1303 commits

Author	SHA1	Message	Date
Sébastien Han	ea966565f6	feat: improve telemetry (#2590) ... # 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>	2025-07-04 17:29:09 +02:00
Derek Higgins	4eae0cbfa4	fix(starter): Add missing faiss provider to build.yaml vector_io section (#2625) ... The starter template build.yaml was missing the inline::faiss provider in the vector_io section, while it was properly configured in run.yaml and starter.py's vector_io_providers list. Fixes: #2624 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-07-04 17:28:57 +02:00
Sébastien Han	df6ce8befa	fix: only load mcp when enabled in tool_group (#2621) ... # 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>	2025-07-04 20:27:05 +05:30
Sébastien Han	c4349f532b	feat: consolidate most distros into "starter" (#2516) ... # What does this PR do? * Removes a bunch of distros * Removed distros were added into the "starter" distribution * Doc for "starter" has been added * Partially reverts https://github.com/meta-llama/llama-stack/pull/2482 since inference providers are disabled by default and can be turned on manually via env variable. * Disables safety in starter distro Closes: https://github.com/meta-llama/llama-stack/issues/2502. ~Needs: https://github.com/meta-llama/llama-stack/pull/2482 for Ollama to work properly in the CI.~ TODO: - [ ] We can only update `install.sh` when we get a new release. - [x] Update providers documentation - [ ] Update notebooks to reference starter instead of ollama Signed-off-by: Sébastien Han <seb@redhat.com>	2025-07-04 15:58:03 +02:00
Derek Higgins	f77d4d91f5	fix: handle encoding errors when adding files to vector store (#2574) ... - Add try-catch block around data.decode() to handle UnicodeDecodeError - Implement UTF-8 fallback when detected encoding fails - Return empty string when both encodings fail - add unit tests Fixes #2572: UnicodeDecodeError when uploading files with problematic encodings Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-07-04 12:10:18 +02:00
Matthew Farrellee	ef26259209	feat: add llama guard 4 model (#2579) ... add support for Llama Guard 4 model to the llama_guard safety provider test with - 0. NVIDIA_API_KEY=... llama stack build --image-type conda --image-name env-nvidia --providers inference=remote::nvidia,safety=inline::llama-guard --run 1. llama-stack-client models register meta-llama/Llama-Guard-4-12B --provider-model-id meta/llama-guard-4-12b 2. pytest tests/integration/safety/test_llama_guard.py Co-authored-by: raghotham <rsm@meta.com>	2025-07-03 22:29:04 -07:00
Derek Higgins	0422b4fc63	fix: CI flakiness in vector IO tests by pinning pymilvus>=2.4.10 (#2610) ... This occurred when marshmallow 4.0.0 was installed (which removed __version_info__) By pinning pymilvus to >=2.4.10, we ensure marshmallow doesn't get installed. Also set the dependency in InlineProviderSpec as this is the one that takes effect when using the "inline::milvus" provider. Fixes https://github.com/meta-llama/llama-stack/issues/2588 Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-07-04 10:27:23 +05:30
Francisco Arceo	ea80ea63ac	chore: Updating chunk id generation to ensure uniqueness (#2618) ... # What does this PR do? This handles an edge case for `generate_chunk_id` if the concatenation of the `document_id` and `chunk_text` combination are not unique. Adding the window location ensures uniqueness. ## Test Plan Added unit test Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-04 10:26:35 +05:30
Francisco Arceo	4afd619c56	chore: Add support for vector-stores files api for Milvus (#2582) ... # What does this PR do? ### Summary This pull request implements support for the OpenAI Vector Store Files API for the Milvus vector store provider in `llama_stack`. It enables storing, loading, updating, and deleting file metadata and file contents in Milvus collections, allowing OpenAI vector store files to be managed directly within Milvus. ### Main Changes - **Milvus Vector Store Files API Implementation** - Implements all required methods for storing, loading, updating, and deleting vector store file metadata and contents (`_save_openai_vector_store_file`, `_load_openai_vector_store_file`, `_load_openai_vector_store_file_contents`, `_update_openai_vector_store_file`, `_delete_openai_vector_store_file_from_storage`). - Uses two Milvus collections: `openai_vector_store_files` (for metadata) and `openai_vector_store_files_contents` (for chunked file contents). - Collections are created dynamically if they do not exist, with appropriate schema definitions. - **Collection Name Sanitization** - Adds a `sanitize_collection_name` utility to ensure Milvus collection