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llama_stack/providers/adapters/inference/bedrock/bedrock.py
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@ -1,445 +1,451 @@
# 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 * # noqa: F403
import boto3
from botocore.client import BaseClient
from botocore.config import Config
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.adapters.inference.bedrock.config import BedrockConfig
BEDROCK_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
}
class BedrockInferenceAdapter(Inference, RoutableProviderForModels):
@staticmethod
def _create_bedrock_client(config: BedrockConfig) -> BaseClient:
retries_config = {
k: v
for k, v in dict(
total_max_attempts=config.total_max_attempts,
mode=config.retry_mode,
).items()
if v is not None
}
config_args = {
k: v
for k, v in dict(
region_name=config.region_name,
retries=retries_config if retries_config else None,
connect_timeout=config.connect_timeout,
read_timeout=config.read_timeout,
).items()
if v is not None
}
boto3_config = Config(**config_args)
session_args = {
k: v
for k, v in dict(
aws_access_key_id=config.aws_access_key_id,
aws_secret_access_key=config.aws_secret_access_key,
aws_session_token=config.aws_session_token,
region_name=config.region_name,
profile_name=config.profile_name,
).items()
if v is not None
}
boto3_session = boto3.session.Session(**session_args)
return boto3_session.client("bedrock-runtime", config=boto3_config)
def __init__(self, config: BedrockConfig) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
)
self._config = config
self._client = BedrockInferenceAdapter._create_bedrock_client(config)
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@property
def client(self) -> BaseClient:
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
self.client.close()
async def completion(
self,
model: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
raise NotImplementedError()
@staticmethod
def _bedrock_stop_reason_to_stop_reason(bedrock_stop_reason: str) -> StopReason:
if bedrock_stop_reason == "max_tokens":
return StopReason.out_of_tokens
return StopReason.end_of_turn
@staticmethod
def _builtin_tool_name_to_enum(tool_name_str: str) -> Union[BuiltinTool, str]:
for builtin_tool in BuiltinTool:
if builtin_tool.value == tool_name_str:
return builtin_tool
else:
return tool_name_str
@staticmethod
def _bedrock_message_to_message(converse_api_res: Dict) -> Message:
stop_reason = BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
converse_api_res["stopReason"]
)
bedrock_message = converse_api_res["output"]["message"]
role = bedrock_message["role"]
contents = bedrock_message["content"]
tool_calls = []
text_content = []
for content in contents:
if "toolUse" in content:
tool_use = content["toolUse"]
tool_calls.append(
ToolCall(
tool_name=BedrockInferenceAdapter._builtin_tool_name_to_enum(
tool_use["name"]
),
arguments=tool_use["input"] if "input" in tool_use else None,
call_id=tool_use["toolUseId"],
)
)
elif "text" in content:
text_content.append(content["text"])
return CompletionMessage(
role=role,
content=text_content,
stop_reason=stop_reason,
tool_calls=tool_calls,
)
@staticmethod
def _messages_to_bedrock_messages(
messages: List[Message],
) -> Tuple[List[Dict], Optional[List[Dict]]]:
bedrock_messages = []
system_bedrock_messages = []
user_contents = []
assistant_contents = None
for message in messages:
role = message.role
content_list = (
message.content
if isinstance(message.content, list)
else [message.content]
)
if role == "ipython" or role == "user":
if not user_contents:
user_contents = []
if role == "ipython":
user_contents.extend(
[
{
"toolResult": {
"toolUseId": message.call_id,
"content": [
{"text": content} for content in content_list
],
}
}
]
)
else:
user_contents.extend(
[{"text": content} for content in content_list]
)
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
assistant_contents = None
elif role == "system":
system_bedrock_messages.extend(
[{"text": content} for content in content_list]
)
elif role == "assistant":
if not assistant_contents:
assistant_contents = []
assistant_contents.extend(
[
{
"text": content,
}
for content in content_list
]
+ [
{
"toolUse": {
"input": tool_call.arguments,
"name": (
tool_call.tool_name
if isinstance(tool_call.tool_name, str)
else tool_call.tool_name.value
),
"toolUseId": tool_call.call_id,
}
}
for tool_call in message.tool_calls
]
)
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
user_contents = None
else:
# Unknown role
pass
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
if system_bedrock_messages:
return bedrock_messages, system_bedrock_messages
return bedrock_messages, None
@staticmethod
def get_bedrock_inference_config(sampling_params: Optional[SamplingParams]) -> Dict:
inference_config = {}
if sampling_params:
param_mapping = {
"max_tokens": "maxTokens",
"temperature": "temperature",
"top_p": "topP",
}
for k, v in param_mapping.items():
if getattr(sampling_params, k):
inference_config[v] = getattr(sampling_params, k)
return inference_config
@staticmethod
def _tool_parameters_to_input_schema(
tool_parameters: Optional[Dict[str, ToolParamDefinition]]
) -> Dict:
input_schema = {"type": "object"}
if not tool_parameters:
return input_schema
json_properties = {}
required = []
for name, param in tool_parameters.items():
json_property = {
"type": param.param_type,
}
if param.description:
json_property["description"] = param.description
if param.required:
required.append(name)
json_properties[name] = json_property
input_schema["properties"] = json_properties
if required:
input_schema["required"] = required
return input_schema
@staticmethod
def _tools_to_tool_config(
tools: Optional[List[ToolDefinition]], tool_choice: Optional[ToolChoice]
) -> Optional[Dict]:
if not tools:
return None
bedrock_tools = []
for tool in tools:
tool_name = (
tool.tool_name
if isinstance(tool.tool_name, str)
else tool.tool_name.value
)
tool_spec = {
"toolSpec": {
"name": tool_name,
"inputSchema": {
"json": BedrockInferenceAdapter._tool_parameters_to_input_schema(
tool.parameters
),
},
}
}
if tool.description:
tool_spec["toolSpec"]["description"] = tool.description
bedrock_tools.append(tool_spec)
tool_config = {
"tools": bedrock_tools,
}
if tool_choice:
tool_config["toolChoice"] = (
{"any": {}}
if tool_choice.value == ToolChoice.required
else {"auto": {}}
)
return tool_config
async def chat_completion(
self,
model: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
# zero-shot tool definitions as input to the model
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> (
AsyncGenerator
): # Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]:
bedrock_model = self.map_to_provider_model(model)
inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
sampling_params
)
tool_config = BedrockInferenceAdapter._tools_to_tool_config(tools, tool_choice)
bedrock_messages, system_bedrock_messages = (
BedrockInferenceAdapter._messages_to_bedrock_messages(messages)
)
converse_api_params = {
"modelId": bedrock_model,
"messages": bedrock_messages,
}
if inference_config:
converse_api_params["inferenceConfig"] = inference_config
# Tool use is not supported in streaming mode
if tool_config and not stream:
converse_api_params["toolConfig"] = tool_config
if system_bedrock_messages:
converse_api_params["system"] = system_bedrock_messages
if not stream:
converse_api_res = self.client.converse(**converse_api_params)
output_message = BedrockInferenceAdapter._bedrock_message_to_message(
converse_api_res
)
yield ChatCompletionResponse(
completion_message=output_message,
logprobs=None,
)
else:
converse_stream_api_res = self.client.converse_stream(**converse_api_params)
event_stream = converse_stream_api_res["stream"]
for chunk in event_stream:
if "messageStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
elif "contentBlockStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=ToolCall(
tool_name=chunk["contentBlockStart"]["toolUse"][
"name"
],
call_id=chunk["contentBlockStart"]["toolUse"][
"toolUseId"
],
),
parse_status=ToolCallParseStatus.started,
),
)
)
elif "contentBlockDelta" in chunk:
if "text" in chunk["contentBlockDelta"]["delta"]:
delta = chunk["contentBlockDelta"]["delta"]["text"]
else:
delta = ToolCallDelta(
content=ToolCall(
arguments=chunk["contentBlockDelta"]["delta"][
"toolUse"
]["input"]
),
parse_status=ToolCallParseStatus.success,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
)
)
elif "contentBlockStop" in chunk:
# Ignored
pass
elif "messageStop" in chunk:
stop_reason = (
BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
chunk["messageStop"]["stopReason"]
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
