The commit makes the follwing changes.
- Import statements updated: MilvusClient → AsyncMilvusClient
- Removed asyncio.to_thread() wrappers: All Milvus operations now use native async/await
- Test compatibility: Mock objects and fixtures updated to work with AsyncMilvusClient
Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
A few changes to the storage layer to ensure we reduce unnecessary
contention arising out of our design choices (and letting the database
layer do its correct thing):
- SQL stores now share a single `SqlAlchemySqlStoreImpl` per backend,
and `kvstore_impl` caches instances per `(backend, namespace)`. This
avoids spawning multiple SQLite connections for the same file, reducing
lock contention and aligning the cache story for all backends.
- Added an async upsert API (with SQLite/Postgres dialect inserts) and
routed it through `AuthorizedSqlStore`, then switched conversations and
responses to call it. Using native `ON CONFLICT DO UPDATE` eliminates
the insert-then-update retry window that previously caused long WAL lock
retries.
### Test Plan
Existing tests, added a unit test for `upsert()`
Fixes issues in the storage system by guaranteeing immediate durability
for responses and ensuring background writers stay alive. Three related
fixes:
* Responses to the OpenAI-compatible API now write directly to
Postgres/SQLite inside the request instead of detouring through an async
queue that might never drain; this restores the expected
read-after-write behavior and removes the "response not found" races
reported by users.
* The access-control shim was stamping owner_principal/access_attributes
as SQL NULL, which Postgres interprets as non-public rows; fixing it to
use the empty-string/JSON-null pattern means conversations and responses
stored without an authenticated user stay queryable (matching SQLite).
* The inference-store queue remains for batching, but its worker tasks
now start lazily on the live event loop so server startup doesn't cancel
them—writes keep flowing even when the stack is launched via llama stack
run.
Closes#4115
### Test Plan
Added a matrix entry to test our "base" suite against Postgres as the
store.
# What does this PR do?
- Updates `/vector_stores/{vector_store_id}/files/{file_id}/content` to
allow returning `embeddings` and `metadata` using the `extra_query`
- Updates the UI accordingly to display them.
- Update UI to support CRUD operations in the Vector Stores section and
adds a new modal exposing the functionality.
- Updates Vector Store update to fail if a user tries to update Provider
ID (which doesn't make sense to allow)
```python
In [1]: client.vector_stores.files.content(
vector_store_id=vector_store.id,
file_id=file.id,
extra_query={"include_embeddings": True, "include_metadata": True}
)
Out [1]: FileContentResponse(attributes={}, content=[Content(text='This is a test document to check if embeddings are generated properly.\n', type='text', embedding=[0.33760684728622437, ...,], chunk_metadata={'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'source': None, 'created_timestamp': 1762053437, 'updated_timestamp': 1762053437, 'chunk_window': '0-13', 'chunk_tokenizer': 'DEFAULT_TIKTOKEN_TOKENIZER', 'chunk_embedding_model': 'sentence-transformers/nomic
-ai/nomic-embed-text-v1.5', 'chunk_embedding_dimension': 768, 'content_token_count': 13, 'metadata_token_count': 9}, metadata={'filename': 'test-embedding.txt', 'chunk_id': '62a63ae0-c202-f060-1b86-0a688995b8d3', 'document_id': 'file-27291dbc679642ac94ffac6d2810c339', 'token_count': 13, 'metadata_token_count': 9})], file_id='file-27291dbc679642ac94ffac6d2810c339', filename='test-embedding.txt')
```
Screenshots of UI are displayed below:
### List Vector Store with Added "Create New Vector Store"
<img width="1912" height="491" alt="Screenshot 2025-11-06 at 10 47
25 PM"
src="https://github.com/user-attachments/assets/a3a3ddd9-758d-4005-ac9c-5047f03916f3"
/>
### Create New Vector Store
<img width="1918" height="1048" alt="Screenshot 2025-11-06 at 10 47
49 PM"
src="https://github.com/user-attachments/assets/b4dc0d31-696f-4e68-b109-27915090f158"
/>
### Edit Vector Store
<img width="1916" height="1355" alt="Screenshot 2025-11-06 at 10 48
32 PM"
src="https://github.com/user-attachments/assets/ec879c63-4cf7-489f-bb1e-57ccc7931414"
/>
### Vector Store Files Contents page (with Embeddings)
<img width="1914" height="849" alt="Screenshot 2025-11-06 at 11 54
32 PM"
src="https://github.com/user-attachments/assets/3095520d-0e90-41f7-83bd-652f6c3fbf27"
/>
### Vector Store Files Contents Details page (with Embeddings)
<img width="1916" height="1221" alt="Screenshot 2025-11-06 at 11 55
00 PM"
src="https://github.com/user-attachments/assets/e71dbdc5-5b49-472b-a43a-5785f58d196c"
/>
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
Tests added for Middleware extension and Provider failures.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
the inspect API lacked any mechanism to get all
non-deprecated APIs (v1, v1alpha, v1beta)
change default to this behavior
'v1' filter can be used for user' wanting a list
of stable APIs
## Test Plan
1. pull the PR
2. launch a LLS server
3. run `curl http://beanlab3.bss.redhat.com:8321/v1/inspect/routes`
4. note there are APIs for `v1`, `v1alpha`, and `v1beta` but no
deprecated APIs
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
Delete ~2,000 lines of dead code from the old bespoke inference API that
was replaced by OpenAI-only API. This includes removing unused type
conversion functions, dead provider methods, and event_logger.py.
Clean up imports across the codebase to remove references to deleted
types. This eliminates unnecessary
code and dependencies, helping isolate the API package as a
self-contained module.
This is the last interdependency between the .api package and "exterior"
packages, meaning that now every other package in llama stack imports
the API, not the other way around.
## Test Plan
this is a structural change, no tests needed.
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# Problem
Responses API uses max_tool_calls parameter to limit the number of tool
calls that can be generated in a response. Currently, LLS implementation
of the Responses API does not support this parameter.
# What does this PR do?
This pull request adds the max_tool_calls field to the response object
definition and updates the inline provider. it also ensures that:
- the total number of calls to built-in and mcp tools do not exceed
max_tool_calls
- an error is thrown if max_tool_calls < 1 (behavior seen with the
OpenAI Responses API, but we can change this if needed)
Closes #[3563](https://github.com/llamastack/llama-stack/issues/3563)
## Test Plan
- Tested manually for change in model response w.r.t supplied
max_tool_calls field.
- Added integration tests to test invalid max_tool_calls parameter.
- Added integration tests to check max_tool_calls parameter with
built-in and function tools.
- Added integration tests to check max_tool_calls parameter in the
returned response object.
- Recorded OpenAI Responses API behavior using a sample script:
https://github.com/s-akhtar-baig/llama-stack-examples/blob/main/responses/src/max_tool_calls.py
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
# What does this PR do?
Adds OCI GenAI PaaS models for openai chat completion endpoints.
## Test Plan
In an OCI tenancy with access to GenAI PaaS, perform the following
steps:
1. Ensure you have IAM policies in place to use service (check docs
included in this PR)
2. For local development, [setup OCI
cli](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliinstall.htm)
and configure the CLI with your region, tenancy, and auth
[here](https://docs.oracle.com/en-us/iaas/Content/API/SDKDocs/cliconfigure.htm)
3. Once configured, go through llama-stack setup and run llama-stack
(uses config based auth) like:
```bash
OCI_AUTH_TYPE=config_file \
OCI_CLI_PROFILE=CHICAGO \
OCI_REGION=us-chicago-1 \
OCI_COMPARTMENT_OCID=ocid1.compartment.oc1..aaaaaaaa5...5a \
llama stack run oci
```
4. Hit the `models` endpoint to list models after server is running:
```bash
curl http://localhost:8321/v1/models | jq
...
{
"identifier": "meta.llama-4-scout-17b-16e-instruct",
"provider_resource_id": "ocid1.generativeaimodel.oc1.us-chicago-1.am...q",
"provider_id": "oci",
"type": "model",
"metadata": {
"display_name": "meta.llama-4-scout-17b-16e-instruct",
"capabilities": [
"CHAT"
],
"oci_model_id": "ocid1.generativeaimodel.oc1.us-chicago-1.a...q"
},
"model_type": "llm"
},
...
