forked from phoenix/litellm-mirror
feat(bedrock_httpx.py): add support for bedrock converse api
closes https://github.com/BerriAI/litellm/issues/4000
This commit is contained in:
Krrish Dholakia
parent
0d3e52373c
commit
a76a9b7d11
6 changed files with 846 additions and 31 deletions
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@ -765,7 +765,7 @@ from .llms.sagemaker import SagemakerConfig
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from .llms.ollama import OllamaConfig
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from .llms.ollama_chat import OllamaChatConfig
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from .llms.maritalk import MaritTalkConfig
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from .llms.bedrock_httpx import AmazonCohereChatConfig
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from .llms.bedrock_httpx import AmazonCohereChatConfig, AmazonConverseConfig
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from .llms.bedrock import (
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AmazonTitanConfig,
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AmazonAI21Config,
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@ -38,6 +38,8 @@ from .prompt_templates.factory import (
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extract_between_tags,
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parse_xml_params,
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contains_tag,
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_bedrock_converse_messages_pt,
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_bedrock_tools_pt,
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)
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from .base import BaseLLM
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@ -118,6 +120,8 @@ class AmazonCohereChatConfig:
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"presence_penalty",
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"seed",
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"stop",
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"tools",
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"tool_choice",
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]
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def map_openai_params(
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@ -1069,6 +1073,384 @@ class BedrockLLM(BaseLLM):
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return super().embedding(*args, **kwargs)
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class AmazonConverseConfig:
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"""
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Reference - https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html
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"""
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maxTokens: Optional[int]
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stopSequences: Optional[List[str]]
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temperature: Optional[int]
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topP: Optional[int]
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def __init__(
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self,
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maxTokens: Optional[int] = None,
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stopSequences: Optional[List[str]] = None,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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) -> None:
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locals_ = locals()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_supported_openai_params(self) -> List[str]:
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return [
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"max_tokens",
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"stream",
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"stream_options",
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"stop",
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"temperature",
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"top_p",
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"tools",
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"tool_choice",
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]
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def get_supported_image_types(self) -> List[str]:
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return ["png", "jpeg", "gif", "webp"]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict
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) -> dict:
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for param, value in non_default_params.items():
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if param == "max_tokens":
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optional_params["maxTokens"] = value
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if param == "stream":
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optional_params["stream"] = value
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if param == "stop":
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if isinstance(value, str):
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value = [value]
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optional_params["stop_sequences"] = value
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if param == "temperature":
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optional_params["temperature"] = value
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if param == "top_p":
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optional_params["topP"] = value
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return optional_params
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class BedrockConverseLLM(BaseLLM):
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def __init__(self) -> None:
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super().__init__()
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def encode_model_id(self, model_id: str) -> str:
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"""
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Double encode the model ID to ensure it matches the expected double-encoded format.
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Args:
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model_id (str): The model ID to encode.
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Returns:
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str: The double-encoded model ID.