elif "metadata" in chunk:
# Ignored
pass
else:
# Ignored
pass
# 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 * # noqa: F403
import boto3
from botocore.client import BaseClient
from botocore.config import Config
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.adapters.inference.bedrock.config import BedrockConfig
BEDROCK_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
}
# NOTE: this is not quite tested after the recent refactors
class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: BedrockConfig) -> None:
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
)
self._config = config
self._client = _create_bedrock_client(config)
self.formatter = ChatFormat(Tokenizer.get_instance())
@property
def client(self) -> BaseClient:
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
self.client.close()
def completion(
self,
model: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
raise NotImplementedError()
@staticmethod
def _bedrock_stop_reason_to_stop_reason(bedrock_stop_reason: str) -> StopReason:
if bedrock_stop_reason == "max_tokens":
return StopReason.out_of_tokens
return StopReason.end_of_turn
@staticmethod
def _builtin_tool_name_to_enum(tool_name_str: str) -> Union[BuiltinTool, str]:
for builtin_tool in BuiltinTool:
if builtin_tool.value == tool_name_str:
return builtin_tool
else:
return tool_name_str
@staticmethod
def _bedrock_message_to_message(converse_api_res: Dict) -> Message:
stop_reason = BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
converse_api_res["stopReason"]
)
bedrock_message = converse_api_res["output"]["message"]
role = bedrock_message["role"]
contents = bedrock_message["content"]
tool_calls = []
text_content = []
for content in contents:
if "toolUse" in content:
tool_use = content["toolUse"]
tool_calls.append(
ToolCall(
tool_name=BedrockInferenceAdapter._builtin_tool_name_to_enum(
tool_use["name"]
),
arguments=tool_use["input"] if "input" in tool_use else None,
call_id=tool_use["toolUseId"],
)
)
elif "text" in content:
text_content.append(content["text"])
return CompletionMessage(
role=role,
content=text_content,
stop_reason=stop_reason,
tool_calls=tool_calls,
)
@staticmethod
def _messages_to_bedrock_messages(
messages: List[Message],
) -> Tuple[List[Dict], Optional[List[Dict]]]:
bedrock_messages = []
system_bedrock_messages = []
user_contents = []
assistant_contents = None
for message in messages:
role = message.role
content_list = (
message.content
if isinstance(message.content, list)
else [message.content]
)
if role == "ipython" or role == "user":
if not user_contents:
user_contents = []
if role == "ipython":
user_contents.extend(
[
{
"toolResult": {
"toolUseId": message.call_id,
"content": [
{"text": content} for content in content_list
],
}
}
]
)
else:
user_contents.extend(
[{"text": content} for content in content_list]
)
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
assistant_contents = None
elif role == "system":
system_bedrock_messages.extend(
[{"text": content} for content in content_list]
)
elif role == "assistant":
if not assistant_contents:
assistant_contents = []
assistant_contents.extend(
[
{
"text": content,
}
for content in content_list
]
+ [
{
"toolUse": {
"input": tool_call.arguments,
"name": (
tool_call.tool_name
if isinstance(tool_call.tool_name, str)
else tool_call.tool_name.value
),
"toolUseId": tool_call.call_id,
}
}
for tool_call in message.tool_calls
]
)
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
user_contents = None
else:
# Unknown role
pass
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
if system_bedrock_messages:
return bedrock_messages, system_bedrock_messages
return bedrock_messages, None
@staticmethod
def get_bedrock_inference_config(sampling_params: Optional[SamplingParams]) -> Dict:
inference_config = {}
if sampling_params:
param_mapping = {
"max_tokens": "maxTokens",
"temperature": "temperature",
"top_p": "topP",
}
for k, v in param_mapping.items():
if getattr(sampling_params, k):
inference_config[v] = getattr(sampling_params, k)
return inference_config
@staticmethod
def _tool_parameters_to_input_schema(
tool_parameters: Optional[Dict[str, ToolParamDefinition]],
) -> Dict:
input_schema = {"type": "object"}
if not tool_parameters:
return input_schema
json_properties = {}
required = []
for name, param in tool_parameters.items():
json_property = {
"type": param.param_type,
}
if param.description:
json_property["description"] = param.description
if param.required:
required.append(name)
json_properties[name] = json_property
input_schema["properties"] = json_properties
if required:
input_schema["required"] = required
return input_schema
@staticmethod
def _tools_to_tool_config(
tools: Optional[List[ToolDefinition]], tool_choice: Optional[ToolChoice]
) -> Optional[Dict]:
if not tools:
return None
bedrock_tools = []
for tool in tools:
tool_name = (
tool.tool_name
if isinstance(tool.tool_name, str)
else tool.tool_name.value
)
tool_spec = {
"toolSpec": {
"name": tool_name,
"inputSchema": {
"json": BedrockInferenceAdapter._tool_parameters_to_input_schema(
tool.parameters
),
},
}
}
if tool.description:
tool_spec["toolSpec"]["description"] = tool.description
bedrock_tools.append(tool_spec)
tool_config = {
"tools": bedrock_tools,
}
if tool_choice:
tool_config["toolChoice"] = (
{"any": {}}
if tool_choice.value == ToolChoice.required
else {"auto": {}}
)
return tool_config
def chat_completion(
self,
model: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
# zero-shot tool definitions as input to the model
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> (
AsyncGenerator
): # Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]:
bedrock_model = self.map_to_provider_model(model)
inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
sampling_params
)
tool_config = BedrockInferenceAdapter._tools_to_tool_config(tools, tool_choice)
bedrock_messages, system_bedrock_messages = (
BedrockInferenceAdapter._messages_to_bedrock_messages(messages)
)
converse_api_params = {
"modelId": bedrock_model,
"messages": bedrock_messages,
}
if inference_config:
converse_api_params["inferenceConfig"] = inference_config
# Tool use is not supported in streaming mode
if tool_config and not stream:
converse_api_params["toolConfig"] = tool_config
if system_bedrock_messages:
converse_api_params["system"] = system_bedrock_messages
if not stream:
converse_api_res = self.client.converse(**converse_api_params)
output_message = BedrockInferenceAdapter._bedrock_message_to_message(
converse_api_res
)
yield ChatCompletionResponse(
completion_message=output_message,
logprobs=None,
)
else:
converse_stream_api_res = self.client.converse_stream(**converse_api_params)
event_stream = converse_stream_api_res["stream"]
for chunk in event_stream:
if "messageStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
elif "contentBlockStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=ToolCall(
tool_name=chunk["contentBlockStart"]["toolUse"][
"name"
],
call_id=chunk["contentBlockStart"]["toolUse"][
"toolUseId"
],
),
parse_status=ToolCallParseStatus.started,
),
)
)
elif "contentBlockDelta" in chunk:
if "text" in chunk["contentBlockDelta"]["delta"]:
delta = chunk["contentBlockDelta"]["delta"]["text"]
else:
delta = ToolCallDelta(
content=ToolCall(
arguments=chunk["contentBlockDelta"]["delta"][
"toolUse"
]["input"]
),
parse_status=ToolCallParseStatus.success,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
)
)
elif "contentBlockStop" in chunk:
# Ignored
pass
elif "messageStop" in chunk:
stop_reason = (
BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
chunk["messageStop"]["stopReason"]
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
elif "metadata" in chunk:
# Ignored
pass
else:
# Ignored
pass
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()
def _create_bedrock_client(config: BedrockConfig) -> BaseClient:
retries_config = {
k: v
for k, v in dict(
total_max_attempts=config.total_max_attempts,
mode=config.retry_mode,
).items()
if v is not None
}
config_args = {
k: v
for k, v in dict(
region_name=config.region_name,
retries=retries_config if retries_config else None,
connect_timeout=config.connect_timeout,
read_timeout=config.read_timeout,
).items()
if v is not None
}
boto3_config = Config(**config_args)
session_args = {
k: v
for k, v in dict(
aws_access_key_id=config.aws_access_key_id,
aws_secret_access_key=config.aws_secret_access_key,
aws_session_token=config.aws_session_token,
region_name=config.region_name,
profile_name=config.profile_name,
).items()
if v is not None
}
boto3_session = boto3.session.Session(**session_args)
return boto3_session.client("bedrock-runtime", config=boto3_config)

