# What does this PR do?
`AgentEventLogger` only supports streaming responses, so I suggest
adding a comment near the bottom of `demo_script.py` letting the user
know this, e.g., if they change the `stream` value to `False` in the
call to `create_turn`, they need to comment out the logging lines.
See https://github.com/llamastack/llama-stack-client-python/issues/15
<!-- 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: Dean Wampler <dean.wampler@ibm.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Remove pure venv (without uv) references in docs
<!-- 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.* -->
A bunch of miscellaneous cleanup focusing on tests, but ended up
speeding up starter distro substantially.
- Pulled llama stack client init for tests into `pytest_sessionstart` so
it does not clobber output
- Profiling of that told me where we were doing lots of heavy imports
for starter, so lazied them
- starter now starts 20seconds+ faster on my Mac
- A few other smallish refactors for `compat_client`
As the title says. Distributions is in, Templates is out.
`llama stack build --template` --> `llama stack build --distro`. For
backward compatibility, the previous option is kept but results in a
warning.
Updated `server.py` to remove the "config_or_template" backward
compatibility since it has been a couple releases since that change.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This PR is responsible for removal of Conda support in Llama Stack
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#2539
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
# What does this PR do?
Adds support to Vector store Open AI APIs in Qdrant.
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2463
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
This PR (1) enables the files API for Weaviate and (2) enables
integration tests for Weaviate, which adds a docker container to the
github action.
This PR also handles a couple of edge cases for in creating the
collection and ensuring the tests all pass.
## Test Plan
CI enabled
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
**Description**
This PR adjusts the external providers documentation to align with the
new providers format. Splits up sections into the existing external
providers and how to create them as well.
<img width="1049" height="478" alt="Screenshot 2025-07-31 at 9 48 26 AM"
src="https://github.com/user-attachments/assets/f13599cb-2fd1-4e57-8ca9-27b067264e33"
/>
Open to feedback and adjusting titles
What does this PR do?
This PR adds support for Direct Preference Optimization (DPO) training
via the existing HuggingFace inline provider. It introduces a new DPO
training recipe, config schema updates, dataset integration, and
end-to-end testing to support preference-based fine-tuning with TRL.
Test Plan
Added integration test:
tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize
Ran tests on both CPU and CUDA environments
---------
Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
**Description**
This PR removes some of the warnings when uv builds the docs
- Errors appear when generating docs about .md files not appearing in
toctree. ~~Adding content to the `providers-gen.py ` file that adds `---
orphan: true ---` to to each file.~~. Added a toctree generator to the
`providers-gen.py` file, this gets rid of the errors in the builds.
- Deletes the `_openai_compat` files, extension of PR #2849
- Adds the `files` APIs section to the `providers` toctree on the index
page
- Manually adds the `--- orphan: true ---` to the advanced apis. Ill try
to find a way to modify the providers code gen so it automatically adds
it, but this fixes the errors.
- Adds the `testing.md` to the `contributing` toctree
- Adds `starting_llama_stack_server.md` to `distributions` toctree
There are some other warnings im still looking at but this PR gets rid
of most of the toctree errors
Theres also an issue with the actual distribution-codegen that I can
investigate in another PR. Opened a bug for it here #2873
We tried to always keep Ollama enabled. However doing so makes the
provider implementation half-assed -- should it error when it cannot
connect to Ollama or not? What happens during periodic model refresh?
Etc. Instead do the same thing we do for vLLM -- use the `OLLAMA_URL` to
conditionally enable the provider.
## Test Plan
Run `uv run llama stack build --template starter --image-type venv
--run` with and without `OLLAMA_URL` set. Verify using
`llama-stack-client provider list` that ollama is correctly enabled.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Updates provider template from outdated `ollama` to `starter`
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes: #2839
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
# What does this PR do?
- Add base_url field to OpenAIConfig with default
"https://api.openai.com/v1"
- Update sample_run_config to support OPENAI_BASE_URL environment
variable
- Modify get_base_url() to return configured base_url instead of
hardcoded value
- Add comprehensive test suite covering:
- Default base URL behavior
- Custom base URL from config
- Environment variable override
- Config precedence over environment variables
- Client initialization with configured URL
- Model availability checks using configured URL
This enables users to configure custom OpenAI-compatible API endpoints
via environment variables or configuration files.
