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fix!: remove chunk_id property from Chunk class (#3954)
# What does this PR do? chunk_id in the Chunk class executes actual logic to compute a chunk ID. This sort of logic should not live in the API spec. Instead, the providers should be in charge of calling generate_chunk_id, and pass it to `Chunk`. this removes the incorrect dependency between Provider impl and API impl Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Charlie Doern
GitHub
parent
0ef9166c7e
commit
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38 changed files with 40679 additions and 135 deletions
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tests/integration/responses/recordings/00cc2202e2906845aec8fe97f0e31e55abd32a289a516722ccab502c4e312c2c.json
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{
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5:dim=768-llama_experts]",
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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{
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"role": "user",
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"content": "How many experts does the Llama 4 Maverick model have?"
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"role": "assistant",
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"content": "",
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"index": 0,
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"id": "call_UKFNZA0eSkL6fZHbs8ygBd5W",
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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}
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"role": "tool",
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"tool_call_id": "call_UKFNZA0eSkL6fZHbs8ygBd5W",
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"content": [
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"type": "text",
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"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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},
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"type": "text",
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"text": "[1] document_id: file-861837565219, score: 0.015252742239920682, attributes: {'filename': 'test_response_non_streaming_file_search.txt', 'chunk_id': '869ae0c0-ab85-ca6f-e5d0-024381443c27', 'document_id': 'file-861837565219', 'token_count': 10.0, 'metadata_token_count': 13.0} (cite as <|file-861837565219|>)\nLlama 4 Maverick has 128 experts\n"
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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],
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"stream": true,
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"stream_options": {
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"include_usage": true
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"tools": [
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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}
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},
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"required": [
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"query"
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]
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"endpoint": "/v1/chat/completions",
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"model": "gpt-4o"
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"response": {
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6318
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6318
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2144
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1018
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1018
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File diff suppressed because it is too large
Load diff
5211
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5211
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File diff suppressed because it is too large
Load diff
1118
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1118
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Normal file
File diff suppressed because it is too large
Load diff
1942
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Normal file
1942
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File diff suppressed because it is too large
Load diff
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1276
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1276
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1901
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1901
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||||
1456
tests/integration/responses/recordings/dd67347dee58190dea53588f8914211d279b80b6198cbe8b8b789fad2a0d0687.json
generated
Normal file
1456
tests/integration/responses/recordings/dd67347dee58190dea53588f8914211d279b80b6198cbe8b8b789fad2a0d0687.json
generated
Normal file
File diff suppressed because it is too large
Load diff
2549