251
llama_stack/providers/adapters/inference/databricks/databricks.py
View file

@ -6,39 +6,41 @@
from typing import AsyncGenerator
from openai import OpenAI
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from openai import OpenAI
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
from .config import DatabricksImplConfig
DATABRICKS_SUPPORTED_MODELS = {
"Llama3.1-70B-Instruct": "databricks-meta-llama-3-1-70b-instruct",
"Llama3.1-405B-Instruct": "databricks-meta-llama-3-1-405b-instruct",
}
class DatabricksInferenceAdapter(Inference):
class DatabricksInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: DatabricksImplConfig) -> None:
self.config = config
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@property
def client(self) -> OpenAI:
return OpenAI(
base_url=self.config.url,
api_key=self.config.api_token
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=DATABRICKS_SUPPORTED_MODELS
)
self.config = config
self.formatter = ChatFormat(Tokenizer.get_instance())
async def initialize(self) -> None:
return
@ -46,47 +48,10 @@ class DatabricksInferenceAdapter(Inference):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
# these are the model names the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def completion(self, request: CompletionRequest) -> AsyncGenerator:
def completion(self, request: CompletionRequest) -> AsyncGenerator:
raise NotImplementedError()
def _messages_to_databricks_messages(self, messages: list[Message]) -> list:
databricks_messages = []
for message in messages:
if message.role == "ipython":
role = "tool"
else:
role = message.role
databricks_messages.append({"role": role, "content": message.content})
return databricks_messages
def resolve_databricks_model(self, model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True)
in DATABRICKS_SUPPORTED_MODELS
), f"Unsupported model: {model_name}, use one of the supported models: {','.join(DATABRICKS_SUPPORTED_MODELS.keys())}"
return DATABRICKS_SUPPORTED_MODELS.get(
model.descriptor(shorten_default_variant=True)
)
def get_databricks_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(request.sampling_params, attr):
options[attr] = getattr(request.sampling_params, attr)
return options
async def chat_completion(
def chat_completion(
self,
model: str,
messages: List[Message],
@ -108,150 +73,46 @@ class DatabricksInferenceAdapter(Inference):
logprobs=logprobs,
)
messages = augment_messages_for_tools(request)
options = self.get_databricks_chat_options(request)
databricks_model = self.resolve_databricks_model(request.model)
if not request.stream:
r = self.client.chat.completions.create(
model=databricks_model,
messages=self._messages_to_databricks_messages(messages),
stream=False,
**options,
)
stop_reason = None
if r.choices[0].finish_reason:
if r.choices[0].finish_reason == "stop":
stop_reason = StopReason.end_of_turn
elif r.choices[0].finish_reason == "length":
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
r.choices[0].message.content, stop_reason
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
if stream:
return self._stream_chat_completion(request, client)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
return self._nonstream_chat_completion(request, client)
buffer = ""
ipython = False
stop_reason = None
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest, client: OpenAI
) -> ChatCompletionResponse:
params = self._get_params(request)
r = client.completions.create(**params)
return process_chat_completion_response(request, r, self.formatter)
for chunk in self.client.chat.completions.create(
model=databricks_model,
messages=self._messages_to_databricks_messages(messages),
stream=True,
**options,
):
if chunk.choices[0].finish_reason:
if (
stop_reason is None
and chunk.choices[0].finish_reason == "stop"
):
stop_reason = StopReason.end_of_turn
elif (
stop_reason is None
and chunk.choices[0].finish_reason == "length"
):
stop_reason = StopReason.out_of_tokens
break
async def _stream_chat_completion(
self, request: ChatCompletionRequest, client: OpenAI
) -> AsyncGenerator:
params = self._get_params(request)
text = chunk.choices[0].delta.content
async def _to_async_generator():
s = client.completions.create(**params)
for chunk in s:
yield chunk
if text is None:
continue
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(
request, stream, self.formatter
):
yield chunk
# check if its a tool call ( aka starts with <|python_tag|> )
if not ipython and text.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer += text
continue
def _get_params(self, request: ChatCompletionRequest) -> dict:
return {
"model": self.map_to_provider_model(request.model),
"prompt": chat_completion_request_to_prompt(request, self.formatter),
"stream": request.stream,
**get_sampling_options(request),
}
if ipython:
if text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
continue
elif text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
continue
buffer += text
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
else:
buffer += text
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=text,
stop_reason=stop_reason,
)
)
# parse tool calls and report errors
message = self.formatter.decode_assistant_message_from_content(
buffer, stop_reason
)
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

227
llama_stack/providers/adapters/inference/fireworks/fireworks.py
View file

@ -10,14 +10,19 @@ from fireworks.client import Fireworks
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
from .config import FireworksImplConfig
@ -27,21 +32,18 @@ FIREWORKS_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
"Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
"Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
"Llama3.2-1B-Instruct": "fireworks/llama-v3p2-1b-instruct",
"Llama3.2-3B-Instruct": "fireworks/llama-v3p2-3b-instruct",
}
class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
def __init__(self, config: FireworksImplConfig) -> None:
RoutableProviderForModels.__init__(
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
)
self.config = config
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@property
def client(self) -> Fireworks:
return Fireworks(api_key=self.config.api_key)
self.formatter = ChatFormat(Tokenizer.get_instance())
async def initialize(self) -> None:
return
@ -49,7 +51,7 @@ class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
async def shutdown(self) -> None:
pass
async def completion(
def completion(
self,
model: str,
content: InterleavedTextMedia,
@ -59,27 +61,7 @@ class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
) -> AsyncGenerator:
raise NotImplementedError()
def _messages_to_fireworks_messages(self, messages: list[Message]) -> list:
fireworks_messages = []
for message in messages:
if message.role == "ipython":
role = "tool"
else:
role = message.role
fireworks_messages.append({"role": role, "content": message.content})
return fireworks_messages
def get_fireworks_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(request.sampling_params, attr):
options[attr] = getattr(request.sampling_params, attr)
return options
async def chat_completion(
def chat_completion(
self,
model: str,
messages: List[Message],
@ -101,147 +83,48 @@ class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
logprobs=logprobs,
)
messages = augment_messages_for_tools(request)
# accumulate sampling params and other options to pass to fireworks
options = self.get_fireworks_chat_options(request)
fireworks_model = self.map_to_provider_model(request.model)
if not request.stream:
r = await self.client.chat.completions.acreate(
model=fireworks_model,
messages=self._messages_to_fireworks_messages(messages),
stream=False,
**options,
)
stop_reason = None
if r.choices[0].finish_reason:
if r.choices[0].finish_reason == "stop":
stop_reason = StopReason.end_of_turn
elif r.choices[0].finish_reason == "length":
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
r.choices[0].message.content, stop_reason
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
client = Fireworks(api_key=self.config.api_key)
if stream:
return self._stream_chat_completion(request, client)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
return self._nonstream_chat_completion(request, client)
buffer = ""
ipython = False
stop_reason = None
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest, client: Fireworks
) -> ChatCompletionResponse:
params = self._get_params(request)
r = await client.completion.acreate(**params)
return process_chat_completion_response(request, r, self.formatter)
async for chunk in self.client.chat.completions.acreate(
model=fireworks_model,
messages=self._messages_to_fireworks_messages(messages),
stream=True,
**options,
):
if chunk.choices[0].finish_reason:
if stop_reason is None and chunk.choices[0].finish_reason == "stop":
stop_reason = StopReason.end_of_turn
elif (
stop_reason is None
and chunk.choices[0].finish_reason == "length"
):
stop_reason = StopReason.out_of_tokens
break
async def _stream_chat_completion(
self, request: ChatCompletionRequest, client: Fireworks
) -> AsyncGenerator:
params = self._get_params(request)
text = chunk.choices[0].delta.content
if text is None:
continue
stream = client.completion.acreate(**params)
async for chunk in process_chat_completion_stream_response(
request, stream, self.formatter
):
yield chunk
# check if its a tool call ( aka starts with <|python_tag|> )
if not ipython and text.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer += text
continue
def _get_params(self, request: ChatCompletionRequest) -> dict:
prompt = chat_completion_request_to_prompt(request, self.formatter)
# Fireworks always prepends with BOS
if prompt.startswith("<|begin_of_text|>"):
prompt = prompt[len("<|begin_of_text|>") :]
if ipython:
if text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
continue
elif text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
continue
options = get_sampling_options(request)
options.setdefault("max_tokens", 512)
return {
"model": self.map_to_provider_model(request.model),
"prompt": prompt,
"stream": request.stream,
**options,
}
buffer += text
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
else:
buffer += text
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=text,
stop_reason=stop_reason,
)
)
# parse tool calls and report errors
message = self.formatter.decode_assistant_message_from_content(
buffer, stop_reason
)
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

