235 commits

Author	SHA1	Message	Date
Matthew Farrellee	57745101be	chore: internal change, make Model.provider_model_id non-optional (#2690) ... - POST /v1/models accepts optional provider_model_id - ModelsRoutingTable.register_model handler ensures it is non-None, providing a default usage of Model.provider_model_id will no longer need to detect None	2025-07-17 08:26:57 -07:00
Francisco Arceo	31b088978a	fix: Fix /vector-stores/create API when vector store with duplicate name (#2617) ... # What does this PR do? Resolves https://github.com/meta-llama/llama-stack/issues/2735 Currently, if you test against OpenAI's Vector Stores API the `client.vector_stores.search` call fails with an invalid vector_db during routing (see the script referenced in the clickable item under the Test Plan section). This PR ensures that `client.vector_stores.search()` is compatible with OpenAI's Vector Stores API. Two biggest changes: 1. The `name`, which was previously used as the `vector_db_id`, has been changed to be consistent with OpenAI's `vs_{uuid}` format. 2. The vector store ID has to be referenced by the ID, the name is not reliable as every `client.vector_stores.create` results in a new vector store. NOTE: I believe this is a breaking change for end users as they'll need to update their VectorDB identifiers. ## Test Plan Unit tests: ```bash ./scripts/unit-tests.sh tests/unit/providers/vector_io/ -v ``` Integration tests: ```bash ENABLE_MILVUS=milvus llama stack run /Users/farceo/dev/llama-stack/llama_stack/templates/starter/run.yaml --image-type venv LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/vector_io/test_openai_vector_stores.py --embedding-model=all-MiniLM-L6-v2 -vv ``` Unit tests and test script below 👇 <details> <summary>Click here for script used to test OpenAI and Llama Stack Vector Store implementation</summary> ```python import json import argparse from openai import OpenAI, pagination import logging from colorama import Fore, Style, init import traceback import os # Initialize colorama for color support in terminal init(autoreset=True) # Setup basic logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') DEMO_VECTOR_STORE_NAME = "Support FAQ FJA" global DEMO_VECTOR_STORE_ID global DEMO_VECTOR_STORE_ID2 def colored_print(color, text): """Prints text to the console with the specified color.""" print(f"{color}{text}{Style.RESET_ALL}") def log_and_print(color, message, level=logging.INFO): """Logs a message and prints it to the console with the specified color.""" logging.log(level, message) colored_print(color, message) def run_tests(client, prefix="openai"): """ Runs all tests using the provided OpenAI client and saves the output to JSON files with the given prefix. """ # Create the directory if it doesn't exist os.makedirs('openai_testing', exist_ok=True) # Default values in case tests fail global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = None DEMO_VECTOR_STORE_ID2 = None def test_idempotent_vector_store_creation(): """ Test that creating a vector store with the same name is idempotent. """ log_and_print(Fore.BLUE, "Starting vector store creation test...") try: vector_store = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Attempt to create the same vector store again vector_store2 = client.vector_stores.create( name=DEMO_VECTOR_STORE_NAME, ) # Check instead of assert if vector_store2.id != vector_store.id: log_and_print(Fore.YELLOW, f"FAILED IDEMPOTENCY: the same VectorStore name for {prefix.upper()} does not return the same ID", level=logging.WARNING) else: log_and_print(Fore.GREEN, f"PASSED IDEMPOTENCY: f{vector_store2.id} == {vector_store.id} the same VectorStore name for {prefix.upper()} returns the same ID") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.create = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_create.json', 'w') as f: json.dump(vector_store_data, f, indent=2) global DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 DEMO_VECTOR_STORE_ID = vector_store.id DEMO_VECTOR_STORE_ID2 = vector_store2.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 except Exception as e: log_and_print(Fore.RED, f"Idempotent vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Create a fallback vector store ID if needed if 'vector_store' in locals() and vector_store: DEMO_VECTOR_STORE_ID = vector_store.id return DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 def test_vector_store_list(): """ Test listing vector stores. """ log_and_print(Fore.BLUE, "Starting vector store list test...") try: vector_stores = client.vector_stores.list() # Check instead of assert if not isinstance(vector_stores, pagination.SyncCursorPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of vector stores, got {type(vector_stores)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Vector store list test passed!") vector_stores_data = vector_stores.to_dict() log_and_print(Fore.WHITE, f"vector_stores.list = {json.dumps(vector_stores_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_list.json', 'w') as f: json.dump(vector_stores_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Vector store list test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_retrieve_vector_store(): """ Test retrieving a specific vector store. """ log_and_print(Fore.BLUE, "Starting retrieve vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping retrieve vector store test - no vector store ID available", level=logging.WARNING) return try: vector_store = client.vector_stores.retrieve( vector_store_id=DEMO_VECTOR_STORE_ID, ) # Check instead of assert if vector_store.id != DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "FAILED: Retrieved vector store ID does not match", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Retrieve vector store test passed!") vector_store_data = vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.retrieve = {json.dumps(vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_retrieve.json', 'w') as f: json.dump(vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Retrieve vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_modify_vector_store(): """ Test modifying a vector store. """ log_and_print(Fore.BLUE, "Starting modify vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping modify vector store test - no vector store ID available", level=logging.WARNING) return try: updated_vector_store = client.vector_stores.update( vector_store_id=DEMO_VECTOR_STORE_ID, name="Updated Support FAQ FJA", ) # Check instead of assert if updated_vector_store.name != "Updated Support FAQ FJA": log_and_print(Fore.YELLOW, "FAILED: Vector store name was not updated correctly", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Modify vector store test passed!") updated_vector_store_data = updated_vector_store.to_dict() log_and_print(Fore.WHITE, f"vector_stores.modify = {json.dumps(updated_vector_store_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_modify.json', 'w') as f: json.dump(updated_vector_store_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Modify vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_delete_vector_store(): """ Test deleting a vector store. """ log_and_print(Fore.BLUE, "Starting delete vector store test...") if not DEMO_VECTOR_STORE_ID2: log_and_print(Fore.YELLOW, "Skipping delete vector store test - no second vector store ID available", level=logging.WARNING) return try: response = client.vector_stores.delete( vector_store_id=DEMO_VECTOR_STORE_ID2, ) log_and_print(Fore.GREEN, "Delete vector store test passed!") response_data = response.to_dict() log_and_print(Fore.WHITE, f"Vector store delete response = {json.dumps(response_data, indent=2)}") with open(f'openai_testing/{prefix}_vector_store_delete.json', 'w') as f: json.dump(response_data, f, indent=2) except Exception as e: log_and_print(Fore.RED, f"Delete vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_create_vector_store_file(): log_and_print(Fore.BLUE, "Starting create vector store file test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping create vector store file test - no vector store ID available", level=logging.WARNING) return try: # create jsonl of files as an example with open("mydata.jsonl", "w") as f: f.write('{"text": "What is the return policy?", "metadata": {"category": "support"}}\n') f.write('{"text": "How do I reset my password?", "metadata": {"category": "support"}}\n') f.write('{"text": "Where can I find my order history?", "metadata": {"category": "support"}}\n') f.write('{"text": "What are the shipping options?", "metadata": {"category": "support"}}\n') f.write('{"text": "What is your favorite banana?", "metadata": {"category": "support"}}\n') # Create a simple text file if my_data_small.txt doesn't exist if not os.path.exists("my_data_small.txt"): with open("my_data_small.txt", "w") as f: f.write("This is a test file for vector store testing.\n") created_file = client.files.create( file=open("my_data_small.txt", "rb"), purpose="assistants", ) created_file_data = created_file.to_dict() log_and_print(Fore.WHITE, f"Created file {json.dumps(created_file_data, indent=2)}") with open(f'openai_testing/{prefix}_file_create.json', 'w') as f: json.dump(created_file_data, f, indent=2) retrieved_files = client.files.retrieve(created_file.id) retrieved_files_data = retrieved_files.to_dict() log_and_print(Fore.WHITE, f"Retrieved file {json.dumps(retrieved_files_data, indent=2)}") with open(f'openai_testing/{prefix}_file_retrieve.json', 'w') as f: json.dump(retrieved_files_data, f, indent=2) vector_store_file = client.vector_stores.files.create( vector_store_id=DEMO_VECTOR_STORE_ID, file_id=created_file.id, ) log_and_print(Fore.GREEN, "Create vector store file test passed!") except Exception as e: log_and_print(Fore.RED, f"Create vector store file test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) def test_search_vector_store(): """ Test searching a vector store. """ log_and_print(Fore.BLUE, "Starting search vector store test...") if not DEMO_VECTOR_STORE_ID: log_and_print(Fore.YELLOW, "Skipping search vector store test - no vector store ID available", level=logging.WARNING) return try: query = "What is the banana policy?" search_results = client.vector_stores.search( vector_store_id=DEMO_VECTOR_STORE_ID, query=query, max_num_results=10, ranking_options={ 'ranker': 'default-2024-11-15', 'score_threshold': 0.0, }, rewrite_query=False, ) # Check instead of assert if not isinstance(search_results, pagination.SyncPage): log_and_print(Fore.YELLOW, f"FAILED: Expected a list of search results, got {type(search_results)}", level=logging.WARNING) else: log_and_print(Fore.GREEN, "Search vector store test passed!") search_results_dict = search_results.to_dict() log_and_print(Fore.WHITE, f"Search results = {search_results_dict}") with open(f'openai_testing/{prefix}_vector_store_search.json', 'w') as f: json.dump(search_results_dict, f, indent=2) log_and_print(Fore.WHITE, f"vector_stores.search = {search_results.to_json()}") except Exception as e: log_and_print(Fore.RED, f"Search vector store test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) # Run all tests in sequence, even if some fail test_results = [] try: result = test_idempotent_vector_store_creation() if result and len(result) == 2: DEMO_VECTOR_STORE_ID, DEMO_VECTOR_STORE_ID2 = result test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"Vector store creation test failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) for test_func in [ test_vector_store_list, test_retrieve_vector_store, test_modify_vector_store, test_delete_vector_store, test_create_vector_store_file, test_search_vector_store ]: try: test_func() test_results.append(True) except Exception as e: log_and_print(Fore.RED, f"{test_func.__name__} failed: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) test_results.append(False) if all(test_results): log_and_print(Fore.GREEN, f"All {prefix} tests completed successfully!") else: failed_count = test_results.count(False) log_and_print(Fore.YELLOW, f"{failed_count} {prefix} test(s) failed, but script completed.") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run OpenAI and/or LlamaStack tests.") parser.add_argument( "--provider", type=str, default="llama", choices=["openai", "llama", "both"], help="Specify which environment to test: openai, llama, or both. Default is both.", ) args = parser.parse_args() try: if args.provider in ("openai", "both"): openai_client = OpenAI() run_tests(openai_client, prefix="openai") if args.provider in ("llama", "both"): llama_client = OpenAI(base_url="http://localhost:8321/v1/openai/v1", api_key="none") run_tests(llama_client, prefix="llama") log_and_print(Fore.GREEN, "All tests completed!") except Exception as e: log_and_print(Fore.RED, f"Tests failed to complete: {e}", level=logging.ERROR) logging.error(traceback.format_exc()) ``` </details> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-07-15 11:24:41 -04:00
