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Unverified Commit 77e80b7f authored by Souyama's avatar Souyama Committed by GitHub
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Fix: calling AWS Bedrock models (#10443)

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docs/examples/embeddings/bedrock.ipynb
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%% Cell type:markdown id: tags:
<a href="https://colab.research.google.com/github/jerryjliu/llama_index/blob/main/docs/examples/embeddings/bedrock.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
%% Cell type:markdown id: tags:
# Bedrock Embeddings
If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
%% Cell type:code id: tags:
``` python
import os
from llama_index.embeddings import BedrockEmbedding
```
%% Cell type:code id: tags:
``` python
embed_model = BedrockEmbedding.from_credentials(
embed_model = BedrockEmbedding(
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
aws_session_token=os.getenv("AWS_SESSION_TOKEN"),
aws_region="<aws-region>",
aws_profile="<aws-profile>",
region_name="<aws-region>",
profile_name="<aws-profile>",
)
```
%% Cell type:code id: tags:
``` python
embedding = embed_model.get_text_embedding("hello world")
print(embedding)
```
%% Cell type:markdown id: tags:
## List supported models
To check list of supported models of Amazon Bedrock on LlamaIndex, call `BedrockEmbedding.list_supported_models()` as follows.
%% Cell type:code id: tags:
``` python
from llama_index.embeddings import BedrockEmbedding
import json
supported_models = BedrockEmbedding.list_supported_models()
print(json.dumps(supported_models, indent=2))
```
%% Output
{
"amazon": [
"amazon.titan-embed-text-v1",
"amazon.titan-embed-g1-text-02",
"cohere.embed-english-v3",
"cohere.embed-multilingual-v3"
],
"cohere": [
"amazon.titan-embed-text-v1",
"amazon.titan-embed-g1-text-02",
"cohere.embed-english-v3",
"cohere.embed-multilingual-v3"
]
}
%% Cell type:markdown id: tags:
## Provider: Amazon
Amazon Bedrock Titan embeddings.
%% Cell type:code id: tags:
``` python
from llama_index.embeddings import BedrockEmbedding
model = BedrockEmbedding().from_credentials(
model_name="amazon.titan-embed-g1-text-02"
)
model = BedrockEmbedding(model_name="amazon.titan-embed-g1-text-02")
embeddings = model.get_text_embedding("hello world")
print(embeddings)
```
%% Cell type:markdown id: tags:
## Provider: Cohere
### cohere.embed-english-v3
%% Cell type:markdown id: tags:
Embed text for search
%% Cell type:code id: tags:
``` python
model = BedrockEmbedding().from_credentials(
model_name="cohere.embed-english-v3"
)
coherePayload = {
"texts": ["This is a test document", "This is another test document"],
"input_type": "search_document",
"truncate": "NONE",
}
embeddings = model.get_text_embedding(coherePayload)
model = BedrockEmbedding(model_name="cohere.embed-english-v3")
coherePayload = ["This is a test document", "This is another test document"]
embeddings = model.get_text_embedding_batch(coherePayload)
print(embeddings)
```
%% Cell type:markdown id: tags:
Embed query for question answering
%% Cell type:code id: tags:
``` python
model = BedrockEmbedding(model_name="cohere.embed-english-v3")
coherePayload = "What is gravity?"
embeddings = model._get_query_embedding(coherePayload)
print(embeddings)
```
%% Cell type:markdown id: tags:
### MultiLingual Embeddings from Cohere
%% Cell type:code id: tags:
``` python
model = BedrockEmbedding().from_credentials(
model_name="cohere.embed-multilingual-v3"
)
coherePayload = {
"texts": [
"This is a test document",
"తెలుగు అనేది ద్రావిడ భాషల కుటుంబానికి చెందిన భాష.",
],
"input_type": "search_document",
"truncate": "NONE",
}
embeddings = model.get_text_embedding(coherePayload)
model = BedrockEmbedding(model_name="cohere.embed-multilingual-v3")
coherePayload = [
"This is a test document",
"తెలుగు అనేది ద్రావిడ భాషల కుటుంబానికి చెందిన భాష.",
]
embeddings = model.get_text_embedding_batch(coherePayload)
print(embeddings)
```
......
docs/examples/llm/bedrock.ipynb
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− 35
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This diff is collapsed.
llama_index/embeddings/bedrock.py
+ 114
− 146
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import json
import os
import warnings
from enum import Enum
from typing import Any, Dict, List, Literal, Optional
from typing import Any, Callable, Dict, List, Literal, Optional, Sequence
from llama_index.bridge.pydantic import PrivateAttr
from llama_index.bridge.pydantic import Field, PrivateAttr
from llama_index.callbacks.base import CallbackManager
from llama_index.constants import DEFAULT_EMBED_BATCH_SIZE
from llama_index.core.embeddings.base import BaseEmbedding, Embedding
from llama_index.core.llms.types import ChatMessage
from llama_index.types import BaseOutputParser, PydanticProgramMode
class PROVIDERS(str, Enum):
......@@ -33,169 +32,140 @@ PROVIDER_SPECIFIC_IDENTIFIERS = {
class BedrockEmbedding(BaseEmbedding):
model: str = Field(description="The modelId of the Bedrock model to use.")
profile_name: Optional[str] = Field(
description="The name of aws profile to use. If not given, then the default profile is used.",
exclude=True,
)
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 client."
)
_client: Any = PrivateAttr()
_verbose: bool = PrivateAttr()
def __init__(
self,
model_name: str = Models.TITAN_EMBEDDING,
client: Any = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
verbose: bool = False,
):
self._client = client
self._verbose = verbose
super().__init__(
model_name=model_name,
client=client,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
)
@staticmethod
def list_supported_models() -> Dict[str, List[str]]:
list_models = {}
for provider in PROVIDERS:
list_models[provider.value] = [m.value for m in Models]
return list_models
@classmethod
def class_name(self) -> str:
return "BedrockEmbedding"
def set_credentials(
self,
aws_region: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
aws_profile: Optional[str] = None,
) -> None:
aws_region = aws_region or os.getenv("AWS_REGION")
aws_access_key_id = aws_access_key_id or os.getenv("AWS_ACCESS_KEY_ID")
aws_secret_access_key = aws_secret_access_key or os.getenv(
"AWS_SECRET_ACCESS_KEY"
)
aws_session_token = aws_session_token or os.getenv("AWS_SESSION_TOKEN")
if aws_region is None:
warnings.warn(
"AWS_REGION not found. Set environment variable AWS_REGION or set aws_region"
)
if aws_access_key_id is None:
warnings.warn(
"AWS_ACCESS_KEY_ID not found. Set environment variable AWS_ACCESS_KEY_ID or set aws_access_key_id"
)
assert aws_access_key_id is not None
if aws_secret_access_key is None:
warnings.warn(
"AWS_SECRET_ACCESS_KEY not found. Set environment variable AWS_SECRET_ACCESS_KEY or set aws_secret_access_key"
)
assert aws_secret_access_key is not None
if aws_session_token is None:
warnings.warn(
"AWS_SESSION_TOKEN not found. Set environment variable AWS_SESSION_TOKEN or set aws_session_token"
)
assert aws_session_token is not None
session_kwargs = {
"profile_name": aws_profile,
"region_name": aws_region,
"aws_access_key_id": aws_access_key_id,
"aws_secret_access_key": aws_secret_access_key,
"aws_session_token": aws_session_token,
}
try:
import boto3
session = boto3.Session(**session_kwargs)
except ImportError:
raise ImportError(
"boto3 package not found, install with" "'pip install boto3'"
)
if "bedrock-runtime" in session.get_available_services():
self._client = session.client("bedrock-runtime")
else:
self._client = session.client("bedrock")
@classmethod
def from_credentials(
cls,
model_name: str = Models.TITAN_EMBEDDING,
aws_region: Optional[str] = None,
model: str = Models.TITAN_EMBEDDING,
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,
aws_profile: Optional[str] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
region_name: Optional[str] = None,
client: Optional[Any] = None,
botocore_session: Optional[Any] = None,
botocore_config: Optional[Any] = None,
additional_kwargs: Optional[Dict[str, Any]] = None,
max_retries: int = 10,
timeout: float = 60.0,
callback_manager: Optional[CallbackManager] = None,
verbose: bool = False,
) -> "BedrockEmbedding":
"""
Instantiate using AWS credentials.