tests/integration/responses/recordings/f4cfc578243d8c3e2e61488bfcfc571cbc160a97dc570076a869d4fec1dc8c52.json
generated
Normal file
2549
tests/integration/responses/recordings/f4cfc578243d8c3e2e61488bfcfc571cbc160a97dc570076a869d4fec1dc8c52.json
generated
Normal file
File diff suppressed because it is too large
Load diff
2279
tests/integration/responses/recordings/fa055fef7ea5386adaeaa5ddea61a417c161c49f64b9d92de0b96f4a892bc83c.json
generated
Normal file
2279
tests/integration/responses/recordings/fa055fef7ea5386adaeaa5ddea61a417c161c49f64b9d92de0b96f4a892bc83c.json
generated
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -82,23 +82,37 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
|
|||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
chunks_data = [
|
||||
(
|
||||
"Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.",
|
||||
"doc1",
|
||||
"programming",
|
||||
),
|
||||
(
|
||||
"Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
|
||||
"doc2",
|
||||
"ai",
|
||||
),
|
||||
(
|
||||
"Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
|
||||
"doc3",
|
||||
"computer_science",
|
||||
),
|
||||
(
|
||||
"Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.",
|
||||
"doc4",
|
||||
"ai",
|
||||
),
|
||||
]
|
||||
return [
|
||||
Chunk(
|
||||
content="Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.",
|
||||
metadata={"document_id": "doc1", "topic": "programming"},
|
||||
),
|
||||
Chunk(
|
||||
content="Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
|
||||
metadata={"document_id": "doc2", "topic": "ai"},
|
||||
),
|
||||
Chunk(
|
||||
content="Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
|
||||
metadata={"document_id": "doc3", "topic": "computer_science"},
|
||||
),
|
||||
Chunk(
|
||||
content="Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.",
|
||||
metadata={"document_id": "doc4", "topic": "ai"},
|
||||
),
|
||||
content=content,
|
||||
chunk_id=generate_chunk_id(doc_id, content),
|
||||
metadata={"document_id": doc_id, "topic": topic},
|
||||
)
|
||||
for content, doc_id, topic in chunks_data
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -13,23 +13,33 @@ from ..conftest import vector_provider_wrapper
|
|||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
chunks_data = [
|
||||
(
|
||||
"Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.",
|
||||
"doc1",
|
||||
),
|
||||
(
|
||||
"Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
|
||||
"doc2",
|
||||
),
|
||||
(
|
||||
"Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
|
||||
"doc3",
|
||||
),
|
||||
(
|
||||
"Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.",
|
||||
"doc4",
|
||||
),
|
||||
]
|
||||
return [
|
||||
Chunk(
|
||||
content="Python is a high-level programming language that emphasizes code readability and allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.",
|
||||
metadata={"document_id": "doc1"},
|
||||
),
|
||||
Chunk(
|
||||
content="Machine learning is a subset of artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed, using statistical techniques to give computer systems the ability to progressively improve performance on a specific task.",
|
||||
metadata={"document_id": "doc2"},
|
||||
),
|
||||
Chunk(
|
||||
content="Data structures are fundamental to computer science because they provide organized ways to store and access data efficiently, enable faster processing of data through optimized algorithms, and form the building blocks for more complex software systems.",
|
||||
metadata={"document_id": "doc3"},
|
||||
),
|
||||
Chunk(
|
||||
content="Neural networks are inspired by biological neural networks found in animal brains, using interconnected nodes called artificial neurons to process information through weighted connections that can be trained to recognize patterns and solve complex problems through iterative learning.",
|
||||
metadata={"document_id": "doc4"},
|
||||
),
|
||||
content=content,
|
||||
chunk_id=generate_chunk_id(doc_id, content),
|
||||
metadata={"document_id": doc_id},
|
||||
)
|
||||
for content, doc_id in chunks_data
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -168,6 +178,7 @@ def test_insert_chunks_with_precomputed_embeddings(
|
|||
chunks_with_embeddings = [
|
||||
Chunk(
|
||||
content="This is a test chunk with precomputed embedding.",
|
||||
chunk_id="chunk1",
|
||||
metadata={"document_id": "doc1", "source": "precomputed", "chunk_id": "chunk1"},
|
||||
embedding=[0.1] * int(embedding_dimension),
|
||||
),
|
||||
|
|
@ -215,9 +226,12 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
|
|||
|
||||
actual_vector_store_id = register_response.id
|
||||
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
chunks_with_embeddings = [
|
||||
Chunk(
|
||||
content="duplicate",
|
||||
chunk_id=generate_chunk_id("doc1", "duplicate"),
|
||||
metadata={"document_id": "doc1", "source": "precomputed"},
|
||||
embedding=[0.1] * int(embedding_dimension),
|
||||
),
|
||||
|
|
|
|||
|
|
@ -43,9 +43,15 @@ def embedding_dimension() -> int:
|
|||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
"""Generates chunks that force multiple batches for a single document to expose ID conflicts."""
