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chore(migrate apis): move VectorDBWithIndex from embeddings to openai_embeddings (#3294)

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# What does this PR do?

migrates VectorDBWithIndex to use openai_embeddings

part of #2365 

## Test Plan

existing unit tests
This commit is contained in:
Matthew Farrellee 2025-08-31 17:48:35 -04:00 • committed by GitHub
parent b12cd528ef
commit 478b4ff1e6
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
20 changed files with 8376 additions and 13 deletions
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10
llama_stack/providers/utils/memory/vector_store.py
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@ -294,12 +294,12 @@ class VectorDBWithIndex:
_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension) _validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
if chunks_to_embed: if chunks_to_embed:
resp = await self.inference_api.embeddings( resp = await self.inference_api.openai_embeddings(
self.vector_db.embedding_model, self.vector_db.embedding_model,
[c.content for c in chunks_to_embed], [c.content for c in chunks_to_embed],
) )
for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False): for c, data in zip(chunks_to_embed, resp.data, strict=False):
c.embedding = embedding c.embedding = data.embedding
embeddings = np.array([c.embedding for c in chunks], dtype=np.float32) embeddings = np.array([c.embedding for c in chunks], dtype=np.float32)
await self.index.add_chunks(chunks, embeddings) await self.index.add_chunks(chunks, embeddings)
@ -334,8 +334,8 @@ class VectorDBWithIndex:
if mode == "keyword": if mode == "keyword":
return await self.index.query_keyword(query_string, k, score_threshold) return await self.index.query_keyword(query_string, k, score_threshold)
embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string]) embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string])
query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32) query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32)
if mode == "hybrid": if mode == "hybrid":
return await self.index.query_hybrid( return await self.index.query_hybrid(
query_vector, query_string, k, score_threshold, reranker_type, reranker_params query_vector, query_string, k, score_threshold, reranker_type, reranker_params

422
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}
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}
}

422
tests/integration/recordings/responses/f6d655e91ac3.json Normal file
View file

@ -0,0 +1,422 @@
{
"request": {
"method": "POST",
"url": "http://0.0.0.0:11434/v1/v1/embeddings",
"headers": {},
"body": {
"model": "all-minilm:l6-v2",
"input": [
"This is a test file"
],
"encoding_format": "float"
},
"endpoint": "/v1/embeddings",
"model": "all-minilm:l6-v2"
},
"response": {
"body": {
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11
tests/unit/providers/vector_io/test_qdrant.py
View file

@ -11,7 +11,8 @@ from unittest.mock import AsyncMock, MagicMock, patch
import pytest import pytest
from llama_stack.apis.inference import EmbeddingsResponse, Inference from llama_stack.apis.inference import Inference
from llama_stack.apis.inference.inference import OpenAIEmbeddingData, OpenAIEmbeddingsResponse, OpenAIEmbeddingUsage
from llama_stack.apis.vector_io import ( from llama_stack.apis.vector_io import (
QueryChunksResponse, QueryChunksResponse,
VectorDB, VectorDB,
@ -68,7 +69,13 @@ def mock_vector_db_store(mock_vector_db) -> MagicMock:
@pytest.fixture @pytest.fixture
def mock_api_service(sample_embeddings): def mock_api_service(sample_embeddings):
mock_api_service = MagicMock(spec=Inference) mock_api_service = MagicMock(spec=Inference)
mock_api_service.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings)) mock_api_service.openai_embeddings = AsyncMock(
return_value=OpenAIEmbeddingsResponse(
model="mock-embedding-model",
data=[OpenAIEmbeddingData(embedding=sample, index=i) for i, sample in enumerate(sample_embeddings)],
usage=OpenAIEmbeddingUsage(prompt_tokens=10, total_tokens=10),
)
)
return mock_api_service return mock_api_service

