feat: Updating files/content response to return additional fields

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-08-06 16:55:14 -04:00
parent e12524af85
commit a19c16428f
143 changed files with 6907 additions and 15104 deletions

View file

@ -18,6 +18,7 @@ from llama_stack.apis.files import Files, OpenAIFileObject
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
ChunkMetadata,
QueryChunksResponse,
SearchRankingOptions,
VectorStoreChunkingStrategy,
@ -516,31 +517,68 @@ class OpenAIVectorStoreMixin(ABC):
raise ValueError(f"Unsupported filter type: {filter_type}")
def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
created_ts = None
if chunk.chunk_metadata is not None:
created_ts = getattr(chunk.chunk_metadata, "created_timestamp", None)
metadata_dict = {}
if chunk.chunk_metadata:
if hasattr(chunk.chunk_metadata, "model_dump"):
metadata_dict = chunk.chunk_metadata.model_dump()
else:
metadata_dict = vars(chunk.chunk_metadata)
user_metadata = chunk.metadata or {}
base_meta = {**metadata_dict, **user_metadata}
# content is InterleavedContent
if isinstance(chunk.content, str):
content = [
VectorStoreContent(
type="text",
text=chunk.content,
embedding=chunk.embedding,
created_timestamp=created_ts,
metadata=user_metadata,
chunk_metadata=ChunkMetadata(**base_meta) if base_meta else None,
)
]
elif isinstance(chunk.content, list):
# TODO: Add support for other types of content
content = [
VectorStoreContent(
type="text",
text=item.text,
)
for item in chunk.content
if item.type == "text"
]
content = []
for item in chunk.content:
if hasattr(item, "type") and item.type == "text":
item_meta = {**base_meta}
item_user_meta = getattr(item, "metadata", {}) or {}
if item_user_meta:
item_meta.update(item_user_meta)
content.append(
VectorStoreContent(
type="text",
text=item.text,
embedding=getattr(item, "embedding", None),
created_timestamp=created_ts,
metadata=item_user_meta,
chunk_metadata=ChunkMetadata(**item_meta) if item_meta else None,
)
)
else:
if chunk.content.type != "text":
raise ValueError(f"Unsupported content type: {chunk.content.type}")
content_item = chunk.content
if content_item.type != "text":
raise ValueError(f"Unsupported content type: {content_item.type}")
item_user_meta = getattr(content_item, "metadata", {}) or {}
combined_meta = {**base_meta, **item_user_meta}
content = [
VectorStoreContent(
type="text",
text=chunk.content.text,
text=content_item.text,
embedding=getattr(content_item, "embedding", None),
created_timestamp=created_ts,
metadata=item_user_meta,
chunk_metadata=ChunkMetadata(**combined_meta) if combined_meta else None,
)
]
return content

View file

@ -302,23 +302,25 @@ class VectorDBWithIndex:
mode = params.get("mode")
score_threshold = params.get("score_threshold", 0.0)
# Get ranker configuration
ranker = params.get("ranker")
if ranker is None:
# Default to RRF with impact_factor=60.0
reranker_type = RERANKER_TYPE_RRF
reranker_params = {"impact_factor": 60.0}
else:
reranker_type = ranker.type
reranker_params = (
{"impact_factor": ranker.impact_factor} if ranker.type == RERANKER_TYPE_RRF else {"alpha": ranker.alpha}
)
strategy = ranker.get("strategy", "rrf")
if strategy == "weighted":
weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
reranker_type = RERANKER_TYPE_WEIGHTED
reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
else:
reranker_type = RERANKER_TYPE_RRF
k_value = ranker.get("params", {}).get("k", 60.0)
reranker_params = {"impact_factor": k_value}
query_string = interleaved_content_as_str(query)
if mode == "keyword":
return await self.index.query_keyword(query_string, k, score_threshold)
# Calculate embeddings for both vector and hybrid modes
embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
if mode == "hybrid":