```
5. Use the "display_name" field to use the model in a
`/chat/completions` request:
```bash
# Streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": true,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
# Non-streaming result
curl -X POST http://localhost:8321/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "meta.llama-4-scout-17b-16e-instruct",
"stream": false,
"temperature": 0.9,
"messages": [
{
"role": "system",
"content": "You are a funny comedian. You can be crass."
},
{
"role": "user",
"content": "Tell me a funny joke about programming."
}
]
}'
```
6. Try out other models from the `/models` endpoint.
Mark all register_* / unregister_* APIs as deprecated across models,
shields, tool groups, datasets, benchmarks, and scoring functions. This
is the first step toward moving resource mutations to an `/admin`
namespace as outlined in
https://github.com/llamastack/llama-stack/issues/3809#issuecomment-3492931585.
The deprecation flag will be reflected in the OpenAPI schema to warn API
users that these endpoints are being phased out. Next step will be
implementing the `/admin` route namespace for these resource management
operations.
- `register_model` / `unregister_model`
- `register_shield` / `unregister_shield`
- `register_tool_group` / `unregister_toolgroup`
- `register_dataset` / `unregister_dataset`
- `register_benchmark` / `unregister_benchmark`
- `register_scoring_function` / `unregister_scoring_function`
# 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.
---------
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
Resolves#4102
1. Added `web_search_2025_08_26` to the `WebSearchToolTypes` list and
the `OpenAIResponseInputToolWebSearch.type` Literal union
2. No changes needed to tool execution logic - all `web_search` types
map to the same underlying tool
3. Backward compatibility is maintained - existing `web_search`,
`web_search_preview`, and `web_search_preview_2025_03_11` types continue
to work
4. Added an integration test case using {"type":
"web_search_2025_08_26"} to verify it works correctly
5. Updated `docs/docs/providers/openai_responses_limitations.mdx` to
reflect that `web_search_2025_08_26` is now supported.
6. Removed incorrect references to `MOD1/MOD2/MOD3` (which don't exist
in the codebase)
<!-- 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.* -->
---------
Signed-off-by: Aakanksha Duggal <aduggal@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This dependency has been bothering folks for a long time (cc @leseb). We
really needed it due to "library client" which is primarily used for our
tests and is not a part of the Stack server. Anyone who needs to use the
library client can certainly install `llama-stack-client` in their
environment to make that work.
Updated the notebook references to install `llama-stack-client`
additionally when setting things up.
# What does this PR do?
Remove circular dependency by moving tracing from API protocol
definitions
to router implementation layer.
This gets us closer to having a self contained API package with no other
cross-cutting dependencies to other parts of the llama stack codebase.
To the best of our ability, the llama_stack.api should only be type and
protocol definitions.
Changes:
- Create apis/common/tracing.py with marker decorator (zero core
dependencies)
- Add the _new_ `@telemetry_traceable` marker decorator to 11 protocol
classes
- Apply actual tracing in core/resolver.py in `instantiate_provider`
based on protocol marker
- Move MetricResponseMixin from core to apis (it's an API response type)
- APIs package is now self-contained with zero core dependencies
The tracing functionality remains identical - actual trace_protocol from
core
is applied to router implementations at runtime when both telemetry is
enabled
and the protocol has the `__marked_for_tracing__` marker.
## Test Plan
Manual integration test confirms identical behavior to main branch:
```bash
llama stack list-deps --format uv starter | sh
export OLLAMA_URL=http://localhost:11434
llama stack run starter
curl -X POST http://localhost:8321/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "ollama/gpt-oss:20b",
"messages": [{"role": "user", "content": "Say hello"}],
"max_tokens": 10}'
```
Verified identical between main and this branch:
- trace_id present in response
- metrics array with prompt_tokens, completion_tokens, total_tokens
- Server logs show trace_protocol applied to all routers
Existing telemetry integration tests (tests/integration/telemetry/) validate
trace context propagation and span attributes.
relates to #3895
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
We'd like to remove the dependence of `llama-stack` on
`llama-stack-client`. This is a necessary step.
