# What does this PR do?
* Use a single env variable to setup OTEL endpoint
* Update telemetry provider doc
* Update general telemetry doc with the metric with generate
* Left a script to setup telemetry for testing
Closes: https://github.com/meta-llama/llama-stack/issues/783
Note to reviewer: the `setup_telemetry.sh` script was useful for me, it
was nicely generated by AI, if we don't want it in the repo, and I can
delete it, and I would understand.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
The agent code is currently importing MCP modules even when MCP isn’t
enabled. Do we consider this worth fixing, or are we treating MCP as a
first-class dependency? I believe we should treat it as such.
If everyone agrees, let’s go ahead and close this.
Note: The current setup breaks if someone builds a distro without
including MCP in tool_group but still serves the agent API.
Also, we should bump the MCP version to support streamable responses, as
SSE is being deprecated.
Signed-off-by: Sébastien Han <seb@redhat.com>
Resolves access control error visibility issues where 500 errors were
returned instead of proper 403 responses with actionable error messages.
• Enhance AccessDeniedError with detailed context and improve exception
handling
• Enhanced AccessDeniedError class to include user, action, and resource
context
- Added constructor parameters for action, resource, and user
- Generate detailed error messages showing user principal, attributes,
and attempted resource
- Backward compatible with existing usage (falls back to generic
message)
• Updated exception handling in server.py
- Import AccessDeniedError from access_control module
- Return proper 403 status codes with detailed error messages
- Separate handling for PermissionError (generic) vs AccessDeniedError
(detailed)
• Enhanced error context at raise sites
- Updated routing_tables/common.py to pass action, resource, and user
context
- Updated agents persistence to include context in access denied errors
- Provides better debugging information for access control issues
• Added comprehensive unit tests
- Created tests/unit/server/test_server.py with 13 test cases
- Covers AccessDeniedError with and without context
- Tests all exception types (ValidationError, BadRequestError,
AuthenticationRequiredError, etc.)
- Validates proper HTTP status codes and error message formats
# 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
```
server:
port: 8321
access_policy:
- permit:
principal: admin
actions: [create, read, delete]
when: user with admin in groups
- permit:
actions: [read]
when: user with system:authenticated in roles
```
then:
```
curl --request POST --url http://localhost:8321/v1/vector-dbs \
--header "Authorization: Bearer your-bearer" \
--data '{
"vector_db_id": "my_demo_vector_db",
"embedding_model": "ibm-granite/granite-embedding-125m-english",
"embedding_dimension": 768,
"provider_id": "milvus"
}'
```
depending if user is in group admin or not, you should get the
`AccessDeniedError`. Before this PR, this was leading to an error 500
and `Traceback` displayed in the logs.
After the PR, logs display a simpler error (unless DEBUG logging is set)
and a 403 Forbidden error is returned on the HTTP side.
---------
Signed-off-by: Akram Ben Aissi <<akram.benaissi@gmail.com>>
# What does this PR do?
We were not using conditionals correctly, conditionals can only be used
when the env variable is set, so `${env.ENVIRONMENT:+}` would return
None is ENVIRONMENT is not set.
If you want to create a conditional value, you need to do
`${env.ENVIRONMENT:=}`, this will pick the value of ENVIRONMENT if set,
otherwise will return None.
Closes: https://github.com/meta-llama/llama-stack/issues/2564
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
Simple approach to get some provider pages in the docs.
Add or update description fields in the provider configuration class
using Pydantic’s Field, ensuring these descriptions are clear and
complete, as they will be used to auto-generate provider documentation
via ./scripts/distro_codegen.py instead of editing the docs manually.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR creates a webmethod for deleting open AI responses, adds and
implementation for it and makes an integration test for the OpenAI
delete response method.
[//]: # (If resolving an issue, uncomment and update the line below)
# (Closes#2077)
## Test Plan
Ran the standard tests and the pre-commit hooks and the unit tests.
# (## Documentation)
For this pr I made the routes and implementation based on the current
get and create methods. The unit tests were not able to handle this test
due to the mock interface in use, which did not allow for effective CRUD
to be tested. I instead created an integration test to match the
existing ones in the test_openai_responses.
# What does this PR do?
Closes https://github.com/meta-llama/llama-stack/issues/2461
## Test Plan
Tested with the `ollama` distriubtion template and updated the vector_io
provider to:
```yaml
vector_io:
- provider_id: milvus
provider_type: inline::milvus
config:
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ollama}/milvus_store.db
kvstore:
type: sqlite
db_name: milvus_registry.db
```
Ran the stack
```bash
llama stack run ./llama_stack/templates/ollama/run.yaml --image-type venv --env OLLAMA_URL="http://0.0.0.0:11434"
```
Ran the tests:
```
pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2
```
Output passed.
