# What does this PR do?
This PR makes it possible to switch between agentic and non-agentic RAG
when running the respective Playground page.
When non-agentic RAG is selected, user queries are answered by directly
querying the vector DB, augmenting the prompt, and sending the extended
prompt to the model via Inference API.
## Test Plan
- Launch the Playground and go to the RAG page;
- Select the vector DB ID;
- Adjust other configuration parameters if necessary;
- Set the radio button to Agent-based RAG;
- Send a message to the chat;
- The query will be answered by an agent using the knowledge search tool
as indicated by the output;
- Click the 'Clear Chat' button to make it possible to switch modes;
- Send a message to the chat again;
- This time, the query will be answered by the model directly as can be
deduced from the reply.
# What does this PR do?
closes https://github.com/meta-llama/llama-stack/issues/1586
this issue arises when loading an mcp_endpoint from run.yaml. the issue
does not manifest for mcp servers added via a running distro server. the
existing tests only cover the case of adding to a running server.
the code for loading run.yaml strips type information from mcp_endpoint,
passing `{"uri": ...}` instead of `URL(uri=...)` along to the resource
provider registration.
## Test Plan
1. run an mcp server
2. add an mcp tool config to the dev.py, e.g.
```
diff --git a/llama_stack/templates/dev/dev.py b/llama_stack/templates/dev/dev.py
index 69924acb..e0dc7189 100644
--- a/llama_stack/templates/dev/dev.py
+++ b/llama_stack/templates/dev/dev.py
@@ -6,6 +6,8 @@
from typing import List, Tuple
+from llama_stack.apis.common.content_types import URL
+
from llama_stack.apis.models.models import ModelType
from llama_stack.distribution.datatypes import (
ModelInput,
@@ -154,6 +156,11 @@ def get_distribution_template() -> DistributionTemplate:
toolgroup_id="builtin::code_interpreter",
provider_id="code-interpreter",
),
+ ToolGroupInput(
+ toolgroup_id="mcp::filesystem",
+ provider_id="model-context-protocol",
+ mcp_endpoint=URL(uri="http://localhost:8002/sse"),
+ ),
]
embedding_model = ModelInput(
model_id="all-MiniLM-L6-v2",
```
3. run distro_codegen.py
4. llama stack build --template dev --run
before this pr, the `llama stack run` would fail w/ `AttributeError:
'dict' object has no attribute 'uri'`, after it will succeed.
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
Fixes a couple of errors in PVC/Secret setup and adds context for
expected Hugging Face token
[//]: # (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)
# What does this PR do?
## Test Plan
(myenv) ➜ llama-stack python tests/verifications/generate_report.py
--providers fireworks,together,openai --run-tests
# What does this PR do?
Another doc enhancement for
https://github.com/meta-llama/llama-stack/issues/1818
Summary of changes:
- `docs/source/distributions/configuration.md`
- Updated dropdown title to include a more user-friendly description.
- `docs/_static/css/my_theme.css`
- Added styling for `<h3>` elements to set a normal font weight.
- `docs/source/distributions/starting_llama_stack_server.md`
- Changed section headers from bold text to proper markdown headers
(e.g., `##`).
- Improved descriptions for starting Llama Stack server using different
methods (library, container, conda, Kubernetes).
- Enhanced clarity and structure by converting instructions into
markdown headers and improved formatting.
- `docs/source/getting_started/index.md`
- Major restructuring of the "Quick Start" guide:
- Added new introductory section for Llama Stack and its capabilities.
- Reorganized steps into clearer subsections with proper markdown
headers.
- Replaced dropdowns with tabbed content for OS-specific instructions.
- Added detailed steps for setting up and running the Llama Stack server
and client.
- Introduced new sections for running basic inference and building
agents.
- Enhanced readability and visual structure with emojis, admonitions,
and examples.
- `docs/source/providers/index.md`
- Updated the list of LLM inference providers to include "Ollama."
- Expanded the list of vector databases to include "SQLite-Vec."
Let me know if you need further details!
## Test Plan
Renders locally, included screenshot.
