Update the "InterleavedTextMedia" type (#635)

## What does this PR do?

This is a long-pending change and particularly important to get done
now.

Specifically:
- we cannot "localize" (aka download) any URLs from media attachments
anywhere near our modeling code. it must be done within llama-stack.
- `PIL.Image` is infesting all our APIs via `ImageMedia ->
InterleavedTextMedia` and that cannot be right at all. Anything in the
API surface must be "naturally serializable". We need a standard `{
type: "image", image_url: "<...>" }` which is more extensible
- `UserMessage`, `SystemMessage`, etc. are moved completely to
llama-stack from the llama-models repository.

See https://github.com/meta-llama/llama-models/pull/244 for the
corresponding PR in llama-models.

## Test Plan

```bash
cd llama_stack/providers/tests

pytest -s -v -k "fireworks or ollama or together" inference/test_vision_inference.py
pytest -s -v -k "(fireworks or ollama or together) and llama_3b" inference/test_text_inference.py
pytest -s -v -k chroma memory/test_memory.py \
  --env EMBEDDING_DIMENSION=384 --env CHROMA_DB_PATH=/tmp/foobar

pytest -s -v -k fireworks agents/test_agents.py  \
   --safety-shield=meta-llama/Llama-Guard-3-8B \
   --inference-model=meta-llama/Llama-3.1-8B-Instruct
```

Updated the client sdk (see PR ...), installed the SDK in the same
environment and then ran the SDK tests:

```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=together pytest -s -v agents/test_agents.py
LLAMA_STACK_CONFIG=ollama pytest -s -v memory/test_memory.py

# this one needed a bit of hacking in the run.yaml to ensure I could register the vision model correctly
INFERENCE_MODEL=llama3.2-vision:latest LLAMA_STACK_CONFIG=ollama pytest -s -v inference/test_inference.py
```

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -10,21 +10,24 @@ import uuid
 
 from botocore.client import BaseClient
 from llama_models.datatypes import CoreModelId
-
 from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import ToolParamDefinition
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_stack.providers.utils.inference.model_registry import (
     build_model_alias,
     ModelRegistryHelper,
 )
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    content_has_media,
+    interleaved_content_as_str,
+)
 
 from llama_stack.apis.inference import *  # noqa: F403
 
-
 from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
-from llama_stack.providers.utils.inference.prompt_adapter import content_has_media
 
 
 MODEL_ALIASES = [
@@ -65,7 +68,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
     async def completion(
         self,
         model_id: str,
-        content: InterleavedTextMedia,
+        content: InterleavedContent,
         sampling_params: Optional[SamplingParams] = SamplingParams(),
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
@@ -450,7 +453,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
     async def embeddings(
         self,
         model_id: str,
-        contents: List[InterleavedTextMedia],
+        contents: List[InterleavedContent],
     ) -> EmbeddingsResponse:
         model = await self.model_store.get_model(model_id)
         embeddings = []
@@ -458,7 +461,7 @@ class BedrockInferenceAdapter(ModelRegistryHelper, Inference):
             assert not content_has_media(
                 content
             ), "Bedrock does not support media for embeddings"
-            input_text = interleaved_text_media_as_str(content)
+            input_text = interleaved_content_as_str(content)
             input_body = {"inputText": input_text}
             body = json.dumps(input_body)
             response = self.client.invoke_model(