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
currently only the last saved model is reported as a checkpoint and
associated with the job UUID. since the HF trainer handles checkpoint
collection during training, we need to add all of the `checkpoint-*`
folders as Checkpoint objects. Adjust the save strategy to be per-epoch
to make this easier and to use less storage
Signed-off-by: Charlie Doern <cdoern@redhat.com>
The error message was misleading as it appeared to be an Ollama
connectivity issue, but actually occurred during faiss vector database
initialization.
## 🔍 Root Cause Analysis
The issue was in the faiss vector database serialization logic in
`llama_stack/providers/inline/vector_io/faiss/faiss.py`:
1. **Saving**: `faiss.serialize_index()` returns binary data (uint8
numpy array)
2. **Bug**: Code incorrectly used `np.savetxt()` which converts binary
to text with scientific notation (e.g., `7.300000000000000000e+01`)
3. **Loading**: `np.loadtxt(buffer, dtype=np.uint8)` failed to parse
scientific notation back to uint8
4. **Result**: Server crashed during initialization before reaching
Ollama connectivity check
## ✅ Solution
Replaced text-based serialization with proper binary serialization:
```
**After (fixed):**
```python
# Saving - proper binary format
np.save(buffer, np_index, allow_pickle=False)
# Loading - proper binary format
self.index = faiss.deserialize_index(np.load(buffer,
allow_pickle=False))
```
## 🧪 Testing
- ✅ Binary serialization/deserialization works correctly
- ✅ Backward compatible with existing functionality
- ✅ No security concerns (allow_pickle=False maintained)
- ✅ Resolves the specific ValueError mentioned in the issue
## 📊 Impact
This fix resolves:
- ValueError during server startup with Ollama templates
## 🔗 Related Issues
- Closes#2519
- Affects all users of Ollama template and faiss vector_io configurations
## 📝 Files Changed
- `llama_stack/providers/inline/vector_io/faiss/faiss.py` - Fixed serialization methods in `initialize()` and `_save_index()`
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
I get errors when trying to query spans. It appears to be a result of
traces being inserted where there is no root_span_id which causes a
pydantic validation error on trying to load the data for a query
response (and in any case having no span referenced undermines the
purpose of the trace). The root cause as far as I can see is an invalid
test in the code that inserts the trace, where it is testing for the
string "true" against an object set to the python value True.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2493
## 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.* -->
With this change I can query spans.
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
This commit significantly improves the environment variable substitution
functionality in Llama Stack configuration files:
* The version field in configuration files has been changed from string
to integer type for better type consistency across build and run
configurations.
* The environment variable substitution system for ${env.FOO:} was fixed
and properly returns an error
* The environment variable substitution system for ${env.FOO+} returns
None instead of an empty strings, it better matches type annotations in
config fields
* The system includes automatic type conversion for boolean, integer,
and float values.
* The error messages have been enhanced to provide clearer guidance when
environment variables are missing, including suggestions for using
default values or conditional syntax.
* Comprehensive documentation has been added to the configuration guide
explaining all supported syntax patterns, best practices, and runtime
override capabilities.
* Multiple provider configurations have been updated to use the new
conditional syntax for optional API keys, making the system more
flexible for different deployment scenarios. The telemetry configuration
has been improved to properly handle optional endpoints with appropriate
validation, ensuring that required endpoints are specified when their
corresponding sinks are enabled.
* There were many instances of ${env.NVIDIA_API_KEY:} that should have
caused the code to fail. However, due to a bug, the distro server was
still being started, and early validation wasn’t triggered. As a result,
failures were likely being handled downstream by the providers. I’ve
maintained similar behavior by using ${env.NVIDIA_API_KEY:+}, though I
believe this is incorrect for many configurations. I’ll leave it to each
provider to correct it as needed.
* Environment variable substitution now uses the same syntax as Bash
parameter expansion.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
We still had a few enum declared to behave like string as well as enum.
Let's use StrEnum for those.
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
* Given that our API packages use "import *" in `__init.py__` we don't
need to do `from llama_stack.apis.models.models` but simply from
llama_stack.apis.models. The decision to use `import *` is debatable and
should probably be revisited at one point.
* Remove unneeded Ruff F401 rule
* Consolidate Ruff F403 rule in the pyprojectfrom
llama_stack.apis.models.models
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
These are a couple of fixes to get an example LangChain app working with
our OpenAI Responses API implementation.
The Responses API spec requires an annotations array in
`output[*].content[*].annotations` and we were not providing one. So,
this adds that as an empty list, even though we don't do anything to
populate it yet. This prevents an error from client libraries like
Langchain that expect this field to always exist, even if an empty list.
