[AUR-363, AUR-433, AUR-434] Add Base Tool interface with Wikipedia/Google tools (#30)

* 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>

tests/test_tools.py

@@ -0,0 +1,70 @@
+import json
+from pathlib import Path
+
+import pytest
+from openai.api_resources.embedding import Embedding
+
+from kotaemon.docstores import InMemoryDocumentStore
+from kotaemon.documents.base import Document
+from kotaemon.embeddings.openai import AzureOpenAIEmbeddings
+from kotaemon.pipelines.indexing import IndexVectorStoreFromDocumentPipeline
+from kotaemon.pipelines.retrieving import RetrieveDocumentFromVectorStorePipeline
+from kotaemon.pipelines.tools import ComponentTool, GoogleSearchTool, WikipediaTool
+from kotaemon.vectorstores import ChromaVectorStore
+
+with open(Path(__file__).parent / "resources" / "embedding_openai.json") as f:
+    openai_embedding = json.load(f)
+
+
+@pytest.fixture(scope="function")
+def mock_openai_embedding(monkeypatch):
+    monkeypatch.setattr(Embedding, "create", lambda *args, **kwargs: openai_embedding)
+
+
+def test_google_tool():
+    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
+
+
+def test_pipeline_tool(mock_openai_embedding, tmp_path):
+    db = ChromaVectorStore(path=str(tmp_path))
+    doc_store = InMemoryDocumentStore()
+    embedding = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        deployment="embedding-deployment",
+        openai_api_base="https://test.openai.azure.com/",
+        openai_api_key="some-key",
+    )
+
+    index_pipeline = IndexVectorStoreFromDocumentPipeline(
+        vector_store=db, embedding=embedding, doc_store=doc_store
+    )
+    retrieval_pipeline = RetrieveDocumentFromVectorStorePipeline(
+        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