Add support for Amazon Bedrock models #35
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,157 @@ | ||
| try: | ||
| import boto3 | ||
| from botocore.config import Config | ||
|
|
||
| except ImportError: | ||
| raise ImportError("If you'd like to use Amazon Bedrock models, please install the boto3 package by running `pip install boto3`") | ||
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||
| import os | ||
| import platformdirs | ||
| from tenacity import ( | ||
| retry, | ||
| stop_after_attempt, | ||
| wait_random_exponential, | ||
| ) | ||
| from .base import EngineLM, CachedEngine | ||
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| class ChatBedrock(EngineLM, CachedEngine): | ||
| SYSTEM_PROMPT = "You are a helpful, creative, and smart assistant." | ||
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| def __init__( | ||
| self, | ||
| model_string="anthropic.claude-3-sonnet-20240229-v1:0", | ||
| system_prompt=SYSTEM_PROMPT, | ||
| **kwargs | ||
| ): | ||
| self.system_prompt_supported = True | ||
| if "anthropic" in model_string: | ||
| self.system_prompt_supported = True | ||
| if "meta" in model_string: | ||
| self.system_prompt_supported = True | ||
| if "cohere" in model_string: | ||
| self.system_prompt_supported = True | ||
| if "mistral" in model_string: | ||
| if "instruct" in model_string: | ||
| self.system_prompt_supported = False | ||
| else: | ||
| self.system_prompt_supported = True | ||
| if "amazon" in model_string: | ||
| self.system_prompt_supported = False | ||
| if "premier" in model_string: | ||
| raise ValueError("amazon-titan-premier not supported yet") | ||
| if "ai21" in model_string: | ||
| self.system_prompt_supported = False | ||
| raise ValueError("ai21 not supported yet") | ||
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| self.max_tokens = kwargs.get("max_tokens", None) | ||
| self.aws_region = kwargs.get("region", None) | ||
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| # handle both AWS interaction options: with default credential or providing AWS ACCESS KEY and SECRET KEY | ||
| if boto3._get_default_session().get_credentials() is not None: | ||
| if self.aws_region: | ||
| self.my_config = Config(region_name = self.aws_region) | ||
| self.client = boto3.client(service_name='bedrock-runtime', config=self.my_config) | ||
| else: | ||
| self.client = boto3.client(service_name='bedrock-runtime') | ||
| else: | ||
| access_key_id = os.getenv("AWS_ACCESS_KEY_ID", None) | ||
| secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY", None) | ||
| session_token = os.getenv("AWS_SESSION_TOKEN", None) | ||
| if self.aws_region is None: | ||
| self.aws_region = os.getenv("AWS_DEFAULT_REGION", None) | ||
| if self.aws_region is None: | ||
| raise ValueError("AWS region not specified. Please add it in get_engine parameters or as AWS_DEFAULT_REGION env var. You can also provide an AWS role to this environment to use default session credentials") | ||
| if access_key_id is None: | ||
| raise ValueError("AWS access key ID cannot be 'None'. You can also provide an AWS role to this environment to use default session credentials") | ||
| if secret_access_key is None: | ||
| raise ValueError("AWS secret access key cannot be 'None'. You can also provide an AWS role to this environment to use default session credentials") | ||
| session = boto3.Session( | ||
| aws_access_key_id=access_key_id, | ||
| aws_secret_access_key=secret_access_key, | ||
| aws_session_token=session_token | ||
| ) | ||
| self.my_config = Config(region_name = self.aws_region) | ||
| self.client = session.client(service_name='bedrock-runtime', config=self.my_config) | ||
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| root = platformdirs.user_cache_dir("textgrad") | ||
| cache_path = os.path.join(root, f"cache_bedrock_{model_string}.db") | ||
| super().__init__(cache_path=cache_path) | ||
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| self.model_string = model_string | ||
| self.system_prompt = system_prompt | ||
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| assert isinstance(self.system_prompt, str) | ||
|
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| @retry(wait=wait_random_exponential(min=1, max=5), stop=stop_after_attempt(5)) | ||
| def __call__(self, prompt, **kwargs): | ||
| return self.generate(prompt, **kwargs) | ||
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| def generate_conversation(self, model_id="", system_prompts=[], messages=[], temperature=0.5, top_k=200, top_p=0.99, max_tokens=2048): | ||
| """ | ||
| Sends messages to a model. | ||
| Args: | ||
| bedrock_client: The Boto3 Bedrock runtime client. | ||
| model_id (str): The model ID to use. | ||
| system_prompts (JSON) : The system prompts for the model to use. | ||
| messages (JSON) : The messages to send to the model. | ||
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| Returns: | ||
| response (JSON): The conversation that the model generated. | ||
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| """ | ||
|
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| # Base inference parameters to use. | ||
| inference_config = {"temperature": temperature, "topP": top_p, "maxTokens": self.max_tokens if self.max_tokens else max_tokens} | ||
| if("anthropic" in model_id): | ||
| # Additional inference parameters to use. | ||
| additional_model_fields = {"top_k": top_k} | ||
| else: | ||
| additional_model_fields = {} | ||
|
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| # Send the message. | ||
| if self.system_prompt_supported: | ||
| response = self.client.converse( | ||
| modelId=model_id, | ||
| messages=messages, | ||
| system=system_prompts, | ||
| inferenceConfig=inference_config, | ||
| additionalModelRequestFields=additional_model_fields | ||
| ) | ||
| else: | ||
| response = self.client.converse( | ||
| modelId=model_id, | ||
| messages=messages, | ||
| inferenceConfig=inference_config, | ||
| additionalModelRequestFields=additional_model_fields | ||
| ) | ||
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| return response | ||
|
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| def generate( | ||
|
There was a problem hiding this comment. this is great! Do you want to add support for multiple inputs to this engine? Like here? There was a problem hiding this comment. yes I can do! Can you provide me a simple example to test the implementation out? Regarding to images only Anthropic models in Bedrock support vision so I'll enable that only for them! |
||
| self, prompt, system_prompt=None, temperature=0, max_tokens=2048, top_p=0.99 | ||
| ): | ||
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| sys_prompt_arg = system_prompt if system_prompt else self.system_prompt | ||
| sys_prompt_args = [{"text": sys_prompt_arg}] | ||
| cache_or_none = self._check_cache(sys_prompt_arg + prompt) | ||
| if cache_or_none is not None: | ||
| return cache_or_none | ||
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| if self.system_prompt_supported: | ||
| messages = [{ | ||
| "role": "user", | ||
| "content": [{"text": prompt}] | ||
| }] | ||
| else: | ||
| messages = [ | ||
| { | ||
| "role": "user", | ||
| "content": [{"text": sys_prompt_arg + "\n\n" + prompt}] | ||
| }] | ||
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| response = self.generate_conversation(self.model_string, system_prompts=sys_prompt_args, messages=messages, temperature=temperature, top_p=top_p, max_tokens=max_tokens) | ||
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| response = response["output"]["message"]["content"][0]["text"] | ||
| self._save_cache(sys_prompt_arg + prompt, response) | ||
| return response | ||
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can we add the related environment variables that would be needed here? i.e. AWS_ACCESS_KEY_ID etc,
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Lines 63/68 handles the variables needed. But if you prefer I can move them also here