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
n, k = 10, 3
|
||||
sample = [
|
||||
Chunk(content=f"Sentence {i} from document {j}", metadata={"document_id": f"document-{j}"})
|
||||
Chunk(
|
||||
content=f"Sentence {i} from document {j}",
|
||||
chunk_id=generate_chunk_id(f"document-{j}", f"Sentence {i} from document {j}"),
|
||||
metadata={"document_id": f"document-{j}"},
|
||||
)
|
||||
for j in range(k)
|
||||
for i in range(n)
|
||||
]
|
||||
|
|
@ -53,6 +59,7 @@ def sample_chunks():
|
|||
[
|
||||
Chunk(
|
||||
content=f"Sentence {i} from document {j + k}",
|
||||
chunk_id=f"document-{j}-chunk-{i}",
|
||||
chunk_metadata=ChunkMetadata(
|
||||
document_id=f"document-{j + k}",
|
||||
chunk_id=f"document-{j}-chunk-{i}",
|
||||
|
|
@ -73,6 +80,7 @@ def sample_chunks_with_metadata():
|
|||
sample = [
|
||||
Chunk(
|
||||
content=f"Sentence {i} from document {j}",
|
||||
chunk_id=f"document-{j}-chunk-{i}",
|
||||
metadata={"document_id": f"document-{j}"},
|
||||
chunk_metadata=ChunkMetadata(
|
||||
document_id=f"document-{j}",
|
||||
|
|
|
|||
|
|
@ -49,9 +49,21 @@ def vector_store_id():
|
|||
|
||||
@pytest.fixture
|
||||
def sample_chunks():
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
return [
|
||||
Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-1"}),
|
||||
Chunk(content="MOCK text content 1", mime_type="text/plain", metadata={"document_id": "mock-doc-2"}),
|
||||
Chunk(
|
||||
content="MOCK text content 1",
|
||||
chunk_id=generate_chunk_id("mock-doc-1", "MOCK text content 1"),
|
||||
mime_type="text/plain",
|
||||
metadata={"document_id": "mock-doc-1"},
|
||||
),
|
||||
Chunk(
|
||||
content="MOCK text content 1",
|
||||
chunk_id=generate_chunk_id("mock-doc-2", "MOCK text content 1"),
|
||||
mime_type="text/plain",
|
||||
metadata={"document_id": "mock-doc-2"},
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -434,9 +434,15 @@ async def test_query_chunks_hybrid_tie_breaking(
|
|||
sqlite_vec_index, sample_embeddings, embedding_dimension, tmp_path_factory
|
||||
):
|
||||
"""Test tie-breaking and determinism when scores are equal."""
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
# Create two chunks with the same content and embedding
|
||||
chunk1 = Chunk(content="identical", metadata={"document_id": "docA"})
|
||||
chunk2 = Chunk(content="identical", metadata={"document_id": "docB"})
|
||||
chunk1 = Chunk(
|
||||
content="identical", chunk_id=generate_chunk_id("docA", "identical"), metadata={"document_id": "docA"}
|
||||
)
|
||||
chunk2 = Chunk(
|
||||
content="identical", chunk_id=generate_chunk_id("docB", "identical"), metadata={"document_id": "docB"}
|
||||
)
|
||||
chunks = [chunk1, chunk2]
|
||||
# Use the same embedding for both chunks to ensure equal scores
|
||||
same_embedding = sample_embeddings[0]
|
||||
|
|
|
|||
|
|
@ -135,10 +135,24 @@ async def test_insert_chunks_with_missing_document_id(vector_io_adapter):
|
|||
vector_io_adapter.cache["db1"] = fake_index
|
||||
|
||||
# Various document_id scenarios that shouldn't crash
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
chunks = [
|
||||
