14 commits

Author	SHA1	Message	Date
Bill Murdock	5d711d4bcb	fix: Update watsonx.ai provider to use LiteLLM mixin and list all models (#3674) ... # What does this PR do? - The watsonx.ai provider now uses the LiteLLM mixin instead of using IBM's library, which does not seem to be working (see #3165 for context). - The watsonx.ai provider now lists all the models available by calling the watsonx.ai server instead of having a hard coded list of known models. (That list gets out of date quickly) - An edge case in [llama_stack/core/routers/inference.py](https://github.com/llamastack/llama-stack/pull/3674/files#diff-a34bc966ed9befd9f13d4883c23705dff49be0ad6211c850438cdda6113f3455) is addressed that was causing my manual tests to fail. - Fixes `b64_encode_openai_embeddings_response` which was trying to enumerate over a dictionary and then reference elements of the dictionary using .field instead of ["field"]. That method is called by the LiteLLM mixin for embedding models, so it is needed to get the watsonx.ai embedding models to work. - A unit test along the lines of the one in #3348 is added. A more comprehensive plan for automatically testing the end-to-end functionality for inference providers would be a good idea, but is out of scope for this PR. - Updates to the watsonx distribution. Some were in response to the switch to LiteLLM (e.g., updating the Python packages needed). Others seem to be things that were already broken that I found along the way (e.g., a reference to a watsonx specific doc template that doesn't seem to exist). Closes #3165 Also it is related to a line-item in #3387 but doesn't really address that goal (because it uses the LiteLLM mixin, not the OpenAI one). I tried the OpenAI one and it doesn't work with watsonx.ai, presumably because the watsonx.ai service is not OpenAI compatible. It works with LiteLLM because LiteLLM has a provider implementation for watsonx.ai. ## Test Plan The test script below goes back and forth between the OpenAI and watsonx providers. The idea is that the OpenAI provider shows how it should work and then the watsonx provider output shows that it is also working with watsonx. Note that the result from the MCP test is not as good (the Llama 3.3 70b model does not choose tools as wisely as gpt-4o), but it is still working and providing a valid response. For more details on setup and the MCP server being used for testing, see [the AI Alliance sample notebook](https://github.com/The-AI-Alliance/llama-stack-examples/blob/main/notebooks/01-responses/) that these examples are drawn from. ```python #!/usr/bin/env python3 import json from llama_stack_client import LlamaStackClient from litellm import completion import http.client def print_response(response): """Print response in a nicely formatted way""" print(f"ID: {response.id}") print(f"Status: {response.status}") print(f"Model: {response.model}") print(f"Created at: {response.created_at}") print(f"Output items: {len(response.output)}") for i, output_item in enumerate(response.output): if len(response.output) > 1: print(f"\n--- Output Item {i+1} ---") print(f"Output type: {output_item.type}") if output_item.type in ("text", "message"): print(f"Response content: {output_item.content[0].text}") elif output_item.type == "file_search_call": print(f" Tool Call ID: {output_item.id}") print(f" Tool Status: {output_item.status}") # 'queries' is a list, so we join it for clean printing print(f" Queries: {', '.join(output_item.queries)}") # Display results if they exist, otherwise note they are empty print(f" Results: {output_item.results if output_item.results else 'None'}") elif output_item.type == "mcp_list_tools": print_mcp_list_tools(output_item) elif output_item.type == "mcp_call": print_mcp_call(output_item) else: print(f"Response content: {output_item.content}") def print_mcp_call(mcp_call): """Print MCP call in a nicely formatted way""" print(f"\n🛠️ MCP Tool Call: {mcp_call.name}") print(f" Server: {mcp_call.server_label}") print(f" ID: {mcp_call.id}") print(f" Arguments: {mcp_call.arguments}") if mcp_call.error: print("Error: {mcp_call.error}") elif mcp_call.output: print("Output:") # Try to format JSON output nicely try: parsed_output = json.loads(mcp_call.output) print(json.dumps(parsed_output, indent=4)) except: # If not valid JSON, print as-is print(f" {mcp_call.output}") else: print(" ⏳ No output yet") def print_mcp_list_tools(mcp_list_tools): """Print MCP list tools in a nicely formatted way""" print(f"\n🔧 MCP Server: {mcp_list_tools.server_label}") print(f" ID: {mcp_list_tools.id}") print(f" Available Tools: {len(mcp_list_tools.tools)}") print("=" * 80) for i, tool in enumerate(mcp_list_tools.tools, 1): print(f"\n{i}. {tool.name}") print(f" Description: {tool.description}") # Parse and display input schema schema = tool.input_schema if schema and 'properties' in schema: properties = schema['properties'] required = schema.get('required', []) print(" Parameters:") for param_name, param_info in properties.items(): param_type = param_info.get('type', 'unknown') param_desc = param_info.get('description', 'No description') required_marker = " (required)" if param_name in required else " (optional)" print(f" • {param_name} ({param_type}){required_marker}") if param_desc: print(f" {param_desc}") if i < len(mcp_list_tools.tools): print("-" * 40) def main(): """Main function to run all the tests""" # Configuration LLAMA_STACK_URL = "http://localhost:8321/" LLAMA_STACK_MODEL_IDS = [ "openai/gpt-3.5-turbo", "openai/gpt-4o", "llama-openai-compat/Llama-3.3-70B-Instruct", "watsonx/meta-llama/llama-3-3-70b-instruct" ] # Using gpt-4o for this demo, but feel free to try one of the others or add more to run.yaml. OPENAI_MODEL_ID = LLAMA_STACK_MODEL_IDS[1] WATSONX_MODEL_ID = LLAMA_STACK_MODEL_IDS[-1] NPS_MCP_URL = "http://localhost:3005/sse/" print("=== Llama Stack Testing Script ===") print(f"Using OpenAI model: {OPENAI_MODEL_ID}") print(f"Using WatsonX model: {WATSONX_MODEL_ID}") print(f"MCP URL: {NPS_MCP_URL}") print() # Initialize client print("Initializing LlamaStackClient...") client = LlamaStackClient(base_url="http://localhost:8321") # Test 1: List models print("\n=== Test 1: List Models ===") try: models = client.models.list() print(f"Found {len(models)} models") except Exception as e: print(f"Error listing models: {e}") raise e # Test 2: Basic chat completion with OpenAI print("\n=== Test 2: Basic Chat Completion (OpenAI) ===") try: chat_completion_response = client.chat.completions.create( model=OPENAI_MODEL_ID, messages=[{"role": "user", "content": "What is the capital of France?"}] ) print("OpenAI Response:") for chunk in chat_completion_response.choices[0].message.content: print(chunk, end="", flush=True) print() except Exception as e: print(f"Error with OpenAI chat completion: {e}") raise e # Test 3: Basic chat completion with WatsonX print("\n=== Test 3: Basic Chat Completion (WatsonX) ===") try: chat_completion_response_wxai = client.chat.completions.create( model=WATSONX_MODEL_ID, messages=[{"role": "user", "content": "What is the capital of France?"}], ) print("WatsonX Response:") for chunk in chat_completion_response_wxai.choices[0].message.content: print(chunk, end="", flush=True) print() except Exception as e: print(f"Error with WatsonX chat completion: {e}") raise e # Test 4: Tool calling with OpenAI print("\n=== Test 4: Tool Calling (OpenAI) ===") tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather for a specific location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g., San Francisco, CA", }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] }, }, "required": ["location"], }, }, } ] messages = [ {"role": "user", "content": "What's the weather like in Boston, MA?"} ] try: print("--- Initial API Call ---") response = client.chat.completions.create( model=OPENAI_MODEL_ID, messages=messages, tools=tools, tool_choice="auto", # "auto" is the default ) print("OpenAI tool calling response received") except Exception as e: print(f"Error with OpenAI tool calling: {e}") raise e # Test 5: Tool calling with WatsonX print("\n=== Test 5: Tool Calling (WatsonX) ===") try: wxai_response = client.chat.completions.create( model=WATSONX_MODEL_ID, messages=messages, tools=tools, tool_choice="auto", # "auto" is the default ) print("WatsonX tool calling response received") except Exception as e: print(f"Error with WatsonX tool calling: {e}") raise e # Test 6: Streaming with WatsonX print("\n=== Test 6: Streaming Response (WatsonX) ===") try: chat_completion_response_wxai_stream = client.chat.completions.create( model=WATSONX_MODEL_ID, messages=[{"role": "user", "content": "What is the capital of France?"