Mark Campbell	618ccea090	feat: add input validation for search mode of rag query config (#2275) ... # What does this PR do? Adds input validation for mode in RagQueryConfig This will prevent users from inputting search modes other than `vector` and `keyword` for the time being with `hybrid` to follow when that functionality is implemented. ## 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.*] ``` # Check out this PR and enter the LS directory uv sync --extra dev ``` Run the quickstart [example](https://llama-stack.readthedocs.io/en/latest/getting_started/#step-3-run-the-demo) Alter the Agent to include a query_config ``` agent = Agent( client, model=model_id, instructions="You are a helpful assistant", tools=[ { "name": "builtin::rag/knowledge_search", "args": { "vector_db_ids": [vector_db_id], "query_config": { "mode": "i-am-not-vector", # Test for non valid search mode "max_chunks": 6 } }, } ], ) ``` Ensure you get the following error: ``` 400: {'errors': [{'loc': ['mode'], 'msg': "Value error, mode must be either 'vector' or 'keyword' if supported by the vector_io provider", 'type': 'value_error'}]} ``` ## Running unit tests ``` uv sync --extra dev uv run pytest tests/unit/rag/test_rag_query.py -v ``` [//]: # (## Documentation)	2025-07-14 09:11:34 -04:00
Mustafa Elbehery	cd0ad21111	chore(api): add mypy coverage to apis (#2648) ... # What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> This PR adds static type coverage to `llama-stack/apis` Part of https://github.com/meta-llama/llama-stack/issues/2647 <!-- 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: Mustafa Elbehery <melbeher@redhat.com>	2025-07-09 12:55:16 +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
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
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
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
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
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
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
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
Ben Browning	f394c7f2d9	feat: Add missing Vector Store Files API surface (#2468) ... # What does this PR do? This adds the ability to list, retrieve, update, and delete Vector Store Files. It implements these new APIs for the faiss and sqlite-vec providers, since those are the two that also have the rest of the vector store files implementation. Closes #2445 ## Test Plan ### test_openai_vector_stores Integration Tests There are a number of new integration tests added, which I ran for each provider as outlined below. faiss (from ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` sqlite-vec (from starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io/test_openai_vector_stores.py \ --embedding-model=all-MiniLM-L6-v2 ``` ### file_search verification tests I also ensured the file_search verification tests continue to work, both for faiss and sqlite-vec. faiss (ollama distro): ``` INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run llama_stack/templates/ollama/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` sqlite-vec (starter distro): ``` llama stack run llama_stack/templates/starter/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=together/meta-llama/Llama-3.2-3B-Instruct-Turbo ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-19 11:08:24 -04:00
Charlie Doern	d12f195f56	feat: drop python 3.10 support (#2469) ... # What does this PR do? dropped python3.10, updated pyproject and dependencies, and also removed some blocks of code with special handling for enum.StrEnum Closes #2458 Signed-off-by: Charlie Doern <cdoern@redhat.com>	2025-06-19 12:07:14 +05:30
ehhuang	db2cd9e8f3	feat: support filters in file search (#2472) ... # What does this PR do? Move to use vector_stores.search for file search tool in Responses, which supports filters. closes #2435 ## Test Plan Added e2e test with fitlers. myenv ❯ llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search and filters' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct	2025-06-18 21:50:55 -07:00
Rohan Awhad	436c7aa751	feat: Add url field to PaginatedResponse and populate it using route … (#2419) ... …path # What does this PR do? Closes #1847 Changes: - llama_stack/apis/common/responses.py: adds optional `url` field to PaginatedResponse - llama_stack/distribution/server/server.py: automatically populate the URL field with route path ## Test Plan - Built and ran llama stack server using the following cmds: ```bash export INFERENCE_MODEL=llama3.1:8b llama stack build --run --template ollama --image-type container llama stack run llama_stack/templates/ollama/run.yaml ``` - Ran `curl` to test if we are seeing the `url` param in response: ```bash curl -X 'GET' \ 'http://localhost:8321/v1/agents' \ -H 'accept: application/json' ``` - Expected and Received Output: `{"data":[],"has_more":false,"url":"/v1/agents"}` --------- Co-authored-by: Rohan Awhad <rawhad@redhat.com>	2025-06-16 11:19:48 +02:00
