Add Amazon Bedrock Embedding function (#1361)

https://docs.aws.amazon.com/bedrock/latest/userguide/embeddings.html

## Description of changes

 - New functionality
	 - Support Amazon Bedrock embedding function

## Test plan

- [ ] Tests pass locally with `pytest` for python, `yarn test` for js

Tested locally by given profile_name with appropreate `~/.aws/config`

```py
>>> import boto3
>>> from chromadb.utils.embedding_functions import AmazonBedrockEmbeddingFunction
>>> session = boto3.Session(profile_name="myprofile", region_name="us-east-1")
>>> ef = AmazonBedrockEmbeddingFunction(session=session)
>>> ef(["Hello Bedrock"])
[[-0.73046875, 0.390625, 0.24511719, 0.111816406, 0.83203125, 0.79296875,...,]]
```

## Documentation Changes
Written docstrings as much as possible.

---------

Co-authored-by: Ben Eggers <[email protected]>

chromadb/utils/embedding_functions.py

@@ -710,6 +710,55 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
         return embeddings
 
 
+class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Documents]):
+    def __init__(
+        self,
+        session: "boto3.Session",  # Quote for forward reference
+        model_name: str = "amazon.titan-embed-text-v1",
+        **kwargs: Any,
+    ):
+        """Initialize AmazonBedrockEmbeddingFunction.
+
+        Args:
+            session (boto3.Session): The boto3 session to use.
+            model_name (str, optional): Identifier of the model, defaults to "amazon.titan-embed-text-v1"
+            **kwargs: Additional arguments to pass to the boto3 client.
+
+        Example:
+            >>> import boto3
+            >>> session = boto3.Session(profile_name="profile", region_name="us-east-1")
+            >>> bedrock = AmazonBedrockEmbeddingFunction(session=session)
+            >>> texts = ["Hello, world!", "How are you?"]
+            >>> embeddings = bedrock(texts)
+        """
+
+        self._model_name = model_name
+
+        self._client = session.client(
+            service_name="bedrock-runtime",
+            **kwargs,
+        )
+
+    def __call__(self, input: Documents) -> Embeddings:
+        import json
+
+        accept = "application/json"
+        content_type = "application/json"
+        embeddings = []
+        for text in input:
+            input_body = {"inputText": text}
+            body = json.dumps(input_body)
+            response = self._client.invoke_model(
+                body=body,
+                modelId=self._model_name,
+                accept=accept,
+                contentType=content_type,
+            )
+            embedding = json.load(response.get("body")).get("embedding")
+            embeddings.append(embedding)
+        return embeddings
+
+
 class HuggingFaceEmbeddingServer(EmbeddingFunction[Documents]):
     """
     This class is used to get embeddings for a list of texts using the HuggingFace Embedding server (https://github.com/huggingface/text-embeddings-inference).