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4: finished rename I think

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This commit is contained in:
Ashwin Bharambe 2025-10-20 19:27:24 -07:00
parent 3d7b463a80
commit 44f104baae
15 changed files with 273 additions and 272 deletions
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2
llama_stack/apis/vector_dbs/__init__.py → llama_stack/apis/vector_stores/__init__.py
View file

@ -4,4 +4,4 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .vector_dbs import *
from .vector_stores import *

0
llama_stack/apis/vector_dbs/vector_dbs.py → llama_stack/apis/vector_stores/vector_stores.py
View file

2
llama_stack/providers/datatypes.py
View file

@ -17,7 +17,7 @@ from llama_stack.apis.models import Model
from llama_stack.apis.scoring_functions import ScoringFn
from llama_stack.apis.shields import Shield
from llama_stack.apis.tools import ToolGroup
from llama_stack.apis.vector_dbs import VectorStore
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.schema_utils import json_schema_type

51
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
View file

@ -17,7 +17,7 @@ from pydantic import TypeAdapter
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files, OpenAIFileObject
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.apis.vector_io import (
Chunk,
OpenAICreateVectorStoreFileBatchRequestWithExtraBody,
@ -63,7 +63,7 @@ MAX_CONCURRENT_FILES_PER_BATCH = 3 # Maximum concurrent file processing within
FILE_BATCH_CHUNK_SIZE = 10 # Process files in chunks of this size
VERSION = "v3"
VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"
VECTOR_DBS_PREFIX = f"vector_stores:{VERSION}::"
OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:{VERSION}::"
OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:{VERSION}::"
@ -321,19 +321,19 @@ class OpenAIVectorStoreMixin(ABC):
pass
@abstractmethod
async def register_vector_db(self, vector_db: VectorDB) -> None:
async def register_vector_store(self, vector_store: VectorStore) -> None:
"""Register a vector database (provider-specific implementation)."""
pass
@abstractmethod
async def unregister_vector_db(self, vector_db_id: str) -> None:
async def unregister_vector_store(self, vector_store_id: str) -> None:
"""Unregister a vector database (provider-specific implementation)."""
pass
@abstractmethod
async def insert_chunks(
self,
vector_db_id: str,
vector_store_id: str,
chunks: list[Chunk],
ttl_seconds: int | None = None,
) -> None:
@ -342,7 +342,7 @@ class OpenAIVectorStoreMixin(ABC):
@abstractmethod
async def query_chunks(
self, vector_db_id: str, query: Any, params: dict[str, Any] | None = None
self, vector_store_id: str, query: Any, params: dict[str, Any] | None = None
) -> QueryChunksResponse:
"""Query chunks from a vector database (provider-specific implementation)."""
pass
@ -358,7 +358,7 @@ class OpenAIVectorStoreMixin(ABC):
extra_body = params.model_extra or {}
metadata = params.metadata or {}
provider_vector_db_id = extra_body.get("provider_vector_db_id")
provider_vector_store_id = extra_body.get("provider_vector_store_id")
# Use embedding info from metadata if available, otherwise from extra_body
if metadata.get("embedding_model"):
@ -389,8 +389,8 @@ class OpenAIVectorStoreMixin(ABC):
# use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
provider_id = extra_body.get("provider_id") or getattr(self, "__provider_id__", None)
# Derive the canonical vector_db_id (allow override, else generate)
vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
# Derive the canonical vector_store_id (allow override, else generate)
vector_store_id = provider_vector_store_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
if embedding_model is None:
raise ValueError("embedding_model is required")
@ -398,19 +398,20 @@ class OpenAIVectorStoreMixin(ABC):
if embedding_dimension is None:
raise ValueError("Embedding dimension is required")
# Register the VectorDB backing this vector store
# Register the VectorStore backing this vector store
if provider_id is None:
raise ValueError("Provider ID is required but was not provided")
vector_db = VectorDB(
identifier=vector_db_id,
# call to the provider to create any index, etc.
vector_store = VectorStore(
identifier=vector_store_id,
embedding_dimension=embedding_dimension,
embedding_model=embedding_model,
provider_id=provider_id,
provider_resource_id=vector_db_id,
vector_db_name=params.name,
provider_resource_id=vector_store_id,
vector_store_name=params.name,
)
await self.register_vector_db(vector_db)
await self.register_vector_store(vector_store)
# Create OpenAI vector store metadata
status = "completed"
@ -424,7 +425,7 @@ class OpenAIVectorStoreMixin(ABC):
total=0,
)
store_info: dict[str, Any] = {
"id": vector_db_id,
"id": vector_store_id,
"object": "vector_store",
"created_at": created_at,
"name": params.name,
@ -441,23 +442,23 @@ class OpenAIVectorStoreMixin(ABC):
# Add provider information to metadata if provided
if provider_id:
metadata["provider_id"] = provider_id
if provider_vector_db_id:
metadata["provider_vector_db_id"] = provider_vector_db_id
if provider_vector_store_id:
metadata["provider_vector_store_id"] = provider_vector_store_id
store_info["metadata"] = metadata
# Save to persistent storage (provider-specific)
await self._save_openai_vector_store(vector_db_id, store_info)
await self._save_openai_vector_store(vector_store_id, store_info)
# Store in memory cache
self.openai_vector_stores[vector_db_id] = store_info
self.openai_vector_stores[vector_store_id] = store_info
# Now that our vector store is created, attach any files that were provided
file_ids = params.file_ids or []
tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids]
tasks = [self.openai_attach_file_to_vector_store(vector_store_id, file_id) for file_id in file_ids]
await asyncio.gather(*tasks)
# Get the updated store info and return it
store_info = self.openai_vector_stores[vector_db_id]
store_info = self.openai_vector_stores[vector_store_id]
return VectorStoreObject.model_validate(store_info)
async def openai_list_vector_stores(
@ -567,7 +568,7 @@ class OpenAIVectorStoreMixin(ABC):
# Also delete the underlying vector DB
try:
await self.unregister_vector_db(vector_store_id)
await self.unregister_vector_store(vector_store_id)
except Exception as e:
logger.warning(f"Failed to delete underlying vector DB {vector_store_id}: {e}")
@ -618,7 +619,7 @@ class OpenAIVectorStoreMixin(ABC):
# TODO: Add support for ranking_options.ranker
response = await self.query_chunks(
vector_db_id=vector_store_id,
vector_store_id=vector_store_id,
query=search_query,
params=params,
)
@ -812,7 +813,7 @@ class OpenAIVectorStoreMixin(ABC):
)
else:
await self.insert_chunks(
vector_db_id=vector_store_id,
vector_store_id=vector_store_id,
chunks=chunks,
)
vector_store_file_object.status = "completed"