Closes#2910
## Test Plan
run unit tests
# What does this PR do?
external provider docs mention setting provider_id in the build yaml.
Since we changed that to just be provider_type and module, remove
instances of provider_id
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
Today, external providers are installed via the `external_providers_dir`
in the config. This necessitates users to understand the `ProviderSpec`
and set up their directories accordingly. This process splits up the
config for the stack across multiple files, directories, and formats.
Most (if not all) external providers today have a
[get_provider_spec](559cb18fbb/src/ramalama_stack/provider.py (L9))
method that sits unused. Utilizing this method rather than the
providers.d route allows for a much easier installation process for
external providers and limits the amount of extra configuration a
regular user has to do to get their stack off the ground.
To accomplish this and wire it throughout the build process, Introduce
the concept of a `module` for users to specify for an external provider
upon build time. In order to facilitate this, align the build and run
spec to use `Provider` class rather than the stringified provider_type
that build currently uses.
For example, say this is in your build config:
```
- provider_id: ramalama
provider_type: remote::ramalama
module: ramalama_stack
```
during build (in the various `build_...` scripts), additionally to
installing any pip dependencies we will also install this module and use
the `get_provider_spec` method to retrieve the ProviderSpec that is
currently specified using `providers.d`.
In production so far, providing instructions for installing external
providers for users has been difficult: they need to install the module
as a pre-req, create the providers.d directory, copy in the provider
spec, and also copy in the necessary build/run yaml files. Accessing an
external provider should be as easy as possible, and pointing to its
installable module aligns more with the rest of our build and dependency
management process.
For now, `external_providers_dir` still exists as an alternate more
declarative method of using external providers.
## Test Plan
added an integration test installing an external provider from module
and more unit test coverage for `get_provider_registry`
( the warning in yellow is expected, the module is installed inside of
the build env, not where we are running the command)
<img width="1119" height="400" alt="Screenshot 2025-07-24 at 11 30
48 AM"
src="https://github.com/user-attachments/assets/1efbaf45-b9e8-451a-bd63-264ed664706d"
/>
<img width="1154" height="618" alt="Screenshot 2025-07-24 at 11 31
14 AM"
src="https://github.com/user-attachments/assets/feb2b3ea-c5dd-418e-9662-9a3bd5dd6bdc"
/>
---------
Signed-off-by: Charlie Doern <cdoern@redhat.com>
# What does this PR do?
- Added ability to specify `required_scope` when declaring an API. This
is part of the `@webmethod` decorator.
- If auth is enabled, a user can access an API only if
`user.attributes['scope']` includes the `required_scope`
- We add `required_scope='telemetry.read'` to the telemetry read APIs.
## Test Plan
CI with added tests
1. Enable server.auth with github token
2. Observe `client.telemetry.query_traces()` returns 403
# What does this PR do?
Prototype on a new feature to allow new APIs to be plugged in Llama
Stack. Opened for early feedback on the approach and test appetite on
the functionality.
@ashwinb @raghotham open for early feedback, thanks!
---------
Signed-off-by: Sébastien Han <seb@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
This flips #2823 and #2805 by making the Stack periodically query the
providers for models rather than the providers going behind the back and
calling "register" on to the registry themselves. This also adds support
for model listing for all other providers via `ModelRegistryHelper`.
Once this is done, we do not need to manually list or register models
via `run.yaml` and it will remove both noise and annoyance (setting
`INFERENCE_MODEL` environment variables, for example) from the new user
experience.
In addition, it adds a configuration variable `allowed_models` which can
be used to optionally restrict the set of models exposed from a
provider.
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
This pull request adds documentation to clarify the differences between
the Agents API and the OpenAI Responses API, including use cases for
each. It also updates the index page to reference the new documentation.