The other fix is `web_search_preview` is a valid name for the web search
tool in the Responses API, but we only responded to `web_search` or
`web_search_preview_2025_03_11`.
## Test Plan
The existing Responses unit tests were expanded to test these cases,
via:
```
pytest -sv tests/unit/providers/agents/meta_reference/test_openai_responses.py
```
The existing test_openai_responses.py integration tests still pass with
this change, tested as below with Fireworks:
```
uv run llama stack run llama_stack/templates/starter/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
uv run pytest -sv tests/integration/agents/test_openai_responses.py \
--text-model accounts/fireworks/models/llama4-scout-instruct-basic
```
Lastly, this example LangChain app now works with Llama stack (tested
with Ollama in the starter template in this case). This LangChain code
is using the example snippets for using Responses API at
https://python.langchain.com/docs/integrations/chat/openai/#responses-api
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="http://localhost:8321/v1/openai/v1",
api_key="fake",
model="ollama/meta-llama/Llama-3.2-3B-Instruct",
)
tool = {"type": "web_search_preview"}
llm_with_tools = llm.bind_tools([tool])
response = llm_with_tools.invoke("What was a positive news story from today?")
print(response.content)
```
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# 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?
Inference/Response stores now store user attributes when inserting, and
respects them when fetching.
## Test Plan
pytest tests/unit/utils/test_sqlstore.py
# 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?
dropped python3.10, updated pyproject and dependencies, and also removed
some blocks of code with special handling for enum.StrEnum
Closes#2458
Signed-off-by: Charlie Doern <cdoern@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?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
To add health check for faiss inline vector_io provider.
I tried adding `async def health(self) -> HealthResponse:` like in
inference provider, but it didn't worked for `inline->vector_io->faiss`
provider. And via debug logs, I understood the critical issue, that the
health responses are being stored with the API name as the key, not as a
nested dictionary with provider IDs. This means that all providers of
the same API type (e.g., "vector_io") will share the same health
response, and only the last one processed will be visible in the API
response.
I've created a patch file that fixes this issue by:
- Storing the original get_providers_health method
- Creating a patched version that correctly maps health responses to
providers
- Applying the patch to the `ProviderImpl` class
Not an expert, so please let me know, if there can be any other
workaround using which I can get the health status updated directly from
`faiss.py`.
<!-- 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.* -->
Added unit tests to test the provider patch implementation in the PR.
Adding a screenshot with the FAISS inline vector_io health status as
"OK"
# 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>
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?
Adds try-catch to faiss `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 appends `(1.0
/ sys.float_info.min)` to `scores` to 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="faiss",
)
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")
```
### Running unit tests
`uv run pytest tests/unit/rag/test_rag_query.py -v`
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
Co-authored-by: Ben Browning <bbrownin@redhat.com>
The non-streaming version is just a small layer on top of the streaming
version - just pluck off the final `response.completed` event and return
that as the response!
This PR also includes a couple other changes which I ended up making
while working on it on a flight:
- changes to `ollama` so it does not pull embedding models
unconditionally
- a small fix to library client to make the stream and non-stream cases
a bit more symmetric
This allows a set of rules to be defined for determining access to
resources. The rules are (loosely) based on the cedar policy format.
A rule defines a list of action either to permit or to forbid. It may
specify a principal or a resource that must match for the rule to take
effect. It may also specify a condition, either a 'when' or an 'unless',
with additional constraints as to where the rule applies.
A list of rules is held for each type to be protected and tried in order
to find a match. If a match is found, the request is permitted or
forbidden depening on the type of rule. If no match is found, the
request is denied. If no rules are specified for a given type, a rule
that allows any action as long as the resource attributes match the user
attributes is added (i.e. the previous behaviour is the default.
Some examples in yaml:
```
model:
- permit:
principal: user-1
actions: [create, read, delete]
comment: user-1 has full access to all models
- permit:
principal: user-2
actions: [read]
resource: model-1
comment: user-2 has read access to model-1 only
- permit:
actions: [read]
when:
user_in: resource.namespaces
comment: any user has read access to models with matching attributes
vector_db:
- forbid:
actions: [create, read, delete]
unless:
user_in: role::admin
comment: only user with admin role can use vector_db resources
```
---------
Signed-off-by: Gordon Sim <gsim@redhat.com>
# What does this PR do?
This adds the missing `text` parameter to the Responses API that is how
users control structured outputs. All we do with that parameter is map
it to the corresponding chat completion response_format.
## Test Plan
The new unit tests exercise the various permutations allowed for this
property, while a couple of new verification tests actually use it for
real to verify the model outputs are following the format as expected.