18
llama_stack/providers/utils/memory/vector_store.py
View file

@ -23,7 +23,7 @@ from llama_stack.apis.common.content_types import (
)
from llama_stack.apis.inference import OpenAIEmbeddingsRequestWithExtraBody
from llama_stack.apis.tools import RAGDocument
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_stores import VectorStore
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
from llama_stack.log import get_logger
from llama_stack.models.llama.llama3.tokenizer import Tokenizer
@ -187,7 +187,7 @@ def make_overlapped_chunks(
updated_timestamp=int(time.time()),
chunk_window=chunk_window,
chunk_tokenizer=default_tokenizer,
chunk_embedding_model=None, # This will be set in `VectorDBWithIndex.insert_chunks`
chunk_embedding_model=None, # This will be set in `VectorStoreWithIndex.insert_chunks`
content_token_count=len(toks),
metadata_token_count=len(metadata_tokens),
)
@ -255,8 +255,8 @@ class EmbeddingIndex(ABC):
@dataclass
class VectorDBWithIndex:
vector_db: VectorDB
class VectorStoreWithIndex:
vector_store: VectorStore
index: EmbeddingIndex
inference_api: Api.inference
@ -269,14 +269,14 @@ class VectorDBWithIndex:
if c.embedding is None:
chunks_to_embed.append(c)
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
c.chunk_metadata.chunk_embedding_model = self.vector_store.embedding_model
c.chunk_metadata.chunk_embedding_dimension = self.vector_store.embedding_dimension
else:
_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
_validate_embedding(c.embedding, i, self.vector_store.embedding_dimension)
if chunks_to_embed:
params = OpenAIEmbeddingsRequestWithExtraBody(
model=self.vector_db.embedding_model,
model=self.vector_store.embedding_model,
input=[c.content for c in chunks_to_embed],
)
resp = await self.inference_api.openai_embeddings(params)
@ -319,7 +319,7 @@ class VectorDBWithIndex:
return await self.index.query_keyword(query_string, k, score_threshold)
params = OpenAIEmbeddingsRequestWithExtraBody(
model=self.vector_db.embedding_model,
model=self.vector_store.embedding_model,
input=[query_string],
)
embeddings_response = await self.inference_api.openai_embeddings(params)