feat: Add ChunkMetadata to Chunk (#2497)

# What does this PR do?
Adding `ChunkMetadata` so we can properly delete embeddings later.

More specifically, this PR refactors and extends the chunk metadata
handling in the vector database and introduces a distinction between
metadata used for model context and backend-only metadata required for
chunk management, storage, and retrieval. It also improves chunk ID
generation and propagation throughout the stack, enhances test coverage,
and adds new utility modules.

```python
class ChunkMetadata(BaseModel):
    """
    `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that
        will NOT be inserted into the context during inference, but is required for backend functionality.
        Use `metadata` in `Chunk` for metadata that will be used during inference.
    """
    document_id: str | None = None
    chunk_id: str | None = None
    source: str | None = None
    created_timestamp: int | None = None
    updated_timestamp: int | None = None
    chunk_window: str | None = None
    chunk_tokenizer: str | None = None
    chunk_embedding_model: str | None = None
    chunk_embedding_dimension: int | None = None
    content_token_count: int | None = None
    metadata_token_count: int | None = None
```
Eventually we can migrate the document_id out of the `metadata` field.
I've introduced the changes so that `ChunkMetadata` is backwards
compatible with `metadata`.

<!-- If resolving an issue, uncomment and update the line below -->
Closes https://github.com/meta-llama/llama-stack/issues/2501 

## Test Plan
Added unit tests

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/apis/vector_io/vector_io.py

@@ -8,6 +8,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import uuid
 from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
@@ -15,21 +16,80 @@ from pydantic import BaseModel, Field
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
+from llama_stack.providers.utils.vector_io.chunk_utils import generate_chunk_id
 from llama_stack.schema_utils import json_schema_type, webmethod
 from llama_stack.strong_typing.schema import register_schema
 
 
+@json_schema_type
+class ChunkMetadata(BaseModel):
+    """
+    `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that
+        will not be used in the context during inference, but is required for backend functionality. The `ChunkMetadata`
+        is set during chunk creation in `MemoryToolRuntimeImpl().insert()`and is not expected to change after.
+        Use `Chunk.metadata` for metadata that will be used in the context during inference.
+    :param chunk_id: The ID of the chunk. If not set, it will be generated based on the document ID and content.
+    :param document_id: The ID of the document this chunk belongs to.
+    :param source: The source of the content, such as a URL, file path, or other identifier.
+    :param created_timestamp: An optional timestamp indicating when the chunk was created.
+    :param updated_timestamp: An optional timestamp indicating when the chunk was last updated.
+    :param chunk_window: The window of the chunk, which can be used to group related chunks together.
+    :param chunk_tokenizer: The tokenizer used to create the chunk. Default is Tiktoken.
+    :param chunk_embedding_model: The embedding model used to create the chunk's embedding.
+    :param chunk_embedding_dimension: The dimension of the embedding vector for the chunk.
+    :param content_token_count: The number of tokens in the content of the chunk.
+    :param metadata_token_count: The number of tokens in the metadata of the chunk.
+    """
+
+    chunk_id: str | None = None
+    document_id: str | None = None
+    source: str | None = None
+    created_timestamp: int | None = None
+    updated_timestamp: int | None = None
+    chunk_window: str | None = None
+    chunk_tokenizer: str | None = None
+    chunk_embedding_model: str | None = None
+    chunk_embedding_dimension: int | None = None
+    content_token_count: int | None = None
+    metadata_token_count: int | None = None
+
+
+@json_schema_type
 class Chunk(BaseModel):
     """
     A chunk of content that can be inserted into a vector database.
     :param content: The content of the chunk, which can be interleaved text, images, or other types.
     :param embedding: Optional embedding for the chunk. If not provided, it will be computed later.
-    :param metadata: Metadata associated with the chunk, such as document ID, source, or other relevant information.
+    :param metadata: Metadata associated with the chunk that will be used in the model context during inference.
+    :param stored_chunk_id: The chunk ID that is stored in the vector database. Used for backend functionality.
+    :param chunk_metadata: Metadata for the chunk that will NOT be used in the context during inference.
+        The `chunk_metadata` is required backend functionality.
     """
 
     content: InterleavedContent
     metadata: dict[str, Any] = Field(default_factory=dict)
     embedding: list[float] | None = None
+    # The alias parameter serializes the field as "chunk_id" in JSON but keeps the internal name as "stored_chunk_id"
+    stored_chunk_id: str | None = Field(default=None, alias="chunk_id")
+    chunk_metadata: ChunkMetadata | None = None
+
+    model_config = {"populate_by_name": True}
+
+    def model_post_init(self, __context):
+        # Extract chunk_id from metadata if present
+        if self.metadata and "chunk_id" in self.metadata:
+            self.stored_chunk_id = self.metadata.pop("chunk_id")
+
+    @property
+    def chunk_id(self) -> str:
+        """Returns the chunk ID, which is either an input `chunk_id` or a generated one if not set."""
+        if self.stored_chunk_id:
+            return self.stored_chunk_id
+
+        if "document_id" in self.metadata:
+            return generate_chunk_id(self.metadata["document_id"], str(self.content))
+
+        return generate_chunk_id(str(uuid.uuid4()), str(self.content))
 
 
 @json_schema_type