diff --git a/.github/workflows/test-external-providers.yml b/.github/workflows/test-external-providers.yml index b4deba6..52e3298 100644 --- a/.github/workflows/test-external-providers.yml +++ b/.github/workflows/test-external-providers.yml @@ -72,7 +72,6 @@ jobs: # temporary hack for file writing that should be done by the pip setup script # https://github.com/containers/ramalama-stack/issues/53 mkdir -p ~/.llama/distributions/ramalama/ - cp -r $GITHUB_WORKSPACE/src/ramalama_stack/providers.d/ ~/.llama/ cp $GITHUB_WORKSPACE/src/ramalama_stack/ramalama-run.yaml ~/.llama/distributions/ramalama/ramalama-run.yaml - name: Run 'test-build.sh' diff --git a/.github/workflows/test-lls-integration.yml b/.github/workflows/test-lls-integration.yml index d4b699e..b11d882 100644 --- a/.github/workflows/test-lls-integration.yml +++ b/.github/workflows/test-lls-integration.yml @@ -56,7 +56,6 @@ jobs: # temporary hack for file writing that should be done by the pip setup script # https://github.com/containers/ramalama-stack/issues/53 mkdir -p ~/.llama/distributions/ramalama/ - cp -r $GITHUB_WORKSPACE/src/ramalama_stack/providers.d/ ~/.llama/ cp $GITHUB_WORKSPACE/src/ramalama_stack/ramalama-run.yaml ~/.llama/distributions/ramalama/ramalama-run.yaml - name: Run 'test-build.sh' diff --git a/MANIFEST.in b/MANIFEST.in index 31fadaf..9f32ace 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,2 +1 @@ -recursive-include ramalama_stack/providers.d * include ramalama_stack/ramalama-run.yaml diff --git a/README.md b/README.md index 1b7b422..3380b09 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,6 @@ This will install Llama Stack and RamaLama as well if they are not installed alr > [!WARNING] > The following workaround is currently needed to run this provider - see https://github.com/containers/ramalama-stack/issues/53 for more details > ```bash -> curl --create-dirs --output ~/.llama/providers.d/remote/inference/ramalama.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.5/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml > curl --create-dirs --output ~/.llama/distributions/ramalama/ramalama-run.yaml https://raw.githubusercontent.com/containers/ramalama-stack/refs/tags/v0.2.5/src/ramalama_stack/ramalama-run.yaml > ``` diff --git a/pyproject.toml b/pyproject.toml index 2e2a95d..75d38fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,8 +21,10 @@ dependencies = [ "datasets>=3.6.0", "fastapi>=0.115.12", "httpx>=0.28.1", - "llama-stack==0.2.14", + "llama-stack==0.6.0", + "llama-stack-client==0.6.0", "mcp>=1.9.2", + "milvus-lite>=2.5.1", "numpy>=2.2.6", "openai>=1.82.0", "opentelemetry-exporter-otlp-proto-http>=1.33.1", @@ -31,7 +33,6 @@ dependencies = [ "psutil>=7.0.0", "pydantic>=2.11.5", "pymilvus>=2.5.10", - "ramalama==0.10.1", "requests>=2.32.3", "sentence-transformers>=3.0.0", "six>=1.17.0", @@ -40,6 +41,7 @@ dependencies = [ "trl>=0.18.1", "urllib3>=2.4.0", "uvicorn>=0.34.2", + "setuptools<70", ] [dependency-groups] @@ -62,7 +64,7 @@ package-dir = { "" = "src" } include-package-data = true [tool.setuptools.package-data] -"ramalama_stack" = ["providers.d/**/*", "ramalama-run.yaml"] +"ramalama_stack" = ["ramalama-run.yaml"] [tool.ruff] extend-exclude = ["*.ipynb"] diff --git a/requirements.txt b/requirements.txt index 37a9792..27f516a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,17 +6,19 @@ accelerate==1.7.0 # trl aiohappyeyeballs==2.6.1 # via aiohttp -aiohttp==3.12.7 +aiohttp==3.13.3 # via # fsspec # llama-stack # ramalama-stack -aiosignal==1.3.2 +aiosignal==1.4.0 # via aiohttp aiosqlite==0.21.0 # via # llama-stack # ramalama-stack +annotated-doc==0.0.4 + # via fastapi annotated-types==0.7.0 # via pydantic anyio==4.9.0 @@ -27,8 +29,6 @@ anyio==4.9.0 # openai # sse-starlette # starlette -argcomplete==3.6.2 - # via ramalama asyncpg==0.30.0 # via llama-stack attrs==25.3.0 @@ -46,13 +46,18 @@ certifi==2025.4.26 # via # httpcore # httpx + # oci # requests +cffi==2.0.0 ; platform_python_implementation != 'PyPy' + # via cryptography chardet==5.2.0 # via ramalama-stack charset-normalizer==3.4.2 # via requests chevron==0.14.0 # via autoevals +circuitbreaker==2.1.3 + # via oci click==8.2.1 # via # llama-stack-client @@ -61,15 +66,16 @@ colorama==0.4.6 ; sys_platform == 'win32' # via # click # tqdm +cryptography==45.0.7 + # via + # oci + # oracledb + # pyjwt + # pyopenssl datasets==3.6.0 # via # ramalama-stack # trl -deprecated==1.2.18 - # via - # opentelemetry-api - # opentelemetry-exporter-otlp-proto-http - # opentelemetry-semantic-conventions dill==0.3.8 # via # datasets @@ -78,11 +84,10 @@ distro==1.9.0 # via # llama-stack-client # openai -ecdsa==0.19.1 - # via python-jose -fastapi==0.115.12 +fastapi==0.129.0 # via # llama-stack + # llama-stack-api # ramalama-stack filelock==3.18.0 # via @@ -106,7 +111,7 @@ fsspec==2025.3.0 # torch googleapis-common-protos==1.70.0 # via opentelemetry-exporter-otlp-proto-http -greenlet==3.2.2 ; (python_full_version < '3.14' and platform_machine == 'AMD64') or (python_full_version < '3.14' and platform_machine == 'WIN32') or (python_full_version < '3.14' and platform_machine == 'aarch64') or (python_full_version < '3.14' and platform_machine == 'amd64') or (python_full_version < '3.14' and platform_machine == 'ppc64le') or (python_full_version < '3.14' and platform_machine == 'win32') or (python_full_version < '3.14' and platform_machine == 'x86_64') +greenlet==3.2.2 # via sqlalchemy grpcio==1.67.1 # via pymilvus @@ -115,7 +120,7 @@ h11==0.16.0 # httpcore # llama-stack # uvicorn -hf-xet==1.1.2 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' +hf-xet==1.2.0 ; platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' # via huggingface-hub httpcore==1.0.9 # via httpx @@ -128,11 +133,10 @@ httpx==0.28.1 # ramalama-stack httpx-sse==0.4.0 # via mcp -huggingface-hub==0.32.4 +huggingface-hub==0.36.2 # via # accelerate # datasets - # llama-stack # peft # sentence-transformers # tokenizers @@ -157,24 +161,32 @@ jsonschema==4.24.0 # via # autoevals # llama-stack + # llama-stack-api + # mcp jsonschema-specifications==2025.4.1 # via jsonschema -llama-stack==0.2.14 +llama-stack==0.6.0 # via ramalama-stack -llama-stack-client==0.2.14 +llama-stack-api==0.5.1 # via llama-stack +llama-stack-client==0.6.0 + # via ramalama-stack lxml==5.4.0 # via blobfile markdown-it-py==3.0.0 # via rich markupsafe==3.0.2 # via jinja2 -mcp==1.9.2 - # via ramalama-stack +mcp==1.26.0 + # via + # llama-stack + # ramalama-stack mdurl==0.1.2 # via markdown-it-py -milvus-lite==2.4.12 ; sys_platform != 'win32' - # via pymilvus +milvus-lite==2.5.1 + # via + # pymilvus + # ramalama-stack mpmath==1.3.0 # via sympy multidict==6.4.4 @@ -185,10 +197,11 @@ multiprocess==0.70.16 # via datasets networkx==3.5 # via torch -numpy==2.2.6 +numpy==2.4.2 # via # accelerate # datasets + # llama-stack # pandas # peft # ramalama-stack @@ -232,37 +245,54 @@ nvidia-nvjitlink-cu12==12.6.85 ; platform_machine == 'x86_64' and sys_platform = # torch nvidia-nvtx-cu12==12.6.77 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch -openai==1.84.0 +oci==2.168.0 + # via llama-stack +openai==2.21.0 # via # llama-stack + # llama-stack-api # ramalama-stack -opentelemetry-api==1.33.1 +opentelemetry-api==1.39.1 # via + # opentelemetry-distro # opentelemetry-exporter-otlp-proto-http + # opentelemetry-instrumentation # opentelemetry-sdk # opentelemetry-semantic-conventions -opentelemetry-exporter-otlp-proto-common==1.33.1 +opentelemetry-distro==0.60b1 + # via llama-stack +opentelemetry-exporter-otlp-proto-common==1.39.1 # via opentelemetry-exporter-otlp-proto-http -opentelemetry-exporter-otlp-proto-http==1.33.1 +opentelemetry-exporter-otlp-proto-http==1.39.1 # via # llama-stack + # llama-stack-api # ramalama-stack -opentelemetry-proto==1.33.1 +opentelemetry-instrumentation==0.60b1 + # via opentelemetry-distro +opentelemetry-proto==1.39.1 # via # opentelemetry-exporter-otlp-proto-common # opentelemetry-exporter-otlp-proto-http -opentelemetry-sdk==1.33.1 +opentelemetry-sdk==1.39.1 # via # llama-stack + # llama-stack-api + # opentelemetry-distro # opentelemetry-exporter-otlp-proto-http # ramalama-stack -opentelemetry-semantic-conventions==0.54b1 - # via opentelemetry-sdk +opentelemetry-semantic-conventions==0.60b1 + # via + # opentelemetry-instrumentation + # opentelemetry-sdk +oracledb==3.4.2 + # via llama-stack packaging==25.0 # via # accelerate # datasets # huggingface-hub + # opentelemetry-instrumentation # peft # transformers pandas==2.2.3 @@ -273,9 +303,7 @@ pandas==2.2.3 peft==0.15.2 # via ramalama-stack pillow==11.2.1 - # via - # llama-stack - # sentence-transformers + # via sentence-transformers polyleven==0.9.0 # via autoevals prompt-toolkit==3.0.51 @@ -296,59 +324,69 @@ psutil==7.0.0 # accelerate # peft # ramalama-stack -pyaml==25.5.0 +psycopg2-binary==2.9.11 + # via llama-stack +pyaml==26.2.1 # via llama-stack-client pyarrow==20.0.0 # via datasets -pyasn1==0.6.1 - # via - # python-jose - # rsa +pycparser==3.0 ; implementation_name != 'PyPy' and platform_python_implementation != 'PyPy' + # via cffi pycryptodomex==3.23.0 # via blobfile -pydantic==2.11.5 +pydantic==2.12.5 # via # fastapi # llama-stack + # llama-stack-api # llama-stack-client # mcp # openai # pydantic-settings # ramalama-stack -pydantic-core==2.33.2 +pydantic-core==2.41.5 # via pydantic pydantic-settings==2.9.1 # via mcp pygments==2.19.1 # via rich +pyjwt==2.11.0 + # via + # llama-stack + # mcp pymilvus==2.5.10 # via ramalama-stack +pyopenssl==25.3.0 + # via oci python-dateutil==2.9.0.post0 - # via pandas + # via + # oci + # pandas python-dotenv==1.1.0 # via # llama-stack # pydantic-settings # pymilvus -python-jose==3.5.0 - # via llama-stack -python-multipart==0.0.20 +python-multipart==0.0.22 # via # llama-stack # mcp pytz==2025.2 - # via pandas + # via + # oci + # pandas +pywin32==311 ; sys_platform == 'win32' + # via mcp pyyaml==6.0.2 # via # accelerate # autoevals # datasets # huggingface-hub + # llama-stack # peft # pyaml # transformers -ramalama==0.10.1 - # via ramalama-stack referencing==0.36.2 # via # jsonschema @@ -374,8 +412,6 @@ rpds-py==0.25.1 # via # jsonschema # referencing -rsa==4.9.1 - # via python-jose safetensors==0.5.3 # via # accelerate @@ -389,14 +425,14 @@ scipy==1.15.3 # sentence-transformers sentence-transformers==4.1.0 # via ramalama-stack -setuptools==80.9.0 +setuptools==69.5.1 # via # pymilvus + # ramalama-stack # torch # triton six==1.17.0 # via - # ecdsa # python-dateutil # ramalama-stack sniffio==1.3.1 @@ -405,10 +441,12 @@ sniffio==1.3.1 # llama-stack-client # openai sqlalchemy==2.0.41 - # via ramalama-stack + # via + # llama-stack + # ramalama-stack sse-starlette==2.3.6 # via mcp -starlette==0.46.2 +starlette==0.52.1 # via # fastapi # llama-stack @@ -432,6 +470,8 @@ torch==2.7.0 # peft # ramalama-stack # sentence-transformers +tornado==6.5.4 + # via llama-stack tqdm==4.67.1 # via # datasets @@ -451,33 +491,44 @@ triton==3.3.0 ; platform_machine == 'x86_64' and sys_platform == 'linux' # via torch trl==0.18.1 # via ramalama-stack -typing-extensions==4.14.0 +typing-extensions==4.15.0 # via + # aiosignal # aiosqlite # anyio # fastapi # huggingface-hub # llama-stack-client + # mcp # openai + # opentelemetry-api + # opentelemetry-exporter-otlp-proto-http # opentelemetry-sdk + # opentelemetry-semantic-conventions + # oracledb # pydantic # pydantic-core + # pyopenssl # referencing # sentence-transformers # sqlalchemy + # starlette # torch # typing-inspection -typing-inspection==0.4.1 +typing-inspection==0.4.2 # via + # fastapi + # mcp # pydantic # pydantic-settings tzdata==2025.2 # via pandas ujson==5.10.0 # via pymilvus -urllib3==2.4.0 +urllib3==2.6.3 # via # blobfile + # llama-stack # ramalama-stack # requests uvicorn==0.34.3 @@ -488,7 +539,7 @@ uvicorn==0.34.3 wcwidth==0.2.13 # via prompt-toolkit wrapt==1.17.2 - # via deprecated + # via opentelemetry-instrumentation xxhash==3.5.0 # via datasets yarl==1.20.0 diff --git a/setup.py b/setup.py index d4d4163..17d7f9f 100644 --- a/setup.py +++ b/setup.py @@ -9,28 +9,17 @@ def run(self): # Run the standard install super().run() - # Write 'providers.d' to '~/.llama/providers.d' - # This allows users to see the remote providers - providers_dir = os.path.join(self.install_lib, "ramalama_stack", "providers.d") - target_dir_1 = os.path.expanduser("~/.llama/providers.d") - try: - os.makedirs(target_dir_1, exist_ok=True) - shutil.copytree(providers_dir, target_dir_1, dirs_exist_ok=True) - print(f"Copied {providers_dir} to {target_dir_1}") - except Exception as error: - print(f"Failed to copy {providers_dir} to {target_dir_1}. Error: {error}") - raise - # Write `ramalama-run.yaml` to '~/.llama/distributions/ramalama' # This allows users to run the stack - run_yaml = os.path.join(self.install_lib, "ramalama_stack", "ramalama-run.yaml") - target_dir_2 = os.path.expanduser("~/.llama/distributions/ramalama") + run_yaml = os.path.join(self.install_lib, "ramalama_stack", + "ramalama-run.yaml") + target_dir = os.path.expanduser("~/.llama/distributions/ramalama") try: - os.makedirs(target_dir_2, exist_ok=True) - shutil.copy(run_yaml, target_dir_2) - print(f"Copied {run_yaml} to {target_dir_2}") + os.makedirs(target_dir, exist_ok=True) + shutil.copy(run_yaml, target_dir) + print(f"Copied {run_yaml} to {target_dir}") except Exception as error: - print(f"Failed to copy {providers_dir} to {target_dir_1}. Error: {error}") + print(f"Failed to copy {run_yaml} to {target_dir}. Error: {error}") raise diff --git a/src/ramalama_stack/__init__.py b/src/ramalama_stack/__init__.py index 7bc0e44..79c0d2a 100644 --- a/src/ramalama_stack/__init__.py +++ b/src/ramalama_stack/__init__.py @@ -4,6 +4,5 @@ async def get_adapter_impl(config: RamalamaImplConfig, _deps): from .ramalama_adapter import RamalamaInferenceAdapter - impl = RamalamaInferenceAdapter(config.url) - await impl.initialize() + impl = RamalamaInferenceAdapter(config=config) return impl diff --git a/src/ramalama_stack/config.py b/src/ramalama_stack/config.py index 514a9a3..b0c1bcc 100644 --- a/src/ramalama_stack/config.py +++ b/src/ramalama_stack/config.py @@ -1,15 +1,19 @@ -from typing import Any, Dict +from typing import Any -from pydantic import BaseModel +from pydantic import Field, HttpUrl -DEFAULT_RAMALAMA_URL = "http://localhost:8080" +from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig +from llama_stack_api import json_schema_type - -class RamalamaImplConfig(BaseModel): - url: str = DEFAULT_RAMALAMA_URL +@json_schema_type +class RamalamaImplConfig(RemoteInferenceProviderConfig): + base_url: HttpUrl | None = Field( + default=HttpUrl("http://localhost:8080/v1"), + description="The URL for the Ramalama server", + ) @classmethod - def sample_run_config( - cls, url: str = "${env.RAMALAMA_URL:http://localhost:8080}", **kwargs - ) -> Dict[str, Any]: - return {"url": url} + def sample_run_config(cls, **kwargs) -> dict[str, Any]: + return { + "base_url": "http://localhost:8080/v1", + } diff --git a/src/ramalama_stack/models.py b/src/ramalama_stack/models.py deleted file mode 100644 index 42e3641..0000000 --- a/src/ramalama_stack/models.py +++ /dev/null @@ -1,103 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the terms described in the LICENSE file in -# the root directory of this source tree. - -from llama_stack.apis.models.models import ModelType -from llama_stack.models.llama.sku_types import CoreModelId -from llama_stack.providers.utils.inference.model_registry import ( - ProviderModelEntry, - build_hf_repo_model_entry, - build_model_entry, -) - -model_entries = [ - build_hf_repo_model_entry( - "llama3.1:8b-instruct-fp16", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_model_entry( - "llama3.1:8b", - CoreModelId.llama3_1_8b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.1:70b-instruct-fp16", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_model_entry( - "llama3.1:70b", - CoreModelId.llama3_1_70b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.1:405b-instruct-fp16", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_model_entry( - "llama3.1:405b", - CoreModelId.llama3_1_405b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2:1b-instruct-fp16", - CoreModelId.llama3_2_1b_instruct.value, - ), - build_model_entry( - "llama3.2:1b", - CoreModelId.llama3_2_1b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2:3b-instruct-fp16", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_model_entry( - "llama3.2:3b", - CoreModelId.llama3_2_3b_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2-vision:11b-instruct-fp16", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_model_entry( - "llama3.2-vision:latest", - CoreModelId.llama3_2_11b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.2-vision:90b-instruct-fp16", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_model_entry( - "llama3.2-vision:90b", - CoreModelId.llama3_2_90b_vision_instruct.value, - ), - build_hf_repo_model_entry( - "llama3.3:70b", - CoreModelId.llama3_3_70b_instruct.value, - ), - # The Llama Guard models don't have their full fp16 versions - # so we are going to alias their default version to the canonical SKU - build_hf_repo_model_entry( - "llama-guard3:8b", - CoreModelId.llama_guard_3_8b.value, - ), - build_hf_repo_model_entry( - "llama-guard3:1b", - CoreModelId.llama_guard_3_1b.value, - ), - ProviderModelEntry( - provider_model_id="all-minilm:latest", - aliases=["all-minilm"], - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 384, - "context_length": 512, - }, - ), - ProviderModelEntry( - provider_model_id="nomic-embed-text", - model_type=ModelType.embedding, - metadata={ - "embedding_dimension": 768, - "context_length": 8192, - }, - ), -] diff --git a/src/ramalama_stack/openai_compat.py b/src/ramalama_stack/openai_compat.py deleted file mode 100644 index 9cd5b42..0000000 --- a/src/ramalama_stack/openai_compat.py +++ /dev/null @@ -1,373 +0,0 @@ -import logging -import warnings -from typing import ( - Any, - AsyncGenerator, - Dict, - List, - Optional, -) -from llama_stack.providers.utils.inference.openai_compat import ( - convert_message_to_openai_dict_new, - convert_tooldef_to_openai_tool, - _convert_openai_finish_reason, - convert_message_to_openai_dict, - get_sampling_options, -) -from openai import AsyncStream -from openai.types.chat.chat_completion import ( - Choice as OpenAIChoice, -) -from openai.types.completion_choice import Logprobs as OpenAICompletionLogprobs -from pydantic import BaseModel - -from llama_stack.apis.inference import ( - ChatCompletionRequest, - CompletionRequest, - CompletionResponse, - CompletionResponseStreamChunk, - GrammarResponseFormat, - GreedySamplingStrategy, - Message, - TokenLogProbs, - ToolChoice, - TopKSamplingStrategy, - TopPSamplingStrategy, - UserMessage, -) -from llama_stack.apis.inference.inference import ( - JsonSchemaResponseFormat, - OpenAICompletion, -) - -logger = logging.getLogger(__name__) - - -class OpenAICompatCompletionChoiceDelta(BaseModel): - content: str - - -class OpenAICompatLogprobs(BaseModel): - text_offset: Optional[List[int]] = None - - token_logprobs: Optional[List[float]] = None - - tokens: Optional[List[str]] = None - - top_logprobs: Optional[List[Dict[str, float]]] = None - - -class OpenAICompatCompletionChoice(BaseModel): - finish_reason: Optional[str] = None - text: Optional[str] = None - delta: Optional[OpenAICompatCompletionChoiceDelta] = None - logprobs: Optional[OpenAICompatLogprobs] = None - - -class OpenAICompatCompletionResponse(BaseModel): - choices: List[OpenAICompatCompletionChoice] - - -async def convert_chat_completion_request( - request: ChatCompletionRequest, - n: int = 1, -) -> dict: - """ - Convert a ChatCompletionRequest to an OpenAI API-compatible dictionary. - """ - # model -> model - # messages -> messages - # sampling_params TODO(mattf): review strategy - # strategy=greedy -> nvext.top_k = -1, temperature = temperature - # strategy=top_p -> nvext.top_k = -1, top_p = top_p - # strategy=top_k -> nvext.top_k = top_k - # temperature -> temperature - # top_p -> top_p - # top_k -> nvext.top_k - # max_tokens -> max_tokens - # repetition_penalty -> nvext.repetition_penalty - # response_format -> GrammarResponseFormat TODO(mf) - # response_format -> JsonSchemaResponseFormat: response_format = "json_object" & nvext["guided_json"] = json_schema - # tools -> tools - # tool_choice ("auto", "required") -> tool_choice - # tool_prompt_format -> TBD - # stream -> stream - # logprobs -> logprobs - - if request.response_format and not isinstance( - request.response_format, JsonSchemaResponseFormat - ): - raise ValueError( - f"Unsupported response format: {request.response_format}. Only JsonSchemaResponseFormat is supported." - ) - - nvext = {} - payload: Dict[str, Any] = dict( - model=request.model, - messages=[ - await convert_message_to_openai_dict_new(message) - for message in request.messages - ], - stream=request.stream, - n=n, - extra_body=dict(nvext=nvext), - extra_headers={ - b"User-Agent": b"llama-stack: nvidia-inference-adapter", - }, - ) - - if request.response_format: - # server bug - setting guided_json changes the behavior of response_format resulting in an error - # payload.update(response_format="json_object") - nvext.update(guided_json=request.response_format.json_schema) - - if request.tools: - payload.update( - tools=[convert_tooldef_to_openai_tool(tool) for tool in request.tools] - ) - if request.tool_config.tool_choice: - payload.update( - tool_choice=request.tool_config.tool_choice.value - ) # we cannot include tool_choice w/o tools, server will complain - - if request.logprobs: - payload.update(logprobs=True) - payload.update(top_logprobs=request.logprobs.top_k) - - if request.sampling_params: - nvext.update(repetition_penalty=request.sampling_params.repetition_penalty) - - if request.sampling_params.max_tokens: - payload.update(max_tokens=request.sampling_params.max_tokens) - - strategy = request.sampling_params.strategy - if isinstance(strategy, TopPSamplingStrategy): - nvext.update(top_k=-1) - payload.update(top_p=strategy.top_p) - payload.update(temperature=strategy.temperature) - elif isinstance(strategy, TopKSamplingStrategy): - if strategy.top_k != -1 and strategy.top_k < 1: - warnings.warn("top_k must be -1 or >= 1", stacklevel=2) - nvext.update(top_k=strategy.top_k) - elif isinstance(strategy, GreedySamplingStrategy): - nvext.update(top_k=-1) - else: - raise ValueError(f"Unsupported sampling strategy: {strategy}") - - return payload - - -def convert_completion_request( - request: CompletionRequest, - n: int = 1, -) -> dict: - """ - Convert a ChatCompletionRequest to an OpenAI API-compatible dictionary. - """ - # model -> model - # prompt -> prompt - # sampling_params TODO(mattf): review strategy - # strategy=greedy -> nvext.top_k = -1, temperature = temperature - # strategy=top_p -> nvext.top_k = -1, top_p = top_p - # strategy=top_k -> nvext.top_k = top_k - # temperature -> temperature - # top_p -> top_p - # top_k -> nvext.top_k - # max_tokens -> max_tokens - # repetition_penalty -> nvext.repetition_penalty - # response_format -> nvext.guided_json - # stream -> stream - # logprobs.top_k -> logprobs - - nvext = {} - payload: Dict[str, Any] = dict( - model=request.model, - prompt=request.content, - stream=request.stream, - extra_body=dict(nvext=nvext), - extra_headers={ - b"User-Agent": b"llama-stack: nvidia-inference-adapter", - }, - n=n, - ) - - if request.response_format: - # this is not openai compliant, it is a nim extension - nvext.update(guided_json=request.response_format.json_schema) - - if request.logprobs: - payload.update(logprobs=request.logprobs.top_k) - - if request.sampling_params: - nvext.update(repetition_penalty=request.sampling_params.repetition_penalty) - - if request.sampling_params.max_tokens: - payload.update(max_tokens=request.sampling_params.max_tokens) - - if request.sampling_params.strategy == "top_p": - nvext.update(top_k=-1) - payload.update(top_p=request.sampling_params.top_p) - elif request.sampling_params.strategy == "top_k": - if ( - request.sampling_params.top_k != -1 - and request.sampling_params.top_k < 1 - ): - warnings.warn("top_k must be -1 or >= 1", stacklevel=2) - nvext.update(top_k=request.sampling_params.top_k) - elif request.sampling_params.strategy == "greedy": - nvext.update(top_k=-1) - payload.update(temperature=request.sampling_params.temperature) - - return payload - - -def _convert_openai_completion_logprobs( - logprobs: Optional[OpenAICompletionLogprobs], -) -> Optional[List[TokenLogProbs]]: - """ - Convert an OpenAI CompletionLogprobs into a list of TokenLogProbs. - """ - if not logprobs: - return None - - return [ - TokenLogProbs(logprobs_by_token=logprobs) for logprobs in logprobs.top_logprobs - ] - - -def convert_openai_completion_choice( - choice: OpenAIChoice, -) -> CompletionResponse: - """ - Convert an OpenAI Completion Choice into a CompletionResponse. - """ - return CompletionResponse( - content=choice.text, - stop_reason=_convert_openai_finish_reason(choice.finish_reason), - logprobs=_convert_openai_completion_logprobs(choice.logprobs), - ) - - -async def convert_openai_completion_stream( - stream: AsyncStream[OpenAICompletion], -) -> AsyncGenerator[CompletionResponse, None]: - """ - Convert a stream of OpenAI Completions into a stream - of ChatCompletionResponseStreamChunks. - """ - async for chunk in stream: - choice = chunk.choices[0] - yield CompletionResponseStreamChunk( - delta=choice.text, - stop_reason=_convert_openai_finish_reason(choice.finish_reason), - logprobs=_convert_openai_completion_logprobs(choice.logprobs), - ) - - -def _merge_context_into_content(message: Message) -> Message: # type: ignore - """ - Merge the ``context`` field of a Llama Stack ``Message`` object into - the content field for compabilitiy with OpenAI-style APIs. - Generates a content string that emulates the current behavior - of ``llama_models.llama3.api.chat_format.encode_message()``. - :param message: Message that may include ``context`` field - :returns: A version of ``message`` with any context merged into the - ``content`` field. - """ - if not isinstance(message, UserMessage): # Separate type check for linter - return message - if message.context is None: - return message - return UserMessage( - role=message.role, - # Emumate llama_models.llama3.api.chat_format.encode_message() - content=message.content + "\n\n" + message.context, - context=None, - ) - - -async def llama_stack_chat_completion_to_openai_chat_completion_dict( - request: ChatCompletionRequest, -) -> dict: - """ - Convert a chat completion request in Llama Stack format into an - equivalent set of arguments to pass to an OpenAI-compatible - chat completions API. - :param request: Bundled request parameters in Llama Stack format. - :returns: Dictionary of key-value pairs to use as an initializer - for a dataclass or to be converted directly to JSON and sent - over the wire. - """ - - converted_messages = [ - # This mystery async call makes the parent function also be async - await convert_message_to_openai_dict( - _merge_context_into_content(m), download=True - ) - for m in request.messages - ] - # converted_tools = _llama_stack_tools_to_openai_tools(request.tools) - - # Llama will try to use built-in tools with no tool catalog, so don't enable - # tool choice unless at least one tool is enabled. - converted_tool_choice = "none" - if ( - request.tool_config is not None - and request.tool_config.tool_choice == ToolChoice.auto - and request.tools is not None - and len(request.tools) > 0 - ): - converted_tool_choice = "auto" - - # TODO: Figure out what to do with the tool_prompt_format argument. - # Other connectors appear to drop it quietly. - - # Use Llama Stack shared code to translate sampling parameters. - sampling_options = get_sampling_options(request.sampling_params) - - # get_sampling_options() translates repetition penalties to an option that - # OpenAI's APIs don't know about. - # vLLM's OpenAI-compatible API also handles repetition penalties wrong. - # For now, translate repetition penalties into a format that vLLM's broken - # API will handle correctly. Two wrongs make a right... - if "repeat_penalty" in sampling_options: - del sampling_options["repeat_penalty"] - if ( - request.sampling_params.repetition_penalty is not None - and request.sampling_params.repetition_penalty != 1.0 - ): - sampling_options["repetition_penalty"] = ( - request.sampling_params.repetition_penalty - ) - - # Convert a single response format into four different parameters, per - # the OpenAI spec - guided_decoding_options = dict() - if request.response_format is None: - # Use defaults - pass - elif isinstance(request.response_format, JsonSchemaResponseFormat): - guided_decoding_options["guided_json"] = request.response_format.json_schema - elif isinstance(request.response_format, GrammarResponseFormat): - guided_decoding_options["guided_grammar"] = request.response_format.bnf - else: - raise TypeError( - f"ResponseFormat object is of unexpected subtype '{type(request.response_format)}'" - ) - - logprob_options = dict() - if request.logprobs is not None: - logprob_options["logprobs"] = request.logprobs.top_k - - # Marshall together all the arguments for a ChatCompletionRequest - request_options = { - "model": request.model, - "messages": converted_messages, - "tool_choice": converted_tool_choice, - "stream": request.stream, - **sampling_options, - **guided_decoding_options, - **logprob_options, - } - - return request_options diff --git a/src/ramalama_stack/provider.py b/src/ramalama_stack/provider.py index f97ffee..99ba0f1 100644 --- a/src/ramalama_stack/provider.py +++ b/src/ramalama_stack/provider.py @@ -1,18 +1,16 @@ -from llama_stack.providers.datatypes import ( +from llama_stack_api.datatypes import ( ProviderSpec, Api, - AdapterSpec, - remote_provider_spec, + RemoteProviderSpec, ) def get_provider_spec() -> ProviderSpec: - return remote_provider_spec( + return RemoteProviderSpec( api=Api.inference, - adapter=AdapterSpec( - adapter_type="ramalama", - pip_packages=[], - config_class="ramalama_stack.config.RamalamaImplConfig", - module="ramalama_stack", - ), + provider_type="remote::ramalama", + adapter_type="ramalama", + pip_packages=[], + config_class="ramalama_stack.config.RamalamaImplConfig", + module="ramalama_stack", ) diff --git a/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml b/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml deleted file mode 100644 index 76d7564..0000000 --- a/src/ramalama_stack/providers.d/remote/inference/ramalama.yaml +++ /dev/null @@ -1,7 +0,0 @@ -adapter: - adapter_type: ramalama - pip_packages: [] - config_class: ramalama_stack.config.RamalamaImplConfig - module: ramalama_stack -api_dependencies: [] -optional_api_dependencies: [] diff --git a/src/ramalama_stack/ramalama-run.yaml b/src/ramalama_stack/ramalama-run.yaml index f5a211c..412ff7f 100644 --- a/src/ramalama_stack/ramalama-run.yaml +++ b/src/ramalama_stack/ramalama-run.yaml @@ -1,148 +1,234 @@ -version: '2' -image_name: ramalama +version: 2 +distro_name: ramalama apis: - agents +- batches - datasetio - eval +- files - inference - post_training - safety - scoring -- telemetry - tool_runtime - vector_io providers: - # all available providers can be found here: https://llama-stack.readthedocs.io/en/latest/providers/index.html + inference: + - provider_id: ${env.VLLM_URL:+ramalama} + provider_type: remote::vllm + config: + base_url: ${env.VLLM_URL:=} + max_tokens: ${env.VLLM_MAX_TOKENS:=4096} + - provider_id: ${env.LLAMA_CPP_SERVER_URL:+ramalama} + provider_type: remote::llama-cpp-server + config: + base_url: ${env.LLAMA_CPP_SERVER_URL:=} + - provider_id: ${env.RAMALAMA_URL:+ramalama} + provider_type: remote::ramalama + module: ramalama_stack + config: + base_url: ${env.RAMALAMA_URL:=} + - provider_id: sentence-transformers + provider_type: inline::sentence-transformers + vector_io: + - provider_id: ${env.MILVUS_URL:+milvus} + provider_type: inline::milvus + config: + db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/ramalama}/milvus.db + persistence: + namespace: vector_io::milvus + backend: kv_default + files: + - provider_id: meta-reference-files + provider_type: inline::localfs + config: + storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/ramalama/files} + metadata_store: + table_name: files_metadata + backend: sql_default + safety: + - provider_id: llama-guard + provider_type: inline::llama-guard + config: + excluded_categories: [] + - provider_id: code-scanner + provider_type: inline::code-scanner agents: - provider_id: meta-reference provider_type: inline::meta-reference config: - persistence_store: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/agents_store.db - responses_store: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/responses_store.db - datasetio: - - provider_id: huggingface - provider_type: remote::huggingface - config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/huggingface_datasetio.db - - provider_id: localfs - provider_type: inline::localfs + persistence: + agent_state: + namespace: agents + backend: kv_default + responses: + table_name: responses + backend: sql_default + max_write_queue_size: 10000 + num_writers: 4 + post_training: + - provider_id: torchtune-cpu + provider_type: inline::torchtune-cpu config: - kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/localfs_datasetio.db + checkpoint_format: meta eval: - provider_id: meta-reference provider_type: inline::meta-reference config: kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/meta_reference_eval.db - inference: - - provider_id: ramalama - provider_type: remote::ramalama - config: - url: ${env.RAMALAMA_URL:=http://localhost:8080} - - provider_id: sentence-transformers - provider_type: inline::sentence-transformers - config: {} - post_training: + namespace: eval + backend: kv_default + datasetio: - provider_id: huggingface - provider_type: inline::huggingface + provider_type: remote::huggingface config: - checkpoint_format: huggingface - distributed_backend: null - device: cpu - safety: - - provider_id: llama-guard - provider_type: inline::llama-guard + kvstore: + namespace: datasetio::huggingface + backend: kv_default + - provider_id: localfs + provider_type: inline::localfs config: - excluded_categories: [] + kvstore: + namespace: datasetio::localfs + backend: kv_default scoring: - provider_id: basic provider_type: inline::basic - config: {} - provider_id: llm-as-judge provider_type: inline::llm-as-judge - config: {} - provider_id: braintrust provider_type: inline::braintrust config: - openai_api_key: ${env.OPENAI_API_KEY:+} - telemetry: - - provider_id: meta-reference - provider_type: inline::meta-reference - config: - service_name: ${env.OTEL_SERVICE_NAME:=llamastack} - sinks: ${env.TELEMETRY_SINKS:=console,sqlite} - sqlite_db_path: ${env.SQLITE_DB_PATH:=~/.llama/distributions/ramalama}/trace_store.db + openai_api_key: ${env.OPENAI_API_KEY:=} tool_runtime: - provider_id: brave-search provider_type: remote::brave-search config: - api_key: ${env.BRAVE_SEARCH_API_KEY:+} + api_key: ${env.BRAVE_SEARCH_API_KEY:=} max_results: 3 - provider_id: tavily-search provider_type: remote::tavily-search config: - api_key: ${env.TAVILY_SEARCH_API_KEY:+} + api_key: ${env.TAVILY_SEARCH_API_KEY:=} max_results: 3 - provider_id: rag-runtime provider_type: inline::rag-runtime - config: {} - provider_id: model-context-protocol provider_type: remote::model-context-protocol - config: {} - provider_id: wolfram-alpha provider_type: remote::wolfram-alpha config: api_key: ${env.WOLFRAM_ALPHA_API_KEY:+} - vector_io: - - provider_id: milvus - provider_type: inline::milvus + batches: + - provider_id: reference + provider_type: inline::reference config: - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama/milvus.db} kvstore: - type: sqlite - namespace: null - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama/milvus_registry.db} -metadata_store: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/registry.db -inference_store: - type: sqlite - db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/inference_store.db -models: -- metadata: {} - model_id: ${env.INFERENCE_MODEL} - provider_id: ramalama - model_type: llm -- metadata: - embedding_dimension: 384 - model_id: all-MiniLM-L6-v2 - provider_id: sentence-transformers - model_type: embedding -shields: [] -vector_dbs: [] -datasets: [] -scoring_fns: [] -benchmarks: [] -tool_groups: -- toolgroup_id: builtin::websearch - provider_id: tavily-search -- toolgroup_id: builtin::rag - provider_id: rag-runtime -- toolgroup_id: builtin::wolfram_alpha - provider_id: wolfram-alpha + namespace: batches + backend: kv_default +storage: + backends: + kv_default: + type: kv_sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/kvstore.db + sql_default: + type: sql_sqlite + db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ramalama}/sql_store.db + stores: + metadata: + namespace: registry + backend: kv_default + inference: + table_name: inference_store + backend: sql_default + max_write_queue_size: 10000 + num_writers: 4 + conversations: + table_name: openai_conversations + backend: sql_default + prompts: + namespace: prompts + backend: kv_default + connectors: + namespace: connectors + backend: kv_default +registered_resources: + models: + - metadata: {} + model_id: ${env.INFERENCE_MODEL} + provider_id: ramalama + model_type: llm + shields: + - shield_id: llama-guard + provider_id: ${env.SAFETY_MODEL:+llama-guard} + provider_shield_id: ${env.SAFETY_MODEL:=} + - shield_id: code-scanner + provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner} + provider_shield_id: ${env.CODE_SCANNER_MODEL:=} + vector_dbs: [] + datasets: [] + scoring_fns: [] + benchmarks: [] + tool_groups: + - toolgroup_id: builtin::websearch + provider_id: tavily-search + - toolgroup_id: builtin::rag + provider_id: rag-runtime + - toolgroup_id: builtin::wolfram_alpha + provider_id: wolfram-alpha server: port: 8321 -external_providers_dir: ${env.EXTERNAL_PROVIDERS_DIR:=~/.llama/providers.d} +vector_stores: + default_provider_id: milvus + default_embedding_model: + provider_id: sentence-transformers + model_id: nomic-ai/nomic-embed-text-v1.5 + file_search_params: + header_template: 'knowledge_search tool found {num_chunks} chunks: + + BEGIN of knowledge_search tool results. + + ' + footer_template: 'END of knowledge_search tool results. + + ' + context_prompt_params: + chunk_annotation_template: 'Result {index} + + Content: {chunk.content} + + Metadata: {metadata} + + ' + context_template: 'The above results were retrieved to help answer the user''s + query: "{query}". Use them as supporting information only in answering this + query. {annotation_instruction} + + ' + annotation_prompt_params: + enable_annotations: true + annotation_instruction_template: Cite sources immediately at the end of sentences + before punctuation, using `<|file-id|>` format like 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'. + Do not add extra punctuation. Use only the file IDs provided, do not invent + new ones. + chunk_annotation_template: '[{index}] {metadata_text} cite as <|{file_id}|> + + {chunk_text} + + ' + file_ingestion_params: + default_chunk_size_tokens: 512 + default_chunk_overlap_tokens: 128 + chunk_retrieval_params: + chunk_multiplier: 5 + max_tokens_in_context: 4000 + default_reranker_strategy: rrf + rrf_impact_factor: 60.0 + weighted_search_alpha: 0.5 + file_batch_params: + max_concurrent_files_per_batch: 3 + file_batch_chunk_size: 10 + cleanup_interval_seconds: 86400 +safety: + default_shield_id: llama-guard +connectors: [] diff --git a/src/ramalama_stack/ramalama_adapter.py b/src/ramalama_stack/ramalama_adapter.py index 939f547..fd20e09 100644 --- a/src/ramalama_stack/ramalama_adapter.py +++ b/src/ramalama_stack/ramalama_adapter.py @@ -1,337 +1,15 @@ -from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union +from .config import RamalamaImplConfig -from openai import AsyncOpenAI, BadRequestError -from openai.types.chat.chat_completion_chunk import ( - ChatCompletionChunk as OpenAIChatCompletionChunk, -) +from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin -from llama_stack.apis.common.content_types import ( - InterleavedContent, - InterleavedContentItem, - TextContentItem, -) -from llama_stack.apis.inference import ( - ChatCompletionRequest, - CompletionRequest, - EmbeddingsResponse, - EmbeddingTaskType, - Inference, - LogProbConfig, - Message, - ResponseFormat, - SamplingParams, - TextTruncation, - ToolChoice, - ToolConfig, - ToolDefinition, - ToolPromptFormat, - OpenAIEmbeddingsResponse, -) -from llama_stack.apis.inference.inference import ( - OpenAIChatCompletion, - OpenAICompletion, - OpenAIMessageParam, - OpenAIResponseFormatParam, -) -from llama_stack.apis.models import Model -from llama_stack.log import get_logger -from llama_stack.providers.datatypes import ModelsProtocolPrivate -from llama_stack.providers.utils.inference.model_registry import ( - ModelRegistryHelper, -) -from llama_stack.providers.utils.inference.openai_compat import ( - convert_openai_chat_completion_choice, - convert_openai_chat_completion_stream, - prepare_openai_completion_params, -) -from .openai_compat import ( - convert_chat_completion_request, - convert_completion_request, - convert_openai_completion_choice, - convert_openai_completion_stream, -) -from .models import model_entries +class RamalamaInferenceAdapter(OpenAIMixin): + config: RamalamaImplConfig -logger = get_logger(name=__name__, category="inference") + def get_api_key(self) -> str | None: + if self.config.auth_credential is None: + return "NO KEY REQUIRED" + return self.config.auth_credential.get_secret_value() - -class RamalamaInferenceAdapter(Inference, ModelsProtocolPrivate): - def __init__(self, url: str) -> None: - self.register_helper = ModelRegistryHelper(model_entries) - self.url = url - - async def initialize(self) -> None: - logger.info(f"checking connectivity to Ramalama at `{self.url}`...") - self.client = AsyncOpenAI(base_url=self.url, api_key="NO KEY") - logger.info(f"successfully connected to Ramalama at `{self.url}`...") - - async def shutdown(self) -> None: - pass - - async def unregister_model(self, model_id: str) -> None: - pass - - async def completion( - self, - model_id: str, - content: InterleavedContent, - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = convert_completion_request( - request=CompletionRequest( - model=model.provider_resource_id, - content=content, - sampling_params=sampling_params, - response_format=response_format, - stream=stream, - logprobs=logprobs, - ) - ) - - response = await self.client.completions.create(**request) - if stream: - return convert_openai_completion_stream(response) - else: - # we pass n=1 to get only one completion - return convert_openai_completion_choice(response.choices[0]) - - async def chat_completion( - self, - model_id: str, - messages: List[Message], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_choice: Optional[ToolChoice] = ToolChoice.auto, - tool_prompt_format: Optional[ToolPromptFormat] = None, - stream: Optional[bool] = False, - logprobs: Optional[LogProbConfig] = None, - tool_config: Optional[ToolConfig] = None, - ) -> AsyncGenerator: - if sampling_params is None: - sampling_params = SamplingParams() - model = await self.model_store.get_model(model_id) - request = await convert_chat_completion_request( - request=ChatCompletionRequest( - model=model.provider_resource_id, - messages=messages, - sampling_params=sampling_params, - tools=tools or [], - stream=stream, - logprobs=logprobs, - response_format=response_format, - tool_config=tool_config, - ), - n=1, - ) - s = await self.client.chat.completions.create(**request) - if stream: - return convert_openai_chat_completion_stream( - s, enable_incremental_tool_calls=True - ) - else: - # we pass n=1 to get only one completion - return convert_openai_chat_completion_choice(s.choices[0]) - - async def embeddings( - self, - model_id: str, - contents: List[str] | List[InterleavedContentItem], - text_truncation: Optional[TextTruncation] = TextTruncation.none, - output_dimension: Optional[int] = None, - task_type: Optional[EmbeddingTaskType] = None, - ) -> EmbeddingsResponse: - flat_contents = [ - content.text if isinstance(content, TextContentItem) else content - for content in contents - ] - input = [ - content.text if isinstance(content, TextContentItem) else content - for content in flat_contents - ] - model = self.get_provider_model_id(model_id) - - extra_body = {} - - if text_truncation is not None: - text_truncation_options = { - TextTruncation.none: "NONE", - TextTruncation.end: "END", - TextTruncation.start: "START", - } - extra_body["truncate"] = text_truncation_options[text_truncation] - - if output_dimension is not None: - extra_body["dimensions"] = output_dimension - - if task_type is not None: - task_type_options = { - EmbeddingTaskType.document: "passage", - EmbeddingTaskType.query: "query", - } - extra_body["input_type"] = task_type_options[task_type] - - try: - response = await self._client.embeddings.create( - model=model, - input=input, - extra_body=extra_body, - ) - except BadRequestError as e: - raise ValueError(f"Failed to get embeddings: {e}") from e - - return EmbeddingsResponse( - embeddings=[embedding.embedding for embedding in response.data] - ) - - async def register_model(self, model: Model) -> Model: - res = await self.client.models.list() - available_models = [m.id async for m in res] - # Ramalama handles paths on MacOS and Linux differently - if (model.provider_resource_id.split("/")[-1] not in available_models) and ( - model.provider_resource_id not in available_models - ): - raise ValueError( - f"Model {model.provider_resource_id} is not being served by Ramalama. " - f"Available models: {', '.join(available_models)}" - ) - return model - - async def openai_embeddings( - self, - model: str, - input: str | list[str], - encoding_format: str | None = "float", - dimensions: int | None = None, - user: str | None = None, - ) -> OpenAIEmbeddingsResponse: - raise NotImplementedError() - - async def openai_completion( - self, - model: str, - prompt: Union[str, List[str], List[int], List[List[int]]], - best_of: Optional[int] = None, - echo: Optional[bool] = None, - frequency_penalty: Optional[float] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - presence_penalty: Optional[float] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - guided_choice: Optional[List[str]] = None, - prompt_logprobs: Optional[int] = None, - suffix: str | None = None, - ) -> OpenAICompletion: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - prompt=prompt, - best_of=best_of, - echo=echo, - frequency_penalty=frequency_penalty, - logit_bias=logit_bias, - logprobs=logprobs, - max_tokens=max_tokens, - n=n, - presence_penalty=presence_penalty, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - top_p=top_p, - user=user, - suffix=suffix, - ) - return await self.client.completions.create(**params) # type: ignore - - async def openai_chat_completion( - self, - model: str, - messages: List[OpenAIMessageParam], - frequency_penalty: Optional[float] = None, - function_call: Optional[Union[str, Dict[str, Any]]] = None, - functions: Optional[List[Dict[str, Any]]] = None, - logit_bias: Optional[Dict[str, float]] = None, - logprobs: Optional[bool] = None, - max_completion_tokens: Optional[int] = None, - max_tokens: Optional[int] = None, - n: Optional[int] = None, - parallel_tool_calls: Optional[bool] = None, - presence_penalty: Optional[float] = None, - response_format: Optional[OpenAIResponseFormatParam] = None, - seed: Optional[int] = None, - stop: Optional[Union[str, List[str]]] = None, - stream: Optional[bool] = None, - stream_options: Optional[Dict[str, Any]] = None, - temperature: Optional[float] = None, - tool_choice: Optional[Union[str, Dict[str, Any]]] = None, - tools: Optional[List[Dict[str, Any]]] = None, - top_logprobs: Optional[int] = None, - top_p: Optional[float] = None, - user: Optional[str] = None, - ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]: - model_obj = await self.model_store.get_model(model) - params = await prepare_openai_completion_params( - model=model_obj.provider_resource_id, - messages=messages, - frequency_penalty=frequency_penalty, - function_call=function_call, - functions=functions, - logit_bias=logit_bias, - logprobs=logprobs, - max_completion_tokens=max_completion_tokens, - max_tokens=max_tokens, - n=n, - parallel_tool_calls=parallel_tool_calls, - presence_penalty=presence_penalty, - response_format=response_format, - seed=seed, - stop=stop, - stream=stream, - stream_options=stream_options, - temperature=temperature, - tool_choice=tool_choice, - tools=tools, - top_logprobs=top_logprobs, - top_p=top_p, - user=user, - ) - return await self.client.chat.completions.create(**params) # type: ignore - - async def batch_completion( - self, - model_id: str, - content_batch: List[InterleavedContent], - sampling_params: Optional[SamplingParams] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, - ): - raise NotImplementedError("Batch completion is not supported for Ramalama") - - async def batch_chat_completion( - self, - model_id: str, - messages_batch: List[List[Message]], - sampling_params: Optional[SamplingParams] = None, - tools: Optional[List[ToolDefinition]] = None, - tool_config: Optional[ToolConfig] = None, - response_format: Optional[ResponseFormat] = None, - logprobs: Optional[LogProbConfig] = None, - ): - raise NotImplementedError("Batch chat completion is not supported for Ramalama") + def get_base_url(self) -> str: + return str(self.config.base_url) diff --git a/tests/test-build.sh b/tests/test-build.sh index 23e33d2..8cb049b 100755 --- a/tests/test-build.sh +++ b/tests/test-build.sh @@ -1,18 +1,8 @@ #!/bin/bash function test_file_writes { - PROVIDER_DIR="$HOME/.llama/providers.d" RUN_YAML="$HOME/.llama/distributions/ramalama/ramalama-run.yaml" - # check for PROVIDER_DIR - if [ -d "$PROVIDER_DIR" ]; then - echo "$PROVIDER_DIR found" - else - echo "$PROVIDER_DIR not found" - echo "===> test_file_writes: fail" - exit 1 - fi - # check for RUN_YAML if [ -f "$RUN_YAML" ]; then echo "$RUN_YAML found" diff --git a/tests/utils.sh b/tests/utils.sh index e365fe3..9b8f33c 100755 --- a/tests/utils.sh +++ b/tests/utils.sh @@ -27,7 +27,7 @@ function start_and_wait_for_ramalama_server { function start_and_wait_for_llama_stack_server { # Start llama stack run with logging to 'lls-$INFERENCE_MODEL_NO_COLON.log' - LLAMA_STACK_LOG_FILE="lls-$INFERENCE_MODEL_NO_COLON.log" nohup uv run llama stack run ~/.llama/distributions/ramalama/ramalama-run.yaml --image-type venv & + LLAMA_STACK_LOG_FILE="lls-$INFERENCE_MODEL_NO_COLON.log" nohup uv run llama stack run ~/.llama/distributions/ramalama/ramalama-run.yaml & LLS_PID=$! echo "Started Llama Stack server with PID: $LLS_PID" @@ -38,7 +38,7 @@ function start_and_wait_for_llama_stack_server { resp=$(curl -s http://localhost:8321/v1/health) if [ "$resp" == '{"status":"OK"}' ]; then echo "Llama Stack server is up!" - if grep -q -e "remote::ramalama from .*providers.d/remote/inference/ramalama.yaml" "lls-$INFERENCE_MODEL_NO_COLON.log"; then + if grep -q -e "remote::ramalama" "lls-$INFERENCE_MODEL_NO_COLON.log"; then echo "Llama Stack server is using RamaLama provider" return else @@ -64,6 +64,7 @@ function start_and_wait_for_llama_stack_container { --env INFERENCE_MODEL="$INFERENCE_MODEL" \ --env RAMALAMA_URL=http://0.0.0.0:8080 \ --name llama-stack \ + --rm \ quay.io/ramalama/llama-stack:latest LLS_PID=$! echo "Started Llama Stack container with PID: $LLS_PID" @@ -75,7 +76,7 @@ function start_and_wait_for_llama_stack_container { resp=$(curl -s http://localhost:8321/v1/health) if [ "$resp" == '{"status":"OK"}' ]; then echo "Llama Stack server is up!" - if podman logs llama-stack | grep -q -e "remote::ramalama from .*providers.d/remote/inference/ramalama.yaml"; then + if podman logs llama-stack 2>&1 | grep -q -e "remote::ramalama"; then echo "Llama Stack server is using RamaLama provider" return else @@ -102,6 +103,7 @@ function test_ramalama_models { return else echo "===> test_ramalama_models: fail" + echo "Response: $resp" echo "RamaLama logs:" cat "ramalama-$INFERENCE_MODEL_NO_COLON.log" exit 1 @@ -142,7 +144,7 @@ function test_llama_stack_models { function test_llama_stack_openai_models { echo "===> test_llama_stack_openai_models: start" # shellcheck disable=SC2016 - resp=$(curl -sS http://localhost:8321/v1/openai/v1/models) + resp=$(curl -sS http://localhost:8321/v1/models) if echo "$resp" | grep -q "$INFERENCE_MODEL"; then echo "===> test_llama_stack_openai_models: pass" return @@ -158,11 +160,12 @@ function test_llama_stack_chat_completion { echo "===> test_llama_stack_chat_completion: start" nohup uv run llama-stack-client configure --endpoint http://localhost:8321 --api-key none resp=$(nohup uv run llama-stack-client inference chat-completion --message "tell me a joke") - if echo "$resp" | grep -q "OpenAIChatCompletion"; then + if echo "$resp" | grep -q "CompletionCreateResponse"; then echo "===> test_llama_stack_chat_completion: pass" return else echo "===> test_llama_stack_chat_completion: fail" + echo "Response: $resp" echo "Server logs:" cat "lls-$INFERENCE_MODEL_NO_COLON.log" || podman logs llama-stack exit 1 @@ -172,14 +175,15 @@ function test_llama_stack_chat_completion { function test_llama_stack_openai_chat_completion { echo "===> test_llama_stack_openai_chat_completion: start" # shellcheck disable=SC2016 - resp=$(curl -sS -X POST http://localhost:8321/v1/openai/v1/chat/completions \ + resp=$(curl -sS -X POST http://localhost:8321/v1/chat/completions \ -H "Content-Type: application/json" \ - -d "{\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"model\": \"$INFERENCE_MODEL\"}") + -d "{\"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"model\": \"ramalama/$INFERENCE_MODEL\"}") if echo "$resp" | grep -q "choices"; then echo "===> test_llama_stack_openai_chat_completion: pass" return else echo "===> test_llama_stack_openai_chat_completion: fail" + echo "Response: $resp" echo "Server logs:" cat "lls-$INFERENCE_MODEL_NO_COLON.log" || podman logs 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