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"""
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return urllib.parse.quote(model_id, safe="")
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def get_credentials(
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self,
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aws_access_key_id: Optional[str] = None,
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aws_secret_access_key: Optional[str] = None,
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aws_region_name: Optional[str] = None,
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aws_session_name: Optional[str] = None,
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aws_profile_name: Optional[str] = None,
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aws_role_name: Optional[str] = None,
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aws_web_identity_token: Optional[str] = None,
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):
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"""
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Return a boto3.Credentials object
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"""
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import boto3
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## CHECK IS 'os.environ/' passed in
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params_to_check: List[Optional[str]] = [
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aws_access_key_id,
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aws_secret_access_key,
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aws_region_name,
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aws_session_name,
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aws_profile_name,
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aws_role_name,
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aws_web_identity_token,
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]
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# Iterate over parameters and update if needed
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for i, param in enumerate(params_to_check):
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if param and param.startswith("os.environ/"):
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_v = get_secret(param)
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if _v is not None and isinstance(_v, str):
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params_to_check[i] = _v
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# Assign updated values back to parameters
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(
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aws_access_key_id,
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aws_secret_access_key,
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aws_region_name,
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aws_session_name,
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aws_profile_name,
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aws_role_name,
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aws_web_identity_token,
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) = params_to_check
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### CHECK STS ###
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if (
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aws_web_identity_token is not None
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and aws_role_name is not None
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and aws_session_name is not None
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):
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oidc_token = get_secret(aws_web_identity_token)
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if oidc_token is None:
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raise BedrockError(
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message="OIDC token could not be retrieved from secret manager.",
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status_code=401,
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)
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sts_client = boto3.client("sts")
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# https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRoleWithWebIdentity.html
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# https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sts/client/assume_role_with_web_identity.html
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sts_response = sts_client.assume_role_with_web_identity(
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RoleArn=aws_role_name,
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RoleSessionName=aws_session_name,
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WebIdentityToken=oidc_token,
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DurationSeconds=3600,
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)
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session = boto3.Session(
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aws_access_key_id=sts_response["Credentials"]["AccessKeyId"],
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aws_secret_access_key=sts_response["Credentials"]["SecretAccessKey"],
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aws_session_token=sts_response["Credentials"]["SessionToken"],
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region_name=aws_region_name,
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)
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return session.get_credentials()
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elif aws_role_name is not None and aws_session_name is not None:
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sts_client = boto3.client(
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"sts",
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aws_access_key_id=aws_access_key_id, # [OPTIONAL]
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aws_secret_access_key=aws_secret_access_key, # [OPTIONAL]
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)
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sts_response = sts_client.assume_role(
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RoleArn=aws_role_name, RoleSessionName=aws_session_name
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)
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# Extract the credentials from the response and convert to Session Credentials
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sts_credentials = sts_response["Credentials"]
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from botocore.credentials import Credentials
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credentials = Credentials(
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access_key=sts_credentials["AccessKeyId"],
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secret_key=sts_credentials["SecretAccessKey"],
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token=sts_credentials["SessionToken"],
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)
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return credentials
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elif aws_profile_name is not None: ### CHECK SESSION ###
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# uses auth values from AWS profile usually stored in ~/.aws/credentials
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client = boto3.Session(profile_name=aws_profile_name)
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return client.get_credentials()
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else:
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session = boto3.Session(
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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region_name=aws_region_name,
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)
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return session.get_credentials()
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def completion(
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self,
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model: str,
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messages: list,
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custom_prompt_dict: dict,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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logging_obj,
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optional_params: dict,
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acompletion: bool,
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timeout: Optional[Union[float, httpx.Timeout]],
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litellm_params=None,
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logger_fn=None,
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extra_headers: Optional[dict] = None,
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client: Optional[Union[AsyncHTTPHandler, HTTPHandler]] = None,
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):
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try:
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import boto3
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from botocore.auth import SigV4Auth
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from botocore.awsrequest import AWSRequest
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from botocore.credentials import Credentials
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except ImportError as e:
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raise ImportError("Missing boto3 to call bedrock. Run 'pip install boto3'.")
