forked from phoenix-oss/llama-stack-mirror
71 lines
2.1 KiB
Python
71 lines
2.1 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 pytest
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from llama_stack_client.types.memory_insert_params import Document
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def test_memory_bank(llama_stack_client):
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providers = llama_stack_client.providers.list()
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if "memory" not in providers:
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pytest.skip("No memory provider available")
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# get memory provider id
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assert len(providers["memory"]) > 0
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memory_provider_id = providers["memory"][0].provider_id
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memory_bank_id = "test_bank"
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llama_stack_client.memory_banks.register(
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memory_bank_id=memory_bank_id,
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params={
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"memory_bank_type": "vector",
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"embedding_model": "all-MiniLM-L6-v2",
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"chunk_size_in_tokens": 512,
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"overlap_size_in_tokens": 64,
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},
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provider_id=memory_provider_id,
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)
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# list to check memory bank is successfully registered
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available_memory_banks = [
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memory_bank.identifier for memory_bank in llama_stack_client.memory_banks.list()
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]
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assert memory_bank_id in available_memory_banks
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# add documents to memory bank
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urls = [
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"memory_optimizations.rst",
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"chat.rst",
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"llama3.rst",
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"datasets.rst",
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]
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documents = [
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Document(
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document_id=f"num-{i}",
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content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
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mime_type="text/plain",
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metadata={},
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)
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for i, url in enumerate(urls)
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]
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llama_stack_client.memory.insert(
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bank_id=memory_bank_id,
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documents=documents,
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)
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# query documents
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response = llama_stack_client.memory.query(
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bank_id=memory_bank_id,
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query="How do I use lora",
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)
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assert len(response.chunks) > 0
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assert len(response.chunks) == len(response.scores)
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contents = [chunk.content for chunk in response.chunks]
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assert "lora" in contents[0].lower()
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