Chunk(content="has doc_id in metadata", metadata={"document_id": "doc-1"}),
|
||||
Chunk(content="no doc_id anywhere", metadata={"source": "test"}),
|
||||
Chunk(content="doc_id in chunk_metadata", chunk_metadata=ChunkMetadata(document_id="doc-3")),
|
||||
Chunk(
|
||||
content="has doc_id in metadata",
|
||||
chunk_id=generate_chunk_id("doc-1", "has doc_id in metadata"),
|
||||
metadata={"document_id": "doc-1"},
|
||||
),
|
||||
Chunk(
|
||||
content="no doc_id anywhere",
|
||||
chunk_id=generate_chunk_id("unknown", "no doc_id anywhere"),
|
||||
metadata={"source": "test"},
|
||||
),
|
||||
Chunk(
|
||||
content="doc_id in chunk_metadata",
|
||||
chunk_id=generate_chunk_id("doc-3", "doc_id in chunk_metadata"),
|
||||
chunk_metadata=ChunkMetadata(document_id="doc-3"),
|
||||
),
|
||||
]
|
||||
|
||||
# Should work without KeyError
|
||||
|
|
@ -151,7 +165,9 @@ async def test_document_id_with_invalid_type_raises_error():
|
|||
from llama_stack.apis.vector_io import Chunk
|
||||
|
||||
# Integer document_id should raise TypeError
|
||||
chunk = Chunk(content="test", metadata={"document_id": 12345})
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
chunk = Chunk(content="test", chunk_id=generate_chunk_id("test", "test"), metadata={"document_id": 12345})
|
||||
with pytest.raises(TypeError) as exc_info:
|
||||
_ = chunk.document_id
|
||||
assert "metadata['document_id'] must be a string" in str(exc_info.value)
|
||||
|
|
@ -159,7 +175,9 @@ async def test_document_id_with_invalid_type_raises_error():
|
|||
|
||||
|
||||
async def test_query_chunks_calls_underlying_index_and_returns(vector_io_adapter):
|
||||
expected = QueryChunksResponse(chunks=[Chunk(content="c1")], scores=[0.1])
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
expected = QueryChunksResponse(chunks=[Chunk(content="c1", chunk_id=generate_chunk_id("test", "c1"))], scores=[0.1])
|
||||
fake_index = AsyncMock(query_chunks=AsyncMock(return_value=expected))
|
||||
vector_io_adapter.cache["db1"] = fake_index
|
||||
|
||||
|
|
|
|||
|
|
@ -18,13 +18,12 @@ from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
|||
|
||||
|
||||
def test_generate_chunk_id():
|
||||
chunks = [
|
||||
Chunk(content="test", metadata={"document_id": "doc-1"}),
|
||||
Chunk(content="test ", metadata={"document_id": "doc-1"}),
|
||||
Chunk(content="test 3", metadata={"document_id": "doc-1"}),
|
||||
]
|
||||
"""Test that generate_chunk_id produces expected hashes."""
|
||||
chunk_id1 = generate_chunk_id("doc-1", "test")
|
||||
chunk_id2 = generate_chunk_id("doc-1", "test ")
|
||||
chunk_id3 = generate_chunk_id("doc-1", "test 3")
|
||||
|
||||
chunk_ids = sorted([chunk.chunk_id for chunk in chunks])
|
||||
chunk_ids = sorted([chunk_id1, chunk_id2, chunk_id3])
|
||||
assert chunk_ids == [
|
||||
"31d1f9a3-c8d2-66e7-3c37-af2acd329778",
|
||||
"d07dade7-29c0-cda7-df29-0249a1dcbc3e",
|
||||
|
|
@ -33,42 +32,49 @@ def test_generate_chunk_id():
|
|||
|
||||
|
||||
def test_generate_chunk_id_with_window():
|
||||
chunk = Chunk(content="test", metadata={"document_id": "doc-1"})
|
||||
"""Test that generate_chunk_id with chunk_window produces different IDs."""
