Added support for models on AWS Bedrock and Sagemaker

docs/api/language_model_clients/aws_models.md

@@ -0,0 +1,86 @@
+---
+sidebar_position: 9
+---
+
+# dspy.AWSMistral, dspy.AWSAnthropic, dspy.AWSMeta
+
+### Usage
+
+```python
+# Notes:
+# 1. Install boto3 to use AWS models.
+# 2. Configure your AWS credentials with the AWS CLI before using these models
+
+# initialize the bedrock aws provider
+bedrock = dspy.Bedrock(region_name="us-west-2")
+# For mixtral on Bedrock
+lm = dspy.AWSMistral(bedrock, "mistral.mixtral-8x7b-instruct-v0:1", **kwargs)
+# For haiku on Bedrock
+lm = dspy.AWSAnthropic(bedrock, "anthropic.claude-3-haiku-20240307-v1:0", **kwargs)
+# For llama2 on Bedrock
+lm = dspy.AWSMeta(bedrock, "meta.llama2-13b-chat-v1", **kwargs)
+
+# initialize the sagemaker aws provider
+sagemaker = dspy.Sagemaker(region_name="us-west-2")
+# For mistral on Sagemaker
+# Note: you need to create a Sagemaker endpoint for the mistral model first
+lm = dspy.AWSMistral(sagemaker, "<YOUR_MISTRAL_ENDPOINT_NAME>", **kwargs)
+
+```
+
+### Constructor
+
+The `AWSMistral` constructor initializes the base class `AWSModel` which itself inherits from the `LM` class.
+
+```python
+class AWSMistral(AWSModel):
+    """Mistral family of models."""
+
+    def __init__(
+        self,
+        aws_provider: AWSProvider,
+        model: str,
+        max_context_size: int = 32768,
+        max_new_tokens: int = 1500,
+        **kwargs
+    ) -> None:
+```
+
+**Parameters:**
+- `aws_provider` (AWSProvider): The aws provider to use. One of `dspy.Bedrock` or `dspy.Sagemaker`.
+- `model` (_str_): Mistral AI pretrained models. For Bedrock, this is the Model ID in https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns. For Sagemaker, this is the endpoint name.
+- `max_context_size` (_Optional[int]_, _optional_): Max context size for this model. Defaults to 32768.
+- `max_new_tokens` (_Optional[int]_, _optional_): Max new tokens possible for this model. Defaults to 1500.
+- `**kwargs`: Additional language model arguments to pass to the API provider.
+
+### Methods
+
+```python
+def _format_prompt(self, raw_prompt: str) -> str:
+```
+This function formats the prompt for the model. Refer to the model card for the specific formatting required.
+
+<br/>
+
+```python
+def _create_body(self, prompt: str, **kwargs) -> tuple[int, dict[str, str | float]]:
+```
+This function creates the body of the request to the model. It takes the prompt and any additional keyword arguments and returns a tuple of the number of tokens to generate and a dictionary of keys including the prompt used to create the body of the request.
+
+<br/>
+
+```python
+def _call_model(self, body: str) -> str:
+```
+This function calls the model using the provider `call_model()` function and extracts the generated text (completion) from the provider-specific response.
+
+<br/>
+
+The above model-specific methods are called by the `AWSModel::basic_request()` method, which is the main method for querying the model. This method takes the prompt and any additional keyword arguments and calls the `AWSModel::_simple_api_call()` which then delegates to the model-specific `_create_body()` and `_call_model()` methods to create the body of the request, call the model and extract the generated text.
+
+
+Refer to [`dspy.OpenAI`](https://dspy-docs.vercel.app/api/language_model_clients/OpenAI) documentation for information on the `LM` base class functionality.
+
+<br/>
+
+`AWSAnthropic` and `AWSMeta` work exactly the same as `AWSMistral`.

docs/api/language_model_clients/aws_providers.md

@@ -0,0 +1,53 @@
+---
+sidebar_position: 9
+---
+
+# dspy.Bedrock, dspy.Sagemaker
+
+### Usage
+
+The `AWSProvider` class is the base class for the AWS providers - `dspy.Bedrock` and `dspy.Sagemaker`. An instance of one of these providers is passed to the constructor when creating an instance of an AWS model class (e.g., `dspy.AWSMistral`) that is ultimately used to query the model.
+
+```python
+# Notes:
+# 1. Install boto3 to use AWS models.
+# 2. Configure your AWS credentials with the AWS CLI before using these models
+
+# initialize the bedrock aws provider
+bedrock = dspy.Bedrock(region_name="us-west-2")
+
+# initialize the sagemaker aws provider
+sagemaker = dspy.Sagemaker(region_name="us-west-2")
+```
+
+### Constructor
+
+The `Bedrock` constructor initializes the base class `AWSProvider`.
+
+```python
+class Bedrock(AWSProvider):
+    """This class adds support for Bedrock models."""
+
+    def __init__(
+        self,
+        region_name: str,
+        profile_name: Optional[str] = None,
+        batch_n_enabled: bool = False,   # This has to be setup manually on Bedrock.
+    ) -> None:
+```
+
+**Parameters:**
+- `region_name` (str): The AWS region where this LM is hosted.
+- `profile_name` (str, optional): boto3 credentials profile.
+- `batch_n_enabled` (bool): If False, call the LM N times rather than batching.
+
+### Methods
+
+```python
+def call_model(self, model_id: str, body: str) -> str:
+```
+This function implements the actual invocation of the model on AWS using the boto3 provider.
+
+<br/>
+
+`Sagemaker` works exactly the same as `Bedrock`.

dsp/modules/__init__.py

@@ -1,6 +1,10 @@
 from .anthropic import Claude
+from .aws_models import AWSAnthropic, AWSMeta, AWSMistral, AWSModel
+
+# Below is obsolete. It has been replaced with Bedrock class in dsp/modules/aws_providers.py
+# from .bedrock import *
+from .aws_providers import Bedrock, Sagemaker
 from .azure_openai import AzureOpenAI
-from .bedrock import *
 from .cache_utils import *
 from .clarifai import *
 from .cohere import *
@@ -17,4 +21,3 @@
 from .pyserini import *
 from .sbert import *
 from .sentence_vectorizer import *
-