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/providers/inline/tool_runtime/rag/memory.py

@@ -81,6 +81,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
         chunks = []
         for doc in documents:
             content = await content_from_doc(doc)
+            # TODO: we should add enrichment here as URLs won't be added to the metadata by default
             chunks.extend(
                 make_overlapped_chunks(
                     doc.document_id,
@@ -157,8 +158,24 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
                 )
                 break
 
-            metadata_subset = {k: v for k, v in metadata.items() if k not in ["token_count", "metadata_token_count"]}
-            text_content = query_config.chunk_template.format(index=i + 1, chunk=chunk, metadata=metadata_subset)
+            # Add useful keys from chunk_metadata to metadata and remove some from metadata
+            chunk_metadata_keys_to_include_from_context = [
+                "chunk_id",
+                "document_id",
+                "source",
+            ]
+            metadata_keys_to_exclude_from_context = [
+                "token_count",
+                "metadata_token_count",
+            ]
+            metadata_for_context = {}
+            for k in chunk_metadata_keys_to_include_from_context:
+                metadata_for_context[k] = getattr(chunk.chunk_metadata, k)
+            for k in metadata:
+                if k not in metadata_keys_to_exclude_from_context:
+                    metadata_for_context[k] = metadata[k]
+
+            text_content = query_config.chunk_template.format(index=i + 1, chunk=chunk, metadata=metadata_for_context)
             picked.append(TextContentItem(text=text_content))
 
         picked.append(TextContentItem(text="END of knowledge_search tool results.\n"))

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -5,12 +5,10 @@
 # the root directory of this source tree.
 
 import asyncio
-import hashlib
 import json
 import logging
 import sqlite3
 import struct
-import uuid
 from typing import Any
 
 import numpy as np
@@ -201,10 +199,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                     batch_embeddings = embeddings[i : i + batch_size]
 
                     # Insert metadata
-                    metadata_data = [
-                        (generate_chunk_id(chunk.metadata["document_id"], chunk.content), chunk.model_dump_json())
-                        for chunk in batch_chunks
-                    ]
+                    metadata_data = [(chunk.chunk_id, chunk.model_dump_json()) for chunk in batch_chunks]
                     cur.executemany(
                         f"""
                         INSERT INTO {self.metadata_table} (id, chunk)
@@ -218,7 +213,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                     embedding_data = [
                         (
                             (
-                                generate_chunk_id(chunk.metadata["document_id"], chunk.content),
+                                chunk.chunk_id,
                                 serialize_vector(emb.tolist()),
                             )
                         )
@@ -230,10 +225,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                     )
 
                     # Insert FTS content
-                    fts_data = [
-                        (generate_chunk_id(chunk.metadata["document_id"], chunk.content), chunk.content)
-                        for chunk in batch_chunks
-                    ]
+                    fts_data = [(chunk.chunk_id, chunk.content) for chunk in batch_chunks]
                     # DELETE existing entries with same IDs (FTS5 doesn't support ON CONFLICT)
                     cur.executemany(
                         f"DELETE FROM {self.fts_table} WHERE id = ?;",
@@ -381,13 +373,12 @@ class SQLiteVecIndex(EmbeddingIndex):
         vector_response = await self.query_vector(embedding, k, score_threshold)
         keyword_response = await self.query_keyword(query_string, k, score_threshold)
 
-        # Convert responses to score dictionaries using generate_chunk_id
+        # Convert responses to score dictionaries using chunk_id
         vector_scores = {
-            generate_chunk_id(chunk.metadata["document_id"], str(chunk.content)): score
-            for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False)
+            chunk.chunk_id: score for chunk, score in zip(vector_response.chunks, vector_response.scores, strict=False)
         }
         keyword_scores = {
-            generate_chunk_id(chunk.metadata["document_id"], str(chunk.content)): score
+            chunk.chunk_id: score
             for chunk, score in zip(keyword_response.chunks, keyword_response.scores, strict=False)
         }
 
@@ -408,13 +399,7 @@ class SQLiteVecIndex(EmbeddingIndex):
         filtered_items = [(doc_id, score) for doc_id, score in top_k_items if score >= score_threshold]
 
         # Create a map of chunk_id to chunk for both responses
-        chunk_map = {}
-        for c in vector_response.chunks:
-            chunk_id = generate_chunk_id(c.metadata["document_id"], str(c.content))
-            chunk_map[chunk_id] = c
-        for c in keyword_response.chunks:
-            chunk_id = generate_chunk_id(c.metadata["document_id"], str(c.content))
-            chunk_map[chunk_id] = c
+        chunk_map = {c.chunk_id: c for c in vector_response.chunks + keyword_response.chunks}
 
         # Use the map to look up chunks by their IDs
         chunks = []
@@ -757,9 +742,3 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
         if vector_db_id not in self.cache:
             raise ValueError(f"Vector DB {vector_db_id} not found")
         return await self.cache[vector_db_id].query_chunks(query, params)
-
-
-def generate_chunk_id(document_id: str, chunk_text: str) -> str:
-    """Generate a unique chunk ID using a hash of document ID and chunk text."""
-    hash_input = f"{document_id}:{chunk_text}".encode()
-    return str(uuid.UUID(hashlib.md5(hash_input).hexdigest()))