Unit tests:
`python -m pytest -s -v
tests/unit/providers/agents/meta_reference/test_openai_responses.py`
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Note that the verification tests can only be run with a real Llama Stack
server (as opposed to using the library client via
`--provider=stack:together`) because the Llama Stack python client is
not yet updated to accept this text field.
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
TSIA
Added Files provider to the fireworks template. Might want to add to all
templates as a follow-up.
## Test Plan
llama-stack pytest tests/unit/files/test_files.py
llama-stack llama stack build --template fireworks --image-type conda
--run
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -s -v
tests/integration/files/
I think the implementation needs more simplification. Spent way too much
time trying to get the tests pass with models not co-operating :(
Finally had to switch claude-sonnet to get things to pass reliably.
### Test Plan
```
export TAVILY_SEARCH_API_KEY=...
export OPENAI_API_KEY=...
uv run pytest -p no:warnings \
-s -v tests/verifications/openai_api/test_responses.py \
--provider=stack:starter \
--model openai/gpt-4o
```
# What does this PR do?
Adds a new endpoint that is compatible with OpenAI for embeddings api.
`/openai/v1/embeddings`
Added providers for OpenAI, LiteLLM and SentenceTransformer.
## Test Plan
```
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004
```
# What does this PR do?
This adds a check to ensure we don't attempt to concatenate `None + str`
or `str + None` when building up our arguments for streaming tool calls
in the Responses API.
## Test Plan
All existing tests pass with this change.
Unit tests:
```
python -m pytest -s -v \
tests/unit/providers/agents/meta_reference/test_openai_responses.py
```
Integration tests:
```
llama stack run llama_stack/templates/together/run.yaml
LLAMA_STACK_CONFIG=http://localhost:8321 \
python -m pytest -s -v \
tests/integration/agents/test_openai_responses.py \
--text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Verification tests:
```
llama stack run llama_stack/templates/together/run.yaml
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url=http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Additionally, the manual example using Codex CLI from #2325 now succeeds
instead of throwing a 500 error.
Closes#2325
Signed-off-by: Ben Browning <bbrownin@redhat.com>
We must store the full (re-hydrated) input not just the original input
in the Response object. Of course, this is not very space efficient and
we should likely find a better storage scheme so that we can only store
unique entries in the database and then re-hydrate them efficiently
later. But that can be done safely later.
Closes https://github.com/meta-llama/llama-stack/issues/2299
## Test Plan
Unit test
# What does this PR do?
Previously prompt guard was hard coded to require cuda which prevented
it from being used on an instance without a cuda support.
This PR allows prompt guard to be configured to use either cpu or cuda.
[//]: # (If resolving an issue, uncomment and update the line below)
Closes [#2133](https://github.com/meta-llama/llama-stack/issues/2133)
## Test Plan (Edited after incorporating suggestion)
1) started stack configured with prompt guard as follows on a system
without a GPU
and validated prompt guard could be used through the APIs
2) validated on a system with a gpu (but without llama stack) that the
python selecting between cpu and cuda support returned the right value
when a cuda device was available.
3) ran the unit tests as per -
https://github.com/meta-llama/llama-stack/blob/main/tests/unit/README.md
[//]: # (## Documentation)
---------
Signed-off-by: Michael Dawson <mdawson@devrus.com>
This adds initial streaming support to the Responses API.
This PR makes sure that the _first_ inference call made to chat
completions streams out.
There's more to be done:
- tool call output tokens need to stream out when possible
- we need to loop through multiple rounds of inference and they all need
to stream out.
## Test Plan
Added a test. Executed as:
```
FIREWORKS_API_KEY=... \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--provider=stack:fireworks --model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Then, started a llama stack fireworks distro and tested against it like
this:
```
OPENAI_API_KEY=blah \
pytest -s -v 'tests/verifications/openai_api/test_responses.py' \
--base-url http://localhost:8321/v1/openai/v1 \
--model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
When registering a MCP endpoint, we cannot list tools (like we used to)
since the MCP endpoint may be behind an auth wall. Registration can
happen much sooner (via run.yaml).
Instead, we do listing only when the _user_ actually calls listing.
Furthermore, we cache the list in-memory in the server. Currently, the
cache is not invalidated -- we may want to periodically re-list for MCP
servers. Note that they must call `list_tools` before calling
`invoke_tool` -- we use this critically.
This will enable us to list MCP servers in run.yaml
## Test Plan
Existing tests, updated tests accordingly.
# What does this PR do?
This is not part of the official OpenAI API, but we'll use this for the
logs UI.
In order to support more filtering options, I'm adopting the newly
introduced sql store in in place of the kv store.
## Test Plan
Added integration/unit tests.
# 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>
