# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- update REAMDE.md format and python version
- update package name: CustomTool was renamed to ClientTool in
https://github.com/meta-llama/llama-stack-client-python/pull/73
<!-- If resolving an issue, uncomment and update the line below -->
Closes#2556
## 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: Wen Zhou <wenzhou@redhat.com>
# What does this PR do?
Clarifies that non-Llama models can be trained via the Post Training API
## Test Plan
Build docs locally
Signed-off-by: Nathan Weinberg <nweinber@redhat.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?
The external providers guide can now be accessed directly from the
sidebar
## Test Plan
Build locally to test the changes
Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
# What does this PR do?
fixes the api_key type when read from env
## Test Plan
run nvidia template w/o api_key in run.yaml and perform inference
before change the inference will fail w/ -
```
File ".../llama-stack/llama_stack/providers/remote/inference/nvidia/nvidia.py", line 118, in _get_client_for_base_url
api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'get_secret_value'
```
## What does this PR do?
Ollama does not support remote images. Only local file paths OR base64
inputs are supported. This PR ensures that the Stack downloads remote
images and passes the base64 down to the inference engine.
## Test Plan
Added a test cases for Responses and ran it for both `fireworks` and
`ollama` providers.
# 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?
Resolves:
```
mypy.....................................................................Failed
- hook id: mypy
- exit code: 1
llama_stack/providers/utils/responses/responses_store.py:119: error: Missing positional argument "policy" in call to "fetch_one" of "AuthorizedSqlStore" [call-arg]
llama_stack/providers/utils/responses/responses_store.py:122: error: "AuthorizedSqlStore" has no attribute "delete" [attr-defined]
Found 2 errors in 1 file (checked 403 source files)
```
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?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- change from https://github.com/meta-llama/llama-stack/issues/2110 need
update documentation. "container" is not valid value for --image-type
- chore: updates from standard output
<!-- 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: Wen Zhou <wenzhou@redhat.com>
Bumps [astral-sh/setup-uv](https://github.com/astral-sh/setup-uv) from
6.3.0 to 6.3.1.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/astral-sh/setup-uv/releases">astral-sh/setup-uv's
releases</a>.</em></p>
<blockquote>
<h2>v6.3.1 🌈 Do not warn when version not in manifest-file</h2>
<h2>Changes</h2>
<p>This is a hotfix to change the warning messages that a version could
not be found in the local manifest-file to info level.</p>
<p>A <code>setup-uv</code> release contains a version-manifest.json file
with infos in all available <code>uv</code> releases. When a new
<code>uv</code> version is released this is not contained in this file
until the file gets updated and a new <code>setup-uv</code> release is
made.
We will overhaul this process in the future but for now the spamming of
warnings is removed.</p>
<h2>🐛 Bug fixes</h2>
<ul>
<li>Do not warn when version not in manifest-file <a
href="https://github.com/eifinger"><code>@eifinger</code></a> (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/462">#462</a>)</li>
</ul>
<h2>🧰 Maintenance</h2>
<ul>
<li>chore: update known versions for 0.7.14 @<a
href="https://github.com/apps/github-actions">github-actions[bot]</a>
(<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/459">#459</a>)</li>
<li>Revert "Set expected cache dir drive to C: on windows (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/451">#451</a>)"
<a href="https://github.com/eifinger"><code>@eifinger</code></a> (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/460">#460</a>)</li>
</ul>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="bd01e18f51"><code>bd01e18</code></a>
Do not warn when version not in manifest-file (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/462">#462</a>)</li>
<li><a
href="c6a5ebaafe"><code>c6a5eba</code></a>
chore: update known versions for 0.7.14 (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/459">#459</a>)</li>
<li><a
href="790df8f465"><code>790df8f</code></a>
Revert "Set expected cache dir drive to C: on windows (<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/451">#451</a>)"
(<a
href="https://redirect.github.com/astral-sh/setup-uv/issues/460">#460</a>)</li>
<li>See full diff in <a
href="445689ea25...bd01e18f51">compare
view</a></li>
</ul>
</details>
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Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
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You can trigger Dependabot actions by commenting on this PR:
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Given the shift to python3.12, we need to explicitly depend on