}], stream=True ) print("Model response: ", end="") for chunk in chat_completion_response_wxai_stream: # Each 'chunk' is a ChatCompletionChunk object. # We want the content from the 'delta' attribute. if hasattr(chunk, 'choices') and chunk.choices is not None: content = chunk.choices[0].delta.content # The first few chunks might have None content, so we check for it. if content is not None: print(content, end="", flush=True) print() except Exception as e: print(f"Error with streaming: {e}") raise e # Test 7: MCP with OpenAI print("\n=== Test 7: MCP Integration (OpenAI) ===") try: mcp_llama_stack_client_response = client.responses.create( model=OPENAI_MODEL_ID, input="Tell me about some parks in Rhode Island, and let me know if there are any upcoming events at them.", tools=[ { "type": "mcp", "server_url": NPS_MCP_URL, "server_label": "National Parks Service tools", "allowed_tools": ["search_parks", "get_park_events"], } ] ) print_response(mcp_llama_stack_client_response) except Exception as e: print(f"Error with MCP (OpenAI): {e}") raise e # Test 8: MCP with WatsonX print("\n=== Test 8: MCP Integration (WatsonX) ===") try: mcp_llama_stack_client_response = client.responses.create( model=WATSONX_MODEL_ID, input="What is the capital of France?" ) print_response(mcp_llama_stack_client_response) except Exception as e: print(f"Error with MCP (WatsonX): {e}") raise e # Test 9: MCP with Llama 3.3 print("\n=== Test 9: MCP Integration (Llama 3.3) ===") try: mcp_llama_stack_client_response = client.responses.create( model=WATSONX_MODEL_ID, input="Tell me about some parks in Rhode Island, and let me know if there are any upcoming events at them.", tools=[ { "type": "mcp", "server_url": NPS_MCP_URL, "server_label": "National Parks Service tools", "allowed_tools": ["search_parks", "get_park_events"], } ] ) print_response(mcp_llama_stack_client_response) except Exception as e: print(f"Error with MCP (Llama 3.3): {e}") raise e # Test 10: Embeddings print("\n=== Test 10: Embeddings ===") try: conn = http.client.HTTPConnection("localhost:8321") payload = json.dumps({ "model": "watsonx/ibm/granite-embedding-278m-multilingual", "input": "Hello, world!", }) headers = { 'Content-Type': 'application/json', 'Accept': 'application/json' } conn.request("POST", "/v1/openai/v1/embeddings", payload, headers) res = conn.getresponse() data = res.read() print(data.decode("utf-8")) except Exception as e: print(f"Error with Embeddings: {e}") raise e print("\n=== Testing Complete ===") if __name__ == "__main__": main() ``` --------- Signed-off-by: Bill Murdock <bmurdock@redhat.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>	2025-10-08 07:29:43 -04:00
Matthew Farrellee	d23ed26238	chore: turn OpenAIMixin into a pydantic.BaseModel (#3671) ... # What does this PR do? - implement get_api_key instead of relying on LiteLLMOpenAIMixin.get_api_key - remove use of LiteLLMOpenAIMixin - add default initialize/shutdown methods to OpenAIMixin - remove __init__s to allow proper pydantic construction - remove dead code from vllm adapter and associated / duplicate unit tests - update vllm adapter to use openaimixin for model registration - remove ModelRegistryHelper from fireworks & together adapters - remove Inference from nvidia adapter - complete type hints on embedding_model_metadata - allow extra fields on OpenAIMixin, for model_store, __provider_id__, etc - new recordings for ollama - enhance the list models error handling - update cerebras (remove cerebras-cloud-sdk) and anthropic (custom model listing) inference adapters - parametrized test_inference_client_caching - remove cerebras, databricks, fireworks, together from blanket mypy exclude - removed unnecessary litellm deps ## Test Plan ci	2025-10-06 11:33:19 -04:00
Matthew Farrellee	d266c59c2a	chore: remove deprecated inference.chat_completion implementations (#3654) ... # What does this PR do? remove unused chat_completion implementations vllm features ported - - requires max_tokens be set, use config value - set tool_choice to none if no tools provided ## Test Plan ci	2025-10-03 07:55:34 -04:00
Matthew Farrellee	f7c5ef4ec0	chore: remove /v1/inference/completion and implementations (#3622) ... # What does this PR do? the /inference/completion route is gone. this removes the implementations. ## Test Plan ci	2025-10-01 11:36:53 -04:00