Hardik Shah	985d0b156c	feat: Add suffix to openai_completions (#2449) ... For code completion apps need "fill in the middle" capabilities. Added option of `suffix` to `openai_completion` to enable this. Updated ollama provider to showcase the same. ### Test Plan ``` pytest -sv --stack-config="inference=ollama" tests/integration/inference/test_openai_completion.py --text-model qwen2.5-coder:1.5b -k test_openai_completion_non_streaming_suffix ``` ### OpenAI Sample script ``` from openai import OpenAI client = OpenAI(base_url="http://localhost:8321/v1/openai/v1") response = client.completions.create( model="qwen2.5-coder:1.5b", prompt="The capital of ", suffix="is Paris.", max_tokens=10, ) print(response.choices[0].text) ``` ### Output ``` France is ____. To answer this question, we ```	2025-06-13 16:06:06 -07:00
Varsha	2e8054bede	feat: Implement hybrid search in SQLite-vec (#2312) ... # What does this PR do? Add support for hybrid search mode in SQLite-vec provider, which combines keyword and vector search for better results. The implementation: - Adds hybrid search mode as a new option alongside vector and keyword search - Implements query_hybrid method in SQLiteVecIndex that: - First performs keyword search to get candidate matches - Then applies vector similarity search on those candidates - Updates documentation to reflect the new search mode This change improves search quality by leveraging both semantic similarity and keyword matching, while maintaining backward compatibility with existing vector and keyword search modes. ## Test Plan ``` pytest tests/unit/providers/vector_io/test_sqlite_vec.py -v -s --tb=short /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:217: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =============================================================================================== test session starts =============================================================================================== platform darwin -- Python 3.10.16, pytest-8.3.5, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.6-arm64-arm-64bit', 'Packages': {'pytest': '8.3.5', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'asyncio': '0.26.0', 'nbval': '0.11.0', 'cov': '6.1.1'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, anyio-4.8.0, asyncio-0.26.0, nbval-0.11.0, cov-6.1.1 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 10 items tests/unit/providers/vector_io/test_sqlite_vec.py::test_add_chunks PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_full_text_search_k_greater_than_results PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_no_keyword_matches PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_score_threshold PASSED tests/unit/providers/vector_io/test_sqlite_vec.py::test_query_chunks_hybrid_different_embedding PASSED ``` --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-06-13 15:54:06 -04:00
Ben Browning	941f505eb0	feat: File search tool for Responses API (#2426) ... # What does this PR do? This is an initial working prototype of wiring up the `file_search` builtin tool for the Responses API to our existing rag knowledge search tool. This is me seeing what I could pull together on top of the bits we already have merged. This may not be the ideal way to implement this, and things like how I shuffle the vector store ids from the original response API tool request to the actual tool execution feel a bit hacky (grep for `tool_kwargs["vector_db_ids"]` in `_execute_tool_call` to see what I mean). ## Test Plan I stubbed in some new tests to exercise this using text and pdf documents. Note that this is currently under tests/verification only because it sometimes flakes with tool calling of the small Llama-3.2-3B model we run in CI (and that I use as an example below). We'd want to make the test a bit more robust in some way if we moved this over to tests/integration and ran it in CI. ### OpenAI SaaS (to verify test correctness) ``` pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=https://api.openai.com/v1 \ --model=gpt-4o ``` ### Fireworks with faiss vector store ``` llama stack run llama_stack/templates/fireworks/run.yaml pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.3-70B-Instruct ``` ### Ollama with faiss vector store This sometimes flakes on Ollama because the quantized small model doesn't always choose to call the tool to answer the user's question. But, it often works. ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" pytest -sv tests/verifications/openai_api/test_responses.py \ -k'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=meta-llama/Llama-3.2-3B-Instruct ``` ### OpenAI provider with sqlite-vec vector store ``` llama stack run ./llama_stack/templates/starter/run.yaml --image-type venv pytest -sv tests/verifications/openai_api/test_responses.py \ -k 'file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model=openai/gpt-4o-mini ``` ### Ensure existing vector store integration tests still pass ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" LLAMA_STACK_CONFIG=http://localhost:8321 \ pytest -sv tests/integration/vector_io \ --text-model "meta-llama/Llama-3.2-3B-Instruct" \ --embedding-model=all-MiniLM-L6-v2 ``` --------- Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-13 14:32:48 -04:00
Hardik Shah	0bc1747ed8	feat: update search for vector_stores (#2441) ... Updated the `search` functionality return response to match openai. ## Test Plan ``` pytest -sv --stack-config=http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-12 15:34:22 -07:00
Hardik Shah	de37a04c3e	fix: set appropriate defaults for params (#2434) ... Setting defaults to be `| None` else they get marked as required params in open-api spec.	2025-06-11 17:30:34 -07:00
Hardik Shah	d55100d9b7	feat: OpenAIVectorIOMixin for vector_stores common logic (#2427) ... Extracts common OpenAI vector-store code into its own mixin so that all providers can share the same core logic. This also makes it easy for Llama Stack to support both vector-stores and Llama Stack APIs in the interim so that both share the same underlying vector-dbs. Each provider contains storage specific logic to `create / edit / delete / list` vector dbs while the plumbing logic is standardized in the common code. Ensured that this works well with both faiss and sqllite-vec. ### Test Plan ``` llama stack run starter pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-11 15:40:57 -07:00