A few small cleanups
- Enables `embeddings` now also
- Remove ModelRegistryHelper dependency (unused)
- Consolidate to auth_credential field via RemoteInferenceProviderConfig
- Implement list_models() to fetch from downstream /v1/models
## Test Plan
Tested using this script
https://gist.github.com/ashwinb/6356463d10f989c0682ab3bff8589581
Output:
```
Listing models from downstream server...
Available models: ['passthrough/ollama/nomic-embed-text:latest', 'passthrough/ollama/all-minilm:l6-v2', 'passthrough/ollama/llama3.2-vision:11b', 'passthrough/ollama/llama3.2-vision:latest', 'passthrough/ollama/llama-guard3:1b', 'passthrough/o
llama/llama3.2:1b', 'passthrough/ollama/all-minilm:latest', 'passthrough/ollama/llama3.2:3b', 'passthrough/ollama/llama3.2:3b-instruct-fp16', 'passthrough/bedrock/meta.llama3-1-8b-instruct-v1:0', 'passthrough/bedrock/meta.llama3-1-70b-instruct
-v1:0', 'passthrough/bedrock/meta.llama3-1-405b-instruct-v1:0', 'passthrough/sentence-transformers/nomic-ai/nomic-embed-text-v1.5']
Using LLM model: passthrough/ollama/llama3.2-vision:11b
Making inference request...
Response: 4.
--- Testing streaming ---
Streamed response: ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='1', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='', index=0, logprobs=None)], created=1762381674, m
odel='passthrough/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None)
...
5ChatCompletionChunk(id='chatcmpl-64', choices=[Choice(delta=ChoiceDelta(content='', reasoning_content=None, refusal=None, role='assistant', tool_calls=None), finish_reason='stop', index=0, logprobs=None)], created=1762381674, model='passthrou
gh/ollama/llama3.2-vision:11b', object='chat.completion.chunk', usage=None)
```
# What does this PR do?
- when create vector store is called without chunk strategy, we actually
the strategy used so that the value is persisted instead of
strategy='None'
## Test Plan
updated tests
## What does this PR do?
The starter distribution now comes with all the required packages to
support persistent stores—like the agent store, metadata, and
inference—using PostgreSQL. Users can enable PostgreSQL support by
setting the `ENABLE_POSTGRES_STORE=1` environment variable.
This PR consolidates the functionality from the removed `postgres-demo`
distribution into the starter distribution, reducing maintenance
overhead.
**Closes: #2619**
**Supersedes: #2851** (rebased and updated)
## Changes Made
1. **Added PostgreSQL support to starter distribution**
- New `run-with-postgres-store.yaml` configuration
- Automatic config switching via `ENABLE_POSTGRES_STORE` environment
variable
- Removed separate `postgres-demo` distribution
2. **Updated to new build system**
- Integrated postgres switching logic into Containerfile entrypoint
- Uses new `storage_backends` and `storage_stores` API
- Properly configured both PostgreSQL KV store and SQL store
3. **Updated dependencies**
- Added `psycopg2-binary` and `asyncpg` to starter distribution
- All postgres-related dependencies automatically included
## How to Use
### With Docker (PostgreSQL):
```bash
docker run \
-e ENABLE_POSTGRES_STORE=1 \
-e POSTGRES_HOST=your_postgres_host \
-e POSTGRES_PORT=5432 \
-e POSTGRES_DB=llamastack \
-e POSTGRES_USER=llamastack \
-e POSTGRES_PASSWORD=llamastack \
-e OPENAI_API_KEY=your_key \
llamastack/distribution-starter
```
### PostgreSQL environment variables:
- `POSTGRES_HOST`: Postgres host (default: `localhost`)
- `POSTGRES_PORT`: Postgres port (default: `5432`)
- `POSTGRES_DB`: Postgres database name (default: `llamastack`)
- `POSTGRES_USER`: Postgres username (default: `llamastack`)
- `POSTGRES_PASSWORD`: Postgres password (default: `llamastack`)
## Test Plan
All pre-commit hooks pass (mypy, ruff, distro-codegen)
`llama stack list-deps starter` confirms psycopg2-binary is included
Storage configuration correctly uses PostgreSQL backends
Container builds successfully with postgres support
## Credits
Original work by @leseb in #2851. Rebased and updated by @r-bit-rry to
work with latest main.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Sébastien Han @leseb
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
list-deps takes positional args OR things like --providers
the issue with this, is that these args need to be optional since by
nature, one or the other can be specified.
