renesis / libs /kotaemon /tests /test_tools.py
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import json
from pathlib import Path
from unittest.mock import patch
from openai.types.create_embedding_response import CreateEmbeddingResponse
from kotaemon.agents.tools import ComponentTool, GoogleSearchTool, WikipediaTool
from kotaemon.base import Document
from kotaemon.embeddings import AzureOpenAIEmbeddings
from kotaemon.indices.vectorindex import VectorIndexing, VectorRetrieval
from kotaemon.storages import ChromaVectorStore, InMemoryDocumentStore
with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
openai_embedding = CreateEmbeddingResponse.model_validate(json.load(f))
def test_google_tool(mock_google_search):
tool = GoogleSearchTool()
assert tool.name
assert tool.description
output = tool("What is Cinnamon AI")
assert output
def test_wikipedia_tool():
tool = WikipediaTool()
assert tool.name
assert tool.description
output = tool("Cinnamon")
assert output
@patch(
"openai.resources.embeddings.Embeddings.create",
side_effect=lambda *args, **kwargs: openai_embedding,
)
def test_pipeline_tool(tmp_path):
db = ChromaVectorStore(path=str(tmp_path))
doc_store = InMemoryDocumentStore()
embedding = AzureOpenAIEmbeddings(
azure_deployment="embedding-deployment",
azure_endpoint="https://test.openai.azure.com/",
api_key="some-key",
api_version="version",
)
index_pipeline = VectorIndexing(
vector_store=db, embedding=embedding, doc_store=doc_store
)
retrieval_pipeline = VectorRetrieval(
vector_store=db, doc_store=doc_store, embedding=embedding
)
index_tool = ComponentTool(
name="index_document",
description="A tool to use to index a document to be searched later",
component=index_pipeline,
)
output = index_tool({"text": Document(text="Cinnamon AI")})
retrieval_tool = ComponentTool(
name="search_document",
description="A tool to use to search a document in a vectorstore",
component=retrieval_pipeline,
)
output = retrieval_tool("Cinnamon AI")
assert output