`setuptools` for the pkg_resources import
## Test Plan
Run
```
cd local/llama-stack
UV_PROJECT_ENVIRONMENT=/tmp/docs uv sync --frozen --group docs
cd /tmp/docs
uv run python -m sphinx -T -b html -d _build/doctrees -D language=en \
~/local/llama-stack/docs/source/ \
/tmp/docs/html
```
# 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?
- llama_stack/exceptions.py: Add UnsupportedModelError class
- remote inference ollama.py and utils/inference/model_registry.py:
Changed ValueError in favor of UnsupportedModelError
- utils/inference/litellm_openai_mixin.py: remove `register_model`
function implementation from `LiteLLMOpenAIMixin` class. Now uses the
parent class `ModelRegistryHelper`'s function implementation
Closes#2517
## Test Plan
1. Create a new `test_run_openai.yaml` and paste the following config in
it:
```yaml
version: '2'
image_name: test-image
apis:
- inference
providers:
inference:
- provider_id: openai
provider_type: remote::openai
config:
max_tokens: 8192
models:
- metadata: {}
model_id: "non-existent-model"
provider_id: openai
model_type: llm
server:
port: 8321
```
And run the server with:
```bash
uv run llama stack run test_run_openai.yaml
```
You should now get a `llama_stack.exceptions.UnsupportedModelError` with
the supported list of models in the error message.
---
Tested for the following remote inference providers, and they all raise
the `UnsupportedModelError`:
- Anthropic
- Cerebras
- Fireworks
- Gemini
- Groq
- Ollama
- OpenAI
- SambaNova
- Together
- Watsonx
---------
Co-authored-by: Rohan Awhad <rawhad@redhat.com>
# What does this PR do?
Fixes CVE-2025-4565 and the following warning:
```
warning: `aiohttp==3.11.13` is yanked (reason: "Regression: https://github.com/aio-libs/aiohttp/issues/10617")
```
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
Fixes an error when inferring dataset provider_id with metadata
Closes #[2506](https://github.com/meta-llama/llama-stack/issues/2506)
Signed-off-by: Juanma Barea <juanmabareamartinez@gmail.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- conditionally created folder /.llama/providers.d if
external_providers_dir is set
- do not create /.cache folder, not in use anywhere
- combine chmod and copy to one command
<!-- 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.* -->
updated test:
```
export CONTAINER_BINARY=podman
LLAMA_STACK_DIR=. uv run llama stack build --template remote-vllm --image-type container --image-name <name>
```
log:
```
Containerfile created successfully in /tmp/tmp.rPMunE39Aw/Containerfile
FROM python:3.11-slim
WORKDIR /app
RUN apt-get update && apt-get install -y iputils-ping net-tools iproute2 dnsutils telnet curl wget telnet git procps psmisc lsof traceroute bubblewrap gcc && rm -rf /var/lib/apt/lists/*
ENV UV_SYSTEM_PYTHON=1
RUN pip install uv
RUN uv pip install --no-cache sentencepiece pillow pypdf transformers pythainlp faiss-cpu opentelemetry-sdk requests datasets chardet scipy nltk numpy matplotlib psycopg2-binary aiosqlite langdetect autoevals tree_sitter tqdm pandas chromadb-client opentelemetry-exporter-otlp-proto-http redis scikit-learn openai pymongo emoji sqlalchemy[asyncio] mcp aiosqlite fastapi fire httpx uvicorn opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
RUN uv pip install --no-cache sentence-transformers --no-deps
RUN uv pip install --no-cache torch torchvision --index-url https://download.pytorch.org/whl/cpu
# Allows running as non-root user
RUN mkdir -p /.llama/providers.d /.cache
RUN uv pip install --no-cache llama-stack
RUN pip uninstall -y uv
ENTRYPOINT ["python", "-m", "llama_stack.distribution.server.server", "--template", "remote-vllm"]
RUN chmod -R g+rw /app /.llama /.cache
PWD: /tmp/llama-stack
Containerfile: /tmp/tmp.rPMunE39Aw/Containerfile
+ podman build --progress=plain --security-opt label=disable --platform linux/amd64 -t distribution-remote-vllm:0.2.12 -f /tmp/tmp.rPMunE39Aw/Containerfile /tmp/llama-stack
....
Success!
Build Successful!
You can find the newly-built template here: /tmp/llama-stack/llama_stack/templates/remote-vllm/run.yaml
You can run the new Llama Stack distro via: llama stack run /tmp/llama-stack/llama_stack/templates/remote-vllm/run.yaml --image-type container
```
```
podman tag localhost/distribution-remote-vllm:dev quay.io/wenzhou/distribution-remote-vllm:2492_2
podman push quay.io/wenzhou/distribution-remote-vllm:2492_2
docker run --rm -p 8321:8321 -e INFERENCE_MODEL="meta-llama/Llama-2-7b-chat-hf" -e VLLM_URL="http://localhost:8000/v1" quay.io/wenzhou/distribution-remote-vllm:2492_2 --port 8321
INFO 2025-06-26 13:47:31,813 __main__:436 server: Using template remote-vllm config file:
/app/llama-stack-source/llama_stack/templates/remote-vllm/run.yaml
INFO 2025-06-26 13:47:31,818 __main__:438 server: Run configuration:
INFO 2025-06-26 13:47:31,826 __main__:440 server: apis:
- agents
- datasetio
- eval
- inference
- safety
- scoring
- telemetry
- tool_runtime
- vector_io
benchmarks: []
container_image: null
....