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes#2368
# What does this PR do?
This PR implements the openai compatible endpoints for chromadb
Closes#2462
## Test Plan
Ran ollama llama stack server and ran the command
`pytest -sv --stack-config=http://localhost:8321
tests/integration/vector_io/test_openai_vector_stores.py
--embedding-model all-MiniLM-L6-v2`
8 failed, 27 passed, 8 skipped, 1 xfailed
The failed ones are regarding files api
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: sarthakdeshpande <sarthak.deshpande@engati.com>
Co-authored-by: Francisco Javier Arceo <farceo@redhat.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
# What does this PR do?
add an `OpenAIMixin` for use by inference providers who remote endpoints
support an OpenAI compatible API.
use is demonstrated by refactoring
- OpenAIInferenceAdapter
- NVIDIAInferenceAdapter (adds embedding support)
- LlamaCompatInferenceAdapter
## Test Plan
existing unit and integration tests
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
minor update of the pgvector doc, changing 'faiss' to 'pgvector'
<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
# What does this PR do?
This PR adds the quickstart as a file to the docs so that it can be more
easily maintained and run, as mentioned in
https://github.com/meta-llama/llama-stack/pull/2800.
## Test Plan
I could add this as a test in the CI but I wasn't sure if we wanted to
add additional jobs there. 😅
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
Just like #2805 but for vLLM.
We also make VLLM_URL env variable optional (not required) -- if not
specified, the provider silently sits idle and yells eventually if
someone tries to call a completion on it. This is done so as to allow
this provider to be present in the `starter` distribution.
## Test Plan
Set up vLLM, copy the starter template and set `{ refresh_models: true,
refresh_models_interval: 10 }` for the vllm provider and then run:
```
ENABLE_VLLM=vllm VLLM_URL=http://localhost:8000/v1 \
uv run llama stack run --image-type venv /tmp/starter.yaml
```
Verify that `llama-stack-client models list` brings up the model
correctly from vLLM.
Inline _inference_ providers haven't proved to be very useful -- they
are rarely used. And for good reason -- it is almost never a good idea
to include a complex (distributed) inference engine bundled into a
distributed stateful front-end server serving many other things.
Responsibility should be split properly.
See Discord discussion:
1395849853
For self-hosted providers like Ollama (or vLLM), the backing server is
running a set of models. That server should be treated as the source of
truth and the Stack registry should just be a cache for those models. Of
course, in production environments, you may not want this (because you
know what model you are running statically) hence there's a config
boolean to control this behavior.
_This is part of a series of PRs aimed at removing the requirement of
needing to set `INFERENCE_MODEL` env variables for running Llama Stack
server._
## Test Plan
Copy and modify the starter.yaml template / config and enable
`refresh_models: true, refresh_models_interval: 10` for the ollama
provider. Then, run:
```
LLAMA_STACK_LOGGING=all=debug \
ENABLE_OLLAMA=ollama uv run llama stack run --image-type venv /tmp/starter.yaml
```
See a gargantuan amount of logs, but verify that the provider is
periodically refreshing models. Stop and prune a model from ollama
server, restart the server. Verify that the model goes away when I call
`uv run llama-stack-client models list`
# What does this PR do?
'build' command didn't take into account ENABLE flags for starter distro
for some reason, I was having issues with HuggingFace access for the
embedding model, so added a tip for that as well
Closes#2779
## Test Plan
I ran the described steps manually, but it would be nice if someone else
could try it and verify this still works
We might consider having some CI job ensure the QSG remains functional -
it's not a great experience for new users if they try Llama Stack for
the first time and it doesn't work as we describe
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Adds new documentation that was missing for the Llama Stack Python
Client as well as updates old/outdated docs
# What does this PR do?
This PR adds the keyword search implementation for Milvus. Along with
the implementation for remote Milvus, the tests require us to start a
Milvus containers locally.