Hardik Shah	5ac43268e8	feat: Add OpenAI compat /v1/vector_store APIs (#2423) ... Adding OpenAI compat `/v1/vector-store` apis. This PR implements the `faiss` provider with followup PRs coming up for other providers. Added routes to create, update, delete, list vector stores. Also added route to search a vector store Inserting into vector stores is missing and will be a follow up diff. ### Test Plan - Added new integration test for testing the faiss provider ``` pytest -sv --stack-config http://localhost:8321 tests/integration/vector_io/test_openai_vector_stores.py --embedding-model all-MiniLM-L6-v2 ```	2025-06-10 13:07:39 -07:00
Ashwin Bharambe	ed69c1b3cc	feat(responses): add more streaming response types (#2375)	2025-06-03 15:48:41 -07:00
Ben Browning	8bee2954be	feat: Structured output for Responses API (#2324) ... # What does this PR do? This adds the missing `text` parameter to the Responses API that is how users control structured outputs. All we do with that parameter is map it to the corresponding chat completion response_format. ## Test Plan The new unit tests exercise the various permutations allowed for this property, while a couple of new verification tests actually use it for real to verify the model outputs are following the format as expected. Unit tests: `python -m pytest -s -v tests/unit/providers/agents/meta_reference/test_openai_responses.py` Verification tests: ``` llama stack run llama_stack/templates/together/run.yaml pytest -s -vv 'tests/verifications/openai_api/test_responses.py' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Note that the verification tests can only be run with a real Llama Stack server (as opposed to using the library client via `--provider=stack:together`) because the Llama Stack python client is not yet updated to accept this text field. Signed-off-by: Ben Browning <bbrownin@redhat.com>	2025-06-03 14:43:00 -07:00
Ashwin Bharambe	dbe4e84aca	feat(responses): implement full multi-turn support (#2295) ... I think the implementation needs more simplification. Spent way too much time trying to get the tests pass with models not co-operating :( Finally had to switch claude-sonnet to get things to pass reliably. ### Test Plan ``` export TAVILY_SEARCH_API_KEY=... export OPENAI_API_KEY=... uv run pytest -p no:warnings \ -s -v tests/verifications/openai_api/test_responses.py \ --provider=stack:starter \ --model openai/gpt-4o ```	2025-06-02 15:35:49 -07:00
ehhuang	31a3ae60f4	feat: openai files api (#2321) ... # What does this PR do? * Adds the OpenAI compatible Files API * Modified doc gen script to support multipart parameter ## Test Plan --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/2321). * #2330 * __->__ #2321	2025-06-02 11:45:53 -07:00
Hardik Shah	b21050935e	feat: New OpenAI compat embeddings API (#2314) ... # What does this PR do? Adds a new endpoint that is compatible with OpenAI for embeddings api. `/openai/v1/embeddings` Added providers for OpenAI, LiteLLM and SentenceTransformer. ## Test Plan ``` LLAMA_STACK_CONFIG=http://localhost:8321 pytest -sv tests/integration/inference/test_openai_embeddings.py --embedding-model all-MiniLM-L6-v2,text-embedding-3-small,gemini/text-embedding-004 ```	2025-05-31 22:11:47 -07:00
Francisco Arceo	f328436831	feat: Enable ingestion of precomputed embeddings (#2317)	2025-05-31 04:03:37 -06:00
Ashwin Bharambe	5cdb29758a	feat(responses): add output_text delta events to responses (#2265) ... This adds initial streaming support to the Responses API. This PR makes sure that the _first_ inference call made to chat completions streams out. There's more to be done: - tool call output tokens need to stream out when possible - we need to loop through multiple rounds of inference and they all need to stream out. ## Test Plan Added a test. Executed as: ``` FIREWORKS_API_KEY=... \ pytest -s -v 'tests/verifications/openai_api/test_responses.py' \ --provider=stack:fireworks --model meta-llama/Llama-4-Scout-17B-16E-Instruct ``` Then, started a llama stack fireworks distro and tested against it like this: ``` OPENAI_API_KEY=blah \ pytest -s -v 'tests/verifications/openai_api/test_responses.py' \ --base-url http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-4-Scout-17B-16E-Instruct ```	2025-05-27 13:07:14 -07:00
Sébastien Han	7710b2f43b	chore: removed unused class (#2268) ... Signed-off-by: Sébastien Han <seb@redhat.com>	2025-05-26 08:41:37 -07:00
Ashwin Bharambe	ce33d02443	fix(tools): do not index tools, only index toolgroups (#2261) ... When registering a MCP endpoint, we cannot list tools (like we used to) since the MCP endpoint may be behind an auth wall. Registration can happen much sooner (via run.yaml). Instead, we do listing only when the _user_ actually calls listing. Furthermore, we cache the list in-memory in the server. Currently, the cache is not invalidated -- we may want to periodically re-list for MCP servers. Note that they must call `list_tools` before calling `invoke_tool` -- we use this critically. This will enable us to list MCP servers in run.yaml ## Test Plan Existing tests, updated tests accordingly.	2025-05-25 13:27:52 -07:00
Ashwin Bharambe	3faf1e4a79	feat: enable MCP execution in Responses impl (#2240) ... ## Test Plan ``` pytest -s -v 'tests/verifications/openai_api/test_responses.py' \ --provider=stack:together --model meta-llama/Llama-4-Scout-17B-16E-Instruct ```	2025-05-24 14:20:42 -07:00
ehhuang	15b0a67555	feat: add responses input items api (#2239) ... # What does this PR do? TSIA ## Test Plan added integration and unit tests	2025-05-24 07:05:53 -07:00
ehhuang	5844c2da68	feat: add list responses API (#2233) ... # What does this PR do? This is not part of the official OpenAI API, but we'll use this for the logs UI. In order to support more filtering options, I'm adopting the newly introduced sql store in in place of the kv store. ## Test Plan Added integration/unit tests.	2025-05-23 13:16:48 -07:00
Ashwin Bharambe	558d109ab7	fix: signature change to match OpenAI SDK (#2237)	2025-05-23 10:59:30 -07:00