names only contain valid characters (letters, numbers, underscores). - **Testing** - Updates test skip logic to include `"inline::milvus"` for cases where the OpenAI Vector Store Files API is not supported, improving integration test accuracy. - **Other Improvements** - Passes `kvstore` to `MilvusIndex` for consistency. - Removes obsolete NotImplementedErrors and legacy code for file storage. ## Test Plan CI and tested via a test script ## Notes - `VectorDB` currently uses the `name` as the `identifier` in `openai_create_vector_store`. We need to add `name` as a field to `VectorDB` and generate the `identifier` upon creation. OpenAI is not idempotent with respect to the `name` field that they pass (i.e., you can pass the same name multiple times and OpenAI will generate a new identifier). I'll add a follow up PR for this. - The `Files` api needs to use `files-` as a prefix in the identifier. I have updated the Vector Store to use the OpenAI prefix `vs_*`. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-03 12:15:33 -07:00
Akram Ben Aissi	f4950f4ef0	fix: AccessDeniedError leads to HTTP 500 instead of error 403 (#2595) ... 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>>	2025-07-03 10:50:49 -07:00
ehhuang	3c43a2f529	fix: store configs (#2593) ... # What does this PR do? https://github.com/meta-llama/llama-stack/pull/2490 broke postgres_demo, as the config expected a str but the value was converted to int. This PR: 1. Updates the type of port in sqlstore to be int 2. template generation uses `dict` instead of `StackRunConfig` so as to avoid failing pydantic typechecks. 3. Adds `replace_env_vars` to StackRunConfig instantiation in `configure.py` (not sure why this wasn't needed before). ## Test Plan `llama stack build --template postgres_demo --image-type conda --run`	2025-07-03 10:07:23 -07:00
Sébastien Han	aa273944fd	fix: add mcp dependency to agent provider (#2587) ... # What does this PR do? The agent depends on utils.tools.mcp. Closes: https://github.com/meta-llama/llama-stack/issues/2576 Signed-off-by: Sébastien Han <seb@redhat.com>	2025-07-03 14:59:01 +02:00
Nate Harada	5b07755556	docs: Minor spelling fix (#2592) ... # What does this PR do? Minor spelling fix in the comments ## Test Plan No code changes	2025-07-02 20:26:51 -04:00
Jorge	4d0d2d685f	fix: Set parameter usedforsecurity=False when calling hashlib.md5 in order to fix rag_tool.insert on FIPS clusters (#2577) ... # What does this PR do? Set parameter `usedforsecurity=False` when calling hashlib.md5 in order to fix rag_tool.insert on FIPS clusters <!-- If resolving an issue, uncomment and update the line below --> Closes #2571 --------- Signed-off-by: Jorge Garcia Oncins <jgarciao@redhat.com>	2025-07-02 12:07:05 +02:00
Sébastien Han	25268854bc	fix: allow default empty vars for conditionals (#2570) ... # 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>	2025-07-01 14:42:05 +02:00
Francisco Arceo	0066135944	chore: Enabling VectorIO Integration tests for Milvus (#2546)	2025-06-30 19:49:59 -07:00
Francisco Arceo	5785ccda35	fix: Fixing Milvus sample config and updating documentation (#2568)	2025-06-30 19:25:23 -07:00
Matthew Farrellee	13aa367c8a	fix: default api_key from env must be a SecretStr (#2565) ... # 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' ```	2025-06-30 18:08:44 -07:00
Ashwin Bharambe	b333a3c03a	fix(ollama): Download remote image URLs for Ollama (#2551) ... ## 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.	2025-06-30 20:36:11 +05:30
Sébastien Han	c9a49a80e8	docs: auto generated documentation for providers (#2543) ... # 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>	2025-06-30 15:13:20 +02:00
Sébastien Han	8d8e90d78e	fix: add missing argument and methods (#2550) ... # 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>	2025-06-30 14:55:37 +02:00
Krzysztof Malczuk	be9bf68246	feat: Add webmethod for deleting openai responses (#2160) ... # 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.	2025-06-30 11:28:02 +02:00
github-actions[bot]	709eb7da33	build: Bump version to 0.2.13	2025-06-27 23:56:14 +00:00
Francisco Arceo	cc19b56c87	chore: OpenAI compatibility for Milvus (#2470) ... # 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>	2025-06-27 16:00:36 -07:00
Charlie Doern	65b4fae51d	fix: proper checkpointing logic for HF trainer (#2429) ... # 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>	2025-06-27 17:36:25 -04:00
Ramakrishna Reddy Yekulla	03e61e3fcc	fix: ValueError in faiss vector database serialization (resolves #2519) (#2526) ... 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>	2025-06-27 14:34:52 -04:00
