedding_dimension": 384,
}
)
```
The `extra_body` is now unnecessary.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Have been running into flaky unit test failures:
5217035494
Fixing below
1. Shutting down properly by cancelling any stale file batches tasks
running in background.
2. Also, use unique_kvstore_config, so the test dont use same db path
and maintain test isolation
## Test Plan
Ran unit test locally and CI
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- This PR implements keyword and hybrid search for Weaviate DB based on
its inbuilt functions.
- Added fixtures to conftest.py for Weaviate.
- Enabled integration tests for remote Weaviate on all 3 search modes.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#3010
## 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.* -->
Unit tests and integration tests should pass on this PR.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR renames categories of llama_stack loggers.
This PR aligns logging categories as per the package name, as well as
reviews from initial
https://github.com/meta-llama/llama-stack/pull/2868. This is a follow up
to #3061.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Replaces https://github.com/meta-llama/llama-stack/pull/2868
Part of https://github.com/meta-llama/llama-stack/issues/2865
cc @leseb @rhuss
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
# What does this PR do?
This PR adds a step in pre-commit to enforce using `llama_stack` logger.
Currently, various parts of the code base uses different loggers. As a
custom `llama_stack` logger exist and used in the codebase, it is better
to standardize its utilization.
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
Some fixes to MCP tests. And a bunch of fixes for Vector providers.
I also enabled a bunch of Vector IO tests to be used with
`LlamaStackLibraryClient`
## Test Plan
Run Responses tests with llama stack library client:
```
pytest -s -v tests/integration/non_ci/responses/ --stack-config=server:starter \
--text-model openai/gpt-4o \
--embedding-model=sentence-transformers/all-MiniLM-L6-v2 \
-k "client_with_models"
```
Do the same with `-k openai_client`
The rest should be taken care of by CI.
# What does this PR do?
Remove score_threshold based check from `OpenAIVectorStoreMixin`
Closes: https://github.com/meta-llama/llama-stack/issues/3018
<!-- 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.* -->
# What does this PR do?
This PR (1) enables the files API for Weaviate and (2) enables
integration tests for Weaviate, which adds a docker container to the
github action.
This PR also handles a couple of edge cases for in creating the
collection and ensuring the tests all pass.
## Test Plan
CI enabled
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
1. Creates a new `VectorStoreNotFoundError` class
2. Implements the new class where appropriate
Relates to #2379
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
Add support for deleting individual chunks from vector stores
- Add abstract remove_chunk() method to EmbeddingIndex base class
- Implement chunk deletion for Faiss provider, SQLite Vec, Milvus,
PGVector
- Placeholder implementations with NotImplementedError for
Chroma/Qdrant/Weaviate
- Integrate chunk deletion into OpenAI vector store file deletion flow
- removed xfail from
test_openai_vector_store_delete_file_removes_from_vector_store
Closes: #2477
---------
Signed-off-by: Derek Higgins <derekh@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# 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?
Fixes provider weaviate `query_vector` function for when the distance
between the query embedding and an embedding within the vector db is 0
(identical vectors). Catches `ZeroDivisionError` and then sets `score`
to infinity, which represent maximum similarity.
<!-- If resolving an issue, uncomment and update the line below -->
Closes [#2381]
## Test Plan
Checkout this PR
Execute this code and there will no longer be a `ZeroDivisionError`
exception
```
from llama_stack_client import LlamaStackClient
base_url = "http://localhost:8321"
client = LlamaStackClient(base_url=base_url)
models = client.models.list()
embedding_model = (
em := next(m for m in models if m.model_type == "embedding")
).identifier
embedding_dimension = 384
_ = client.vector_dbs.register(
vector_db_id="foo_db",
embedding_model=embedding_model,
embedding_dimension=embedding_dimension,
provider_id="weaviate",
)
chunk = {
"content": "foo",
"mime_type": "text/plain",
"metadata": {
"document_id": "foo-id"
}
}
client.vector_io.insert(vector_db_id="foo_db", chunks=[chunk])
client.vector_io.query(vector_db_id="foo_db", query="foo")
```
# 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
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[//]: # (## 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?
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>
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