add a check to list-deps that checks `if not args.providers and not
args.config`. If this is true, help is printed and we exit.
resolves#4075
## Test Plan
before:
```
╰─ llama stack list-deps
Traceback (most recent call last):
File "/Users/charliedoern/projects/Documents/llama-stack/venv/bin/llama", line 10, in <module>
sys.exit(main())
^^^^^^
File "/Users/charliedoern/projects/Documents/llama-stack/src/llama_stack/cli/llama.py", line 52, in main
parser.run(args)
File "/Users/charliedoern/projects/Documents/llama-stack/src/llama_stack/cli/llama.py", line 43, in run
args.func(args)
File "/Users/charliedoern/projects/Documents/llama-stack/src/llama_stack/cli/stack/list_deps.py", line 51, in _run_stack_list_deps_command
return run_stack_list_deps_command(args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/charliedoern/projects/Documents/llama-stack/src/llama_stack/cli/stack/_list_deps.py", line 135, in run_stack_list_deps_command
normal_deps, special_deps, external_provider_dependencies = get_provider_dependencies(build_config)
^^^^^^^^^^^^
UnboundLocalError: cannot access local variable 'build_config' where it is not associated with a value
```
after:
```
╰─ llama stack list-deps
usage: llama stack list-deps [-h] [--providers PROVIDERS] [--format {uv,deps-only}] [config | distro]
list the dependencies for a llama stack distribution
positional arguments:
config | distro Path to config file to use or name of known distro (llama stack list for a list). (default: None)
options:
-h, --help show this help message and exit
--providers PROVIDERS
sync dependencies for a list of providers and only those providers. This list is formatted like: api1=provider1,api2=provider2. Where there can be multiple
providers per API. (default: None)
--format {uv,deps-only}
Output format: 'uv' shows shell commands, 'deps-only' shows just the list of dependencies without `uv` (default) (default: deps-only)
```
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
It avoids model_limit KeyError while trying to get embedding models for
Watsonx
<!-- If resolving an issue, uncomment and update the line below -->
Closes https://github.com/llamastack/llama-stack/issues/4059
## 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.* -->
Start server with watsonx distro:
```bash
llama stack list-deps watsonx | xargs -L1 uv pip install
uv run llama stack run watsonx
```
Run
```python
client = LlamaStackClient(base_url=base_url)
client.models.list()
```
Check if there is any embedding model available (currently there is not
a single one)
# What does this PR do?
1. Make telemetry tests as easy as possible for users by expanding the
`SpanStub` data class and creating the `MetricStub` dataclass as a way
to consistently marshal telemetry data in test fixtures and unmarshal
and handle it in tests.
2. Structure server and client tests to always follow the same standards
for consistent testing experience by using the `SpanStub` and
`MetricStub` data class objects.
3. Enable Metrics Testing for completions endpoint
4. Correct token metrics to use histograms instead of counts to capture
tokens per request rather than a cumulative count of tokens over the
lifecycle of the server.
## Test Plan
These are tests
# What does this PR do?
Fixes issue #3922 where `llama stack list` only showed distributions
after they were run. This PR makes the command show all available
distributions immediately on a fresh install.
Closes#3922
## Changes
- **Updated `_get_distribution_dirs()`** to discover both built-in and
built distributions:
- Built-in distributions from `src/llama_stack/distributions/` (e.g.,
starter, nvidia, dell)
- Built distributions from `~/.llama/distributions`
- **Added a "Source" column** to distinguish between "built-in" and
"built" distributions
- **Built distributions override built-in ones** with the same name
(expected behavior)
- **Updated config file detection logic** to handle both naming
conventions:
- Built-in: `build.yaml` and `run.yaml`
- Built: `{name}-build.yaml` and `{name}-run.yaml`
## Test Plan
### Unit Tests
Added comprehensive unit tests in
`tests/unit/distribution/test_stack_list.py`:
```bash
uv run pytest tests/unit/distribution/test_stack_list.py -v
```
**Result**: ✅ All 8 tests pass
- `test_builtin_distros_shown_without_running` - Verifies the core fix
for issue #3922
- `test_builtin_and_built_distros_shown_together` - Ensures both types
are shown
- `test_built_distribution_overrides_builtin` - Tests override behavior
- `test_empty_distributions` - Edge case handling
- `test_config_files_detection_builtin` - Config file detection for
built-in distros
- `test_config_files_detection_built` - Config file detection for built
distros
- `test_llamastack_prefix_stripped` - Name normalization
- `test_hidden_directories_ignored` - Filters hidden directories
### Manual Testing
**Before the fix** (simulated with empty `~/.llama/distributions`):
```bash
$ llama stack list
No stacks found in ~/.llama/distributions
```
**After the fix**:
```bash
$ llama stack list
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ ci-tests │ built-in │ /path/to/src/... │ Yes │ Yes │
│ dell │ built-in │ /path/to/src/... │ Yes │ Yes │
│ meta-reference-g… │ built-in │ /path/to/src/... │ Yes │ Yes │
│ nvidia │ built-in │ /path/to/src/... │ Yes │ Yes │
│ open-benchmark │ built-in │ /path/to/src/... │ Yes │ Yes │
│ postgres-demo │ built-in │ /path/to/src/... │ Yes │ Yes │
│ starter │ built-in │ /path/to/src/... │ Yes │ Yes │
│ starter-gpu │ built-in │ /path/to/src/... │ Yes │ Yes │
│ watsonx │ built-in │ /path/to/src/... │ Yes │ Yes │
└───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘
```
**After running a distribution**:
```bash
$ llama stack run starter # Creates ~/.llama/distributions/starter
$ llama stack list
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Stack Name ┃ Source ┃ Path ┃ Build Config ┃ Run Config ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ ... │ built-in │ ... │ Yes │ Yes │
│ starter │ built │ ~/.llama/distri… │ No │ No │
│ ... │ built-in │ ... │ Yes │ Yes │
└───────────────────┴──────────┴───────────────────┴──────────────┴────────────┘
```
Note how `starter` now shows as "built" and points to
`~/.llama/distributions`, overriding the built-in version.
## Breaking Changes
**No breaking changes** - This is a bug fix that improves user
experience with minimal risk:
- No programmatic parsing of output found in the codebase
- Table format is clearly for human consumption
- The new "Source" column helps users understand where distributions
come from
- The behavior change is exactly what users expect (seeing all available
distributions)
---------
Co-authored-by: Claude <noreply@anthropic.com>
# What does this PR do?
These were maybe be included in the webmethod?
The unit test was pointless too since the request was never used
anywhere?
This shouldn't be in the API definition, if we never consume it.
## Test Plan
CI with pre-commit on OpenAPI spec generation.
Signed-off-by: Sébastien Han <seb@redhat.com>
RAG aka file search is implemented via the Responses API by specifying
the file-search tool. The backend implementation remains unchanged. This
PR merely removes the directly exposed API surface which allowed users
to directly perform searches from the client.
This facility is now available via the `client.vector_store.search()`
OpenAI compatible API.
- Removes the deprecated agents (sessions and turns) API that was marked
alpha in 0.3.0
- Cleans up unused imports and orphaned types after the API removal
- Removes `SessionNotFoundError` and `AgentTurnInputType` which are no
longer needed
The agents API is completely superseded by the Responses + Conversations
APIs, and the client SDK Agent class already uses those implementations.
Corresponding client-side PR:
https://github.com/llamastack/llama-stack-client-python/pull/295
This PR removes all routes which we had marked deprecated for the 0.3.0
release.
