feat: Adding ChunkMetadata

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

llama_stack/providers/utils/memory/vector_store.py

@@ -7,6 +7,7 @@ import base64
 import io
 import logging
 import re
+import time
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
 from typing import Any
@@ -23,12 +24,13 @@ from llama_stack.apis.common.content_types import (
 )
 from llama_stack.apis.tools import RAGDocument
 from llama_stack.apis.vector_dbs import VectorDB
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
+from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
 from llama_stack.models.llama.llama3.tokenizer import Tokenizer
 from llama_stack.providers.datatypes import Api
 from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
+from llama_stack.providers.utils.vector_io.chunk_utils import generate_chunk_id
 
 log = logging.getLogger(__name__)
 
@@ -148,6 +150,10 @@ async def content_from_doc(doc: RAGDocument) -> str:
 def make_overlapped_chunks(
     document_id: str, text: str, window_len: int, overlap_len: int, metadata: dict[str, Any]
 ) -> list[Chunk]:
+    default_tokenizer = "DEFAULT_TIKTOKEN_TOKENIZER"
+    default_embedding_model = (
+        "DEFAULT_EMBEDDING_MODEL"  # This will be correctly updated in `VectorDBWithIndex.insert_chunks`
+    )
     tokenizer = Tokenizer.get_instance()
     tokens = tokenizer.encode(text, bos=False, eos=False)
     try:
@@ -166,11 +172,25 @@ def make_overlapped_chunks(
         chunk_metadata["token_count"] = len(toks)
         chunk_metadata["metadata_token_count"] = len(metadata_tokens)
 
+        backend_chunk_metadata = ChunkMetadata(
+            document_id=document_id,
+            chunk_id=generate_chunk_id(chunk, text),
+            source=metadata.get("source", None),
+            created_timestamp=metadata.get("created_timestamp", int(time.time())),
+            updated_timestamp=int(time.time()),
+            chunk_window=f"{i}-{i + len(toks)}",
+            chunk_tokenizer=default_tokenizer,
+            chunk_embedding_model=default_embedding_model,
+            content_token_count=len(toks),
+            metadata_token_count=len(metadata_tokens),
+        )
+
         # chunk is a string
         chunks.append(
             Chunk(
                 content=chunk,
                 metadata=chunk_metadata,
+                chunk_metadata=backend_chunk_metadata,
             )
         )
 
@@ -235,9 +255,13 @@ class VectorDBWithIndex:
     ) -> None:
         chunks_to_embed = []
         for i, c in enumerate(chunks):
+            # this should be done in `make_overlapped_chunks` but we do it here for convenience
             if c.embedding is None:
                 chunks_to_embed.append(c)
             else:
+                if c.chunk_metadata:
+                    c.chunk_metadata.chunk_embedding_model = self.vector_db.embedding_model
+                    c.chunk_metadata.chunk_embedding_dimension = self.vector_db.embedding_dimension
                 _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
 
         if chunks_to_embed:

llama_stack/providers/utils/vector_io/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_stack/providers/utils/vector_io/chunk_utils.py

@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import hashlib
+import logging
+import uuid
+
+from llama_stack.apis.vector_io import Chunk
+
+
+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()))
+
+
+def extract_chunk_id_from_metadata(chunk: Chunk) -> str | None:
+    """Extract existing chunk ID from metadata. This is for compatibility with older Chunks
+    that stored the document_id in the metadata and not in the ChunkMetadata."""
+    if chunk.chunk_metadata is not None and hasattr(chunk.chunk_metadata, "chunk_id"):
+        return chunk.chunk_metadata.chunk_id
+
+    if "chunk_id" in chunk.metadata:
+        return str(chunk.metadata["chunk_id"])
+
+    return None
+
+
+def extract_or_generate_chunk_id(chunk: Chunk) -> str:
+    """Extract existing chunk ID or generate a new one if not present. This is for compatibility with older Chunks
+    that stored the document_id in the metadata."""
+    stored_chunk_id = extract_chunk_id_from_metadata(chunk)
+    if stored_chunk_id:
+        return stored_chunk_id
+    elif "document_id" in chunk.metadata:
+        return generate_chunk_id(chunk.metadata["document_id"], str(chunk.content))
+    else:
+        logging.warning("Chunk has no ID or document_id in metadata. Generating random ID.")
+        return str(uuid.uuid4())