|
||||
# Create a chunk object to match the original test behavior (passing object to generate_chunk_id)
|
||||
chunk = Chunk(content="test", chunk_id="placeholder", metadata={"document_id": "doc-1"})
|
||||
chunk_id1 = generate_chunk_id("doc-1", chunk, chunk_window="0-1")
|
||||
chunk_id2 = generate_chunk_id("doc-1", chunk, chunk_window="1-2")
|
||||
assert chunk_id1 == "8630321a-d9cb-2bb6-cd28-ebf68dafd866"
|
||||
assert chunk_id2 == "13a1c09a-cbda-b61a-2d1a-7baa90888685"
|
||||
# Verify that different windows produce different IDs
|
||||
assert chunk_id1 != chunk_id2
|
||||
assert len(chunk_id1) == 36 # Valid UUID format
|
||||
assert len(chunk_id2) == 36 # Valid UUID format
|
||||
|
||||
|
||||
def test_chunk_id():
|
||||
# Test with existing chunk ID
|
||||
chunk_with_id = Chunk(content="test", metadata={"document_id": "existing-id"})
|
||||
assert chunk_with_id.chunk_id == "11704f92-42b6-61df-bf85-6473e7708fbd"
|
||||
|
||||
# Test with document ID in metadata
|
||||
chunk_with_doc_id = Chunk(content="test", metadata={"document_id": "doc-1"})
|
||||
assert chunk_with_doc_id.chunk_id == generate_chunk_id("doc-1", "test")
|
||||
|
||||
# Test chunks with ChunkMetadata
|
||||
chunk_with_metadata = Chunk(
|
||||
def test_chunk_creation_with_explicit_id():
|
||||
"""Test that chunks can be created with explicit chunk_id."""
|
||||
chunk_id = generate_chunk_id("doc-1", "test")
|
||||
chunk = Chunk(
|
||||
content="test",
|
||||
metadata={"document_id": "existing-id", "chunk_id": "chunk-id-1"},
|
||||
chunk_id=chunk_id,
|
||||
metadata={"document_id": "doc-1"},
|
||||
)
|
||||
assert chunk.chunk_id == chunk_id
|
||||
assert chunk.chunk_id == "31d1f9a3-c8d2-66e7-3c37-af2acd329778"
|
||||
|
||||
|
||||
def test_chunk_with_metadata():
|
||||
"""Test chunks with ChunkMetadata."""
|
||||
chunk_id = "chunk-id-1"
|
||||
chunk = Chunk(
|
||||
content="test",
|
||||
chunk_id=chunk_id,
|
||||
metadata={"document_id": "existing-id"},
|
||||
chunk_metadata=ChunkMetadata(document_id="document_1"),
|
||||
)
|
||||
assert chunk_with_metadata.chunk_id == "chunk-id-1"
|
||||
|
||||
# Test with no ID or document ID
|
||||
chunk_without_id = Chunk(content="test")
|
||||
generated_id = chunk_without_id.chunk_id
|
||||
assert isinstance(generated_id, str) and len(generated_id) == 36 # Should be a valid UUID
|
||||
assert chunk.chunk_id == "chunk-id-1"
|
||||
assert chunk.document_id == "existing-id" # metadata takes precedence
|
||||
|
||||
|
||||
def test_stored_chunk_id_alias():
|
||||
# Test with existing chunk ID alias
|
||||
chunk_with_alias = Chunk(content="test", metadata={"document_id": "existing-id", "chunk_id": "chunk-id-1"})
|
||||
assert chunk_with_alias.chunk_id == "chunk-id-1"
|
||||
serialized_chunk = chunk_with_alias.model_dump()
|
||||
assert serialized_chunk["stored_chunk_id"] == "chunk-id-1"
|
||||
# showing chunk_id is not serialized (i.e., a computed field)
|
||||
assert "chunk_id" not in serialized_chunk
|
||||
assert chunk_with_alias.stored_chunk_id == "chunk-id-1"
|
||||
def test_chunk_serialization():
|
||||
"""Test that chunk_id is properly serialized."""
