llama-stack/llama_stack/providers/tests/memory/test_memory.py
Dinesh Yeduguru 38cce97597
migrate memory banks to Resource and new registration (#411)
* migrate memory banks to Resource and new registration

* address feedback

* address feedback

* fix tests

* pgvector fix

* pgvector fix v2

* remove auto discovery

* change register signature to make params required

* update client

* client fix

* use annotated union to parse

* remove base MemoryBank inheritence

---------

Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com>
2024-11-11 17:10:44 -08:00

145 lines
5.4 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import pytest
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import * # noqa: F403
from llama_stack.apis.memory_banks.memory_banks import VectorMemoryBankParams
# How to run this test:
#
# pytest llama_stack/providers/tests/memory/test_memory.py
# -m "meta_reference"
# -v -s --tb=short --disable-warnings
@pytest.fixture
def sample_documents():
return [
MemoryBankDocument(
document_id="doc1",
content="Python is a high-level programming language.",
metadata={"category": "programming", "difficulty": "beginner"},
),
MemoryBankDocument(
document_id="doc2",
content="Machine learning is a subset of artificial intelligence.",
metadata={"category": "AI", "difficulty": "advanced"},
),
MemoryBankDocument(
document_id="doc3",
content="Data structures are fundamental to computer science.",
metadata={"category": "computer science", "difficulty": "intermediate"},
),
MemoryBankDocument(
document_id="doc4",
content="Neural networks are inspired by biological neural networks.",
metadata={"category": "AI", "difficulty": "advanced"},
),
]
async def register_memory_bank(banks_impl: MemoryBanks):
return await banks_impl.register_memory_bank(
memory_bank_id="test_bank",
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
)
class TestMemory:
@pytest.mark.asyncio
async def test_banks_list(self, memory_stack):
# NOTE: this needs you to ensure that you are starting from a clean state
# but so far we don't have an unregister API unfortunately, so be careful
_, banks_impl = memory_stack
response = await banks_impl.list_memory_banks()
assert isinstance(response, list)
assert len(response) == 0
@pytest.mark.asyncio
async def test_banks_register(self, memory_stack):
# NOTE: this needs you to ensure that you are starting from a clean state
# but so far we don't have an unregister API unfortunately, so be careful
_, banks_impl = memory_stack
bank = await banks_impl.register_memory_bank(
memory_bank_id="test_bank_no_provider",
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
)
response = await banks_impl.list_memory_banks()
assert isinstance(response, list)
assert len(response) == 1
# register same memory bank with same id again will fail
await banks_impl.register_memory_bank(
memory_bank_id="test_bank_no_provider",
params=VectorMemoryBankParams(
embedding_model="all-MiniLM-L6-v2",
chunk_size_in_tokens=512,
overlap_size_in_tokens=64,
),
)
response = await banks_impl.list_memory_banks()
assert isinstance(response, list)
assert len(response) == 1
@pytest.mark.asyncio
async def test_query_documents(self, memory_stack, sample_documents):
memory_impl, banks_impl = memory_stack
with pytest.raises(ValueError):
await memory_impl.insert_documents("test_bank", sample_documents)
await register_memory_bank(banks_impl)
await memory_impl.insert_documents("test_bank", sample_documents)
query1 = "programming language"
response1 = await memory_impl.query_documents("test_bank", query1)
assert_valid_response(response1)
assert any("Python" in chunk.content for chunk in response1.chunks)
# Test case 3: Query with semantic similarity
query3 = "AI and brain-inspired computing"
response3 = await memory_impl.query_documents("test_bank", query3)
assert_valid_response(response3)
assert any(
"neural networks" in chunk.content.lower() for chunk in response3.chunks
)
# Test case 4: Query with limit on number of results
query4 = "computer"
params4 = {"max_chunks": 2}
response4 = await memory_impl.query_documents("test_bank", query4, params4)
assert_valid_response(response4)
assert len(response4.chunks) <= 2
# Test case 5: Query with threshold on similarity score
query5 = "quantum computing" # Not directly related to any document
params5 = {"score_threshold": 0.2}
response5 = await memory_impl.query_documents("test_bank", query5, params5)
assert_valid_response(response5)
print("The scores are:", response5.scores)
assert all(score >= 0.2 for score in response5.scores)
def assert_valid_response(response: QueryDocumentsResponse):
assert isinstance(response, QueryDocumentsResponse)
assert len(response.chunks) > 0
assert len(response.scores) > 0
assert len(response.chunks) == len(response.scores)
for chunk in response.chunks:
assert isinstance(chunk.content, str)
assert chunk.document_id is not None