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322 | class Bedrock(LLM):
"""Bedrock LLM.
Examples:
`pip install llama-index-llms-bedrock`
```python
from llama_index.llms.bedrock import Bedrock
llm = Bedrock(
model="amazon.titan-text-express-v1",
aws_access_key_id="AWS Access Key ID to use",
aws_secret_access_key="AWS Secret Access Key to use",
aws_session_token="AWS Session Token to use",
aws_region_name="AWS Region to use, eg. us-east-1",
)
resp = llm.complete("Paul Graham is ")
print(resp)
```
"""
model: str = Field(description="The modelId of the Bedrock model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
max_tokens: int = Field(description="The maximum number of tokens to generate.")
context_size: int = Field("The maximum number of tokens available for input.")
profile_name: Optional[str] = Field(
description="The name of aws profile to use. If not given, then the default profile is used."
)
aws_access_key_id: Optional[str] = Field(
description="AWS Access Key ID to use", exclude=True
)
aws_secret_access_key: Optional[str] = Field(
description="AWS Secret Access Key to use", exclude=True
)
aws_session_token: Optional[str] = Field(
description="AWS Session Token to use", exclude=True
)
region_name: Optional[str] = Field(
description="AWS region name to use. Uses region configured in AWS CLI if not passed",
exclude=True,
)
botocore_session: Optional[Any] = Field(
description="Use this Botocore session instead of creating a new default one.",
exclude=True,
)
botocore_config: Optional[Any] = Field(
description="Custom configuration object to use instead of the default generated one.",
exclude=True,
)
max_retries: int = Field(
default=10, description="The maximum number of API retries.", gt=0
)
timeout: float = Field(
default=60.0,
description="The timeout for the Bedrock API request in seconds. It will be used for both connect and read timeouts.",
)
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Additional kwargs for the bedrock invokeModel request.",
)
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
_provider: Provider = PrivateAttr()
def __init__(
self,
model: str,
temperature: Optional[float] = DEFAULT_TEMPERATURE,
max_tokens: Optional[int] = 512,
context_size: Optional[int] = None,
profile_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
region_name: Optional[str] = None,
botocore_session: Optional[Any] = None,
client: Optional[Any] = None,
timeout: Optional[float] = 60.0,
max_retries: Optional[int] = 10,
botocore_config: Optional[Any] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
**kwargs: Any,
) -> None:
if context_size is None and model not in BEDROCK_FOUNDATION_LLMS:
raise ValueError(
"`context_size` argument not provided and"
"model provided refers to a non-foundation model."
" Please specify the context_size"
)
session_kwargs = {
"profile_name": profile_name,
"region_name": region_name,
"aws_access_key_id": aws_access_key_id,
"aws_secret_access_key": aws_secret_access_key,
"aws_session_token": aws_session_token,
"botocore_session": botocore_session,
}
config = None
try:
import boto3
from botocore.config import Config
config = (
Config(
retries={"max_attempts": max_retries, "mode": "standard"},
connect_timeout=timeout,
read_timeout=timeout,
)
if botocore_config is None
else botocore_config
)
session = boto3.Session(**session_kwargs)
except ImportError:
raise ImportError(
"boto3 package not found, install with" "'pip install boto3'"
)
# Prior to general availability, custom boto3 wheel files were
# distributed that used the bedrock service to invokeModel.
# This check prevents any services still using those wheel files
# from breaking
if client is not None:
self._client = client
elif "bedrock-runtime" in session.get_available_services():
self._client = session.client("bedrock-runtime", config=config)
else:
self._client = session.client("bedrock", config=config)
additional_kwargs = additional_kwargs or {}
callback_manager = callback_manager or CallbackManager([])
context_size = context_size or BEDROCK_FOUNDATION_LLMS[model]
self._provider = get_provider(model)
messages_to_prompt = messages_to_prompt or self._provider.messages_to_prompt
completion_to_prompt = (
completion_to_prompt or self._provider.completion_to_prompt
)
super().__init__(
model=model,
temperature=temperature,
max_tokens=max_tokens,
context_size=context_size,
profile_name=profile_name,
timeout=timeout,
max_retries=max_retries,
botocore_config=config,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
"""Get class name."""
return "Bedrock_LLM"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(
context_window=self.context_size,
num_output=self.max_tokens,
is_chat_model=self.model in CHAT_ONLY_MODELS,
model_name=self.model,
)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
self._provider.max_tokens_key: self.max_tokens,
}
if type(self._provider) is AnthropicProvider and self.system_prompt:
base_kwargs["system"] = self.system_prompt
return {
**base_kwargs,
**self.additional_kwargs,
}
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
**all_kwargs,
)["body"].read()
response = json.loads(response)
return CompletionResponse(
text=self._provider.get_text_from_response(response), raw=response
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
if self.model in BEDROCK_FOUNDATION_LLMS and self.model not in STREAMING_MODELS:
raise ValueError(f"Model {self.model} does not support streaming")
if not formatted:
prompt = self.completion_to_prompt(prompt)
all_kwargs = self._get_all_kwargs(**kwargs)
request_body = self._provider.get_request_body(prompt, all_kwargs)
request_body_str = json.dumps(request_body)
response = completion_with_retry(
client=self._client,
model=self.model,
request_body=request_body_str,
max_retries=self.max_retries,
stream=True,
**all_kwargs,
)["body"]
def gen() -> CompletionResponseGen:
content = ""
for r in response:
r = json.loads(r["chunk"]["bytes"])
content_delta = self._provider.get_text_from_stream_response(r)
content += content_delta
yield CompletionResponse(text=content, delta=content_delta, raw=r)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
"""Chat asynchronously."""
# TODO: do synchronous chat for now
return self.chat(messages, **kwargs)
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
raise NotImplementedError
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise NotImplementedError
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise NotImplementedError
|