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## SETUP ##
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stream = optional_params.pop("stream", None)
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modelId = optional_params.pop("model_id", None)
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if modelId is not None:
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modelId = self.encode_model_id(model_id=modelId)
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else:
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modelId = model
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provider = model.split(".")[0]
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## CREDENTIALS ##
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# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
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aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
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aws_access_key_id = optional_params.pop("aws_access_key_id", None)
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aws_region_name = optional_params.pop("aws_region_name", None)
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aws_role_name = optional_params.pop("aws_role_name", None)
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aws_session_name = optional_params.pop("aws_session_name", None)
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aws_profile_name = optional_params.pop("aws_profile_name", None)
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aws_bedrock_runtime_endpoint = optional_params.pop(
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"aws_bedrock_runtime_endpoint", None
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) # https://bedrock-runtime.{region_name}.amazonaws.com
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aws_web_identity_token = optional_params.pop("aws_web_identity_token", None)
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### SET REGION NAME ###
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if aws_region_name is None:
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# check env #
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litellm_aws_region_name = get_secret("AWS_REGION_NAME", None)
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if litellm_aws_region_name is not None and isinstance(
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litellm_aws_region_name, str
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):
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aws_region_name = litellm_aws_region_name
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standard_aws_region_name = get_secret("AWS_REGION", None)
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if standard_aws_region_name is not None and isinstance(
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standard_aws_region_name, str
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):
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aws_region_name = standard_aws_region_name
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if aws_region_name is None:
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aws_region_name = "us-west-2"
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credentials: Credentials = self.get_credentials(
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_region_name=aws_region_name,
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aws_session_name=aws_session_name,
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aws_profile_name=aws_profile_name,
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aws_role_name=aws_role_name,
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aws_web_identity_token=aws_web_identity_token,
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)
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### SET RUNTIME ENDPOINT ###
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endpoint_url = ""
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env_aws_bedrock_runtime_endpoint = get_secret("AWS_BEDROCK_RUNTIME_ENDPOINT")
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if aws_bedrock_runtime_endpoint is not None and isinstance(
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aws_bedrock_runtime_endpoint, str
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):
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endpoint_url = aws_bedrock_runtime_endpoint
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elif env_aws_bedrock_runtime_endpoint and isinstance(
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env_aws_bedrock_runtime_endpoint, str
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):
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endpoint_url = env_aws_bedrock_runtime_endpoint
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else:
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endpoint_url = f"https://bedrock-runtime.{aws_region_name}.amazonaws.com"
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if (stream is not None and stream == True) and provider != "ai21":
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endpoint_url = f"{endpoint_url}/model/{modelId}/converse-stream"
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else:
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endpoint_url = f"{endpoint_url}/model/{modelId}/converse"
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sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name)
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# Separate system prompt from rest of message
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system_prompt_indices = []
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system_content_blocks: List[SystemContentBlock] = []
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for idx, message in enumerate(messages):
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if message["role"] == "system":
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_system_content_block = SystemContentBlock(text=message["content"])
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system_content_blocks.append(_system_content_block)
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system_prompt_indices.append(idx)
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if len(system_prompt_indices) > 0:
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for idx in reversed(system_prompt_indices):
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messages.pop(idx)
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inference_params = copy.deepcopy(optional_params)
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additional_request_keys = []
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additional_request_params = {}
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supported_converse_params = AmazonConverseConfig().get_config().keys()
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## TRANSFORMATION ##
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# send all model-specific params in 'additional_request_params'
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for k, v in inference_params.items():
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if k not in supported_converse_params:
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additional_request_params[k] = v
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additional_request_keys.append(k)
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for key in additional_request_keys:
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inference_params.pop(key, None)
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bedrock_messages: List[MessageBlock] = _bedrock_converse_messages_pt(
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messages=messages
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)
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bedrock_tools: List[ToolBlock] = _bedrock_tools_pt(
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inference_params.get("tools", [])
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)
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bedrock_tool_config: Optional[ToolConfigBlock] = None
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if len(bedrock_tools) > 0:
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bedrock_tool_config = ToolConfigBlock(
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tools=bedrock_tools,
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toolChoice=inference_params.get("tool_choice", None),
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)
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data: RequestObject = {
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"messages": bedrock_messages,
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"additionalModelRequestFields": additional_request_params,
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"system": system_content_blocks,
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}
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if bedrock_tool_config is not None:
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data["toolConfig"] = bedrock_tool_config
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## COMPLETION CALL
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headers = {"Content-Type": "application/json"}
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if extra_headers is not None:
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headers = {"Content-Type": "application/json", **extra_headers}
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request = AWSRequest(
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method="POST", url=endpoint_url, data=data, headers=headers
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)
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sigv4.add_auth(request)
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prepped = request.prepare()
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## LOGGING
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logging_obj.pre_call(
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input=messages,
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api_key="",
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additional_args={
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"complete_input_dict": data,
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"api_base": prepped.url,
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"headers": prepped.headers,
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},
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)
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### ROUTING (ASYNC, STREAMING, SYNC)
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try:
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response = self.client.post(url=prepped.url, headers=prepped.headers, data=data) # type: ignore
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response.raise_for_status()
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except httpx.HTTPStatusError as err:
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error_code = err.response.status_code
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raise BedrockError(status_code=error_code, message=response.text)
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except httpx.TimeoutException as e:
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raise BedrockError(status_code=408, message="Timeout error occurred.")