Args:
model_name (str) : Name of the model
aws_access_key_id (str): AWS access key ID
aws_secret_access_key (str): AWS secret access key
aws_session_token (str): AWS session token
aws_region (str): AWS region where the service is located
aws_profile (str): AWS profile, when None, default profile is chosen automatically
Example:
.. code-block:: python
from llama_index.embeddings import BedrockEmbedding
# Define the model name
model_name = "your_model_name"
embeddings = BedrockEmbedding.from_credentials(
model_name,
aws_access_key_id,
aws_secret_access_key,
aws_session_token,
aws_region,
aws_profile,
)
# base class
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,
):
additional_kwargs = additional_kwargs or {}
"""
session_kwargs = {
"profile_name": aws_profile,
"region_name": aws_region,
"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'"
)
if "bedrock-runtime" in session.get_available_services():
client = session.client("bedrock-runtime")
# 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:
client = session.client("bedrock")
return cls(
client=client,
model_name=model_name,
embed_batch_size=embed_batch_size,
self._client = session.client("bedrock", config=config)
super().__init__(
model=model,
max_retries=max_retries,
timeout=timeout,
botocore_config=config,
profile_name=profile_name,
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
region_name=region_name,
botocore_session=botocore_session,
additional_kwargs=additional_kwargs,
callback_manager=callback_manager,
verbose=verbose,
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,
**kwargs,
)
@staticmethod
def list_supported_models() -> Dict[str, List[str]]:
list_models = {}
for provider in PROVIDERS:
list_models[provider.value] = [m.value for m in Models]
return list_models
@classmethod
def class_name(self) -> str:
return "BedrockEmbedding"
def _get_embedding(self, payload: str, type: Literal["text", "query"]) -> Embedding:
if self._client is None:
self.set_credentials()
......@@ -203,12 +173,12 @@ class BedrockEmbedding(BaseEmbedding):
if self._client is None:
raise ValueError("Client not set")
provider = self.model_name.split(".")[0]
provider = self.model.split(".")[0]
request_body = self._get_request_body(provider, payload, type)
response = self._client.invoke_model(
body=request_body,
modelId=self.model_name,
modelId=self.model,
accept="application/json",
contentType="application/json",
)
......@@ -244,8 +214,6 @@ class BedrockEmbedding(BaseEmbedding):
}
"""
if self._verbose:
print("provider: ", provider, PROVIDERS.AMAZON)
if provider == PROVIDERS.AMAZON:
request_body = json.dumps({"inputText": payload})
elif provider == PROVIDERS.COHERE:
......
llama_index/llms/bedrock.py
+ 68
− 37
View file @ 77e80b7f
......@@ -3,6 +3,9 @@ from typing import Any, Callable, Dict, Optional, Sequence
from llama_index.bridge.pydantic import Field, PrivateAttr
from llama_index.callbacks import CallbackManager
from llama_index.constants import (
DEFAULT_TEMPERATURE,
)
from llama_index.core.llms.types import (
ChatMessage,
ChatResponse,
......@@ -41,16 +44,33 @@ class Bedrock(LLM):
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")
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"
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,
)
aws_session_token: Optional[str] = Field(description="AWS Session Token to use")
aws_region_name: Optional[str] = Field(
description="AWS region name to use. Uses region configured in AWS CLI if not passed"
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."
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,
......@@ -64,16 +84,19 @@ class Bedrock(LLM):
def __init__(
self,
model: str,
temperature: Optional[float] = 0.5,
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,
aws_region_name: Optional[str] = None,
timeout: Optional[float] = 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,
......@@ -81,6 +104,7 @@ class Bedrock(LLM):
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(
......@@ -88,39 +112,45 @@ class Bedrock(LLM):
"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
import botocore
from botocore.config import Config
except Exception as e:
raise ImportError(
"You must install the `boto3` package to use Bedrock."
"Please `pip install boto3`"
) from e
try:
if not profile_name and aws_access_key_id:
session = boto3.Session(
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
region_name=aws_region_name,
config = (
Config(
retries={"max_attempts": max_retries, "mode": "standard"},
connect_timeout=timeout,
read_timeout=timeout,
)
else:
session = boto3.Session(profile_name=profile_name)
# 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 "bedrock-runtime" in session.get_available_services():
self._client = session.client("bedrock-runtime")
else:
self._client = session.client("bedrock")
except botocore.exceptions.NoRegionError as e:
raise ValueError(
"If default region is not set in AWS CLI, you must provide"
" the region_name argument to llama_index.llms.Bedrock"
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([])
......@@ -138,6 +168,7 @@ class Bedrock(LLM):
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,
......
tests/embeddings/test_bedrock.py
+ 2
− 2
View file @ 77e80b7f
......@@ -34,7 +34,7 @@ class TestBedrockEmbedding(TestCase):
)
bedrock_embedding = BedrockEmbedding(
model_name=Models.TITAN_EMBEDDING,
model=Models.TITAN_EMBEDDING,
client=self.bedrock_client,
)
......@@ -63,7 +63,7 @@ class TestBedrockEmbedding(TestCase):
)
bedrock_embedding = BedrockEmbedding(
model_name=Models.COHERE_EMBED_ENGLISH_V3,
model=Models.COHERE_EMBED_ENGLISH_V3,
client=self.bedrock_client,
)
......
