e34b1e4c6d
Refactor the `kotaemon/pipelines` module to `kotaemon/indices`. Create the VectorIndex. Note: currently I place `qa` to be inside `kotaemon/indices` since at the moment we only have `qa` in RAG. At the same time, I think `qa` can be an independent module in `kotaemon/qa`. Since this can be changed later, I still go at the 1st option for now to observe if we can change it later.
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
import json
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from pathlib import Path
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from typing import cast
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import pytest
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from openai.resources.embeddings import Embeddings
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from kotaemon.base import Document
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from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
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from kotaemon.indices import VectorIndexing, VectorRetrieval
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from kotaemon.storages import ChromaVectorStore, InMemoryDocumentStore
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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(Embeddings, "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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doc_store = InMemoryDocumentStore()
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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 = VectorIndexing(vector_store=db, embedding=embedding, doc_store=doc_store)
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pipeline.doc_store = cast(InMemoryDocumentStore, pipeline.doc_store)
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pipeline.vector_store = cast(ChromaVectorStore, pipeline.vector_store)
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assert pipeline.vector_store._collection.count() == 0, "Expected empty collection"
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assert len(pipeline.doc_store._store) == 0, "Expected empty doc store"
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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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assert len(pipeline.doc_store._store) == 1, "Expected 1 document"
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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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doc_store = InMemoryDocumentStore()
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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 = VectorIndexing(
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vector_store=db, embedding=embedding, doc_store=doc_store
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)
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retrieval_pipeline = VectorRetrieval(
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vector_store=db, doc_store=doc_store, 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")
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output1 = retrieval_pipeline(text="Hello world")
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assert len(output) == 1, "Expect 1 results"
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assert output == output1, "Expect identical results"
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