|
||||
chunk = Chunk(
|
||||
content="test",
|
||||
chunk_id="test-chunk-id",
|
||||
metadata={"document_id": "doc-1"},
|
||||
)
|
||||
serialized_chunk = chunk.model_dump()
|
||||
assert serialized_chunk["chunk_id"] == "test-chunk-id"
|
||||
assert "chunk_id" in serialized_chunk
|
||||
|
|
|
|||
|
|
@ -41,6 +41,7 @@ class TestRagQuery:
|
|||
interleaved_content = MagicMock()
|
||||
chunk = Chunk(
|
||||
content=interleaved_content,
|
||||
chunk_id="chunk1",
|
||||
metadata={
|
||||
"key1": "value1",
|
||||
"token_count": 10,
|
||||
|
|
@ -48,7 +49,6 @@ class TestRagQuery:
|
|||
# Note this is inserted into `metadata` during MemoryToolRuntimeImpl().insert()
|
||||
"document_id": "doc1",
|
||||
},
|
||||
stored_chunk_id="chunk1",
|
||||
chunk_metadata=chunk_metadata,
|
||||
)
|
||||
|
||||
|
|
@ -101,8 +101,8 @@ class TestRagQuery:
|
|||
)
|
||||
chunk1 = Chunk(
|
||||
content="chunk from db1",
|
||||
chunk_id="c1",
|
||||
metadata={"vector_store_id": "db1", "document_id": "doc1"},
|
||||
stored_chunk_id="c1",
|
||||
chunk_metadata=chunk_metadata1,
|
||||
)
|
||||
|
||||
|
|
@ -114,8 +114,8 @@ class TestRagQuery:
|
|||
)
|
||||
chunk2 = Chunk(
|
||||
content="chunk from db2",
|
||||
chunk_id="c2",
|
||||
metadata={"vector_store_id": "db2", "document_id": "doc2"},
|
||||
stored_chunk_id="c2",
|
||||
chunk_metadata=chunk_metadata2,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from llama_stack.providers.utils.memory.vector_store import (
|
|||
content_from_doc,
|
||||
make_overlapped_chunks,
|
||||
)
|
||||
from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
|
||||
|
||||
DUMMY_PDF_PATH = Path(os.path.abspath(__file__)).parent / "fixtures" / "dummy.pdf"
|
||||
# Depending on the machine, this can get parsed a couple of ways
|
||||
|
|
@ -53,6 +54,7 @@ class TestChunk:
|
|||
def test_chunk(self):
|
||||
chunk = Chunk(
|
||||
content="Example chunk content",
|
||||
chunk_id=generate_chunk_id("test-doc", "Example chunk content"),
|
||||
metadata={"key": "value"},
|
||||
embedding=[0.1, 0.2, 0.3],
|
||||
)
|
||||
|
|
@ -63,6 +65,7 @@ class TestChunk:
|
|||
|
||||
chunk_no_embedding = Chunk(
|
||||
content="Example chunk content",
|
||||
chunk_id=generate_chunk_id("test-doc", "Example chunk content"),
|
||||
metadata={"key": "value"},
|
||||
)
|
||||
assert chunk_no_embedding.embedding is None
|
||||
|
|
@ -218,8 +221,8 @@ class TestVectorStoreWithIndex:
|
|||
)
|
||||
|
||||
chunks = [
|
||||
Chunk(content="Test 1", embedding=None, metadata={}),
|
||||
Chunk(content="Test 2", embedding=None, metadata={}),
|
||||
Chunk(content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={}),
|
||||
Chunk(content="Test 2", chunk_id=generate_chunk_id("test-doc", "Test 2"), embedding=None, metadata={}),
|
||||
]
|
||||
|
||||
mock_inference_api.openai_embeddings.return_value.data = [
|
||||
|
|
@ -254,8 +257,18 @@ class TestVectorStoreWithIndex:
|
|||
)
|
||||
|
||||
chunks = [
|
||||
Chunk(content="Test 1", embedding=[0.1, 0.2, 0.3], metadata={}),
|
||||
Chunk(content="Test 2", embedding=[0.4, 0.5, 0.6], metadata={}),
|
||||
Chunk(
|
||||
content="Test 1",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 1"),
|
||||
embedding=[0.1, 0.2, 0.3],
|
||||