21
tests/unit/rag/test_vector_store.py
View file

@ -13,6 +13,7 @@ from unittest.mock import AsyncMock, MagicMock
import numpy as np import numpy as np
import pytest import pytest
from llama_stack.apis.inference.inference import OpenAIEmbeddingData
from llama_stack.apis.tools import RAGDocument from llama_stack.apis.tools import RAGDocument
from llama_stack.apis.vector_io import Chunk from llama_stack.apis.vector_io import Chunk
from llama_stack.providers.utils.memory.vector_store import ( from llama_stack.providers.utils.memory.vector_store import (
@ -218,11 +219,16 @@ class TestVectorDBWithIndex:
Chunk(content="Test 2", embedding=None, metadata={}), Chunk(content="Test 2", embedding=None, metadata={}),
] ]
mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]] mock_inference_api.openai_embeddings.return_value.data = [
OpenAIEmbeddingData(embedding=[0.1, 0.2, 0.3], index=0),
OpenAIEmbeddingData(embedding=[0.4, 0.5, 0.6], index=1),
]
await vector_db_with_index.insert_chunks(chunks) await vector_db_with_index.insert_chunks(chunks)
mock_inference_api.embeddings.assert_called_once_with("test-model without embeddings", ["Test 1", "Test 2"]) mock_inference_api.openai_embeddings.assert_called_once_with(
"test-model without embeddings", ["Test 1", "Test 2"]
)
mock_index.add_chunks.assert_called_once() mock_index.add_chunks.assert_called_once()
args = mock_index.add_chunks.call_args[0] args = mock_index.add_chunks.call_args[0]
assert args[0] == chunks assert args[0] == chunks
@ -246,7 +252,7 @@ class TestVectorDBWithIndex:
await vector_db_with_index.insert_chunks(chunks) await vector_db_with_index.insert_chunks(chunks)
mock_inference_api.embeddings.assert_not_called() mock_inference_api.openai_embeddings.assert_not_called()
mock_index.add_chunks.assert_called_once() mock_index.add_chunks.assert_called_once()
args = mock_index.add_chunks.call_args[0] args = mock_index.add_chunks.call_args[0]
assert args[0] == chunks assert args[0] == chunks
@ -288,7 +294,7 @@ class TestVectorDBWithIndex:
with pytest.raises(ValueError, match="has dimension 4, expected 3"): with pytest.raises(ValueError, match="has dimension 4, expected 3"):
await vector_db_with_index.insert_chunks(chunks_wrong_dim) await vector_db_with_index.insert_chunks(chunks_wrong_dim)
mock_inference_api.embeddings.assert_not_called() mock_inference_api.openai_embeddings.assert_not_called()
mock_index.add_chunks.assert_not_called() mock_index.add_chunks.assert_not_called()
async def test_insert_chunks_with_partially_precomputed_embeddings(self): async def test_insert_chunks_with_partially_precomputed_embeddings(self):
@ -308,11 +314,14 @@ class TestVectorDBWithIndex:
Chunk(content="Test 3", embedding=None, metadata={}), Chunk(content="Test 3", embedding=None, metadata={}),
] ]
mock_inference_api.embeddings.return_value.embeddings = [[0.1, 0.1, 0.1], [0.3, 0.3, 0.3]] mock_inference_api.openai_embeddings.return_value.data = [
OpenAIEmbeddingData(embedding=[0.1, 0.1, 0.1], index=0),
OpenAIEmbeddingData(embedding=[0.3, 0.3, 0.3], index=1),
]
await vector_db_with_index.insert_chunks(chunks) await vector_db_with_index.insert_chunks(chunks)
mock_inference_api.embeddings.assert_called_once_with( mock_inference_api.openai_embeddings.assert_called_once_with(
"test-model with partial embeddings", ["Test 1", "Test 3"] "test-model with partial embeddings", ["Test 1", "Test 3"]
) )
mock_index.add_chunks.assert_called_once() mock_index.add_chunks.assert_called_once()