287
llama_stack/providers/adapters/inference/ollama/ollama.py
View file

@ -9,35 +9,38 @@ from typing import AsyncGenerator
import httpx
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from ollama import AsyncClient
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from llama_stack.providers.datatypes import ModelsProtocolPrivate
# TODO: Eventually this will move to the llama cli model list command
# mapping of Model SKUs to ollama models
OLLAMA_SUPPORTED_SKUS = {
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
process_chat_completion_response,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
OLLAMA_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
"Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
"Llama3.2-1B-Instruct": "llama3.2:1b-instruct-fp16",
"Llama3.2-3B-Instruct": "llama3.2:3b-instruct-fp16",
"Llama-Guard-3-8B": "xe/llamaguard3:latest",
}
class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
def __init__(self, url: str) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=OLLAMA_SUPPORTED_SKUS
)
self.url = url
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
self.formatter = ChatFormat(Tokenizer.get_instance())
@property
def client(self) -> AsyncClient:
@ -55,7 +58,33 @@ class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
async def shutdown(self) -> None:
pass
async def completion(
async def register_model(self, model: ModelDef) -> None:
raise ValueError("Dynamic model registration is not supported")
async def list_models(self) -> List[ModelDef]:
ollama_to_llama = {v: k for k, v in OLLAMA_SUPPORTED_MODELS.items()}
ret = []
res = await self.client.ps()
for r in res["models"]:
if r["model"] not in ollama_to_llama:
print(f"Ollama is running a model unknown to Llama Stack: {r['model']}")
continue
llama_model = ollama_to_llama[r["model"]]
ret.append(
ModelDef(
identifier=llama_model,
llama_model=llama_model,
metadata={
"ollama_model": r["model"],
},
)
)
return ret
def completion(
self,
model: str,
content: InterleavedTextMedia,
@ -65,32 +94,7 @@ class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
) -> AsyncGenerator:
raise NotImplementedError()
def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
ollama_messages = []
for message in messages:
if message.role == "ipython":
role = "tool"
else:
role = message.role
ollama_messages.append({"role": role, "content": message.content})
return ollama_messages
def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(request.sampling_params, attr):
options[attr] = getattr(request.sampling_params, attr)
if (
request.sampling_params.repetition_penalty is not None
and request.sampling_params.repetition_penalty != 1.0
):
options["repeat_penalty"] = request.sampling_params.repetition_penalty
return options
async def chat_completion(
def chat_completion(
self,
model: str,
messages: List[Message],
@ -111,156 +115,61 @@ class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
stream=stream,
logprobs=logprobs,
)
messages = augment_messages_for_tools(request)
# accumulate sampling params and other options to pass to ollama
options = self.get_ollama_chat_options(request)
ollama_model = self.map_to_provider_model(request.model)
res = await self.client.ps()
need_model_pull = True
for r in res["models"]:
if ollama_model == r["model"]:
need_model_pull = False
break
if need_model_pull:
print(f"Pulling model: {ollama_model}")
status = await self.client.pull(ollama_model)
assert (
status["status"] == "success"
), f"Failed to pull model {self.model} in ollama"
if not request.stream:
r = await self.client.chat(
model=ollama_model,
messages=self._messages_to_ollama_messages(messages),
stream=False,
options=options,
)
stop_reason = None
if r["done"]:
if r["done_reason"] == "stop":
stop_reason = StopReason.end_of_turn
elif r["done_reason"] == "length":
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
r["message"]["content"], stop_reason
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
if stream:
return self._stream_chat_completion(request)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
return self._nonstream_chat_completion(request)
def _get_params(self, request: ChatCompletionRequest) -> dict:
return {
"model": OLLAMA_SUPPORTED_MODELS[request.model],
"prompt": chat_completion_request_to_prompt(request, self.formatter),
"options": get_sampling_options(request),
"raw": True,
"stream": request.stream,
}
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest
) -> ChatCompletionResponse:
params = self._get_params(request)
r = await self.client.generate(**params)
assert isinstance(r, dict)
choice = OpenAICompatCompletionChoice(
finish_reason=r["done_reason"] if r["done"] else None,
text=r["response"],
)
response = OpenAICompatCompletionResponse(
choices=[choice],
)
return process_chat_completion_response(request, response, self.formatter)
async def _stream_chat_completion(
self, request: ChatCompletionRequest
) -> AsyncGenerator:
params = self._get_params(request)
async def _generate_and_convert_to_openai_compat():
s = await self.client.generate(**params)
async for chunk in s:
choice = OpenAICompatCompletionChoice(
finish_reason=chunk["done_reason"] if chunk["done"] else None,
text=chunk["response"],
)
)
stream = await self.client.chat(
model=ollama_model,
messages=self._messages_to_ollama_messages(messages),
stream=True,
options=options,
)
buffer = ""
ipython = False
stop_reason = None
async for chunk in stream:
if chunk["done"]:
if stop_reason is None and chunk["done_reason"] == "stop":
stop_reason = StopReason.end_of_turn
elif stop_reason is None and chunk["done_reason"] == "length":
stop_reason = StopReason.out_of_tokens
break
text = chunk["message"]["content"]
# check if its a tool call ( aka starts with <|python_tag|> )
if not ipython and text.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer += text
continue
if ipython:
if text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
continue
elif text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
continue
buffer += text
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
else:
buffer += text
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=text,
stop_reason=stop_reason,
)
)
# parse tool calls and report errors
message = self.formatter.decode_assistant_message_from_content(
buffer, stop_reason
)
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
yield OpenAICompatCompletionResponse(
choices=[choice],
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
stream = _generate_and_convert_to_openai_compat()
async for chunk in process_chat_completion_stream_response(
request, stream, self.formatter
):
yield chunk
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

6
llama_stack/providers/adapters/inference/sample/sample.py
View file

@ -9,14 +9,12 @@ from .config import SampleConfig
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
class SampleInferenceImpl(Inference, RoutableProvider):
class SampleInferenceImpl(Inference):
def __init__(self, config: SampleConfig):
self.config = config
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
async def register_model(self, model: ModelDef) -> None:
# these are the model names the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass

2
llama_stack/providers/adapters/inference/tgi/config.py
View file

@ -34,7 +34,7 @@ class InferenceEndpointImplConfig(BaseModel):
@json_schema_type
class InferenceAPIImplConfig(BaseModel):
model_id: str = Field(
huggingface_repo: str = Field(
description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
)
api_token: Optional[str] = Field(