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def get_response_stream_shape():
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from botocore.model import ServiceModel
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from botocore.loaders import Loader
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@ -3,14 +3,7 @@ import requests, traceback
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import json, re, xml.etree.ElementTree as ET
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from jinja2 import Template, exceptions, meta, BaseLoader
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from jinja2.sandbox import ImmutableSandboxedEnvironment
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from typing import (
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Any,
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List,
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Mapping,
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MutableMapping,
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Optional,
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Sequence,
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)
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from typing import Any, List, Mapping, MutableMapping, Optional, Sequence, Tuple
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import litellm
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import litellm.types
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from litellm.types.completion import (
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@ -24,7 +17,7 @@ from litellm.types.completion import (
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import litellm.types.llms
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from litellm.types.llms.anthropic import *
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import uuid
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from litellm.types.llms.bedrock import MessageBlock as BedrockMessageBlock
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import litellm.types.llms.vertex_ai
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@ -1460,9 +1453,7 @@ def _load_image_from_url(image_url):
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try:
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from PIL import Image
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except:
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raise Exception(
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"gemini image conversion failed please run `pip install Pillow`"
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)
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raise Exception("image conversion failed please run `pip install Pillow`")
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from io import BytesIO
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try:
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|
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@ -1613,6 +1604,379 @@ def azure_text_pt(messages: list):
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return prompt
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###### AMAZON BEDROCK #######
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from litellm.types.llms.bedrock import (
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ToolResultContentBlock as BedrockToolResultContentBlock,
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ToolResultBlock as BedrockToolResultBlock,
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ToolConfigBlock as BedrockToolConfigBlock,
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ToolUseBlock as BedrockToolUseBlock,
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ImageSourceBlock as BedrockImageSourceBlock,
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ImageBlock as BedrockImageBlock,
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ContentBlock as BedrockContentBlock,
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ToolInputSchemaBlock as BedrockToolInputSchemaBlock,