Ashwin Bharambe	51945f1e57	feat: accept MCP authorization headers for MCP toolgroups (#2230) ... The most interesting MCP servers are those with an authorization wall in front of them. This PR uses the existing `provider_data` mechanism of passing provider API keys for passing MCP access tokens (in fact, arbitrary headers in the style of the OpenAI Responses API) from the client through to the MCP server. ``` class MCPProviderDataValidator(BaseModel): # mcp_endpoint => list of headers to send mcp_headers: dict[str, list[str]] | None = None ``` Note how we must stuff the headers for all MCP endpoints into a single "MCPProviderDataValidator". Unlike existing providers (e.g., Together and Fireworks for inference) where we could name the provider api keys clearly (`together_api_key`, `fireworks_api_key`), we cannot name these keys for MCP. We have a single generic MCP provider which can serve multiple "toolgroups". So we use a dict to combine all the headers for all MCP endpoints you may want to use in an agentic call. ## Test Plan See the added integration test for usage.	2025-05-23 08:52:18 -07:00
Ashwin Bharambe	d8c6ab9bfc	feat: add MCP tool signature to Responses API (#2232)	2025-05-22 16:43:08 -07:00
Varsha	e92301f2d7	feat(sqlite-vec): enable keyword search for sqlite-vec (#1439) ... # What does this PR do? This PR introduces support for keyword based FTS5 search with BM25 relevance scoring. It makes changes to the existing EmbeddingIndex base class in order to support a search_mode and query_str parameter, that can be used for keyword based search implementations. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan run ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto ``` Output: ``` pytest llama_stack/providers/tests/vector_io/test_sqlite_vec.py -v -s --tb=short --disable-warnings --asyncio-mode=auto /Users/vnarsing/miniconda3/envs/stack-client/lib/python3.10/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) ====================================================== test session starts ======================================================= platform darwin -- Python 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /Users/vnarsing/miniconda3/envs/stack-client/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.16', 'Platform': 'macOS-14.7.4-arm64-arm-64bit', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'html': '4.1.1', 'metadata': '3.1.1', 'asyncio': '0.25.3', 'anyio': '4.8.0'}} rootdir: /Users/vnarsing/go/src/github/meta-llama/llama-stack configfile: pyproject.toml plugins: html-4.1.1, metadata-3.1.1, asyncio-0.25.3, anyio-4.8.0 asyncio: mode=auto, asyncio_default_fixture_loop_scope=None collected 7 items llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_add_chunks PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_vector PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_query_chunks_fts PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_chunk_id_conflict PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_register_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_unregister_vector_db PASSED llama_stack/providers/tests/vector_io/test_sqlite_vec.py::test_generate_chunk_id PASSED ``` For reference, with the implementation, the fts table looks like below: ``` Chunk ID: 9fbc39ce-c729-64a2-260f-c5ec9bb2a33e, Content: Sentence 0 from document 0 Chunk ID: 94062914-3e23-44cf-1e50-9e25821ba882, Content: Sentence 1 from document 0 Chunk ID: e6cfd559-4641-33ba-6ce1-7038226495eb, Content: Sentence 2 from document 0 Chunk ID: 1383af9b-f1f0-f417-4de5-65fe9456cc20, Content: Sentence 3 from document 0 Chunk ID: 2db19b1a-de14-353b-f4e1-085e8463361c, Content: Sentence 4 from document 0 Chunk ID: 9faf986a-f028-7714-068a-1c795e8f2598, Content: Sentence 5 from document 0 Chunk ID: ef593ead-5a4a-392f-7ad8-471a50f033e8, Content: Sentence 6 from document 0 Chunk ID: e161950f-021f-7300-4d05-3166738b94cf, Content: Sentence 7 from document 0 Chunk ID: 90610fc4-67c1-e740-f043-709c5978867a, Content: Sentence 8 from document 0 Chunk ID: 97712879-6fff-98ad-0558-e9f42e6b81d3, Content: Sentence 9 from document 0 Chunk ID: aea70411-51df-61ba-d2f0-cb2b5972c210, Content: Sentence 0 from document 1 Chunk ID: b678a463-7b84-92b8-abb2-27e9a1977e3c, Content: Sentence 1 from document 1 Chunk ID: 27bd63da-909c-1606-a109-75bdb9479882, Content: Sentence 2 from document 1 Chunk ID: a2ad49ad-f9be-5372-e0c7-7b0221d0b53e, Content: Sentence 3 from document 1 Chunk ID: cac53bcd-1965-082a-c0f4-ceee7323fc70, Content: Sentence 4 from document 1 ``` Query results: Result 1: Sentence 5 from document 0 Result 2: Sentence 5 from document 1 Result 3: Sentence 5 from document 2 [//]: # (## Documentation) --------- Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>	2025-05-21 15:24:24 -04:00
Derek Higgins	3339844fda	feat: Add "instructions" support to responses API (#2205) ... # What does this PR do? Add support for "instructions" to the responses API. Instructions provide a way to swap out system (or developer) messages in new responses. ## Test Plan unit tests added Signed-off-by: Derek Higgins <derekh@redhat.com>	2025-05-20 09:52:10 -07:00
ehhuang	047303e339	feat: introduce APIs for retrieving chat completion requests (#2145) ... # What does this PR do? This PR introduces APIs to retrieve past chat completion requests, which will be used in the LS UI. Our current `Telemetry` is ill-suited for this purpose as it's untyped so we'd need to filter by obscure attribute names, making it brittle. Since these APIs are 'provided by stack' and don't need to be implemented by inference providers, we introduce a new InferenceProvider class, containing the existing inference protocol, which is implemented by inference providers. The APIs are OpenAI-compliant, with an additional `input_messages` field. ## Test Plan This PR just adds the API and marks them provided_by_stack. S tart stack server -> doesn't crash	2025-05-18 21:43:19 -07:00
Sébastien Han	bb5fca9521	chore: more API validators (#2165) ... # What does this PR do? We added: * make sure docstrings are present with 'params' and 'returns' * fail if someone sets 'returns: None' * fix the failing APIs Signed-off-by: Sébastien Han <seb@redhat.com>	2025-05-15 11:22:51 -07:00
