620b2b03ca
Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
import json
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from pathlib import Path
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import pytest
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from openai.api_resources.embedding import Embedding
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from kotaemon.documents.base import Document
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from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
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from kotaemon.pipelines.indexing import IndexVectorStoreFromDocumentPipeline
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from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
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from kotaemon.vectorstores import ChromaVectorStore
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with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
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openai_embedding = json.load(f)
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@pytest.fixture(scope="function")
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def mock_openai_embedding(monkeypatch):
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monkeypatch.setattr(Embedding, "create", lambda *args, **kwargs: openai_embedding)
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def test_indexing(mock_openai_embedding, tmp_path):
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db = ChromaVectorStore(path=str(tmp_path))
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embedding = AzureOpenAIEmbeddings(
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model="text-embedding-ada-002",
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deployment="embedding-deployment",
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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)
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pipeline = IndexVectorStoreFromDocumentPipeline(
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vector_store=db, embedding=embedding
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)
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assert pipeline.vector_store._collection.count() == 0, "Expected empty collection"
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pipeline(text=Document(text="Hello world"))
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assert pipeline.vector_store._collection.count() == 1, "Index 1 item"
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def test_retrieving(mock_openai_embedding, tmp_path):
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db = ChromaVectorStore(path=str(tmp_path))
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embedding = AzureOpenAIEmbeddings(
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model="text-embedding-ada-002",
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deployment="embedding-deployment",
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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)
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index_pipeline = IndexVectorStoreFromDocumentPipeline(
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vector_store=db, embedding=embedding
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)
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retrieval_pipeline = RetrieveDocumentFromVectorStorePipeline(
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vector_store=db, embedding=embedding
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)
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index_pipeline(text=Document(text="Hello world"))
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output = retrieval_pipeline(text=["Hello world", "Hello world"])
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assert len(output) == 2, "Expected 2 results"
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assert output[0] == output[1], "Expected identical results"
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