Matthew Farrellee	975ead1d6a	chore(api): remove deprecated embeddings impls (#3301) ... # What does this PR do? remove deprecated embeddings implementations	2025-09-29 14:45:09 -04:00
Matthew Farrellee	b67aef2fc4	feat: add static embedding metadata to dynamic model listings for providers using OpenAIMixin (#3547) ... # What does this PR do? - remove auto-download of ollama embedding models - add embedding model metadata to dynamic listing w/ unit test - add support and tests for allowed_models - removed inference provider models.py files where dynamic listing is enabled - store embedding metadata in embedding_model_metadata field on inference providers - make model_entries optional on ModelRegistryHelper and LiteLLMOpenAIMixin - make OpenAIMixin a ModelRegistryHelper - skip base64 embedding test for remote::ollama, always returns floats - only use OpenAI client for ollama model listing - remove unused build_model_entry function - remove unused get_huggingface_repo function ## Test Plan ci w/ new tests	2025-09-25 17:17:00 -04:00
Sébastien Han	65d45c7318	chore: various watsonx fixes (#3428) ... # What does this PR do? use a logger * update the distro to add the Files API otherwise it won't start since it is a dependency of vector * clarify project_id and api_key requirements * disable openai compatible calls since the endpoint returns 404 * disable text_inference structured format tests * fixed openai client initialization ## Test Plan Execute text_inference: ``` WATSONX_API_KEY=... WATSONX_PROJECT_ID=... python -m llama_stack.core.server.server llama_stack/distributions/watsonx/run.yaml LLAMA_STACK_CONFIG=http://localhost:8321 uv run --group test pytest -vvvv -ra --text-model watsonx/meta-llama/llama-3-3-70b-instruct tests/integration/inference/test_text_inference.py ============================================= test session starts ============================================== platform darwin -- Python 3.12.8, pytest-8.4.2, pluggy-1.6.0 -- /Users/leseb/Documents/AI/llama-stack/.venv/bin/python3 cachedir: .pytest_cache metadata: {'Python': '3.12.8', 'Platform': 'macOS-15.6.1-arm64-arm-64bit', 'Packages': {'pytest': '8.4.2', 'pluggy': '1.6.0'}, 'Plugins': {'anyio': '4.9.0', 'html': '4.1.1', 'socket': '0.7.0', 'asyncio': '1.1.0', 'json-report': '1.5.0', 'timeout': '2.4.0', 'metadata': '3.1.1', 'cov': '6.2.1', 'nbval': '0.11.0', 'hydra-core': '1.3.2'}} rootdir: /Users/leseb/Documents/AI/llama-stack configfile: pyproject.toml plugins: anyio-4.9.0, html-4.1.1, socket-0.7.0, asyncio-1.1.0, json-report-1.5.0, timeout-2.4.0, metadata-3.1.1, cov-6.2.1, nbval-0.11.0, hydra-core-1.3.2 asyncio: mode=Mode.AUTO, asyncio_default_fixture_loop_scope=None, asyncio_default_test_loop_scope=function collected 20 items tests/integration/inference/test_text_inference.py::test_text_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:sanity] PASSED [ 5%] tests/integration/inference/test_text_inference.py::test_text_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:sanity] PASSED [ 10%] tests/integration/inference/test_text_inference.py::test_text_completion_stop_sequence[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:stop_sequence] XFAIL [ 15%] tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] XFAIL [ 20%] tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] XFAIL [ 25%] tests/integration/inference/test_text_inference.py::test_text_completion_structured_output[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:structured_output] SKIPPED structured output) [ 30%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:non_streaming_01] PASSED [ 35%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:streaming_01] PASSED [ 40%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 45%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_calling_and_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 50%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_required[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 55%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_tool_choice_none[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling] PASSED [ 60%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_structured_output[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:structured_output] SKIPPEDstructured