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using a property for Chunk.chunk_id

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-06-25 09:49:44 -04:00
parent f90fce218e
commit fa36b672f1
10 changed files with 163 additions and 86 deletions
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12
llama_stack/providers/utils/memory/vector_store.py
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@ -151,9 +151,6 @@ 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:
@ -167,20 +164,22 @@ def make_overlapped_chunks(
for i in range(0, len(tokens), window_len - overlap_len):
toks = tokens[i : i + window_len]
chunk = tokenizer.decode(toks)
chunk_id = generate_chunk_id(chunk, text)
chunk_metadata = metadata.copy()
chunk_metadata["chunk_id"] = chunk_id
chunk_metadata["document_id"] = document_id
chunk_metadata["token_count"] = len(toks)
chunk_metadata["metadata_token_count"] = len(metadata_tokens)
backend_chunk_metadata = ChunkMetadata(
chunk_id=chunk_id,
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,
chunk_embedding_model=None, # This will be set in `VectorDBWithIndex.insert_chunks`
content_token_count=len(toks),
metadata_token_count=len(metadata_tokens),
)
@ -255,13 +254,12 @@ 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
else:
_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
if chunks_to_embed: