[ENH] Create RoboflowEmbeddingFunction (chroma-core#1434)

## Description of changes

This PR adds a new `RoboflowEmbeddingFunction` with which a user can
calculate CLIP text and image embeddings using [Roboflow
Inference](https://inference.roboflow.com).

## Test plan

You can test the embedding function using the following code:

```python
import chromadb
import os
from chromadb.utils.embedding_functions import RoboflowEmbeddingFunction
import uuid
from PIL import Image

client = chromadb.PersistentClient(path="database")

collection = client.create_collection(name="images", metadata={"hnsw:space": "cosine"})
# collection = client.get_collection(name="images")

IMAGE_DIR = "images/train/images/"
SERVER_URL = "https://infer.roboflow.com"
API_KEY = ""

results = []

ef = RoboflowEmbeddingFunction(API_KEY)

documents = [os.path.join(IMAGE_DIR, img) for img in os.listdir(IMAGE_DIR)]
embeddings = ef(images = [img for img in documents])
ids = [str(uuid.uuid4()) for _ in range(len(documents))]

print(len(embeddings))

collection.add(
    embeddings=embeddings,
    documents=documents,
    ids=ids,
)

query = ef(prompt = "baseball")

results = collection.query(
    query_embeddings=query,
    n_results=3
)

top_result = results["documents"]

for i in top_result:
    print(i)
```

You will need a [Roboflow API
key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)

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

## Documentation Changes

I will file a PR to the `chroma-core/docs` repository with
documentation.

---------

Co-authored-by: Anton Troynikov <[email protected]>

chromadb/utils/embedding_functions.py

@@ -16,6 +16,7 @@
     is_document,
 )
 
+from io import BytesIO
 from pathlib import Path
 import os
 import tarfile
@@ -27,6 +28,7 @@
 import inspect
 import json
 import sys
+import base64
 
 try:
     from chromadb.is_thin_client import is_thin_client
@@ -740,6 +742,74 @@ def __call__(self, input: Union[Documents, Images]) -> Embeddings:
         return embeddings
 
 
+class RoboflowEmbeddingFunction(EmbeddingFunction[Union[Documents, Images]]):
+    def __init__(
+        self, api_key: str = "", api_url = "https://infer.roboflow.com"
+    ) -> None:
+        """
+        Create a RoboflowEmbeddingFunction.
+
+        Args:
+            api_key (str): Your API key for the Roboflow API.
+            api_url (str, optional): The URL of the Roboflow API. Defaults to "https://infer.roboflow.com".
+        """
+        if not api_key:
+            api_key = os.environ.get("ROBOFLOW_API_KEY")
+
+        self._api_url = api_url
+        self._api_key = api_key 
+
+        try:
+            self._PILImage = importlib.import_module("PIL.Image")
+        except ImportError:
+            raise ValueError(
+                "The PIL python package is not installed. Please install it with `pip install pillow`"
+            )
+
+    def __call__(self, input: Union[Documents, Images]) -> Embeddings:
+        embeddings = []
+
+        for item in input:
+            if is_image(item):
+                image = self._PILImage.fromarray(item)
+
+                buffer = BytesIO()
+                image.save(buffer, format="JPEG")
+                base64_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+                infer_clip_payload = {
+                    "image": {
+                        "type": "base64",
+                        "value": base64_image,
+                    },
+                }
+
+                res = requests.post(
+                    f"{self._api_url}/clip/embed_image?api_key={self._api_key}",
+                    json=infer_clip_payload,
+                )
+
+                result = res.json()['embeddings']
+
+                embeddings.append(result[0])
+
+            elif is_document(item):
+                infer_clip_payload = {
+                    "text": input,
+                }
+
+                res = requests.post(
+                    f"{self._api_url}/clip/embed_text?api_key={self._api_key}",
+                    json=infer_clip_payload,
+                )
+
+                result = res.json()['embeddings']
+
+                embeddings.append(result[0])
+
+        return embeddings
+
+
 class AmazonBedrockEmbeddingFunction(EmbeddingFunction[Documents]):
     def __init__(
         self,
@@ -885,7 +955,6 @@ def __call__(self, input: Documents) -> Embeddings:
             ],
         )
 
-
 # List of all classes in this module
 _classes = [
     name