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ToolSpecBlock as BedrockToolSpecBlock,
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ToolBlock as BedrockToolBlock,
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)
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|
||||
def get_image_details(image_url) -> Tuple[bytes, str]:
|
||||
try:
|
||||
import base64
|
||||
|
||||
# Send a GET request to the image URL
|
||||
response = requests.get(image_url)
|
||||
response.raise_for_status() # Raise an exception for HTTP errors
|
||||
|
||||
# Check the response's content type to ensure it is an image
|
||||
content_type = response.headers.get("content-type")
|
||||
if not content_type or "image" not in content_type:
|
||||
raise ValueError(
|
||||
f"URL does not point to a valid image (content-type: {content_type})"
|
||||
)
|
||||
|
||||
# Convert the image content to base64 bytes
|
||||
base64_bytes = base64.b64encode(response.content)
|
||||
|
||||
# Get mime-type
|
||||
mime_type = content_type.split("/")[
|
||||
1
|
||||
] # Extract mime-type from content-type header
|
||||
|
||||
return base64_bytes, mime_type
|
||||
|
||||
except requests.RequestException as e:
|
||||
raise Exception(f"Request failed: {e}")
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
||||
def _process_bedrock_converse_image_block(image_url: str) -> BedrockImageBlock:
|
||||
if "base64" in image_url:
|
||||
# Case 1: Images with base64 encoding
|
||||
import base64, re
|
||||
|
||||
# base 64 is passed as data:image/jpeg;base64,<base-64-encoded-image>
|
||||
image_metadata, img_without_base_64 = image_url.split(",")
|
||||
image_format = image_metadata.split("/")[1]
|
||||
|
||||
# read mime_type from img_without_base_64=data:image/jpeg;base64
|
||||
# Extract MIME type using regular expression
|
||||
mime_type_match = re.match(r"data:(.*?);base64", image_metadata)
|
||||
|
||||
if mime_type_match:
|
||||
mime_type = mime_type_match.group(1)
|
||||
else:
|
||||
mime_type = "jpeg"
|
||||
decoded_img = base64.b64decode(img_without_base_64)
|
||||
_blob = BedrockImageSourceBlock(bytes=decoded_img)
|
||||
supported_image_formats = (
|
||||
litellm.AmazonConverseConfig().get_supported_image_types()
|
||||
)
|
||||
if image_format in supported_image_formats:
|
||||
return BedrockImageBlock(source=_blob, format=image_format) # type: ignore
|
||||
else:
|
||||
# Handle the case when the image format is not supported
|
||||
raise ValueError(
|
||||
"Unsupported image format: {}. Supported formats: {}".format(
|
||||
image_format, supported_image_formats
|
||||
)
|
||||
)
|
||||
elif "https:/" in image_url:
|
||||
# Case 2: Images with direct links
|
||||
image_bytes, image_format = get_image_details(image_url)
|
||||
_blob = BedrockImageSourceBlock(bytes=image_bytes)
|
||||
supported_image_formats = (
|
||||
litellm.AmazonConverseConfig().get_supported_image_types()
|
||||
)
|
||||
if image_format in supported_image_formats:
|
||||
return BedrockImageBlock(source=_blob, format=image_format) # type: ignore
|
||||
else:
|
||||
# Handle the case when the image format is not supported
|
||||
raise ValueError(
|
||||
"Unsupported image format: {}. Supported formats: {}".format(
|
||||
image_format, supported_image_formats
|
||||
)
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"Unsupported image type. Expected either image url or base64 encoded string"
|
||||
)
|
||||
|
||||
|
||||
def _convert_to_bedrock_tool_call_invoke(
|
||||
tool_calls: list,
|
||||
) -> List[BedrockContentBlock]:
|
||||
"""
|
||||
OpenAI tool invokes:
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_abc123",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"arguments": "{\n\"location\": \"Boston, MA\"\n}"
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"""
|
||||
"""
|
||||
Bedrock tool invokes:
|
||||
[
|
||||
{
|
||||
"role": "assistant",
|
||||
"toolUse": {
|
||||
"input": {"location": "Boston, MA", ..},
|
||||
"name": "get_current_weather",
|
||||
"toolUseId": "call_abc123"