```
-----
previous test:
local run` >llama stack build --template remote-vllm --image-type
container`
image stored in `quay.io/wenzhou/distribution-remote-vllm:2492`
---------
Signed-off-by: Wen Zhou <wenzhou@redhat.com>
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
Update changelog.
---------
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
Some templates were still using the old environment variable substition
syntax instead of the new one and were not getting substituted properly.
Also, some places didn't handle the new None vs old empty string ("")
values that come from the conditional environment variable substitution.
This gets the starter and remote-vllm distributions starting again, and
I tested various permutations of the starter as chroma and pgvector
needed some adjustments to their config classes to handle the new
possible `None` values. And, I had to tweak our `Provider` class to also
handle `None` values, for cases where we disable providers in the
starter config via environment variables.
This may not have caught everything that was missed, but I did grep
around quite a bit to try and find anything lingering.
## Test Plan
The following permutations now all run (or attempt to run to the point
of complaining that they can't connect to chroma, vllm, etc) when before
they failed immediately on startup because of bad environment variable
substitions:
```
uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_SQLITE_VEC=true uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_PGVECTOR=true uv run llama stack run llama_stack/templates/starter/run.yaml
ENABLE_CHROMADB=true uv run llama stack run llama_stack/templates/starter/run.yaml
uv run llama stack run llama_stack/templates/remote-vllm/run.yaml
```
<!-- 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: Ben Browning <bbrownin@redhat.com>
Co-authored-by: raghotham <rsm@meta.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?
The goal is to promote the minimal set of dependencies the project needs
to run, this includes:
* dependencies needed to work with the CLI
* dependencies needed for the server to run with no providers
This also:
* Relocate redundant dependencies out of the core project and into the
individual providers that actually require them.
* Include all necessary server dependencies so the project can run
standalone, even without any providers.
<!-- 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
Build and run distro a server.
Signed-off-by: Sébastien Han <seb@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?
closes#2522
## Test Plan
added integration test
LLAMA_STACK_CONFIG=http://localhost:8321 pytest -v
tests/integration/agents/test_openai_responses.py --text-model
"accounts/fireworks/models/llama-v3p3-70b-instruct" -vv -k
'function_call'
# 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?
Our starter distro required Ollama to be running (and a large list of
models available in that Ollama) to successfully start. This adjusts
things so that Ollama does not have to be running to use the starter
template / distro.
To accomplish this, a few changes were needed:
* The Ollama provider is now configurable whether it raises an Exception
or just logs a warning when it cannot reach the Ollama server on
startup. The default is to raise an exception (same as previous
behavior), but in the starter template we adjust this to just log a
warning so that we can bring the stack up without needing a running
Ollama server.
* The starter template no longer specifies a default list of models for
Ollama, as any models specified there need to actually be pulled and
available in Ollama. Instead, it adds a new
`OLLAMA_INFERENCE_MODEL` environment variable where users can provide an
optional model to register with the Ollama provider on startup.
Additional models can also be registered via the typical
`models.register(...)` at runtime.
* The vLLM template was adjusted to also allow an optional
`VLLM_INFERENCE_MODEL` specified on startup, so that the behavior
between vLLM and Ollama was consistent here to make it easy to get up
and running quickly.
* The default vector store was changed from sqlite-vec to faiss.
sqlite-vec can enabled via setting the `ENABLE_SQLITE_VEC` environment
variable, like we do for chromadb and pgvector. This is due to
sqlite-vec not shipping proper arm64 binaries, like we previously fixed
in #1530 for the ollama distribution.
## Test Plan
With this change, the following scenarios now work with the starter
template that did not before:
* no Ollama running
* Ollama running but not all of the Llama models pulled locally
* Ollama running with a custom model registered on startup
* vLLM running with a custom model registered on startup
* running the starter template on linux/arm64, like when running
containers on Mac without rosetta emulation
---------
Signed-off-by: Ben Browning <bbrownin@redhat.com>
# What does this PR do?
Add search_mode parameter (vector/keyword/hybrid) to
openai_search_vector_store method. Fixes OpenAPI
code generation by using str instead of Literal type.
Closes: #2459
## 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>
