f9fc02a32a
* add base Tool * minor update test_tool * update test dependency * update test dependency * Fix namespace conflict * update test --------- Co-authored-by: trducng <trungduc1992@gmail.com>
71 lines
2.2 KiB
Python
71 lines
2.2 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.docstores import InMemoryDocumentStore
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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.pipelines.tools import ComponentTool, GoogleSearchTool, WikipediaTool
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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_google_tool():
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tool = GoogleSearchTool()
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assert tool.name
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assert tool.description
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output = tool("What is Cinnamon AI")
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assert output
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def test_wikipedia_tool():
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tool = WikipediaTool()
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assert tool.name
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assert tool.description
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output = tool("Cinnamon")
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assert output
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def test_pipeline_tool(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 = IndexVectorStoreFromDocumentPipeline(
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vector_store=db, embedding=embedding, doc_store=doc_store
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)
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retrieval_pipeline = RetrieveDocumentFromVectorStorePipeline(
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vector_store=db, doc_store=doc_store, embedding=embedding
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)
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index_tool = ComponentTool(
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name="index_document",
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description="A tool to use to index a document to be searched later",
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component=index_pipeline,
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)
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output = index_tool({"text": Document(text="Cinnamon AI")})
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retrieval_tool = ComponentTool(
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name="search_document",
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description="A tool to use to search a document in a vectorstore",
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component=retrieval_pipeline,
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)
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output = retrieval_tool("Cinnamon AI")
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assert output
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