Rohan Awhad	7cb5d3c60f	chore: standardize unsupported model error #2517 (#2518) ... # 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>	2025-06-27 14:26:58 -04:00
Juanma	e7eb9f9adc	fix: dataset metadata without provider_id (#2527) ... # 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>	2025-06-27 08:51:29 -04:00
Wen Zhou	8c3f2762fb	build: update temp. created Containerfile (#2492) ... # 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>	2025-06-27 10:23:12 +02:00
Ben Browning	0883944bc3	fix: Some missed env variable changes from PR 2490 (#2538) ... # 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>	2025-06-26 17:59:15 -07:00
Hardik Shah	eb01a3f1c5	ci: vector_io provider integration tests (#2537) ... Runs integration tests for `vector_io` across the provider matrix. This new workflow adds CI testing across - `inline::faiss`, `remote::chroma`.	2025-06-26 17:04:32 -07:00
grs	68d8f2186f	fix: fix test of root span to match what is being set (#2494) ... # 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>	2025-06-26 11:41:35 -04:00
Sébastien Han	dbdc811d16	chore: isolate bare minimum project dependencies (#2282) ... # 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>	2025-06-26 10:14:27 +02:00
Sébastien Han	43c1f39bd6	refactor(env)!: enhanced environment variable substitution (#2490) ... # 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>	2025-06-26 08:20:08 +05:30
Sébastien Han	36d70637b9	fix: finish conversion to StrEnum (#2514) ... # 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>	2025-06-26 08:01:26 +05:30
Sébastien Han	ac5fd57387	chore: remove nested imports (#2515) ... # 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>	2025-06-26 08:01:05 +05:30
Ben Browning	2d9fd041eb	fix: annotations list and web_search_preview in Responses (#2520) ... # 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>	2025-06-26 07:59:33 +05:30
ehhuang	1d3f27fe5b	fix: resume responses with tool call output (#2524) ... # 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'	2025-06-25 14:43:37 -07:00
Francisco Arceo	82f13fe83e	feat: Add ChunkMetadata to Chunk (#2497) ... # 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>	2025-06-25 15:55:23 -04:00
Ben Browning	fa0b0c13d4	fix: Ollama should be optional in starter distro (#2482) ... # 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>	2025-06-25 15:54:00 +02:00
Varsha	cfee63bd0d	feat: Add search_mode support to OpenAI vector store API (#2500) ... # 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>	2025-06-24 20:38:47 -04:00
ehhuang	114946ae88	chore: fix build script bug (#2507) ... # What does this PR do? Fixes ``` Installing pip dependencies error: Failed to parse: `scikit-learn pymongo pythainlp datasets torch sentencepiece requests aiohttp psycopg2-binary trl pillow pandas chardet nltk scipy ollama faiss-cpu pypdf tree_sitter langdetect openai matplotlib asyncpg peft redis autoevals mcp opentelemetry-exporter-otlp-proto-http sqlalchemy[asyncio] tqdm opentelemetry-sdk aiosqlite numpy chromadb-client emoji transformers aiosqlite fastapi fire httpx uvicorn opentelemetry-sdk opentelemetry-exporter-otlp-proto-http` Caused by: Expected one of `@`, `(`, `<`, `=`, `>`, `~`, `!`, `;`, found `p` scikit-learn pymongo pythainlp datasets torch sentencepiece requests aiohttp psycopg2-binary trl pillow pandas chardet nltk scipy ollama faiss-cpu pypdf tree_sitter langdetect openai matplotlib asyncpg peft redis autoevals mcp opentelemetry-exporter-otlp-proto-http sqlalchemy[asyncio] tqdm opentelemetry-sdk aiosqlite numpy chromadb-client emoji transformers aiosqlite fastapi fire httpx uvicorn opentelemetry-sdk opentelemetry-exporter-otlp-proto-http ^ ERROR 2025-06-24 11:33:33,362 llama_stack.distribution.build:145 uncategorized: Failed to build target myenv with return code 2 Error building stack: Failed to build image myenv ``` ## Test Plan	2025-06-24 12:05:22 -07:00
Ashwin Bharambe	73c18feac4	fix: update the signature of openai_list_files_in_vector_store in all VectorIO impls (#2503)	2025-06-24 18:55:56 +05:30
ehhuang	7fa8f23555	fix(ui): ensure initial data fetch only happens once (#2486) ... # What does this PR do? Bug: 1. go to responses chat logs in UI 2. go to chat completions logs page 3. observe that same data appears in the table twice This is because `fetchData` is called multiple times when multiple renders occur. ## Test Plan manual testing of above bug repro steps	2025-06-24 12:22:55 +02:00
Sébastien Han	9c8be89fb6	chore: bump python supported version to 3.12 (#2475) ... # What does this PR do? The project now supports Python >= 3.12 Signed-off-by: Sébastien Han <seb@redhat.com>	2025-06-24 09:22:04 +05:30
Rohan Awhad	d797f9aec1	fix: #2495 FileNotFound Err in container image (#2498) ... # What does this PR do? Closes #2495 Changes: - Delay the `COPY run.yaml` into docker image step until after external provider handling - Split the check for `external_providers_dir` into “non-empty” and “directory exists" ## Test Plan 0. Create and Activate venv 1. Create a `simple_build.yaml` ```yaml version: '2' distribution_spec: providers: inference: - remote::openai image_type: container image_name: openai-stack ``` 2. Run llama stack build: ```bash llama stack build --config simple_build.yaml ``` 3. Run the docker container: ```bash docker run \ -p 8321:8321 \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ openai_stack:0.2.12 ``` This should show server is running. ``` INFO 2025-06-23 19:07:57,832 llama_stack.distribution.distribution:151 core: Loading external providers from /.llama/providers.d INFO 2025-06-23 19:07:59,324 __main__:572 server: Listening on ['::', '0.0.0.0']:8321 INFO: Started server process [1] INFO: Waiting for application startup. INFO 2025-06-23 19:07:59,336 __main__:156 server: Starting up INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit) ``` Notice the first line: ``` Loading external providers from /.llama/providers.d ``` This is expected behaviour. Co-authored-by: Rohan Awhad <rawhad@redhat.com>	2025-06-24 09:08:08 +05:30
ehhuang	23b7dc7b37	fix: stack build (#2485) ... # What does this PR do? probably related to 3.11 upgrade ^^^^ File "/opt/homebrew/Caskroom/miniconda/base/envs/myenv/lib/python3.11/site-packages/termcolor/termcolor.py", line 147, in colored text = fmt_str % (COLORS[color], text) ~~~~~~^^^^^^^ KeyError: 'light_blue' ## Test Plan	2025-06-20 15:15:43 -07:00
github-actions[bot]	d70573bd47	build: Bump version to 0.2.12	2025-06-20 21:06:17 +00:00
ehhuang	d3b60507d7	feat: support auth attributes in inference/responses stores (#2389) ... # 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	2025-06-20 10:24:45 -07:00
ehhuang	6832e8a658	feat: remove score_threshold constraint (#2479) ... # What does this PR do? See inline comment. fixes test _ test_openai_vector_store_search_with_high_score_filter[llama_stack_client-meta-llama/Llama-3.3-70B-Instruct-meta-llama/Llama-4-Scout-17B-16E-Instruct-all-MiniLM-L6-v2-None-None] _ llama-stack/llama_stack/distribution/library_client.py:98: in convert_to_pydantic return TypeAdapter(annotation).validate_python(value) .venv/lib/python3.10/site-packages/pydantic/type_adapter.py:421: in validate_python return self.validator.validate_python( E pydantic_core._pydantic_core.ValidationError: 1 validation error for nullable[SearchRankingOptions] E score_threshold E Input should be less than or equal to 1 [type=less_than_equal, input_value=1.3458905661753127, input_type=float] E For further information visit https://errors.pydantic.dev/2.11/v/less_than_equal The above exception was the direct cause of the following exception: llama-stack/tests/integration/vector_io/test_openai_vector_stores.py:376: in test_openai_vector_store_search_with_high_score_filter search_response = compat_client.vector_stores.search( .venv/lib/python3.10/site-packages/llama_stack_client/resources/vector_stores/vector_stores.py:356: in search return self._post( .venv/lib/python3.10/site-packages/llama_stack_client/_base_client.py:1232: in post return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)) llama-stack/llama_stack/distribution/library_client.py:177: in request result = loop.run_until_complete(self.async_client.request(*args, **kwargs)) /opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/asyncio/base_events.py:649: in run_until_complete return future.result() llama-stack/llama_stack/distribution/library_client.py:292: in request response = await self._call_non_streaming( llama-stack/llama_stack/distribution/library_client.py:313: in _call_non_streaming body = self._convert_body(path, options.method, body) llama-stack/llama_stack/distribution/library_client.py:425: in _convert_body converted_body[param_name] = convert_to_pydantic(param.annotation, value) llama-stack/llama_stack/distribution/library_client.py:112: in convert_to_pydantic raise ValueError(f"Failed to convert parameter {value} into {annotation}: {e}") from e E ValueError: Failed to convert parameter {'score_threshold': 1.3458905661753127} into llama_stack.apis.vector_io.vector_io.SearchRankingOptions | None: 1 validation error for nullable[SearchRankingOptions] E score_threshold E Input should be less than or equal to 1 [type=less_than_equal, input_value=1.3458905661753127, input_type=float] E For further information visit https://errors.pydantic.dev/2.11/v/less_than_equal ## Test Plan	2025-06-20 09:17:42 +05:30