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chore(package): migrate to src/ layout (#3920)

Browse source 
Migrates package structure to src/ layout following Python packaging
best practices.

All code moved from `llama_stack/` to `src/llama_stack/`. Public API
unchanged - imports remain `import llama_stack.*`.

Updated build configs, pre-commit hooks, scripts, and GitHub workflows
accordingly. All hooks pass, package builds cleanly.

**Developer note**: Reinstall after pulling: `pip install -e .`
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Ashwin Bharambe 2025-10-27 12:02:21 -07:00 • committed by GitHub
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llama_stack/providers/utils/inference/prompt_adapter.py
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# 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 asyncio
import base64
import io
import json
import re
from typing import Any
import httpx
from PIL import Image as PIL_Image
from llama_stack.apis.common.content_types import (
ImageContentItem,
InterleavedContent,
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
CompletionRequest,
Message,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
OpenAIFile,
ResponseFormat,
ResponseFormatType,
SystemMessage,
SystemMessageBehavior,
ToolChoice,
ToolDefinition,
UserMessage,
)
from llama_stack.log import get_logger
from llama_stack.models.llama.datatypes import (
RawContent,
RawContentItem,
RawMediaItem,
RawMessage,
RawTextItem,
Role,
StopReason,
ToolPromptFormat,
)
from llama_stack.models.llama.llama3.chat_format import ChatFormat
from llama_stack.models.llama.llama3.prompt_templates import (
BuiltinToolGenerator,
FunctionTagCustomToolGenerator,
JsonCustomToolGenerator,
PythonListCustomToolGenerator,
SystemDefaultGenerator,
)
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
from llama_stack.models.llama.llama4.prompt_templates.system_prompts import (
PythonListCustomToolGenerator as PythonListCustomToolGeneratorLlama4,
)
from llama_stack.models.llama.sku_list import resolve_model
from llama_stack.models.llama.sku_types import ModelFamily, is_multimodal
from llama_stack.providers.utils.inference import supported_inference_models
log = get_logger(name=__name__, category="providers::utils")
class ChatCompletionRequestWithRawContent(ChatCompletionRequest):
messages: list[RawMessage]
class CompletionRequestWithRawContent(CompletionRequest):
content: RawContent
def decode_assistant_message(content: str, stop_reason: StopReason) -> RawMessage:
formatter = ChatFormat(Tokenizer.get_instance())
return formatter.decode_assistant_message_from_content(content, stop_reason)
def interleaved_content_as_str(
content: Any,
sep: str = " ",
) -> str:
if content is None:
return ""
def _process(c) -> str:
if isinstance(c, str):
return c
elif isinstance(c, TextContentItem) or isinstance(c, OpenAIChatCompletionContentPartTextParam):
return c.text
elif isinstance(c, ImageContentItem) or isinstance(c, OpenAIChatCompletionContentPartImageParam):
return "<image>"
elif isinstance(c, OpenAIFile):
return "<file>"
else:
raise ValueError(f"Unsupported content type: {type(c)}")
if isinstance(content, list):
return sep.join(_process(c) for c in content)
else:
return _process(content)
async def convert_request_to_raw(
request: ChatCompletionRequest | CompletionRequest,
) -> ChatCompletionRequestWithRawContent | CompletionRequestWithRawContent:
if isinstance(request, ChatCompletionRequest):
messages = []
for m in request.messages:
content = await interleaved_content_convert_to_raw(m.content)
d = m.model_dump()
d["content"] = content
messages.append(RawMessage(**d))
d = request.model_dump()
d["messages"] = messages
request = ChatCompletionRequestWithRawContent(**d)
else:
d = request.model_dump()
d["content"] = await interleaved_content_convert_to_raw(request.content)
request = CompletionRequestWithRawContent(**d)
return request
async def interleaved_content_convert_to_raw(
content: InterleavedContent,
) -> RawContent:
"""Download content from URLs / files etc. so plain bytes can be sent to the model"""
async def _localize_single(c: str | InterleavedContentItem) -> str | RawContentItem:
if isinstance(c, str):
return RawTextItem(text=c)
elif isinstance(c, TextContentItem):
return RawTextItem(text=c.text)
elif isinstance(c, ImageContentItem):
image = c.image
if image.url:
# Load image bytes from URL
if image.url.uri.startswith("data"):
match = re.match(r"data:image/(\w+);base64,(.+)", image.url.uri)
if not match:
raise ValueError(f"Invalid data URL format, {image.url.uri[:40]}...")