258
llama_stack/providers/adapters/inference/tgi/tgi.py
View file

@ -6,18 +6,27 @@
import logging
from typing import AsyncGenerator
from typing import AsyncGenerator, List, Optional
from huggingface_hub import AsyncInferenceClient, HfApi
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.distribution.datatypes import RoutableProvider
from llama_models.sku_list import all_registered_models
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
from llama_stack.apis.models import * # noqa: F403
from llama_stack.providers.datatypes import ModelDef, ModelsProtocolPrivate
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
OpenAICompatCompletionChoice,
OpenAICompatCompletionResponse,
process_chat_completion_response,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_model_input_info,
)
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
@ -25,24 +34,39 @@ from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImpl
logger = logging.getLogger(__name__)
class _HfAdapter(Inference, RoutableProvider):
class _HfAdapter(Inference, ModelsProtocolPrivate):
client: AsyncInferenceClient
max_tokens: int
model_id: str
def __init__(self) -> None:
self.tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(self.tokenizer)
self.formatter = ChatFormat(Tokenizer.get_instance())
self.huggingface_repo_to_llama_model_id = {
model.huggingface_repo: model.descriptor()
for model in all_registered_models()
if model.huggingface_repo
}
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
# these are the model names the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def register_model(self, model: ModelDef) -> None:
raise ValueError("Model registration is not supported for HuggingFace models")
async def list_models(self) -> List[ModelDef]:
repo = self.model_id
identifier = self.huggingface_repo_to_llama_model_id[repo]
return [
ModelDef(
identifier=identifier,
llama_model=identifier,
metadata={
"huggingface_repo": repo,
},
)
]
async def shutdown(self) -> None:
pass
async def completion(
def completion(
self,
model: str,
content: InterleavedTextMedia,
@ -52,16 +76,7 @@ class _HfAdapter(Inference, RoutableProvider):
) -> AsyncGenerator:
raise NotImplementedError()
def get_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(request.sampling_params, attr):
options[attr] = getattr(request.sampling_params, attr)
return options
async def chat_completion(
def chat_completion(
self,
model: str,
messages: List[Message],
@ -83,146 +98,71 @@ class _HfAdapter(Inference, RoutableProvider):
logprobs=logprobs,
)
messages = augment_messages_for_tools(request)
model_input = self.formatter.encode_dialog_prompt(messages)
prompt = self.tokenizer.decode(model_input.tokens)
if stream:
return self._stream_chat_completion(request)
else:
return self._nonstream_chat_completion(request)
input_tokens = len(model_input.tokens)
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest
) -> ChatCompletionResponse:
params = self._get_params(request)
r = await self.client.text_generation(**params)
choice = OpenAICompatCompletionChoice(
finish_reason=r.details.finish_reason,
text="".join(t.text for t in r.details.tokens),
)
response = OpenAICompatCompletionResponse(
choices=[choice],
)
return process_chat_completion_response(request, response, self.formatter)
async def _stream_chat_completion(
self, request: ChatCompletionRequest
) -> AsyncGenerator:
params = self._get_params(request)
async def _generate_and_convert_to_openai_compat():
s = await self.client.text_generation(**params)
async for chunk in s:
token_result = chunk.token
choice = OpenAICompatCompletionChoice(text=token_result.text)
yield OpenAICompatCompletionResponse(
choices=[choice],
)
stream = _generate_and_convert_to_openai_compat()
async for chunk in process_chat_completion_stream_response(
request, stream, self.formatter
):
yield chunk
def _get_params(self, request: ChatCompletionRequest) -> dict:
prompt, input_tokens = chat_completion_request_to_model_input_info(
request, self.formatter
)
max_new_tokens = min(
request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
self.max_tokens - input_tokens - 1,
)
options = get_sampling_options(request)
return dict(
prompt=prompt,
stream=request.stream,
details=True,
max_new_tokens=max_new_tokens,
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
**options,
)
print(f"Calculated max_new_tokens: {max_new_tokens}")
options = self.get_chat_options(request)
if not request.stream:
response = await self.client.text_generation(
prompt=prompt,
stream=False,
details=True,
max_new_tokens=max_new_tokens,
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
**options,
)
stop_reason = None
if response.details.finish_reason:
if response.details.finish_reason in ["stop", "eos_token"]:
stop_reason = StopReason.end_of_turn
elif response.details.finish_reason == "length":
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
response.generated_text,
stop_reason,
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
buffer = ""
ipython = False
stop_reason = None
tokens = []
async for response in await self.client.text_generation(
prompt=prompt,
stream=True,
details=True,
max_new_tokens=max_new_tokens,
stop_sequences=["<|eom_id|>", "<|eot_id|>"],
**options,
):
token_result = response.token
buffer += token_result.text
tokens.append(token_result.id)
if not ipython and buffer.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer = buffer[len("<|python_tag|>") :]
continue
if token_result.text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
elif token_result.text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
else:
text = token_result.text
if ipython:
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
else:
delta = text
if stop_reason is None:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
if stop_reason is None:
stop_reason = StopReason.out_of_tokens
# parse tool calls and report errors
message = self.formatter.decode_assistant_message(tokens, stop_reason)
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()
class TGIAdapter(_HfAdapter):
@ -236,7 +176,7 @@ class TGIAdapter(_HfAdapter):
class InferenceAPIAdapter(_HfAdapter):
async def initialize(self, config: InferenceAPIImplConfig) -> None:
self.client = AsyncInferenceClient(
model=config.model_id, token=config.api_token
model=config.huggingface_repo, token=config.api_token
)
endpoint_info = await self.client.get_endpoint_info()
self.max_tokens = endpoint_info["max_total_tokens"]

220
llama_stack/providers/adapters/inference/together/together.py
View file

@ -8,17 +8,22 @@ from typing import AsyncGenerator
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.datatypes import Message
from llama_models.llama3.api.tokenizer import Tokenizer
from together import Together
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
process_chat_completion_response,
process_chat_completion_stream_response,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
)
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from .config import TogetherImplConfig
@ -34,19 +39,14 @@ TOGETHER_SUPPORTED_MODELS = {
class TogetherInferenceAdapter(
Inference, NeedsRequestProviderData, RoutableProviderForModels
ModelRegistryHelper, Inference, NeedsRequestProviderData
):
def __init__(self, config: TogetherImplConfig) -> None:
RoutableProviderForModels.__init__(
ModelRegistryHelper.__init__(
self, stack_to_provider_models_map=TOGETHER_SUPPORTED_MODELS
)
self.config = config
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@property
def client(self) -> Together:
return Together(api_key=self.config.api_key)
self.formatter = ChatFormat(Tokenizer.get_instance())
async def initialize(self) -> None:
return
@ -64,27 +64,7 @@ class TogetherInferenceAdapter(
) -> AsyncGenerator:
raise NotImplementedError()
def _messages_to_together_messages(self, messages: list[Message]) -> list:
together_messages = []
for message in messages:
if message.role == "ipython":
role = "tool"
else:
role = message.role
together_messages.append({"role": role, "content": message.content})
return together_messages
def get_together_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
if getattr(request.sampling_params, attr):
options[attr] = getattr(request.sampling_params, attr)
return options
async def chat_completion(
def chat_completion(
self,
model: str,
messages: List[Message],
@ -95,7 +75,6 @@ class TogetherInferenceAdapter(
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
together_api_key = None
if self.config.api_key is not None:
together_api_key = self.config.api_key
@ -108,7 +87,6 @@ class TogetherInferenceAdapter(
together_api_key = provider_data.together_api_key
client = Together(api_key=together_api_key)
# wrapper request to make it easier to pass around (internal only, not exposed to API)
request = ChatCompletionRequest(
model=model,
messages=messages,
@ -120,146 +98,46 @@ class TogetherInferenceAdapter(
logprobs=logprobs,
)
# accumulate sampling params and other options to pass to together
options = self.get_together_chat_options(request)
together_model = self.map_to_provider_model(request.model)
messages = augment_messages_for_tools(request)
if not request.stream:
# TODO: might need to add back an async here
r = client.chat.completions.create(
model=together_model,
messages=self._messages_to_together_messages(messages),
stream=False,
**options,
)
stop_reason = None
if r.choices[0].finish_reason:
if (
r.choices[0].finish_reason == "stop"
or r.choices[0].finish_reason == "eos"
):
stop_reason = StopReason.end_of_turn
elif r.choices[0].finish_reason == "length":
stop_reason = StopReason.out_of_tokens
completion_message = self.formatter.decode_assistant_message_from_content(
r.choices[0].message.content, stop_reason
)
yield ChatCompletionResponse(
completion_message=completion_message,
logprobs=None,
)
if stream:
return self._stream_chat_completion(request, client)
else:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
return self._nonstream_chat_completion(request, client)
buffer = ""
ipython = False
stop_reason = None
async def _nonstream_chat_completion(
self, request: ChatCompletionRequest, client: Together
) -> ChatCompletionResponse:
params = self._get_params(request)
r = client.completions.create(**params)
return process_chat_completion_response(request, r, self.formatter)
for chunk in client.chat.completions.create(
model=together_model,
messages=self._messages_to_together_messages(messages),
stream=True,
**options,
):
if finish_reason := chunk.choices[0].finish_reason:
if stop_reason is None and finish_reason in ["stop", "eos"]:
stop_reason = StopReason.end_of_turn
elif stop_reason is None and finish_reason == "length":
stop_reason = StopReason.out_of_tokens
break
async def _stream_chat_completion(
self, request: ChatCompletionRequest, client: Together
) -> AsyncGenerator:
params = self._get_params(request)
text = chunk.choices[0].delta.content
if text is None:
continue
# if we shift to TogetherAsyncClient, we won't need this wrapper
async def _to_async_generator():
s = client.completions.create(**params)
for chunk in s:
yield chunk
# check if its a tool call ( aka starts with <|python_tag|> )
if not ipython and text.startswith("<|python_tag|>"):
ipython = True
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.started,
),
)
)
buffer += text
continue
stream = _to_async_generator()
async for chunk in process_chat_completion_stream_response(
request, stream, self.formatter
):
yield chunk
if ipython:
if text == "<|eot_id|>":
stop_reason = StopReason.end_of_turn
text = ""
continue
elif text == "<|eom_id|>":
stop_reason = StopReason.end_of_message
text = ""
continue
def _get_params(self, request: ChatCompletionRequest) -> dict:
return {
"model": self.map_to_provider_model(request.model),
"prompt": chat_completion_request_to_prompt(request, self.formatter),
"stream": request.stream,
**get_sampling_options(request),
}
buffer += text
delta = ToolCallDelta(
content=text,
parse_status=ToolCallParseStatus.in_progress,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
stop_reason=stop_reason,
)
)
else:
buffer += text
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=text,
stop_reason=stop_reason,
)
)
# parse tool calls and report errors
message = self.formatter.decode_assistant_message_from_content(
buffer, stop_reason
)
parsed_tool_calls = len(message.tool_calls) > 0
if ipython and not parsed_tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content="",
parse_status=ToolCallParseStatus.failure,
),
stop_reason=stop_reason,
)
)
for tool_call in message.tool_calls:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=tool_call,
parse_status=ToolCallParseStatus.success,
),
stop_reason=stop_reason,
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
async def embeddings(
self,
model: str,
contents: List[InterleavedTextMedia],
) -> EmbeddingsResponse:
raise NotImplementedError()