|
||||
}
|
||||
}
|
||||
]
|
||||
"""
|
||||
"""
|
||||
- json.loads argument
|
||||
- extract name
|
||||
- extract id
|
||||
"""
|
||||
|
||||
try:
|
||||
_parts_list: List[BedrockContentBlock] = []
|
||||
for tool in tool_calls:
|
||||
if "function" in tool:
|
||||
id = tool["id"]
|
||||
name = tool["function"].get("name", "")
|
||||
arguments = tool["function"].get("arguments", "")
|
||||
arguments_dict = json.loads(arguments)
|
||||
bedrock_tool = BedrockToolUseBlock(
|
||||
input=arguments_dict, name=name, toolUseId=id
|
||||
)
|
||||
bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool)
|
||||
_parts_list.append(bedrock_content_block)
|
||||
return _parts_list
|
||||
except Exception as e:
|
||||
raise Exception(
|
||||
"Unable to convert openai tool calls={} to bedrock tool calls. Received error={}".format(
|
||||
tool_calls, str(e)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _convert_to_bedrock_tool_call_result(
|
||||
message: dict,
|
||||
) -> BedrockMessageBlock:
|
||||
"""
|
||||
OpenAI message with a tool result looks like:
|
||||
{
|
||||
"tool_call_id": "tool_1",
|
||||
"role": "tool",
|
||||
"name": "get_current_weather",
|
||||
"content": "function result goes here",
|
||||
},
|
||||
|
||||
OpenAI message with a function call result looks like:
|
||||
{
|
||||
"role": "function",
|
||||
"name": "get_current_weather",
|
||||
"content": "function result goes here",
|
||||
}
|
||||
"""
|
||||
"""
|
||||
Bedrock result looks like this:
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"toolResult": {
|
||||
"toolUseId": "tooluse_kZJMlvQmRJ6eAyJE5GIl7Q",
|
||||
"content": [
|
||||
{
|
||||
"json": {
|
||||
"song": "Elemental Hotel",
|
||||
"artist": "8 Storey Hike"
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
"""
|
||||
"""
|
||||
-
|
||||
"""
|
||||
content = message.get("content", "")
|
||||
name = message.get("name", "")
|
||||
id = message.get("tool_call_id", str(uuid.uuid4()))
|
||||
|
||||
tool_result_content_block = BedrockToolResultContentBlock(text=content)
|
||||
tool_result = BedrockToolResultBlock(
|
||||
content=tool_result_content_block,
|
||||
toolUseId=id,
|
||||
)
|
||||
content_block = BedrockContentBlock(toolResult=tool_result)
|
||||
|
||||
return BedrockMessageBlock(role="user", content=[content_block])
|
||||
|
||||
|
||||
def _bedrock_converse_messages_pt(messages: List) -> List[BedrockMessageBlock]:
|
||||
"""
|
||||
Converts given messages from OpenAI format to Bedrock format
|
||||
|
||||
- Roles must alternate b/w 'user' and 'model' (same as anthropic -> merge consecutive roles)
|
||||
- Please ensure that function response turn comes immediately after a function call turn
|
||||
"""
|
||||
|
||||
contents: List[BedrockMessageBlock] = []
|
||||
msg_i = 0
|
||||
while msg_i < len(messages):
|
||||
user_content: List[BedrockContentBlock] = []
|
||||
init_msg_i = msg_i
|
||||
## MERGE CONSECUTIVE USER CONTENT ##
|
||||
while msg_i < len(messages) and messages[msg_i]["role"] == "user":
|
||||
if isinstance(messages[msg_i]["content"], list):
|
||||
_parts: List[BedrockContentBlock] = []
|
||||
for element in messages[msg_i]["content"]:
|
||||
if isinstance(element, dict):
|
||||
if element["type"] == "text":
|
||||
_part = BedrockContentBlock(text=element["text"])
|
||||
_parts.append(_part)
|
||||
elif element["type"] == "image_url":
|
||||
image_url = element["image_url"]["url"]
|
||||
_part = _process_bedrock_converse_image_block( # type: ignore
|
||||
image_url=image_url
|
||||
)
|
||||
_parts.append(BedrockContentBlock(image=_part)) # type: ignore
|
||||
user_content.extend(_parts)
|
||||
else:
|
||||
_part = BedrockContentBlock(text=messages[msg_i]["content"])
|
||||
user_content.append(_part)
|
||||
|
||||
msg_i += 1
|
||||
|
||||
if user_content:
|
||||
contents.append(BedrockMessageBlock(role="user", content=user_content))
|
||||
assistant_content: List[BedrockContentBlock] = []
|
||||
## MERGE CONSECUTIVE ASSISTANT CONTENT ##
|
||||
while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
|
||||