_, image_data = match.groups()
data = base64.b64decode(image_data)
elif image.url.uri.startswith("file://"):
path = image.url.uri[len("file://") :]
with open(path, "rb") as f:
data = f.read() # type: ignore
elif image.url.uri.startswith("http"):
async with httpx.AsyncClient() as client:
response = await client.get(image.url.uri)
data = response.content
else:
raise ValueError("Unsupported URL type")
elif image.data:
# data is a base64 encoded string, decode it to bytes for RawMediaItem
data = base64.b64decode(image.data)
else:
raise ValueError("No data or URL provided")
return RawMediaItem(data=data)
else:
raise ValueError(f"Unsupported content type: {type(c)}")
if isinstance(content, list):
return await asyncio.gather(*(_localize_single(c) for c in content))
else:
return await _localize_single(content)
def content_has_media(content: InterleavedContent):
def _has_media_content(c):
return isinstance(c, ImageContentItem)
if isinstance(content, list):
return any(_has_media_content(c) for c in content)
else:
return _has_media_content(content)
def messages_have_media(messages: list[Message]):
return any(content_has_media(m.content) for m in messages)
def request_has_media(request: ChatCompletionRequest | CompletionRequest):
if isinstance(request, ChatCompletionRequest):
return messages_have_media(request.messages)
else:
return content_has_media(request.content)
async def localize_image_content(uri: str) -> tuple[bytes, str] | None:
if uri.startswith("http"):
async with httpx.AsyncClient() as client:
r = await client.get(uri)
content = r.content
content_type = r.headers.get("content-type")
if content_type:
format = content_type.split("/")[-1]
else:
format = "png"
return content, format
elif uri.startswith("data"):
# data:image/{format};base64,{data}
match = re.match(r"data:image/(\w+);base64,(.+)", uri)
if not match:
raise ValueError(f"Invalid data URL format, {uri[:40]}...")
fmt, image_data = match.groups()
content = base64.b64decode(image_data)
return content, fmt
else:
return None
async def convert_image_content_to_url(
media: ImageContentItem, download: bool = False, include_format: bool = True
) -> str:
image = media.image
if image.url and (not download or image.url.uri.startswith("data")):
return image.url.uri
if image.data:
# data is a base64 encoded string, decode it to bytes first
# TODO(mf): do this more efficiently, decode less
content = base64.b64decode(image.data)
pil_image = PIL_Image.open(io.BytesIO(content))
format = pil_image.format
else:
localize_result = await localize_image_content(image.url.uri)
if localize_result is None:
raise ValueError(f"Failed to localize image content from {image.url.uri}")
content, format = localize_result
if include_format:
return f"data:image/{format};base64," + base64.b64encode(content).decode("utf-8")
else:
return base64.b64encode(content).decode("utf-8")
def augment_content_with_response_format_prompt(response_format, content):
if fmt_prompt := response_format_prompt(response_format):
if isinstance(content, list):
return content + [TextContentItem(text=fmt_prompt)]
elif isinstance(content, str):
return [TextContentItem(text=content), TextContentItem(text=fmt_prompt)]
else:
return [content, TextContentItem(text=fmt_prompt)]
return content
async def chat_completion_request_to_prompt(request: ChatCompletionRequest, llama_model: str) -> str:
messages = chat_completion_request_to_messages(request, llama_model)
request.messages = messages
request = await convert_request_to_raw(request)
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
model_input = formatter.encode_dialog_prompt(
request.messages,
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
)
return formatter.tokenizer.decode(model_input.tokens)
async def chat_completion_request_to_model_input_info(
request: ChatCompletionRequest, llama_model: str
) -> tuple[str, int]:
messages = chat_completion_request_to_messages(request, llama_model)
request.messages = messages
request = await convert_request_to_raw(request)
formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
model_input = formatter.encode_dialog_prompt(
request.messages,
tool_prompt_format=request.tool_config.tool_prompt_format or get_default_tool_prompt_format(llama_model),
)
return (
formatter.tokenizer.decode(model_input.tokens),
len(model_input.tokens),
)
def chat_completion_request_to_messages(
request: ChatCompletionRequest,
llama_model: str,
) -> list[Message]:
"""Reads chat completion request and augments the messages to handle tools.
For eg. for llama_3_1, add system message with the appropriate tools or
add user messsage for custom tools, etc.
"""
assert llama_model is not None, "llama_model is required"
model = resolve_model(llama_model)
if model is None:
log.error(f"Could not resolve model {llama_model}")
return request.messages
allowed_models = supported_inference_models()
descriptors = [m.descriptor() for m in allowed_models]
if model.descriptor() not in descriptors:
log.error(f"Unsupported inference model? {model.descriptor()}")
return request.messages
if model.model_family == ModelFamily.llama3_1 or (
model.model_family == ModelFamily.llama3_2 and is_multimodal(model.core_model_id)
):
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
messages = augment_messages_for_tools_llama_3_1(request)
elif model.model_family in (
ModelFamily.llama3_2,
ModelFamily.llama3_3,
):
# llama3.2, llama3.3 follow the same tool prompt format
messages = augment_messages_for_tools_llama(request, PythonListCustomToolGenerator)
elif model.model_family == ModelFamily.llama4:
messages = augment_messages_for_tools_llama(request, PythonListCustomToolGeneratorLlama4)
else:
messages = request.messages
if fmt_prompt := response_format_prompt(request.response_format):
messages.append(UserMessage(content=fmt_prompt))
return messages
def response_format_prompt(fmt: ResponseFormat | None):
if not fmt:
return None
if fmt.type == ResponseFormatType.json_schema.value:
return f"Please respond in JSON format with the schema: {json.dumps(fmt.json_schema)}"
elif fmt.type == ResponseFormatType.grammar.value:
raise NotImplementedError("Grammar response format not supported yet")
else:
raise ValueError(f"Unknown response format {fmt.type}")
def augment_messages_for_tools_llama_3_1(
request: ChatCompletionRequest,
) -> list[Message]:
existing_messages = request.messages
existing_system_message = None
if existing_messages[0].role == Role.system.value:
existing_system_message = existing_messages.pop(0)
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
messages = []
default_gen = SystemDefaultGenerator()
default_template = default_gen.gen()
sys_content = ""
tool_template = None
if request.tools:
tool_gen = BuiltinToolGenerator()
tool_template = tool_gen.gen(request.tools)
sys_content += tool_template.render()
sys_content += "\n"
sys_content += default_template.render()
if existing_system_message:
# TODO: this fn is needed in many places
def _process(c):
if isinstance(c, str):
return c
else:
return "<media>"
sys_content += "\n"
if isinstance(existing_system_message.content, str):
sys_content += _process(existing_system_message.content)
elif isinstance(existing_system_message.content, list):
sys_content += "\n".join([_process(c) for c in existing_system_message.content])
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
if tool_choice_prompt:
sys_content += "\n" + tool_choice_prompt
messages.append(SystemMessage(content=sys_content))
has_custom_tools = request.tools is not None and any(isinstance(dfn.tool_name, str) for dfn in request.tools)
if has_custom_tools:
fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.json
if fmt == ToolPromptFormat.json:
tool_gen = JsonCustomToolGenerator()
elif fmt == ToolPromptFormat.function_tag:
tool_gen = FunctionTagCustomToolGenerator()
else:
raise ValueError(f"Non supported ToolPromptFormat {fmt}")
custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
custom_template = tool_gen.gen(custom_tools)
messages.append(UserMessage(content=custom_template.render()))
# Add back existing messages from the request
messages += existing_messages
return messages
def augment_messages_for_tools_llama(
request: ChatCompletionRequest,
custom_tool_prompt_generator,
) -> list[Message]:
existing_messages = request.messages
existing_system_message = None
if existing_messages[0].role == Role.system.value:
existing_system_message = existing_messages.pop(0)
assert existing_messages[0].role != Role.system.value, "Should only have 1 system message"
sys_content = ""
custom_tools, builtin_tools = [], []
for t in request.tools:
if isinstance(t.tool_name, str):
custom_tools.append(t)
else:
builtin_tools.append(t)
if builtin_tools:
tool_gen = BuiltinToolGenerator()
tool_template = tool_gen.gen(builtin_tools)
sys_content += tool_template.render()
sys_content += "\n"
custom_tools = [dfn for dfn in request.tools if isinstance(dfn.tool_name, str)]
if custom_tools:
fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.python_list
if fmt != ToolPromptFormat.python_list:
raise ValueError(f"Non supported ToolPromptFormat {request.tool_config.tool_prompt_format}")
system_prompt = None
if existing_system_message and request.tool_config.system_message_behavior == SystemMessageBehavior.replace:
system_prompt = existing_system_message.content
tool_template = custom_tool_prompt_generator().gen(custom_tools, system_prompt)
sys_content += tool_template.render()
sys_content += "\n"
if existing_system_message and (
request.tool_config.system_message_behavior == SystemMessageBehavior.append or not custom_tools
):
sys_content += interleaved_content_as_str(existing_system_message.content, sep="\n")
tool_choice_prompt = _get_tool_choice_prompt(request.tool_config.tool_choice, request.tools)
if tool_choice_prompt:
sys_content += "\n" + tool_choice_prompt
messages = [SystemMessage(content=sys_content.strip("\n")), *existing_messages]
return messages
def _get_tool_choice_prompt(tool_choice: ToolChoice | str, tools: list[ToolDefinition]) -> str:
if tool_choice == ToolChoice.auto:
return ""
elif tool_choice == ToolChoice.required:
return "You MUST use one of the provided functions/tools to answer the user query."
elif tool_choice == ToolChoice.none:
# tools are already not passed in
return ""
else:
# specific tool
return f"You MUST use the tool `{tool_choice}` to answer the user query."
def get_default_tool_prompt_format(model: str) -> ToolPromptFormat:
llama_model = resolve_model(model)
if llama_model is None:
log.warning(f"Could not resolve model {model}, defaulting to json tool prompt format")
return ToolPromptFormat.json
if llama_model.model_family == ModelFamily.llama3_1 or (
llama_model.model_family == ModelFamily.llama3_2 and is_multimodal(llama_model.core_model_id)
):
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
return ToolPromptFormat.json
elif llama_model.model_family in (
ModelFamily.llama3_2,
ModelFamily.llama3_3,
ModelFamily.llama4,
):
# llama3.2 and llama3.3 models follow the same tool prompt format
return ToolPromptFormat.python_list
else:
return ToolPromptFormat.json