Merge pull request #25 from SZubarev/feature/amazon-bedrock

Added Amazon Bedrock provider

.envrc.template

@@ -4,3 +4,4 @@ export GROQ_API_KEY=""
 export OLLAMA_HOST_URL=""
 export OPENAI_API_KEY=""
 export XAI_API_KEY=""
+export AMAZON_PROFILE_NAME=""

README.md

@@ -24,10 +24,12 @@ To specify a specific provider or model, you can use the `llm_provider` and `llm
 - [**Ollama**](https://ollama.com)
 - [**OpenAI's GPT**](https://openai.com/gpt)
 - [**xAI's Grok**](https://x.ai/)
+- [**Amazon Bedrock**](https://aws.amazon.com/bedrock/)
 
 If you want to see Simplemind support, additional providers or models, please send a pull request!
 
 ## Why SimpleMind?
+
 - **Intuitive**: Built with Pythonic simplicity and readability in mind.
 - **For Humans**: Emphasizes a human-friendly interface, just like `requests` for HTTP.
 - **Open Source**: Simplemind is open source, and contributions are always welcome!
@@ -56,7 +58,6 @@ Next, import Simplemind and start using it:
 import simplemind as sm
 ```
 
-
 ## Examples
 
 Here are some examples of how to use Simplemind:
@@ -190,6 +191,7 @@ conversation.add_message(
     text="Please write a poem about the moon",
 )
 ```
+
 ```pycon
 >>> conversation.send()
 In the vast expanse where stars do play,
@@ -233,9 +235,10 @@ Simplemind uses [logfire](https://logfire.ai) for logging. To enable logging, ca
 
 Please see the [examples](examples) directory for executable examples.
 
--------------------
+---
 
 ## Contributing
+
 We welcome contributions of all kinds. Feel free to open issues for bug reports or feature requests, and submit pull requests to make SimpleMind even better.
 
 To get started:
@@ -246,7 +249,9 @@ To get started:
 4. Submit a pull request.
 
 ## License
+
 Simplemind is licensed under the Apache 2.0 License.
 
 ## Acknowledgements
+
 Simplemind is inspired by the philosophy of "code for humans" and aims to make working with AI models accessible to all. Special thanks to the open-source community for their contributions and inspiration.

simplemind/providers/__init__.py

@@ -7,5 +7,6 @@ from .groq import Groq
 from .ollama import Ollama
 from .openai import OpenAI
 from .xai import XAI
+from .amazon import Amazon
 
-providers: List[Type[BaseProvider]] = [Anthropic, Gemini, Groq, OpenAI, Ollama, XAI]
+providers: List[Type[BaseProvider]] = [Anthropic, Gemini, Groq, OpenAI, Ollama, XAI, Amazon]

simplemind/providers/amazon.py

@@ -0,0 +1,87 @@
+from typing import Type, TypeVar
+
+import instructor
+import anthropic
+from pydantic import BaseModel
+
+from ._base import BaseProvider
+from ..settings import settings
+
+T = TypeVar("T", bound=BaseModel)
+
+PROVIDER_NAME = "amazon"
+DEFAULT_MODEL = "anthropic.claude-3-sonnet-20240229-v1:0"
+DEFAULT_MAX_TOKENS = 5000
+
+class Amazon(BaseProvider):
+    NAME = PROVIDER_NAME
+    DEFAULT_MODEL = DEFAULT_MODEL
+
+    def __init__(self, profile_name: str | None = None):
+        self.profile_name = profile_name or settings.AMAZON_PROFILE_NAME
+
+    @property
+    def client(self):
+        """The AnthropicBedrock client."""
+        if not self.profile_name:
+            raise ValueError("Profile name is not provided")
+
+        return anthropic.AnthropicBedrock(aws_profile=self.profile_name)
+
+    @property
+    def structured_client(self):
+        """A client patched with Instructor."""
+        return instructor.from_anthropic(self.client)
+
+    def send_conversation(self, conversation: "Conversation", **kwargs):
+        """Send a conversation to the OpenAI API."""
+        from ..models import Message
+
+        messages = [
+            {"role": msg.role, "content": msg.text} for msg in conversation.messages
+        ]
+
+        response = self.client.chat.completions.create(
+            model=conversation.llm_model or DEFAULT_MODEL, messages=messages, **kwargs
+        )
+
+        # Get the response content from the OpenAI response
+        assistant_message = response.choices[0].message
+
+        # Create and return a properly formatted Message instance
+        return Message(
+            role="assistant",
+            text=assistant_message.content or "",
+            raw=response,
+            llm_model=conversation.llm_model or DEFAULT_MODEL,
+            llm_provider=PROVIDER_NAME,
+        )
+
+    def structured_response(self, prompt, response_model: Type[T], *, llm_model: str | None = None, **kwargs) -> T:
+        # Ensure messages are provided in kwargs
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+
+        response = self.structured_client.chat.completions.create(
+            messages=messages,
+            model=llm_model or self.DEFAULT_MODEL,
+            response_model=response_model,
+            max_tokens = DEFAULT_MAX_TOKENS,
+            **kwargs,
+        )
+        return response
+
+    def generate_text(self, prompt, *, llm_model, **kwargs):
+        messages = [
+            {"role": "user", "content": prompt},
+        ]
+
+        response = self.client.messages.create(
+            model=llm_model or self.DEFAULT_MODEL,
+            messages=messages,
+            max_tokens=DEFAULT_MAX_TOKENS,
+            **kwargs,
+        )
+
+        return response.content[0].text

simplemind/settings.py

@@ -41,6 +41,7 @@ class LoggingConfig(BaseSettings):
 class Settings(BaseSettings):
     """The class that holds all the API keys for the application."""
 
+    AMAZON_PROFILE_NAME: Optional[str] = Field("default", description="AWS Named Profile")
     ANTHROPIC_API_KEY: Optional[SecretStr] = Field(
         None, description="API key for Anthropic"
     )

tests/test_generate_data.py

@@ -1,7 +1,9 @@
-from pydantic import BaseModel
 
 import pytest
-from simplemind.providers import Anthropic, Gemini, Groq, Ollama, OpenAI
+
+from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama, Amazon
+
+from pydantic import BaseModel
 
 
 class ResponseModel(BaseModel):
@@ -16,6 +18,7 @@ class ResponseModel(BaseModel):
         OpenAI,
         Groq,
         Ollama,
+        Amazon
     ],
 )
 def test_generate_data(provider_cls):

tests/test_generate_text.py

@@ -1,5 +1,6 @@
 import pytest
-from simplemind.providers import Anthropic, Gemini, Groq, Ollama, OpenAI
+
+from simplemind.providers import Anthropic, Gemini, OpenAI, Groq, Ollama, Amazon
 
 
 @pytest.mark.parametrize(
@@ -10,6 +11,7 @@ from simplemind.providers import Anthropic, Gemini, Groq, Ollama, OpenAI
         OpenAI,
         Groq,
         Ollama,
+        Amazon,
     ],
 )
 def test_generate_text(provider_cls):