84
llama_stack/providers/adapters/memory/chroma/chroma.py
View file

@ -5,16 +5,17 @@
# the root directory of this source tree.
import json
import uuid
from typing import List
from urllib.parse import urlparse
import chromadb
from numpy.typing import NDArray
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from pydantic import parse_obj_as
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
from llama_stack.providers.utils.memory.vector_store import (
BankWithIndex,
EmbeddingIndex,
@ -65,7 +66,7 @@ class ChromaIndex(EmbeddingIndex):
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class ChromaMemoryAdapter(Memory, RoutableProvider):
class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
def __init__(self, url: str) -> None:
print(f"Initializing ChromaMemoryAdapter with url: {url}")
url = url.rstrip("/")
@ -93,56 +94,43 @@ class ChromaMemoryAdapter(Memory, RoutableProvider):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
print(f"[chroma] Registering memory bank routing keys: {routing_keys}")
pass
async def create_memory_bank(
async def register_memory_bank(
self,
name: str,
config: MemoryBankConfig,
url: Optional[URL] = None,
) -> MemoryBank:
bank_id = str(uuid.uuid4())
bank = MemoryBank(
bank_id=bank_id,
name=name,
config=config,
url=url,
)
collection = await self.client.create_collection(
name=bank_id,
metadata={"bank": bank.json()},
memory_bank: MemoryBankDef,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
collection = await self.client.get_or_create_collection(
name=memory_bank.identifier,
metadata={"bank": memory_bank.json()},
)
bank_index = BankWithIndex(
bank=bank, index=ChromaIndex(self.client, collection)
bank=memory_bank, index=ChromaIndex(self.client, collection)
)
self.cache[bank_id] = bank_index
return bank
async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
bank_index = await self._get_and_cache_bank_index(bank_id)
if bank_index is None:
return None
return bank_index.bank
async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
if bank_id in self.cache:
return self.cache[bank_id]
self.cache[memory_bank.identifier] = bank_index
async def list_memory_banks(self) -> List[MemoryBankDef]:
collections = await self.client.list_collections()
for collection in collections:
if collection.name == bank_id:
print(collection.metadata)
bank = MemoryBank(**json.loads(collection.metadata["bank"]))
index = BankWithIndex(
bank=bank,
index=ChromaIndex(self.client, collection),
)
self.cache[bank_id] = index
return index
try:
data = json.loads(collection.metadata["bank"])
bank = parse_obj_as(MemoryBankDef, data)
except Exception:
import traceback
return None
traceback.print_exc()
print(f"Failed to parse bank: {collection.metadata}")
continue
index = BankWithIndex(
bank=bank,
index=ChromaIndex(self.client, collection),
)
self.cache[bank.identifier] = index
return [i.bank for i in self.cache.values()]
async def insert_documents(
self,
@ -150,7 +138,7 @@ class ChromaMemoryAdapter(Memory, RoutableProvider):
documents: List[MemoryBankDocument],
ttl_seconds: Optional[int] = None,
) -> None:
index = await self._get_and_cache_bank_index(bank_id)
index = self.cache.get(bank_id, None)
if not index:
raise ValueError(f"Bank {bank_id} not found")
@ -162,7 +150,7 @@ class ChromaMemoryAdapter(Memory, RoutableProvider):
query: InterleavedTextMedia,
params: Optional[Dict[str, Any]] = None,
) -> QueryDocumentsResponse:
index = await self._get_and_cache_bank_index(bank_id)
index = self.cache.get(bank_id, None)
if not index:
raise ValueError(f"Bank {bank_id} not found")

91
llama_stack/providers/adapters/memory/pgvector/pgvector.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import uuid
from typing import List, Tuple
import psycopg2
@ -12,11 +11,11 @@ from numpy.typing import NDArray
from psycopg2 import sql
from psycopg2.extras import execute_values, Json
from pydantic import BaseModel
from pydantic import BaseModel, parse_obj_as
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
from llama_stack.providers.utils.memory.vector_store import (
ALL_MINILM_L6_V2_DIMENSION,
BankWithIndex,
@ -46,23 +45,17 @@ def upsert_models(cur, keys_models: List[Tuple[str, BaseModel]]):
execute_values(cur, query, values, template="(%s, %s)")
def load_models(cur, keys: List[str], cls):
def load_models(cur, cls):
query = "SELECT key, data FROM metadata_store"
if keys:
placeholders = ",".join(["%s"] * len(keys))
query += f" WHERE key IN ({placeholders})"
cur.execute(query, keys)
else:
cur.execute(query)
cur.execute(query)
rows = cur.fetchall()
return [cls(**row["data"]) for row in rows]
return [parse_obj_as(cls, row["data"]) for row in rows]
class PGVectorIndex(EmbeddingIndex):
def __init__(self, bank: MemoryBank, dimension: int, cursor):
def __init__(self, bank: MemoryBankDef, dimension: int, cursor):
self.cursor = cursor
self.table_name = f"vector_store_{bank.name}"
self.table_name = f"vector_store_{bank.identifier}"
self.cursor.execute(
f"""
@ -119,7 +112,7 @@ class PGVectorIndex(EmbeddingIndex):
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class PGVectorMemoryAdapter(Memory, RoutableProvider):
class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
def __init__(self, config: PGVectorConfig) -> None:
print(f"Initializing PGVectorMemoryAdapter -> {config.host}:{config.port}")
self.config = config
@ -161,57 +154,37 @@ class PGVectorMemoryAdapter(Memory, RoutableProvider):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
print(f"[pgvector] Registering memory bank routing keys: {routing_keys}")
pass
async def create_memory_bank(
async def register_memory_bank(
self,
name: str,
config: MemoryBankConfig,
url: Optional[URL] = None,
) -> MemoryBank:
bank_id = str(uuid.uuid4())
bank = MemoryBank(
bank_id=bank_id,
name=name,
config=config,
url=url,
)
memory_bank: MemoryBankDef,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
upsert_models(
self.cursor,
[
(bank.bank_id, bank),
(memory_bank.identifier, memory_bank),
],
)
index = BankWithIndex(
bank=bank,
index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
bank=memory_bank,
index=PGVectorIndex(memory_bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
)
self.cache[bank_id] = index
return bank
self.cache[memory_bank.identifier] = index
async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
bank_index = await self._get_and_cache_bank_index(bank_id)
if bank_index is None:
return None
return bank_index.bank
async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
if bank_id in self.cache:
return self.cache[bank_id]
banks = load_models(self.cursor, [bank_id], MemoryBank)
if not banks:
return None
bank = banks[0]
index = BankWithIndex(
bank=bank,
index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
)
self.cache[bank_id] = index
return index
async def list_memory_banks(self) -> List[MemoryBankDef]:
banks = load_models(self.cursor, MemoryBankDef)
for bank in banks:
if bank.identifier not in self.cache:
index = BankWithIndex(
bank=bank,
index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
)
self.cache[bank.identifier] = index
return banks
async def insert_documents(
self,
@ -219,7 +192,7 @@ class PGVectorMemoryAdapter(Memory, RoutableProvider):
documents: List[MemoryBankDocument],
ttl_seconds: Optional[int] = None,
) -> None:
index = await self._get_and_cache_bank_index(bank_id)
index = self.cache.get(bank_id, None)
if not index:
raise ValueError(f"Bank {bank_id} not found")
@ -231,7 +204,7 @@ class PGVectorMemoryAdapter(Memory, RoutableProvider):
query: InterleavedTextMedia,
params: Optional[Dict[str, Any]] = None,
) -> QueryDocumentsResponse:
index = await self._get_and_cache_bank_index(bank_id)
index = self.cache.get(bank_id, None)
if not index:
raise ValueError(f"Bank {bank_id} not found")