output) [ 65%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling_tools_absent-True] PASSED [ 70%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:text_then_tool] XFAIL [ 75%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:non_streaming_02] PASSED [ 80%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:streaming_02] PASSED [ 85%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_tool_calling_tools_not_in_request[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_calling_tools_absent-False] PASSED [ 90%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_then_answer] XFAIL [ 95%] tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:array_parameter] XFAIL [100%] =========================================== short test summary info ============================================ SKIPPED [2] tests/integration/inference/test_text_inference.py:49: Model watsonx/meta-llama/llama-3-3-70b-instruct hosted by remote::watsonx doesn't support json_schema structured output XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_stop_sequence[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:stop_sequence] - remote::watsonx doesn't support 'stop' parameter yet XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_non_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] - remote::watsonx doesn't support log probs yet XFAIL tests/integration/inference/test_text_inference.py::test_text_completion_log_probs_streaming[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:completion:log_probs] - remote::watsonx doesn't support log probs yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:text_then_tool] - Not tested for non-llama4 models yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:tool_then_answer] - Not tested for non-llama4 models yet XFAIL tests/integration/inference/test_text_inference.py::test_text_chat_completion_with_multi_turn_tool_calling[txt=watsonx/meta-llama/llama-3-3-70b-instruct-inference:chat_completion:array_parameter] - Not tested for non-llama4 models yet ============================ 12 passed, 2 skipped, 6 xfailed, 14 warnings in 36.88s ============================ ``` --------- Signed-off-by: Sébastien Han <seb@redhat.com>	2025-09-16 13:55:10 +02:00
Cesare Pompeiano	ccaf6aaa51	chore(python-deps): replace ibm_watson_machine_learning with ibm_watsonx_ai (#3302) ... # What does this PR do? This PR updates the Watsonx provider dependencies from `ibm_watson_machine_learning` to `ibm_watsonx_ai`. The old package `ibm_watson_machine_learning` is in **deprecation mode** ([[PyPI link](https://pypi.org/project/ibm-watson-machine-learning/)](https://pypi.org/project/ibm-watson-machine-learning/)) and relies on older versions of dependencies such as `pandas`. Updating to `ibm_watsonx_ai` ensures compatibility with current dependency versions and ongoing support. ## Test Plan I verified the update by running an inference using a model provided by Watsonx. The model ran successfully, confirming that the new dependency works as expected. Co-authored-by: are-ces <cpompeia@redhat.com>	2025-09-03 11:33:35 +02:00
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
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
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
Ihar Hrachyshka	9e6561a1ec	chore: enable pyupgrade fixes (#1806) ... # What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>	2025-05-01 14:23:50 -07:00
Sajikumar JS	6cf6791de1	fix: updated watsonx inference chat apis with new repo changes (#2033) ... # What does this PR do? There are new changes in repo which needs to add some additional functions to the inference which is fixed. Also need one additional params to pass some extra arguments to watsonx.ai [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] [//]: # (## Documentation) --------- Co-authored-by: Sajikumar JS <sajikumar.js@ibm.com>	2025-04-26 10:17:52 -07:00
Sajikumar JS	1bb1d9b2ba	feat: Add watsonx inference adapter (#1895) ... # What does this PR do? IBM watsonx ai added as the inference [#1741 ](https://github.com/meta-llama/llama-stack/issues/1741) [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) --------- Co-authored-by: Sajikumar JS <sajikumar.js@ibm.com>	2025-04-25 11:29:21 -07:00