if isinstance(messages[msg_i]["content"], list):
|
||||
assistants_parts: List[BedrockContentBlock] = []
|
||||
for element in messages[msg_i]["content"]:
|
||||
if isinstance(element, dict):
|
||||
if element["type"] == "text":
|
||||
assistants_part = BedrockContentBlock(text=element["text"])
|
||||
assistants_parts.append(assistants_part)
|
||||
elif element["type"] == "image_url":
|
||||
image_url = element["image_url"]["url"]
|
||||
assistants_part = _process_bedrock_converse_image_block( # type: ignore
|
||||
image_url=image_url
|
||||
)
|
||||
assistants_parts.append(
|
||||
BedrockContentBlock(image=assistants_part) # type: ignore
|
||||
)
|
||||
assistant_content.extend(assistants_parts)
|
||||
elif messages[msg_i].get(
|
||||
"tool_calls", []
|
||||
): # support assistant tool invoke convertion
|
||||
assistant_content.extend(
|
||||
_convert_to_bedrock_tool_call_invoke(messages[msg_i]["tool_calls"])
|
||||
)
|
||||
else:
|
||||
assistant_text = (
|
||||
messages[msg_i].get("content") or ""
|
||||
) # either string or none
|
||||
if assistant_text:
|
||||
assistant_content.append(BedrockContentBlock(text=assistant_text))
|
||||
|
||||
msg_i += 1
|
||||
|
||||
if assistant_content:
|
||||
contents.append(
|
||||
BedrockMessageBlock(role="assistant", content=assistant_content)
|
||||
)
|
||||
|
||||
## APPEND TOOL CALL MESSAGES ##
|
||||
if msg_i < len(messages) and messages[msg_i]["role"] == "tool":
|
||||
tool_call_result = _convert_to_bedrock_tool_call_result(messages[msg_i])
|
||||
contents.append(tool_call_result)
|
||||
msg_i += 1
|
||||
if msg_i == init_msg_i: # prevent infinite loops
|
||||
raise Exception(
|
||||
"Invalid Message passed in - {}. File an issue https://github.com/BerriAI/litellm/issues".format(
|
||||
messages[msg_i]
|
||||
)
|
||||
)
|
||||
|
||||
return contents
|
||||
|
||||
|
||||
def _bedrock_tools_pt(tools: List) -> List[BedrockToolBlock]:
|
||||
"""
|
||||
OpenAI tools looks like:
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
}
|
||||
}
|
||||
]
|
||||
"""
|
||||
"""
|
||||
Bedrock toolConfig looks like:
|
||||
"tools": [
|
||||
{
|
||||
"toolSpec": {
|
||||
"name": "top_song",
|
||||
"description": "Get the most popular song played on a radio station.",
|
||||
"inputSchema": {
|
||||
"json": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"sign": {
|
||||
"type": "string",
|
||||
"description": "The call sign for the radio station for which you want the most popular song. Example calls signs are WZPZ, and WKRP."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"sign"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
"""
|
||||
tool_block_list: List[BedrockToolBlock] = []
|
||||
for tool in tools:
|
||||
parameters = tool.get("function", {}).get("parameters", None)
|
||||
name = tool.get("function", {}).get("name", "")
|
||||
description = tool.get("function", {}).get("description", "")
|
||||
tool_input_schema = BedrockToolInputSchemaBlock(json=parameters)
|
||||
tool_spec = BedrockToolSpecBlock(
|
||||
inputSchema=tool_input_schema, name=name, description=description
|
||||
)
|
||||
tool_block = BedrockToolBlock(toolSpec=tool_spec)
|
||||
tool_block_list.append(tool_block)
|
||||
|
||||
return tool_block_list
|
||||
|
||||
|
||||
# Function call template
|
||||
def function_call_prompt(messages: list, functions: list):
|
||||
function_prompt = """Produce JSON OUTPUT ONLY! Adhere to this format {"name": "function_name", "arguments":{"argument_name": "argument_value"}} The following functions are available to you:"""
|
||||
|
|
|
|||
|
|
@ -300,7 +300,14 @@ def test_completion_claude_3():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_claude_3_function_call():
|
||||
@pytest.mark.parametrize(
|
||||
"model",
|
||||
[
|
||||
# "anthropic/claude-3-opus-20240229",
|
||||
"cohere.command-r-plus-v1:0"
|
||||
],
|
||||
)
|
||||
def test_completion_claude_3_function_call(model):
|
||||
litellm.set_verbose = True
|
||||
tools = [
|
||||
{
|
||||
|
|
@ -331,7 +338,7 @@ def test_completion_claude_3_function_call():
|
|||
try:
|
||||
# test without max tokens
|