# Documentation
For https://github.com/meta-llama/llama-stack/issues/1818
<img width="1332" alt="Screenshot 2025-04-09 at 11 07 12 AM"
src="https://github.com/user-attachments/assets/c106efb9-076c-4059-a4e0-a30fa738585b"
/>
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
While building the "experimental-post-training" distribution, we
encountered a version conflict between torchao with inference requiring
version 0.5.0 and training currently depending on version 0.8.0.
Resolves this error:
```
× No solution found when resolving dependencies:
╰─▶ Because you require torchao==0.5.0 and torchao==0.8.0, we can conclude that your requirements are unsatisfiable.
ERROR 2025-04-10 10:41:22,597 llama_stack.distribution.build:128 uncategorized: Failed to build target test with
return code 1
```
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR fixes two issues with the RAG page of the Playground UI:
1. When the user modifies a configurable setting via a widget (e.g.,
system prompt, temperature, etc.), the agent is not recreated. Thus, the
change has no effect and the user gets no indication of that.
2. After the first issue is fixed, it becomes possible to recreate the
agent mid-conversation or even mid-generation. To mitigate this, widgets
related to agent configuration are now disabled when a conversation is
in progress (i.e., when the chat is non-empty). They are automatically
enabled again when the user resets the chat history.
## Test Plan
- Launch the Playground and go to the RAG page;
- Select the vector DB ID;
- Send a message to the agent via the chat;
- The widgets in charge of the agent parameters will become disabled at
this point;
- Send a second message asking the model about the content of the first
message;
- The reply will indicate that the two messages were sent over the same
session, that is, the agent was not recreated;
- Click the 'Clear Chat' button;
- All widgets will be enabled and a new agent will be created (which can
be validated by sending another message).
# What does this PR do?
- provider and their models now live in config.yaml
- better distinguish different cases within a test
- add model key to surface provider's model_id
- include example command to rerun single test case
## Test Plan
<img width="1173" alt="image"
src="https://github.com/user-attachments/assets/b414baf0-c768-451f-8c3b-c2905cf36fac"
/>
Mirror to https://github.com/meta-llama/llama-models/pull/324 with some
clean up
```
with-proxy pip install -e .
export INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct
export INFERENCE_CHECKPOINT_DIR=../checkpoints/Llama-4-Scout-17B-16E-Instruct
export QUANTIZATION_TYPE=int4_mixed
with-proxy llama stack build --run --template meta-reference-gpu
```
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
# What does this PR do?
* Manage UI deps in pyproject
* Use a new "ui" dep group to pull the deps with "uv"
* Simplify the run command
* Bump versions in requirements.txt
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
1. Adding some lightweight JS to detect the default browser setting for
dark/light mode
3. Setting default screen setting to light mode as to not change default
behavior.
From the docs: https://github.com/MrDogeBro/sphinx_rtd_dark_mode
>This lets you choose which theme the user sees when they load the docs
for the first time ever. After the first time however, this setting has
no effect as the users preference is stored in local storage within
their browser. This option accepts a boolean for the value. If this
option is true (the default option), users will start in dark mode when
first visiting the site. If this option is false, users will start in
light mode when they first visit the site.
# Closes#1915
## Test Plan
Tested locally on my Mac on Safari and Chrome.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
This PR adds the "TAVILY_SEARCH_API_KEY" option to the playground to
enable the use of the websearch tool.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
export TAVILY_SEARCH_API_KEY=***
streamlit run llama_stack/distribution/ui/app.py
```
Without this change the builtin websearch tool will fail due to missing
API key.
[//]: # (## Documentation)
Related to #1902
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
This PR adds an additional page to the playground called "Tools". This
page connects to a llama-stack server and lists all the available LLM
models, builtin tools and MCP tools in the sidebar. Users can select
whatever combination of model and tools they want from the sidebar for
their agent. Once the selections are made, users can chat with their
agent similarly to the RAG page and test out agent tool use.
closes#1902
## Test Plan
Ran the following commands with a llama-stack server and the updated
playground worked as expected.
```
export LLAMA_STACK_ENDPOINT="http://localhost:8321"
streamlit run llama_stack/distribution/ui/app.py
```
[//]: # (## Documentation)
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
**What does this PR do?**
This PR fixes a build issue with the Containerfile caused by missing
requirement `llama-stack`. It updates the Containerfile to include the
necessary requirements and upgrades the Python version to ensure
successful builds.