6
llama_stack/providers/adapters/memory/sample/sample.py
View file

@ -9,14 +9,12 @@ from .config import SampleConfig
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
class SampleMemoryImpl(Memory, RoutableProvider):
class SampleMemoryImpl(Memory):
def __init__(self, config: SampleConfig):
self.config = config
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
async def register_memory_bank(self, memory_bank: MemoryBankDef) -> None:
# these are the memory banks the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass

11
llama_stack/providers/adapters/memory/weaviate/__init__.py
View file

@ -1,8 +1,15 @@
from .config import WeaviateConfig
# 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 .config import WeaviateConfig, WeaviateRequestProviderData # noqa: F401
async def get_adapter_impl(config: WeaviateConfig, _deps):
from .weaviate import WeaviateMemoryAdapter
impl = WeaviateMemoryAdapter(config)
await impl.initialize()
return impl
return impl

10
llama_stack/providers/adapters/memory/weaviate/config.py
View file

@ -4,15 +4,13 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
from pydantic import BaseModel
class WeaviateRequestProviderData(BaseModel):
# if there _is_ provider data, it must specify the API KEY
# if you want it to be optional, use Optional[str]
weaviate_api_key: str
weaviate_cluster_url: str
@json_schema_type
class WeaviateConfig(BaseModel):
collection: str = Field(default="MemoryBank")
pass

218
llama_stack/providers/adapters/memory/weaviate/weaviate.py
View file

@ -1,14 +1,20 @@
# 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 json
import uuid
from typing import List, Optional, Dict, Any
from numpy.typing import NDArray
from typing import Any, Dict, List, Optional
import weaviate
import weaviate.classes as wvc
from numpy.typing import NDArray
from weaviate.classes.init import Auth
from llama_stack.apis.memory import *
from llama_stack.distribution.request_headers import get_request_provider_data
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
from llama_stack.providers.utils.memory.vector_store import (
BankWithIndex,
EmbeddingIndex,
@ -16,162 +22,154 @@ from llama_stack.providers.utils.memory.vector_store import (
from .config import WeaviateConfig, WeaviateRequestProviderData
class WeaviateIndex(EmbeddingIndex):
def __init__(self, client: weaviate.Client, collection: str):
def __init__(self, client: weaviate.Client, collection_name: str):
self.client = client
self.collection = collection
self.collection_name = collection_name
async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
assert len(chunks) == len(embeddings), f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
assert len(chunks) == len(
embeddings
), f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
data_objects = []
for i, chunk in enumerate(chunks):
data_objects.append(wvc.data.DataObject(
properties={
"chunk_content": chunk,
},
vector = embeddings[i].tolist()
))
data_objects.append(
wvc.data.DataObject(
properties={
"chunk_content": chunk.json(),
},
vector=embeddings[i].tolist(),
)
)
# Inserting chunks into a prespecified Weaviate collection
assert self.collection is not None, "Collection name must be specified"
my_collection = self.client.collections.get(self.collection)
await my_collection.data.insert_many(data_objects)
collection = self.client.collections.get(self.collection_name)
# TODO: make this async friendly
collection.data.insert_many(data_objects)
async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
assert self.collection is not None, "Collection name must be specified"
collection = self.client.collections.get(self.collection_name)
my_collection = self.client.collections.get(self.collection)
results = my_collection.query.near_vector(
near_vector = embedding.tolist(),
limit = k,
return_meta_data = wvc.query.MetadataQuery(distance=True)
results = collection.query.near_vector(
near_vector=embedding.tolist(),
limit=k,
return_metadata=wvc.query.MetadataQuery(distance=True),
)
chunks = []
scores = []
for doc in results.objects:
chunk_json = doc.properties["chunk_content"]
try:
chunk = doc.properties["chunk_content"]
chunks.append(chunk)
scores.append(1.0 / doc.metadata.distance)
except Exception as e:
chunk_dict = json.loads(chunk_json)
chunk = Chunk(**chunk_dict)
except Exception:
import traceback
traceback.print_exc()
print(f"Failed to parse document: {e}")
print(f"Failed to parse document: {chunk_json}")
continue
chunks.append(chunk)
scores.append(1.0 / doc.metadata.distance)
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class WeaviateMemoryAdapter(Memory):
class WeaviateMemoryAdapter(
Memory, NeedsRequestProviderData, MemoryBanksProtocolPrivate
):
def __init__(self, config: WeaviateConfig) -> None:
self.config = config
self.client = None
self.client_cache = {}
self.cache = {}
def _get_client(self) -> weaviate.Client:
request_provider_data = get_request_provider_data()
if request_provider_data is not None:
assert isinstance(request_provider_data, WeaviateRequestProviderData)
# Connect to Weaviate Cloud
return weaviate.connect_to_weaviate_cloud(
cluster_url = request_provider_data.weaviate_cluster_url,
auth_credentials = Auth.api_key(request_provider_data.weaviate_api_key),
)
provider_data = self.get_request_provider_data()
assert provider_data is not None, "Request provider data must be set"
assert isinstance(provider_data, WeaviateRequestProviderData)
key = f"{provider_data.weaviate_cluster_url}::{provider_data.weaviate_api_key}"
if key in self.client_cache:
return self.client_cache[key]
client = weaviate.connect_to_weaviate_cloud(
cluster_url=provider_data.weaviate_cluster_url,
auth_credentials=Auth.api_key(provider_data.weaviate_api_key),
)
self.client_cache[key] = client
return client
async def initialize(self) -> None:
try:
self.client = self._get_client()
# Create collection if it doesn't exist
if not self.client.collections.exists(self.config.collection):
self.client.collections.create(
name = self.config.collection,
vectorizer_config = wvc.config.Configure.Vectorizer.none(),
properties=[
wvc.config.Property(
name="chunk_content",
data_type=wvc.config.DataType.TEXT,
),
]
)
except Exception as e:
import traceback
traceback.print_exc()
raise RuntimeError("Could not connect to Weaviate server") from e
pass
async def shutdown(self) -> None:
self.client = self._get_client()
for client in self.client_cache.values():
client.close()
if self.client:
self.client.close()
async def create_memory_bank(
async def register_memory_bank(
self,
name: str,
config: MemoryBankConfig,
url: Optional[URL] = None,
) -> MemoryBank:
bank_id = str(uuid.uuid4())
bank = MemoryBank(
bank_id=bank_id,
name=name,
config=config,
url=url,
)
self.client = self._get_client()
# Store the bank as a new collection in Weaviate
self.client.collections.create(
name=bank_id
)
memory_bank: MemoryBankDef,
) -> None:
assert (
memory_bank.type == MemoryBankType.vector.value
), f"Only vector banks are supported {memory_bank.type}"
client = self._get_client()
# Create collection if it doesn't exist
if not client.collections.exists(memory_bank.identifier):
client.collections.create(
name=memory_bank.identifier,
vectorizer_config=wvc.config.Configure.Vectorizer.none(),
properties=[
wvc.config.Property(
name="chunk_content",
data_type=wvc.config.DataType.TEXT,
),
],
)
index = BankWithIndex(
bank=bank,
index=WeaviateIndex(cleint = self.client, collection = bank_id),
bank=memory_bank,
index=WeaviateIndex(client=client, collection_name=memory_bank.identifier),
)
self.cache[bank_id] = index
return bank
self.cache[memory_bank.identifier] = index
async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
bank_index = await self._get_and_cache_bank_index(bank_id)
if bank_index is None:
return None
return bank_index.bank
async def list_memory_banks(self) -> List[MemoryBankDef]:
# TODO: right now the Llama Stack is the source of truth for these banks. That is
# not ideal. It should be Weaviate which is the source of truth. Unfortunately,
# list() happens at Stack startup when the Weaviate client (credentials) is not
# yet available. We need to figure out a way to make this work.
return [i.bank for i in self.cache.values()]
async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
self.client = self._get_client()
if bank_id in self.cache:
return self.cache[bank_id]
collections = await self.client.collections.list_all().keys()
bank = await self.memory_bank_store.get_memory_bank(bank_id)
if not bank:
raise ValueError(f"Bank {bank_id} not found")
for collection in collections:
if collection == bank_id:
bank = MemoryBank(**json.loads(collection.metadata["bank"]))
index = BankWithIndex(
bank=bank,
index=WeaviateIndex(self.client, collection),
)
self.cache[bank_id] = index
return index
client = self._get_client()
if not client.collections.exists(bank_id):
raise ValueError(f"Collection with name `{bank_id}` not found")
return None
index = BankWithIndex(
bank=bank,
index=WeaviateIndex(client=client, collection_name=bank_id),
)
self.cache[bank_id] = index
return index
async def insert_documents(
self,
bank_id: str,
documents: List[MemoryBankDocument],
ttl_seconds: Optional[int] = None,
) -> None:
index = await self._get_and_cache_bank_index(bank_id)
if not index:
@ -189,4 +187,4 @@ class WeaviateMemoryAdapter(Memory):
if not index:
raise ValueError(f"Bank {bank_id} not found")
return await index.query_documents(query, params)
return await index.query_documents(query, params)