Francisco Arceo	8e7ab146f8	feat: Adding support for customizing chunk context in RAG insertion and querying (#2134) ... # What does this PR do? his PR allows users to customize the template used for chunks when inserted into the context. Additionally, this enables metadata injection into the context of an LLM for RAG. This makes a naive and crude assumption that each chunk should include the metadata, this is obviously redundant when multiple chunks are returned from the same document. In order to remove any sort of duplication of chunks, we'd have to make much more significant changes so this is a reasonable first step that unblocks users requesting this enhancement in https://github.com/meta-llama/llama-stack/issues/1767. In the future, this can be extended to support citations. List of Changes: - `llama_stack/apis/tools/rag_tool.py` - Added `chunk_template` field in `RAGQueryConfig`. - Added `field_validator` to validate the `chunk_template` field in `RAGQueryConfig`. - Ensured the `chunk_template` field includes placeholders `{index}` and `{chunk.content}`. - Updated the `query` method to use the `chunk_template` for formatting chunk text content. - `llama_stack/providers/inline/tool_runtime/rag/memory.py` - Modified the `insert` method to pass `doc.metadata` for chunk creation. - Enhanced the `query` method to format results using `chunk_template` and exclude unnecessary metadata fields like `token_count`. - `llama_stack/providers/utils/memory/vector_store.py` - Updated `make_overlapped_chunks` to include metadata serialization and token count for both content and metadata. - Added error handling for metadata serialization issues. - `pyproject.toml` - Added `pydantic.field_validator` as a recognized `classmethod` decorator in the linting configuration. - `tests/integration/tool_runtime/test_rag_tool.py` - Refactored test assertions to separate `assert_valid_chunk_response` and `assert_valid_text_response`. - Added integration tests to validate `chunk_template` functionality with and without metadata inclusion. - Included a test case to ensure `chunk_template` validation errors are raised appropriately. - `tests/unit/rag/test_vector_store.py` - Added unit tests for `make_overlapped_chunks`, verifying chunk creation with overlapping tokens and metadata integrity. - Added tests to handle metadata serialization errors, ensuring proper exception handling. - `docs/_static/llama-stack-spec.html` - Added a new `chunk_template` field of type `string` with a default template for formatting retrieved chunks in RAGQueryConfig. - Updated the `required` fields to include `chunk_template`. - `docs/_static/llama-stack-spec.yaml` - Introduced `chunk_template` field with a default value for RAGQueryConfig. - Updated the required configuration list to include `chunk_template`. - `docs/source/building_applications/rag.md` - Documented the `chunk_template` configuration, explaining how to customize metadata formatting in RAG queries. - Added examples demonstrating the usage of the `chunk_template` field in RAG tool queries. - Highlighted default values for `RAG` agent configurations. # Resolves https://github.com/meta-llama/llama-stack/issues/1767 ## Test Plan Updated both `test_vector_store.py` and `test_rag_tool.py` and tested end-to-end with a script. I also tested the quickstart to enable this and specified this metadata: ```python document = RAGDocument( document_id="document_1", content=source, mime_type="text/html", metadata={"author": "Paul Graham", "title": "How to do great work"}, ) ``` Which produced the output below:  This highlights the usefulness of the additional metadata. Notice how the metadata is redundant for different chunks of the same document. I think we can update that in a subsequent PR. # Documentation I've added a brief comment about this in the documentation to outline this to users and updated the API documentation. --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>	2025-05-14 21:56:20 -04:00
Ben Browning	8e316c9b1e	feat: function tools in OpenAI Responses (#2094) ... # What does this PR do? This is a combination of what was previously 3 separate PRs - #2069, #2075, and #2083. It turns out all 3 of those are needed to land a working function calling Responses implementation. The web search builtin tool was already working, but this wires in support for custom function calling. I ended up combining all three into one PR because they all had lots of merge conflicts, both with each other but also with #1806 that just landed. And, because landing any of them individually would have only left a partially working implementation merged. The new things added here are: * Storing of input items from previous responses and restoring of those input items when adding previous responses to the conversation state * Handling of multiple input item messages roles, not just "user" messages. * Support for custom tools passed into the Responses API to enable function calling outside of just the builtin websearch tool. Closes #2074 Closes #2080 ## Test Plan ### Unit Tests Several new unit tests were added, and they all pass. Ran via: ``` python -m pytest -s -v tests/unit/providers/agents/meta_reference/test_openai_responses.py ``` ### Responses API Verification Tests I ran our verification run.yaml against multiple providers to ensure we were getting a decent pass rate. Specifically, I ensured the new custom tool verification test passed across multiple providers and that the multi-turn examples passed across at least some of the providers (some providers struggle with the multi-turn workflows still). Running the stack setup for verification testing: ``` llama stack run --image-type venv tests/verifications/openai-api-verification-run.yaml ``` Together, passing 100% as an example: ``` pytest -s -v 'tests/verifications/openai_api/test_responses.py' --provider=together-llama-stack ``` ## Documentation We will need to start documenting the OpenAI APIs, but for now the Responses stuff is still rapidly evolving so delaying that. --------- Signed-off-by: Derek Higgins <derekh@redhat.com> Signed-off-by: Ben Browning <bbrownin@redhat.com> Co-authored-by: Derek Higgins <derekh@redhat.com> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-05-13 11:29:15 -07:00