**Test Plan**
The updated Containerfile has been tested, and the build now completes
successfully with the required dependencies included.
# What does this PR do?
Fixes issue #1537 that causes "500 Internal Server Error" when
unregistering a toolgroup
# (Closes#1537 )
## Test Plan
```console
$ pytest -s -v tests/integration/tool_runtime/test_registration.py --stack-config=ollama --env INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct"
INFO 2025-03-14 21:15:03,999 tests.integration.conftest:41 tests: Setting DISABLE_CODE_SANDBOX=1 for macOS
/opt/homebrew/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-8.3.5, pluggy-1.5.0 -- /opt/homebrew/opt/python@3.10/bin/python3.10
cachedir: .pytest_cache
rootdir: /Users/paolo/Projects/aiplatform/llama-stack
configfile: pyproject.toml
plugins: asyncio-0.25.3, anyio-4.8.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 1 item
tests/integration/tool_runtime/test_registration.py::test_register_and_unregister_toolgroup[None-None-None-None-None] INFO 2025-03-14 21:15:04,478 llama_stack.providers.remote.inference.ollama.ollama:75 inference: checking
connectivity to Ollama at `http://localhost:11434`...
INFO 2025-03-14 21:15:05,350 llama_stack.providers.remote.inference.ollama.ollama:294 inference: Pulling embedding
model `all-minilm:latest` if necessary...
INFO: Started server process [78391]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
INFO: 127.0.0.1:57424 - "GET /sse HTTP/1.1" 200 OK
INFO: 127.0.0.1:57434 - "GET /sse HTTP/1.1" 200 OK
INFO 2025-03-14 21:15:16,129 mcp.client.sse:51 uncategorized: Connecting to SSE endpoint: http://localhost:8000/sse
INFO: 127.0.0.1:57445 - "GET /sse HTTP/1.1" 200 OK
INFO 2025-03-14 21:15:16,146 mcp.client.sse:71 uncategorized: Received endpoint URL:
http://localhost:8000/messages/?session_id=c5b6fc01f8dc4b5e80e38eb1c1b22a9b
INFO 2025-03-14 21:15:16,147 mcp.client.sse:140 uncategorized: Starting post writer with endpoint URL:
http://localhost:8000/messages/?session_id=c5b6fc01f8dc4b5e80e38eb1c1b22a9b
INFO: 127.0.0.1:57447 - "POST /messages/?session_id=c5b6fc01f8dc4b5e80e38eb1c1b22a9b HTTP/1.1" 202 Accepted
INFO: 127.0.0.1:57447 - "POST /messages/?session_id=c5b6fc01f8dc4b5e80e38eb1c1b22a9b HTTP/1.1" 202 Accepted
INFO: 127.0.0.1:57447 - "POST /messages/?session_id=c5b6fc01f8dc4b5e80e38eb1c1b22a9b HTTP/1.1" 202 Accepted
INFO 2025-03-14 21:15:16,155 mcp.server.lowlevel.server:535 uncategorized: Processing request of type
ListToolsRequest
PASSED
=============================================== 1 passed, 4 warnings in 12.17s ================================================
```
---------
Signed-off-by: Paolo Dettori <dettori@us.ibm.com>
# What does this PR do?
download the getting started w/ ollama model instead of downloading and
running it.
directly running it was necessary before
https://github.com/meta-llama/llama-stack/pull/1854
## Test Plan
run the code on the page
# What does this PR do?
closes#1853
## Test Plan
```
uv run llama stack build --image-type conda --image-name ollama --config llama_stack/templates/ollama/build.yaml
ollama pull llama3.2:3b
LLAMA_STACK_CONFIG=http://localhost:8321 uv run pytest tests/integration/inference/test_text_inference.py -v --text-model=llama3.2:3b
```
# What does this PR do?
Providers that live outside of the llama-stack codebase are now
supported.
A new property `external_providers_dir` has been added to the main
config and can be configured as follow:
```
external_providers_dir: /etc/llama-stack/providers.d/
```
Where the expected structure is:
```
providers.d/
inference/
custom_ollama.yaml
vllm.yaml
vector_io/
qdrant.yaml
```
Where `custom_ollama.yaml` is:
```
adapter:
adapter_type: custom_ollama
pip_packages: ["ollama", "aiohttp"]
config_class: llama_stack_ollama_provider.config.OllamaImplConfig
module: llama_stack_ollama_provider
api_dependencies: []
optional_api_dependencies: []
```
Obviously the package must be installed on the system, here is the
`llama_stack_ollama_provider` example:
```
$ uv pip show llama-stack-ollama-provider
Using Python 3.10.16 environment at: /Users/leseb/Documents/AI/llama-stack/.venv
Name: llama-stack-ollama-provider
Version: 0.1.0
Location: /Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages
Editable project location: /private/var/folders/mq/rnm5w_7s2d3fxmtkx02knvhm0000gn/T/tmp.ZBHU5Ezxg4/ollama/llama-stack-ollama-provider
Requires:
Required-by:
```
Closes: https://github.com/meta-llama/llama-stack/issues/658
Signed-off-by: Sébastien Han <seb@redhat.com>
Add the content to use AMD GPU as the vLLM server. Split the original
part to two sub chapters,
1. AMD vLLM server
2. NVIDIA vLLM server (orignal)
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
---------
Signed-off-by: Alex He <alehe@amd.com>
Move the test_context.py under the main tests directory, and fix the
code.
The problem was that the function captures the initial values of the
context variables and then restores those same initial values before
each iteration. This means that any modifications made to the context
variables during iteration are lost when the next iteration starts.
Error was:
```
====================================================== FAILURES =======================================================
______________________________________ test_preserve_contexts_across_event_loops ______________________________________
@pytest.mark.asyncio
async def test_preserve_contexts_across_event_loops():
"""
Test that context variables are preserved across event loop boundaries with nested generators.
This simulates the real-world scenario where:
1. A new event loop is created for each streaming request
2. The async generator runs inside that loop
3. There are multiple levels of nested generators
4. Context needs to be preserved across these boundaries
"""
# Create context variables
request_id = ContextVar("request_id", default=None)
user_id = ContextVar("user_id", default=None)
# Set initial values
# Results container to verify values across thread boundaries
results = []
# Inner-most generator (level 2)
async def inner_generator():
# Should have the context from the outer scope
yield (1, request_id.get(), user_id.get())
# Modify one context variable
user_id.set("user-modified")
# Should reflect the modification
yield (2, request_id.get(), user_id.get())
# Middle generator (level 1)
async def middle_generator():
inner_gen = inner_generator()
# Forward the first yield from inner
item = await inner_gen.__anext__()
yield item
# Forward the second yield from inner
item = await inner_gen.__anext__()
yield item
request_id.set("req-modified")
# Add our own yield with both modified variables
yield (3, request_id.get(), user_id.get())
# Function to run in a separate thread with a new event loop
def run_in_new_loop():
# Create a new event loop for this thread
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
try:
# Outer generator (runs in the new loop)
async def outer_generator():
request_id.set("req-12345")
user_id.set("user-6789")
# Wrap the middle generator
wrapped_gen = preserve_contexts_async_generator(middle_generator(), [request_id, user_id])
# Process all items from the middle generator
async for item in wrapped_gen:
# Store results for verification
results.append(item)
# Run the outer generator in the new loop
loop.run_until_complete(outer_generator())
finally:
loop.close()
# Run the generator chain in a separate thread with a new event loop
with ThreadPoolExecutor(max_workers=1) as executor:
future = executor.submit(run_in_new_loop)
future.result() # Wait for completion
# Verify the results
assert len(results) == 3
# First yield should have original values
assert results[0] == (1, "req-12345", "user-6789")
# Second yield should have modified user_id
assert results[1] == (2, "req-12345", "user-modified")
# Third yield should have both modified values
> assert results[2] == (3, "req-modified", "user-modified")
E AssertionError: assert (3, 'req-modified', 'user-6789') == (3, 'req-modified', 'user-modified')
E
E At index 2 diff: 'user-6789' != 'user-modified'
E
E Full diff:
E (
E 3,
E 'req-modified',
E - 'user-modified',
E + 'user-6789',
E )
tests/unit/distribution/test_context.py:155: AssertionError
-------------------------------------------------- Captured log call --------------------------------------------------
ERROR asyncio:base_events.py:1758 Task was destroyed but it is pending!
task: <Task pending name='Task-7' coro=<<async_generator_athrow without __name__>()>>
================================================== warnings summary ===================================================
.venv/lib/python3.10/site-packages/pydantic/fields.py:1042
/Users/leseb/Documents/AI/llama-stack/.venv/lib/python3.10/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'contentEncoding'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
warn(
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=============================================== short test summary info ===============================================
FAILED tests/unit/distribution/test_context.py::test_preserve_contexts_across_event_loops - AssertionError: assert (3, 'req-modified', 'user-6789') == (3, 'req-modified', 'user-modified')
At index 2 diff: 'user-6789' != 'user-modified'
Full diff:
(
3,
'req-modified',
- 'user-modified',
+ 'user-6789',
)
```
[//]: # (## Documentation)
Signed-off-by: Sébastien Han <seb@redhat.com>
# What does this PR do?
This PR updates the [playground RAG
example](llama_stack/distribution/ui/page/playground/rag.py) so that the
agent is able to use its builtin conversation history. Here we are using
streamlit's `cache_resource` functionality to prevent the agent from
re-initializing after every interaction as well as storing its
session_id in the `session_state`. This allows the agent in the RAG
example to behave more closely to how it works using the python-client
directly.
[//]: # (If resolving an issue, uncomment and update the line below)
Closes#1869
## Test Plan
Without these changes, if you ask it "What is 2 + 2"? followed by the
question "What did I just ask?" It will provide an obviously incorrect
answer.
With these changes, you can ask the same series of questions and it will
provide the correct answer.
[//]: # (## Documentation)
Signed-off-by: Michael Clifford <mcliffor@redhat.com>
# What does this PR do?
## Test Plan
export MODEL=accounts/fireworks/models/llama4-scout-instruct-basic;
LLAMA_STACK_CONFIG=verification pytest -s -v tests/integration/inference
--vision-model $MODEL --text-model $MODEL
# What does this PR do?
Move around bits. This makes the copies from llama-models _much_ easier
to maintain and ensures we don't entangle meta-reference specific
tidbits into llama-models code even by accident.
Also, kills the meta-reference-quantized-gpu distro and rolls
quantization deps into meta-reference-gpu.
## Test Plan
```
LLAMA_MODELS_DEBUG=1 \
with-proxy llama stack run meta-reference-gpu \
--env INFERENCE_MODEL=meta-llama/Llama-4-Scout-17B-16E-Instruct \
--env INFERENCE_CHECKPOINT_DIR=<DIR> \
--env MODEL_PARALLEL_SIZE=4 \
--env QUANTIZATION_TYPE=fp8_mixed
```
Start a server with and without quantization. Point integration tests to
it using:
```
pytest -s -v tests/integration/inference/test_text_inference.py \
--stack-config http://localhost:8321 --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
Running full Tool Calling required some updates to work e2e.
- Remove `python_start` and `python_end` tags
- Tool Call messages and Tool Resposne messages should end with
`<|eom|>`
- System prompt needed updates
```
You are a helpful assisant who can can answer general questions or invoke tools when necessary.
In addition to tool calls, you should also augment your responses by using the tool outputs.
```
### Test Plan
- Start server with meta-reference
```
LLAMA_STACK_DISABLE_VERSION_CHECK=1 LLAMA_MODELS_DEBUG=1 INFERENCE_MODEL=meta-llama/$MODEL llama stack run meta-reference-gpu
```
- Added **NEW** tests with 5 test cases for multi-turn tool calls
```
pytest -s -v --stack-config http://localhost:8321 tests/integration/inference/test_text_inference.py --text-model meta-llama/Llama-4-Scout-17B-16E-Instruct
```
- Also verified all vision and agent tests pass
# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
# What does this PR do?
This PR modifies some of the docs to help them map to (1) the mental
model of software engineers building AI models starting with RAG and
then moving to Agents and (2) aligning the navbar somewhat closer to the
diagram on the home page.
## Test Plan
N/A Tested locally.
# Documentation
Take a look at the screen shot for below and after.
## Before
## After
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
- **chore: mypy for strong_typing**
- **chore: mypy for remote::vllm**
- **chore: mypy for remote::ollama**
- **chore: mypy for providers.datatype**
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