This includes:
- all the `/v1/openai/v1/` routes (the corresponding /v1 routes still
exist of course)
- the /agents API (which is superseded completely by Responses +
Conversations)
- several alpha routes which had a "v1" route to aide transitioning to
"v1alpha"
This is the corresponding client-python change:
https://github.com/llamastack/llama-stack-client-python/pull/294
The llama-stack-client now uses /`v1/openai/v1/models` which returns
OpenAI-compatible model objects with 'id' and 'custom_metadata' fields
instead of the Resource-style 'identifier' field. Updated api_recorder
to handle the new endpoint and modified tests to access model metadata
appropriately. Deleted stale model recordings for re-recording.
**NOTE: CI will be red on this one since it is dependent on
https://github.com/llamastack/llama-stack-client-python/pull/291/files
landing. I verified locally that it is green.**
# What does this PR do?
This API hasn't received any traction and close to zero interest from
the community. Let's revisit in the future if things change.
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
We need to remove `/v1/openai/v1` paths shortly. There is one trouble --
our current `/v1/openai/v1/models` endpoint provides different data than
`/v1/models`. Unfortunately our tests target the latter (llama-stack
customized) behavior. We need to get to true OpenAI compatibility.
This is step 1: adding `custom_metadata` field to `OpenAIModel` that
includes all the extra stuff we add in the native `/v1/models` response.
This can be extracted on the consumer end by look at
`__pydantic_extra__` or other similar fields.
This PR:
- Adds `custom_metadata` field to `OpenAIModel` class in
`src/llama_stack/apis/models/models.py`
- Modified `openai_list_models()` in
`src/llama_stack/core/routing_tables/models.py` to populate
custom_metadata
Next Steps
1. Update stainless client to use `/v1/openai/v1/models` instead of
`/v1/models`
2. Migrate tests to read from `custom_metadata`
3. Remove `/v1/openai/v1/` prefix entirely and consolidate to single
`/v1/models` endpoint
Fixes race condition causing "database is locked" errors during
concurrent writes to SQLite, particularly in streaming responses with
guardrails where multiple inference calls write simultaneously.
Enable Write-Ahead Logging (WAL) mode for SQLite which allows multiple
concurrent readers and one writer without blocking. Set busy_timeout to
5s so SQLite retries instead of failing immediately. Remove the logic
that disabled write queues for SQLite since WAL mode eliminates the
locking issues that prompted disabling them.
Fixes: test_output_safety_guardrails_safe_content[stream=True] flake
# What does this PR do?
llama stack run --providers takes a list of providers in the format of
api1=provider1,api2=provider2
this allows users to run with a simple list of providers.
given the architecture of `create_app`, this run config needs to be
written to disk. use ~/.llama/distribution/providers-run/run.yaml each
time for consistency
resolves#3956
## Test Plan
new unit tests to ensure --providers.
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Allow filtering for v1alpha, v1beta, deprecated and v1. Backward
incompatible change since by default it only returns v1 apis now.
## Test Plan
added unit test
# What does this PR do?
Add rerank API for NVIDIA Inference Provider.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3278
## Test Plan
Unit test:
```
pytest tests/unit/providers/nvidia/test_rerank_inference.py
```
Integration test:
```
pytest -s -v tests/integration/inference/test_rerank.py --stack-config="inference=nvidia" --rerank-model=nvidia/nvidia/nv-rerankqa-mistral-4b-v3 --env NVIDIA_API_KEY="" --env NVIDIA_BASE_URL="https://integrate.api.nvidia.com"
```
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR fixes the handling of the external_providers_dir configuration
field to align with its ongoing deprecation, in favor of the provider
`module` specification approach.
It addresses the issue in #3950, where using the default provided
run.yaml config resulted in the `external_providers_dir` parameter being
set to the literal string `None`, and crashing the llama-stack server
when starting.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#3950
## 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.* -->
- Built a new container image from `podman build . -f
containers/Containerfile --build-arg DISTRO_NAME=starter --tag
llama-stack:starter`
- Tested it locally with `podman run -it localhost/llama-stack:starter`
- Tested it on an OpenShift 4.19 cluster, deployed via the
llama-stack-k8s-operator.
Signed-off-by: Doug Edgar <dedgar@redhat.com>