In order to verify the implementation, run:
```
pytest tests/unit/providers/vector_io/remote/test_milvus.py -v -s --tb=short --disable-warnings --asyncio-mode=auto
```
You can also test the changes using the below script:
```
#!/usr/bin/env python3
import asyncio
import os
import uuid
from typing import List
from llama_stack_client import (
Agent,
AgentEventLogger,
LlamaStackClient,
RAGDocument
)
class MilvusRAGDemo:
def __init__(self, base_url: str = "http://localhost:8321/"):
self.client = LlamaStackClient(base_url=base_url)
self.vector_db_id = f"milvus_rag_demo_{uuid.uuid4().hex[:8]}"
self.model_id = None
self.embedding_model_id = None
self.embedding_dimension = None
def setup_models(self):
"""Get available models and select appropriate ones for LLM and embeddings."""
models = self.client.models.list()
# Select embedding model
embedding_models = [m for m in models if m.model_type == "embedding"]
if not embedding_models:
raise ValueError("No embedding models found")
self.embedding_model_id = embedding_models[0].identifier
self.embedding_dimension = embedding_models[0].metadata["embedding_dimension"]
def register_vector_db(self):
print(f"Registering Milvus vector database: {self.vector_db_id}")
response = self.client.vector_dbs.register(
vector_db_id=self.vector_db_id,
embedding_model=self.embedding_model_id,
embedding_dimension=self.embedding_dimension,
provider_id="milvus-remote", # Use remote Milvus
)
print(f"Vector database registered successfully")
return response
def insert_documents(self):
"""Insert sample documents into the vector database."""
print("\nInserting sample documents...")
# Sample documents about different topics
documents = [
RAGDocument(
document_id="ai_ml_basics",
content="""
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the world.
AI refers to the simulation of human intelligence in machines, while ML is a subset
of AI that enables computers to learn and improve from experience without being
explicitly programmed. Deep learning, a subset of ML, uses neural networks with
multiple layers to process complex patterns in data.
Key concepts in AI/ML include:
- Supervised Learning: Training with labeled data
- Unsupervised Learning: Finding patterns in unlabeled data
- Reinforcement Learning: Learning through trial and error
- Neural Networks: Computing systems inspired by biological brains
""",
mime_type="text/plain",
metadata={"topic": "technology", "category": "ai_ml"},
),
]
# Insert documents with chunking
self.client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=self.vector_db_id,
chunk_size_in_tokens=200, # Smaller chunks for better granularity
)
print(f"Inserted {len(documents)} documents with chunking")
def test_keyword_search(self):
"""Test keyword-based search using BM25."""
queries = [
"neural networks",
"Python frameworks",
"data cleaning",
]
for query in queries:
response = self.client.vector_io.query(
vector_db_id=self.vector_db_id,
query=query,
params={
"mode": "keyword", # Keyword search
"max_chunks": 3,
"score_threshold": 0.0,
}
)
for i, (chunk, score) in enumerate(zip(response.chunks, response.scores)):
print(f" {i+1}. Score: {score:.4f}")
print(f" Content: {chunk.content[:100]}...")
print(f" Metadata: {chunk.metadata}")
def run_demo(self):
try:
self.setup_models()
self.register_vector_db()
self.insert_documents()
self.test_keyword_search()
except Exception as e:
print(f"Error during demo: {e}")
raise
def main():
"""Main function to run the demo."""
# Check if Llama Stack server is running
demo = MilvusRAGDemo()
try:
demo.run_demo()
except Exception as e:
print(f"Demo failed: {e}")
if __name__ == "__main__":
main()
```
[//]: # (## Documentation)
---------
Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
# What does this PR do?
This PR improves documentation clarity around run.yaml file usage. It
adds comprehensive guidance to help users understand that generated
run.yaml files are templates meant to be customized for production use,
not used as-is.
## Changes
- Add new documentation section on customizing run.yaml files
- Clarify that generated run.yaml files are templates, not production
configs
- Add guidance on customization best practices and common scenarios
- Update existing documentation to reference customization guide
- Improve clarity around run.yaml file usage for better user experience
## Test Plan
- Verified new documentation file exists at correct location
- Confirmed documentation is properly integrated into the toctree
structure
- Checked all internal links use correct paths and reference existing
files
- Validated references are added to relevant existing documentation
files
- Documentation build testing will be handled by CI environment
# What does this PR do?
This PR refactors and the VectorIO backend logic for `sqlite-vec` and
adds unit tests and fixtures to make it easy to test both `sqlite-vec`
and `milvus`.
Key changes:
- `sqlite-vec` migrated to `kvstore` registry
- added in-memory cache for sqlite-vec to be consistent with `milvus`
- default fixtures moved to `conftest.py`
- removed redundant tests from sqlite`-vec`
- made `test_vector_io_openai_vector_stores.py` more easily extensible
## Test Plan
Unit tests added testing inline providers.