||||
response = completion(
|
||||
model="anthropic/claude-3-opus-20240229",
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
tool_choice={
|
||||
|
|
@ -364,7 +371,7 @@ def test_completion_claude_3_function_call():
|
|||
)
|
||||
# In the second response, Claude should deduce answer from tool results
|
||||
second_response = completion(
|
||||
model="anthropic/claude-3-opus-20240229",
|
||||
model=model,
|
||||
messages=messages,
|
||||
tools=tools,
|
||||
tool_choice="auto",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import TypedDict, Any, Union, Optional
|
||||
from typing import TypedDict, Any, Union, Optional, Literal, List
|
||||
import json
|
||||
from typing_extensions import (
|
||||
Self,
|
||||
|
|
@ -11,6 +11,81 @@ from typing_extensions import (
|
|||
)
|
||||
|
||||
|
||||
class SystemContentBlock(TypedDict):
|
||||
text: str
|
||||
|
||||
|
||||
class ImageSourceBlock(TypedDict):
|
||||
bytes: Optional[bytes]
|
||||
|
||||
|
||||
class ImageBlock(TypedDict):
|
||||
format: Literal["png", "jpeg", "gif", "webp"]
|
||||
source: ImageSourceBlock
|
||||
|
||||
|
||||
class ToolResultContentBlock(TypedDict, total=False):
|
||||
image: ImageBlock
|
||||
json: dict
|
||||
text: str
|
||||
|
||||
|
||||
class ToolResultBlock(TypedDict, total=False):
|
||||
content: Required[ToolResultContentBlock]
|
||||
toolUseId: Required[str]
|
||||
status: Literal["success", "error"]
|
||||
|
||||
|
||||
class ToolUseBlock(TypedDict):
|
||||
input: dict
|
||||
name: str
|
||||
toolUseId: str
|
||||
|
||||
|
||||
class ContentBlock(TypedDict, total=False):
|
||||
text: str
|
||||
image: ImageBlock
|
||||
toolResult: ToolResultBlock
|
||||
toolUse: ToolUseBlock
|
||||
|
||||
|
||||
class MessageBlock(TypedDict):
|
||||
content: List[ContentBlock]
|
||||
role: Literal["user", "assistant"]
|
||||
|
||||
|
||||
class ToolInputSchemaBlock(TypedDict):
|
||||
json: Optional[dict]
|
||||
|
||||
|
||||
class ToolSpecBlock(TypedDict, total=False):
|
||||
inputSchema: Required[ToolInputSchemaBlock]
|
||||
name: Required[str]
|
||||
description: str
|
||||
|
||||
|
||||
class ToolBlock(TypedDict):
|
||||
toolSpec: Optional[ToolSpecBlock]
|
||||
|
||||
|
||||
class SpecificToolChoiceBlock(TypedDict):
|
||||
name: str
|
||||
|
||||
|
||||
class ToolConfigBlock(TypedDict, total=False):
|
||||
tools: Required[List[ToolBlock]]
|
||||
toolChoice: Union[str, SpecificToolChoiceBlock]
|
||||
|
||||
|
||||
class RequestObject(TypedDict, total=False):
|
||||
additionalModelRequestFields: dict
|
||||
additionalModelResponseFieldPaths: List[str]
|
||||
inferenceConfig: dict
|
||||
messages: Required[List[MessageBlock]]
|
||||
system: List[SystemContentBlock]
|
||||
toolConfig: ToolConfigBlock
|
||||
|
||||
|
||||
class GenericStreamingChunk(TypedDict):
|
||||
text: Required[str]
|
||||
is_finished: Required[bool]
|
||||
|
|
|
|||
|
|
@ -6401,20 +6401,7 @@ def get_supported_openai_params(
|
|||
- None if unmapped
|
||||
"""
|
||||
if custom_llm_provider == "bedrock":
|
||||
if model.startswith("anthropic.claude-3"):
|
||||
return litellm.AmazonAnthropicClaude3Config().get_supported_openai_params()
|
||||
elif model.startswith("anthropic"):
|
||||
return litellm.AmazonAnthropicConfig().get_supported_openai_params()
|
||||
elif model.startswith("ai21"):
|
||||
return ["max_tokens", "temperature", "top_p", "stream"]
|
||||
elif model.startswith("amazon"):
|
||||
return ["max_tokens", "temperature", "stop", "top_p", "stream"]
|
||||
elif model.startswith("meta"):
|
||||
return ["max_tokens", "temperature", "top_p", "stream"]
|
||||
elif model.startswith("cohere"):
|
||||
return ["stream", "temperature", "max_tokens"]
|
||||
elif model.startswith("mistral"):
|
||||
return ["max_tokens", "temperature", "stop", "top_p", "stream"]
|
||||
return litellm.AmazonConverseConfig().get_supported_openai_params()
|
||||
elif custom_llm_provider == "ollama":
|
||||
return litellm.OllamaConfig().get_supported_openai_params()
|
||||
elif custom_llm_provider == "ollama_chat":
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue