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feat: RamaLama Documentation and Templates

Browse source 
RamaLama is a fully Open Source AI Model tool that facilitate
local management of AI Models.

https://github.com/containers/ramalama

It is fully open source and supports pulling models from HuggingFace,
Ollama, OCI Images, and via URI file://, http://, https://

It uses the llama.cpp and vllm AI engines for running the MODELS.

It also defaults to running the models inside of containers.

Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Daniel J Walsh 2025-02-11 13:47:13 -05:00 • committed by Charlie Doern
parent 4de45560bf
commit c9a41288a3
14 changed files with 1331 additions and 354 deletions
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2
llama_stack/providers/remote/inference/ramalama/models.py
View file

@ -5,7 +5,7 @@
# the root directory of this source tree.
from llama_stack.apis.models.models import ModelType
from llama_stack.models.llama.datatypes import CoreModelId
from llama_stack.models.llama.sku_types import CoreModelId
from llama_stack.providers.utils.inference.model_registry import (
ProviderModelEntry,
build_hf_repo_model_entry,

344
llama_stack/providers/remote/inference/ramalama/openai_utils.py
View file

@ -1,344 +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 typing import List, Optional
from llama_stack.apis.inference import (
ChatCompletionRequest,
GrammarResponseFormat,
JsonSchemaResponseFormat,
Message,
ToolChoice,
UserMessage,
)
from llama_stack.providers.utils.inference.openai_compat import (
convert_message_to_openai_dict,
get_sampling_options,
)
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
# 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.
import warnings
from typing import Any, AsyncGenerator, Dict
from openai import AsyncStream
from openai.types.chat.chat_completion import (
Choice as OpenAIChoice,
)
from openai.types.completion import Completion as OpenAICompletion
from openai.types.completion_choice import Logprobs as OpenAICompletionLogprobs
from llama_stack.apis.inference import (
ChatCompletionRequest,
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
TokenLogProbs,
)
from llama_stack.models.llama.datatypes import (
GreedySamplingStrategy,
TopKSamplingStrategy,
TopPSamplingStrategy,
)
from llama_stack.providers.utils.inference.openai_compat import (
_convert_openai_finish_reason,
convert_message_to_openai_dict_new,
convert_tooldef_to_openai_tool,
)
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),
)

149
llama_stack/providers/remote/inference/ramalama/ramalama.py
View file

@ -5,9 +5,12 @@
# the root directory of this source tree.
from typing import AsyncGenerator, List, Optional
from typing import Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Union
from openai import AsyncOpenAI, BadRequestError
from openai.types.chat.chat_completion_chunk import (
ChatCompletionChunk as OpenAIChatCompletionChunk,
)
from llama_stack.apis.common.content_types import (
InterleavedContent,
@ -30,6 +33,12 @@ from llama_stack.apis.inference import (
ToolDefinition,
ToolPromptFormat,
)
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
@ -37,17 +46,16 @@ from llama_stack.providers.utils.inference.model_registry import (
ModelRegistryHelper,
)
from llama_stack.providers.utils.inference.openai_compat import (
convert_chat_completion_request,
convert_completion_request,
convert_openai_chat_completion_choice,
convert_openai_chat_completion_stream,
convert_openai_completion_choice,
convert_openai_completion_stream,
prepare_openai_completion_params,
)
from .models import model_entries
from .openai_utils import (
convert_chat_completion_request,
convert_completion_request,
convert_openai_completion_choice,
convert_openai_completion_stream,
)
logger = get_logger(name=__name__, category="inference")
@ -180,9 +188,132 @@ class RamalamaInferenceAdapter(Inference, ModelsProtocolPrivate):
model = await self.register_helper.register_model(model)
res = await self.client.models.list()
available_models = [m.id async for m in res]
if model.provider_resource_id not in available_models:
# 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 vLLM. "
f"Model {model.provider_resource_id} is not being served by Ramalama. "
f"Available models: {', '.join(available_models)}"
)
return model
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,
) -> 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,
)
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")

292
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -78,6 +78,7 @@ from openai.types.chat.chat_completion_content_part_image_param import (
from openai.types.chat.chat_completion_message_tool_call_param import (
Function as OpenAIFunction,
)
from openai.types.completion_choice import Logprobs as OpenAICompletionLogprobs
from pydantic import BaseModel
from llama_stack.apis.common.content_types import (
@ -97,8 +98,10 @@ from llama_stack.apis.inference import (
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
CompletionMessage,
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
GrammarResponseFormat,
GreedySamplingStrategy,
Message,
SamplingParams,
@ -1466,3 +1469,292 @@ class OpenAIChatCompletionToLlamaStackMixin:
model=model,
object="chat.completion",
)
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