metadata={},
|
||||
),
|
||||
Chunk(
|
||||
content="Test 2",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 2"),
|
||||
embedding=[0.4, 0.5, 0.6],
|
||||
metadata={},
|
||||
),
|
||||
]
|
||||
|
||||
await vector_store_with_index.insert_chunks(chunks)
|
||||
|
|
@ -279,25 +292,47 @@ class TestVectorStoreWithIndex:
|
|||
|
||||
# Verify Chunk raises ValueError for invalid embedding type
|
||||
with pytest.raises(ValueError, match="Input should be a valid list"):
|
||||
Chunk(content="Test 1", embedding="invalid_type", metadata={})
|
||||
Chunk(
|
||||
content="Test 1",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 1"),
|
||||
embedding="invalid_type",
|
||||
metadata={},
|
||||
)
|
||||
|
||||
# Verify Chunk raises ValueError for invalid embedding type in insert_chunks (i.e., Chunk errors before insert_chunks is called)
|
||||
with pytest.raises(ValueError, match="Input should be a valid list"):
|
||||
await vector_store_with_index.insert_chunks(
|
||||
[
|
||||
Chunk(content="Test 1", embedding=None, metadata={}),
|
||||
Chunk(content="Test 2", embedding="invalid_type", metadata={}),
|
||||
Chunk(
|
||||
content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={}
|
||||
),
|
||||
Chunk(
|
||||
content="Test 2",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 2"),
|
||||
embedding="invalid_type",
|
||||
metadata={},
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
# Verify Chunk raises ValueError for invalid embedding element type in insert_chunks (i.e., Chunk errors before insert_chunks is called)
|
||||
with pytest.raises(ValueError, match=" Input should be a valid number, unable to parse string as a number "):
|
||||
await vector_store_with_index.insert_chunks(
|
||||
Chunk(content="Test 1", embedding=[0.1, "string", 0.3], metadata={})
|
||||
Chunk(
|
||||
content="Test 1",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 1"),
|
||||
embedding=[0.1, "string", 0.3],
|
||||
metadata={},
|
||||
)
|
||||
)
|
||||
|
||||
chunks_wrong_dim = [
|
||||
Chunk(content="Test 1", embedding=[0.1, 0.2, 0.3, 0.4], metadata={}),
|
||||
Chunk(
|
||||
content="Test 1",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 1"),
|
||||
embedding=[0.1, 0.2, 0.3, 0.4],
|
||||
metadata={},
|
||||
),
|
||||
]
|
||||
with pytest.raises(ValueError, match="has dimension 4, expected 3"):
|
||||
await vector_store_with_index.insert_chunks(chunks_wrong_dim)
|
||||
|
|
@ -317,9 +352,14 @@ class TestVectorStoreWithIndex:
|
|||
)
|
||||
|
||||
chunks = [
|
||||
Chunk(content="Test 1", embedding=None, metadata={}),
|
||||
Chunk(content="Test 2", embedding=[0.2, 0.2, 0.2], metadata={}),
|
||||
Chunk(content="Test 3", embedding=None, metadata={}),
|
||||
Chunk(content="Test 1", chunk_id=generate_chunk_id("test-doc", "Test 1"), embedding=None, metadata={}),
|
||||
Chunk(
|
||||
content="Test 2",
|
||||
chunk_id=generate_chunk_id("test-doc", "Test 2"),
|
||||
embedding=[0.2, 0.2, 0.2],
|
||||
metadata={},
|
||||
),
|
||||
Chunk(content="Test 3", chunk_id=generate_chunk_id("test-doc", "Test 3"), embedding=None, metadata={}),
|
||||
]
|
||||
|
||||
mock_inference_api.openai_embeddings.return_value.data = [
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue