feat: Adding support for customizing chunk context in RAG insertion and querying (#2134)

# What does this PR do?
his PR allows users to customize the template used for chunks when
inserted into the context. Additionally, this enables metadata injection
into the context of an LLM for RAG. This makes a naive and crude
assumption that each chunk should include the metadata, this is
obviously redundant when multiple chunks are returned from the same
document. In order to remove any sort of duplication of chunks, we'd
have to make much more significant changes so this is a reasonable first
step that unblocks users requesting this enhancement in
https://github.com/meta-llama/llama-stack/issues/1767.

In the future, this can be extended to support citations.


List of Changes:
- `llama_stack/apis/tools/rag_tool.py`
    - Added  `chunk_template` field in `RAGQueryConfig`.
- Added `field_validator` to validate the `chunk_template` field in
`RAGQueryConfig`.
- Ensured the `chunk_template` field includes placeholders `{index}` and
`{chunk.content}`.
- Updated the `query` method to use the `chunk_template` for formatting
chunk text content.
- `llama_stack/providers/inline/tool_runtime/rag/memory.py`
- Modified the `insert` method to pass `doc.metadata` for chunk
creation.
- Enhanced the `query` method to format results using `chunk_template`
and exclude unnecessary metadata fields like `token_count`.
- `llama_stack/providers/utils/memory/vector_store.py`
- Updated `make_overlapped_chunks` to include metadata serialization and
token count for both content and metadata.
    - Added error handling for metadata serialization issues.
- `pyproject.toml`
- Added `pydantic.field_validator` as a recognized `classmethod`
decorator in the linting configuration.
- `tests/integration/tool_runtime/test_rag_tool.py`
- Refactored test assertions to separate `assert_valid_chunk_response`
and `assert_valid_text_response`.
- Added integration tests to validate `chunk_template` functionality
with and without metadata inclusion.
- Included a test case to ensure `chunk_template` validation errors are
raised appropriately.
- `tests/unit/rag/test_vector_store.py`
- Added unit tests for `make_overlapped_chunks`, verifying chunk
creation with overlapping tokens and metadata integrity.
- Added tests to handle metadata serialization errors, ensuring proper
exception handling.
- `docs/_static/llama-stack-spec.html`
- Added a new `chunk_template` field of type `string` with a default
template for formatting retrieved chunks in RAGQueryConfig.
    - Updated the `required` fields to include `chunk_template`.
- `docs/_static/llama-stack-spec.yaml`
- Introduced `chunk_template` field with a default value for
RAGQueryConfig.
- Updated the required configuration list to include `chunk_template`.
- `docs/source/building_applications/rag.md`
- Documented the `chunk_template` configuration, explaining how to
customize metadata formatting in RAG queries.
- Added examples demonstrating the usage of the `chunk_template` field
in RAG tool queries.
    - Highlighted default values for `RAG` agent configurations.

# Resolves https://github.com/meta-llama/llama-stack/issues/1767

## Test Plan
Updated both `test_vector_store.py` and `test_rag_tool.py` and tested
end-to-end with a script.

I also tested the quickstart to enable this and specified this metadata:
```python
document = RAGDocument(
    document_id="document_1",
    content=source,
    mime_type="text/html",
    metadata={"author": "Paul Graham", "title": "How to do great work"},
)
```
Which produced the output below: 

![Screenshot 2025-05-13 at 10 53
43 PM](https://github.com/user-attachments/assets/bb199d04-501e-4217-9c44-4699d43d5519)

This highlights the usefulness of the additional metadata. Notice how
the metadata is redundant for different chunks of the same document. I
think we can update that in a subsequent PR.

# Documentation
I've added a brief comment about this in the documentation to outline
this to users and updated the API documentation.

---------

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

docs/source/building_applications/rag.md

@@ -51,6 +51,7 @@ chunks = [
         "mime_type": "text/plain",
         "metadata": {
             "document_id": "doc1",
+            "author": "Jane Doe",
         },
     },
 ]
@@ -98,6 +99,17 @@ results = client.tool_runtime.rag_tool.query(
 )
 ```
 
+You can configure how the RAG tool adds metadata to the context if you find it useful for your application. Simply add:
+```python
+# Query documents
+results = client.tool_runtime.rag_tool.query(
+    vector_db_ids=[vector_db_id],
+    content="What do you know about...",
+    query_config={
+        "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
+    },
+)
+```
 ### Building RAG-Enhanced Agents
 
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
@@ -115,6 +127,12 @@ agent = Agent(
             "name": "builtin::rag/knowledge_search",
             "args": {
                 "vector_db_ids": [vector_db_id],
+                # Defaults
+                "query_config": {
+                    "chunk_size_in_tokens": 512,
+                    "chunk_overlap_in_tokens": 0,
+                    "chunk_template": "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n",
+                },
             },
         }
     ],