Krzysztof Malczuk	675f34e79d	fix: Syntax error with missing stubs at the end of some function calls (#2116) ... # What does this PR do? This PR adds stubs to the end of functions create_agent_turn, create_openai_response and job_result. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan Ran provided unit tests [//]: # (## Documentation)	2025-05-12 17:05:40 +02:00
Dinesh Yeduguru	fe5f5e530c	feat: add metrics query API (#1394) ... # What does this PR do? Adds the API to query metrics from telemetry. ## Test Plan llama stack run ~/.llama/distributions/fireworks/fireworks-run.yaml --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>	2025-05-07 10:11:26 -07:00
Sébastien Han	c91e3552a3	feat: implementation for agent/session list and describe (#1606) ... Create a new agent: ``` curl --request POST \ --url http://localhost:8321/v1/agents \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --data '{ "agent_config": { "sampling_params": { "strategy": { "type": "greedy" }, "max_tokens": 0, "repetition_penalty": 1 }, "input_shields": [ "string" ], "output_shields": [ "string" ], "toolgroups": [ "string" ], "client_tools": [ { "name": "string", "description": "string", "parameters": [ { "name": "string", "parameter_type": "string", "description": "string", "required": true, "default": null } ], "metadata": { "property1": null, "property2": null } } ], "tool_choice": "auto", "tool_prompt_format": "json", "tool_config": { "tool_choice": "auto", "tool_prompt_format": "json", "system_message_behavior": "append" }, "max_infer_iters": 10, "model": "string", "instructions": "string", "enable_session_persistence": false, "response_format": { "type": "json_schema", "json_schema": { "property1": null, "property2": null } } } }' ``` Get agent: ``` curl http://127.0.0.1:8321/v1/agents/9abad4ab-2c77-45f9-9d16-46b79d2bea1f {"agent_id":"9abad4ab-2c77-45f9-9d16-46b79d2bea1f","agent_config":{"sampling_params":{"strategy":{"type":"greedy"},"max_tokens":0,"repetition_penalty":1.0},"input_shields":["string"],"output_shields":["string"],"toolgroups":["string"],"client_tools":[{"name":"string","description":"string","parameters":[{"name":"string","parameter_type":"string","description":"string","required":true,"default":null}],"metadata":{"property1":null,"property2":null}}],"tool_choice":"auto","tool_prompt_format":"json","tool_config":{"tool_choice":"auto","tool_prompt_format":"json","system_message_behavior":"append"},"max_infer_iters":10,"model":"string","instructions":"string","enable_session_persistence":false,"response_format":{"type":"json_schema","json_schema":{"property1":null,"property2":null}}},"created_at":"2025-03-12T16:18:28.369144Z"}% ``` List agents: ``` curl http://127.0.0.1:8321/v1/agents|jq % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 1680 100 1680 0 0 498k 0 --:--:-- --:--:-- --:--:-- 546k { "data": [ { "agent_id": "9abad4ab-2c77-45f9-9d16-46b79d2bea1f", "agent_config": { "sampling_params": { "strategy": { "type": "greedy" }, "max_tokens": 0, "repetition_penalty": 1.0 }, "input_shields": [ "string" ], "output_shields": [ "string" ], "toolgroups": [ "string" ], "client_tools": [ { "name": "string", "description": "string", "parameters": [ { "name": "string", "parameter_type": "string", "description": "string", "required": true, "default": null } ], "metadata": { "property1": null, "property2": null } } ], "tool_choice": "auto", "tool_prompt_format": "json", "tool_config": { "tool_choice": "auto", "tool_prompt_format": "json", "system_message_behavior": "append" }, "max_infer_iters": 10, "model": "string", "instructions": "string", "enable_session_persistence": false, "response_format": { "type": "json_schema", "json_schema": { "property1": null, "property2": null } } }, "created_at": "2025-03-12T16:18:28.369144Z" }, { "agent_id": "a6643aaa-96dd-46db-a405-333dc504b168", "agent_config": { "sampling_params": { "strategy": { "type": "greedy" }, "max_tokens": 0, "repetition_penalty": 1.0 }, "input_shields": [ "string" ], "output_shields": [ "string" ], "toolgroups": [ "string" ], "client_tools": [ { "name": "string", "description": "string", "parameters": [ { "name": "string", "parameter_type": "string", "description": "string", "required": true, "default": null } ], "metadata": { "property1": null, "property2": null } } ], "tool_choice": "auto", "tool_prompt_format": "json", "tool_config": { "tool_choice": "auto", "tool_prompt_format": "json", "system_message_behavior": "append" }, "max_infer_iters": 10, "model": "string", "instructions": "string", "enable_session_persistence": false, "response_format": { "type": "json_schema", "json_schema": { "property1": null, "property2": null } } }, "created_at": "2025-03-12T16:17:12.811273Z" } ] } ``` Create sessions: ``` curl --request POST \ --url http://localhost:8321/v1/agents/{agent_id}/session \ --header 'Accept: application/json' \ --header 'Content-Type: application/json' \ --data '{ "session_name": "string" }' ``` List sessions: ``` curl http://127.0.0.1:8321/v1/agents/9abad4ab-2c77-45f9-9d16-46b79d2bea1f/sessions|jq % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 263 100 263 0 0 90099 0 --:--:-- --:--:-- --:--:-- 128k [ { "session_id": "2b15c4fc-e348-46c1-ae32-f6d424441ac1", "session_name": "string", "turns": [], "started_at": "2025-03-12T17:19:17.784328" }, { "session_id": "9432472d-d483-4b73-b682-7b1d35d64111", "session_name": "string", "turns": [], "started_at": "2025-03-12T17:19:19.885834" } ] ``` Signed-off-by: Sébastien Han <seb@redhat.com>	2025-05-07 14:49:23 +02:00
Sébastien Han	1a529705da	chore: more mypy fixes (#2029) ... # What does this PR do? Mainly tried to cover the entire llama_stack/apis directory, we only have one left. Some excludes were just noop. Signed-off-by: Sébastien Han <seb@redhat.com>	2025-05-06 09:52:31 -07:00