tests/llms/test_bedrock.py
+ 13
− 13
View file @ 77e80b7f
......@@ -58,7 +58,7 @@ class MockStreamCompletionWithRetry:
) -> dict:
assert json.loads(request_body) == {
"inputText": self.expected_prompt,
"textGenerationConfig": {"maxTokenCount": 512, "temperature": 0.5},
"textGenerationConfig": {"maxTokenCount": 512, "temperature": 0.1},
}
return {
"ResponseMetadata": {
......@@ -84,27 +84,27 @@ class MockStreamCompletionWithRetry:
[
(
"amazon.titan-text-express-v1",
'{"inputText": "test prompt", "textGenerationConfig": {"temperature": 0.5, "maxTokenCount": 512}}',
'{"inputText": "test prompt", "textGenerationConfig": {"temperature": 0.1, "maxTokenCount": 512}}',
'{"inputTextTokenCount": 3, "results": [{"tokenCount": 14, "outputText": "\\n\\nThis is indeed a test", "completionReason": "FINISH"}]}',
'{"inputText": "user: test prompt\\nassistant: ", "textGenerationConfig": {"temperature": 0.5, "maxTokenCount": 512}}',
'{"inputText": "user: test prompt\\nassistant: ", "textGenerationConfig": {"temperature": 0.1, "maxTokenCount": 512}}',
),
(
"ai21.j2-grande-instruct",
'{"prompt": "test prompt", "temperature": 0.5, "maxTokens": 512}',
'{"prompt": "test prompt", "temperature": 0.1, "maxTokens": 512}',
'{"completions": [{"data": {"text": "\\n\\nThis is indeed a test"}}]}',
'{"prompt": "user: test prompt\\nassistant: ", "temperature": 0.5, "maxTokens": 512}',
'{"prompt": "user: test prompt\\nassistant: ", "temperature": 0.1, "maxTokens": 512}',
),
(
"cohere.command-text-v14",
'{"prompt": "test prompt", "temperature": 0.5, "max_tokens": 512}',
'{"prompt": "test prompt", "temperature": 0.1, "max_tokens": 512}',
'{"generations": [{"text": "\\n\\nThis is indeed a test"}]}',
'{"prompt": "user: test prompt\\nassistant: ", "temperature": 0.5, "max_tokens": 512}',
'{"prompt": "user: test prompt\\nassistant: ", "temperature": 0.1, "max_tokens": 512}',
),
(
"anthropic.claude-instant-v1",
'{"prompt": "\\n\\nHuman: test prompt\\n\\nAssistant: ", "temperature": 0.5, "max_tokens_to_sample": 512}',
'{"prompt": "\\n\\nHuman: test prompt\\n\\nAssistant: ", "temperature": 0.1, "max_tokens_to_sample": 512}',
'{"completion": "\\n\\nThis is indeed a test"}',
'{"prompt": "\\n\\nHuman: test prompt\\n\\nAssistant: ", "temperature": 0.5, "max_tokens_to_sample": 512}',
'{"prompt": "\\n\\nHuman: test prompt\\n\\nAssistant: ", "temperature": 0.1, "max_tokens_to_sample": 512}',
),
(
"meta.llama2-13b-chat-v1",
......@@ -112,13 +112,13 @@ class MockStreamCompletionWithRetry:
"honest assistant. Always answer as helpfully as possible and follow "
"ALL given instructions. Do not speculate or make up information. Do "
"not reference any given instructions or context. \\n<</SYS>>\\n\\n "
'test prompt [/INST]", "temperature": 0.5, "max_gen_len": 512}',
'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}',
'{"generation": "\\n\\nThis is indeed a test"}',
'{"prompt": "<s> [INST] <<SYS>>\\n You are a helpful, respectful and '
"honest assistant. Always answer as helpfully as possible and follow "
"ALL given instructions. Do not speculate or make up information. Do "
"not reference any given instructions or context. \\n<</SYS>>\\n\\n "
'test prompt [/INST]", "temperature": 0.5, "max_gen_len": 512}',
'test prompt [/INST]", "temperature": 0.1, "max_gen_len": 512}',
),
],
)
......@@ -128,7 +128,7 @@ def test_model_basic(
llm = Bedrock(
model=model,
profile_name=None,
aws_region_name="us-east-1",
region_name="us-east-1",
aws_access_key_id="test",
)
......@@ -168,7 +168,7 @@ def test_model_streaming(monkeypatch: MonkeyPatch) -> None:
llm = Bedrock(
model="amazon.titan-text-express-v1",
profile_name=None,
aws_region_name="us-east-1",
region_name="us-east-1",
aws_access_key_id="test",
)
test_prompt = "test prompt"
......
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