99
llama_stack/providers/adapters/safety/bedrock/bedrock.py
View file

@ -7,14 +7,13 @@
import json
import logging
import traceback
from typing import Any, Dict, List
import boto3
from llama_stack.apis.safety import * # noqa
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from .config import BedrockSafetyConfig
@ -22,16 +21,17 @@ from .config import BedrockSafetyConfig
logger = logging.getLogger(__name__)
SUPPORTED_SHIELD_TYPES = [
"bedrock_guardrail",
BEDROCK_SUPPORTED_SHIELDS = [
ShieldType.generic_content_shield.value,
]
class BedrockSafetyAdapter(Safety, RoutableProvider):
class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
def __init__(self, config: BedrockSafetyConfig) -> None:
if not config.aws_profile:
raise ValueError(f"Missing boto_client aws_profile in model info::{config}")
self.config = config
self.registered_shields = []
async def initialize(self) -> None:
try:
@ -45,16 +45,23 @@ class BedrockSafetyAdapter(Safety, RoutableProvider):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
for key in routing_keys:
if key not in SUPPORTED_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {key}")
async def register_shield(self, shield: ShieldDef) -> None:
raise ValueError("Registering dynamic shields is not supported")
async def list_shields(self) -> List[ShieldDef]:
raise NotImplementedError(
"""
`list_shields` not implemented; this should read all guardrails from
bedrock and populate guardrailId and guardrailVersion in the ShieldDef.
"""
)
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
if shield_type not in SUPPORTED_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {shield_type}")
shield_def = await self.shield_store.get_shield(shield_type)
if not shield_def:
raise ValueError(f"Unknown shield {shield_type}")
"""This is the implementation for the bedrock guardrails. The input to the guardrails is to be of this format
```content = [
@ -69,52 +76,38 @@ class BedrockSafetyAdapter(Safety, RoutableProvider):
They contain content, role . For now we will extract the content and default the "qualifiers": ["query"]
"""
try:
logger.debug(f"run_shield::{params}::messages={messages}")
if "guardrailIdentifier" not in params:
raise RuntimeError(
"Error running request for BedrockGaurdrails:Missing GuardrailID in request"
)
if "guardrailVersion" not in params:
raise RuntimeError(
"Error running request for BedrockGaurdrails:Missing guardrailVersion in request"
)
shield_params = shield_def.params
logger.debug(f"run_shield::{shield_params}::messages={messages}")
# - convert the messages into format Bedrock expects
content_messages = []
for message in messages:
content_messages.append({"text": {"text": message.content}})
logger.debug(
f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:"
)
# - convert the messages into format Bedrock expects
content_messages = []
for message in messages:
content_messages.append({"text": {"text": message.content}})
logger.debug(
f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:"
)
response = self.boto_client.apply_guardrail(
guardrailIdentifier=params.get("guardrailIdentifier"),
guardrailVersion=params.get("guardrailVersion"),
source="OUTPUT", # or 'INPUT' depending on your use case
content=content_messages,
)
logger.debug(f"run_shield:: response: {response}::")
if response["action"] == "GUARDRAIL_INTERVENED":
user_message = ""
metadata = {}
for output in response["outputs"]:
# guardrails returns a list - however for this implementation we will leverage the last values
user_message = output["text"]
for assessment in response["assessments"]:
# guardrails returns a list - however for this implementation we will leverage the last values
metadata = dict(assessment)
return SafetyViolation(
user_message=user_message,
violation_level=ViolationLevel.ERROR,
metadata=metadata,
)
response = self.boto_client.apply_guardrail(
guardrailIdentifier=shield_params["guardrailIdentifier"],
guardrailVersion=shield_params["guardrailVersion"],
source="OUTPUT", # or 'INPUT' depending on your use case
content=content_messages,
)
if response["action"] == "GUARDRAIL_INTERVENED":
user_message = ""
metadata = {}
for output in response["outputs"]:
# guardrails returns a list - however for this implementation we will leverage the last values
user_message = output["text"]
for assessment in response["assessments"]:
# guardrails returns a list - however for this implementation we will leverage the last values
metadata = dict(assessment)
except Exception:
error_str = traceback.format_exc()
logger.error(
f"Error in apply_guardrails:{error_str}:: RETURNING None !!!!!"
return SafetyViolation(
user_message=user_message,
violation_level=ViolationLevel.ERROR,
metadata=metadata,
)
return None

6
llama_stack/providers/adapters/safety/sample/sample.py
View file

@ -9,14 +9,12 @@ from .config import SampleConfig
from llama_stack.apis.safety import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
class SampleSafetyImpl(Safety, RoutableProvider):
class SampleSafetyImpl(Safety):
def __init__(self, config: SampleConfig):
self.config = config
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
async def register_shield(self, shield: ShieldDef) -> None:
# these are the safety shields the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass

42
llama_stack/providers/adapters/safety/together/together.py
View file

@ -6,26 +6,21 @@
from together import Together
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.safety import (
RunShieldResponse,
Safety,
SafetyViolation,
ViolationLevel,
)
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.apis.safety import * # noqa: F403
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
from .config import TogetherSafetyConfig
SAFETY_SHIELD_TYPES = {
TOGETHER_SHIELD_MODEL_MAP = {
"llama_guard": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-8B": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision-Turbo",
}
class TogetherSafetyImpl(Safety, NeedsRequestProviderData, RoutableProvider):
class TogetherSafetyImpl(Safety, NeedsRequestProviderData, ShieldsProtocolPrivate):
def __init__(self, config: TogetherSafetyConfig) -> None:
self.config = config
@ -35,16 +30,28 @@ class TogetherSafetyImpl(Safety, NeedsRequestProviderData, RoutableProvider):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
for key in routing_keys:
if key not in SAFETY_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {key}")
async def register_shield(self, shield: ShieldDef) -> None:
raise ValueError("Registering dynamic shields is not supported")
async def list_shields(self) -> List[ShieldDef]:
return [
ShieldDef(
identifier=ShieldType.llama_guard.value,
type=ShieldType.llama_guard.value,
params={},
)
]
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
if shield_type not in SAFETY_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {shield_type}")
shield_def = await self.shield_store.get_shield(shield_type)
if not shield_def:
raise ValueError(f"Unknown shield {shield_type}")
model = shield_def.params.get("model", "llama_guard")
if model not in TOGETHER_SHIELD_MODEL_MAP:
raise ValueError(f"Unsupported safety model: {model}")
together_api_key = None
if self.config.api_key is not None:
@ -57,8 +64,6 @@ class TogetherSafetyImpl(Safety, NeedsRequestProviderData, RoutableProvider):
)
together_api_key = provider_data.together_api_key
model_name = SAFETY_SHIELD_TYPES[shield_type]
# messages can have role assistant or user
api_messages = []
for message in messages:
@ -66,7 +71,7 @@ class TogetherSafetyImpl(Safety, NeedsRequestProviderData, RoutableProvider):
api_messages.append({"role": message.role, "content": message.content})
violation = await get_safety_response(
together_api_key, model_name, api_messages
together_api_key, TOGETHER_SHIELD_MODEL_MAP[model], api_messages
)
return RunShieldResponse(violation=violation)
@ -90,7 +95,6 @@ async def get_safety_response(
if parts[0] == "unsafe":
return SafetyViolation(
violation_level=ViolationLevel.ERROR,
user_message="unsafe",
metadata={"violation_type": parts[1]},
)