forked from phoenix-oss/llama-stack-mirror
597869a2aa
# What does this PR do? Generate distro reports to cover inference, agents, and vector_io. ## Test Plan Report generated through `/opt/miniconda3/envs/stack/bin/pytest -s -v tests/client-sdk/ --report` ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests.
93 lines
3 KiB
Python
93 lines
3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import random
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import pytest
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@pytest.fixture(scope="function")
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def empty_vector_db_registry(llama_stack_client):
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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for vector_db_id in vector_dbs:
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llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
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@pytest.fixture(scope="function")
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def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry):
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model="all-MiniLM-L6-v2",
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embedding_dimension=384,
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provider_id="faiss",
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)
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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return vector_dbs
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def test_vector_db_retrieve(
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llama_stack_client, embedding_model, empty_vector_db_registry
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):
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# Register a memory bank first
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model=embedding_model,
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embedding_dimension=384,
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provider_id="faiss",
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)
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# Retrieve the memory bank and validate its properties
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response = llama_stack_client.vector_dbs.retrieve(vector_db_id=vector_db_id)
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assert response is not None
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assert response.identifier == vector_db_id
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assert response.embedding_model == embedding_model
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assert response.provider_id == "faiss"
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assert response.provider_resource_id == vector_db_id
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def test_vector_db_list(llama_stack_client, empty_vector_db_registry):
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vector_dbs_after_register = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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assert len(vector_dbs_after_register) == 0
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def test_vector_db_register(
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llama_stack_client, embedding_model, empty_vector_db_registry
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):
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vector_db_id = f"test_vector_db_{random.randint(1000, 9999)}"
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llama_stack_client.vector_dbs.register(
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vector_db_id=vector_db_id,
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embedding_model=embedding_model,
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embedding_dimension=384,
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provider_id="faiss",
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)
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vector_dbs_after_register = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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assert vector_dbs_after_register == [vector_db_id]
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def test_vector_db_unregister(llama_stack_client, single_entry_vector_db_registry):
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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assert len(vector_dbs) == 1
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vector_db_id = vector_dbs[0]
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llama_stack_client.vector_dbs.unregister(vector_db_id=vector_db_id)
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vector_dbs = [
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vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()
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]
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assert len(vector_dbs) == 0
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