---------
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
# What does this PR do?
Updates some broken or outdated links pointing to the Android Demo App
Signed-off-by: Jorge Garcia Oncins <jgarciao@redhat.com>
- Fix constructor call missing files_api parameter
- Add kvstore field to MilvusVectorIOConfig
- Resolves#2626
# What does this PR do?
[https://github.com/meta-llama/llama-stack/issues/2626]
## Problem
The `MilvusVectorIOAdapter` fails to initialize due to two missing
configuration issues:
1. Missing `files_api` parameter in the constructor call
2. Missing `kvstore` field in the `MilvusVectorIOConfig` class
## Root Cause
1. The adapter constructor expects 3 parameters `(config, inference_api,
files_api)` but the `get_adapter_impl` function only passes 2 parameters
2. The `MilvusVectorIOConfig` class lacks the `kvstore` field that the
adapter's `initialize()` method expects for metadata persistence
## Solution
- Added `files_api = deps.get(Api.files, None)` to safely retrieve files
API from dependencies
- Pass the files_api parameter to MilvusVectorIOAdapter constructor
- Added `kvstore: KVStoreConfig | None = None` field to
MilvusVectorIOConfig
- Maintains backward compatibility since both files_api and kvstore can
be None
Closes#2626
## Test Plan
- [x] Tested with Milvus configuration - server starts successfully
```yaml
vector_io:
- provider_id: milvus
provider_type: remote::milvus
config:
uri: http://localhost:19530
token: root:Milvus
kvstore:
type: sqlite
namespace: null
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/remote-vllm}/milvus_store.db
```
- [x] Vector operations work as expected
```python
from llama_stack_client import LlamaStackClient
from llama_stack_client.types.shared_params.document import Document as RAGDocument
from llama_stack_client.lib.agents.agent import Agent
from llama_stack_client.lib.agents.event_logger import EventLogger as AgentEventLogger
import os
endpoint = os.getenv("LLAMA_STACK_ENDPOINT")
model = os.getenv("INFERENCE_MODEL")
# Initialize the client
client = LlamaStackClient(base_url=endpoint)
vector_db_id = "my_documents"
response = client.vector_dbs.register(
vector_db_id=vector_db_id,
embedding_model="all-MiniLM-L6-v2",
embedding_dimension=384,
provider_id="milvus",
)
urls = ["getting_started/Red_Hat_AI_Inference_Server-3.0-Getting_started-en-US.pdf", "vllm_server_arguments/Red_Hat_AI_Inference_Server-3.0-vLLM_server_arguments-en-US.pdf"]
documents = [
RAGDocument(
document_id=f"num-{i}",
content=f"https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/3.0/pdf/{url}",
mime_type="application/pdf",
metadata={},
)
for i, url in enumerate(urls)
]
client.tool_runtime.rag_tool.insert(
documents=documents,
vector_db_id=vector_db_id,
chunk_size_in_tokens=512,
)
rag_agent = Agent(
client,
model=model,
# Define instructions for the agent (system prompt)
instructions="You are a helpful assistant",
enable_session_persistence=False,
# Define tools available to the agent
tools=[
{
"name": "builtin::rag/knowledge_search",
"args": {
"vector_db_ids": [vector_db_id],
},
}
],
)
session_id = rag_agent.create_session("test-session")
user_prompts = [
"How to start the AI Inference Server container image? use the knowledge_search tool to get information.",
]
for prompt in user_prompts:
print(f"User> {prompt}")
response = rag_agent.create_turn(
messages=[{"role": "user", "content": prompt}],
session_id=session_id,
)
for log in AgentEventLogger().log(response):
log.print()
```
server logs:
```
INFO 2025-07-04 22:18:30,385 __main__:577 server: Listening on ['::', '0.0.0.0']:5000
INFO: Started server process [769725]
INFO: Waiting for application startup.
INFO 2025-07-04 22:18:30,390 __main__:158 server: Starting up
INFO: Application startup complete.
INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit)
INFO 2025-07-04 22:18:52,193 llama_stack.distribution.routing_tables.common:200 core: Setting owner for vector_db 'my_documents' to
20:18:52.194 [START] /v1/vector-dbs
INFO: 192.168.1.249:64170 - "POST /v1/vector-dbs HTTP/1.1" 200 OK
20:18:52.216 [END] /v1/vector-dbs [StatusCode.OK] (21.89ms)
20:18:52.222 [START] /v1/tool-runtime/rag-tool/insert
INFO 2025-07-04 22:18:56,265 llama_stack.providers.utils.inference.embedding_mixin:102 uncategorized: Loading sentence transformer for
all-MiniLM-L6-v2...
WARNING 2025-07-04 22:18:59,214 opentelemetry.trace:537 uncategorized: Overriding of current TracerProvider is not allowed
INFO 2025-07-04 22:18:59,339 sentence_transformers.SentenceTransformer:219 uncategorized: Use pytorch device_name: cuda:0
INFO 2025-07-04 22:18:59,340 sentence_transformers.SentenceTransformer:227 uncategorized: Load pretrained SentenceTransformer: all-MiniLM-L6-v2
INFO: 192.168.1.249:64170 - "POST /v1/tool-runtime/rag-tool/insert HTTP/1.1" 200 OK
INFO: 192.168.1.249:64170 - "POST /v1/agents HTTP/1.1" 200 OK
INFO: 192.168.1.249:64170 - "GET /v1/tools?toolgroup_id=builtin%3A%3Arag%2Fknowledge_search HTTP/1.1" 200 OK
INFO: 192.168.1.249:64170 - "POST /v1/agents/b1f6f063-1691-4780-8d9e-facd81708b91/session HTTP/1.1" 200 OK
20:19:01.834 [END] /v1/tool-runtime/rag-tool/insert [StatusCode.OK] (9612.06ms)
20:19:01.839 [START] /v1/agents
INFO: 192.168.1.249:64170 - "POST /v1/agents/b1f6f063-1691-4780-8d9e-facd81708b91/session/d2706302-bb54-421d-a890-5e25df9cb47f/turn HTTP/1.1" 200 OK
20:19:01.839 [END] /v1/agents [StatusCode.OK] (0.18ms)
20:19:01.844 [START] /v1/tools
INFO 2025-07-04 22:19:01,853 llama_stack.providers.remote.inference.vllm.vllm:330 uncategorized: Initializing vLLM client with
base_url=http://192.168.1.183:8080/v1
20:19:01.858 [END] /v1/tools [StatusCode.OK] (14.92ms)
20:19:01.868 [START] /v1/agents/{agent_id}/session
20:19:01.868 [END] /v1/agents/{agent_id}/session [StatusCode.OK] (0.37ms)
20:19:01.873 [START] /v1/agents/{agent_id}/session/{session_id}/turn
20:19:01.885 [START] inference
20:19:05.506 [END] inference [StatusCode.OK] (3621.19ms)
INFO 2025-07-04 22:19:05,537 llama_stack.providers.inline.agents.meta_reference.agent_instance:890 agents: executing tool call: knowledge_search
with args: {'query': 'How to start the AI Inference Server container image'}
20:19:05.538 [START] tool_execution
20:19:05.928 [END] tool_execution [StatusCode.OK] (390.08ms)
20:19:05.538 [INFO] executing tool call: knowledge_search with args: {'query': 'How to start the AI Inference Server container image'}
20:19:05.935 [START] inference
20:19:17.539 [END] inference [StatusCode.OK] (11603.76ms)
20:19:17.560 [END] /v1/agents/{agent_id}/session/{session_id}/turn [StatusCode.OK] (15686.62ms)
```
- [x] No regressions in functionality
- [x] Configuration properly accepts kvstore settings
---------
Co-authored-by: Peter Gustafsson <peter.gustafsson6@gmail.com>
Co-authored-by: raghotham <rsm@meta.com>
Co-authored-by: Francisco Arceo <farceo@redhat.com>
# What does this PR do?
- fix env variables
- use gpu for vllm
- add eks/apply.py for aws
- add template to set hf secret
## Test Plan
bash apply.sh
Co-authored-by: Eric Huang <erichuang@fb.com>
