From 1096f6aaee36aec08b565211465d3261674607cb Mon Sep 17 00:00:00 2001
From: Shukri <hsm207@users.noreply.github.com>
Date: Wed, 27 Mar 2024 11:54:50 +0100
Subject: [PATCH] Refactor tests (#149)

This pull request updates the fake embeddings used for testing purposes.
It includes the following changes:

- Use consistent fake embeddings instead of openai embeddings.

- Moved all tests to the unit test folder.

- Raised the minimum test coverage to 96.44%

---------

Signed-off-by: hsm207 <hsm207@users.noreply.github.com>
---
 .github/workflows/prebuild.yml                |     4 +-
 tests/integration_tests/fake_docs.json        |  7692 ------
 tests/unit_tests/fake_docs.json               | 23072 ++++++++++++++++
 .../fake_embeddings.py                        |     2 +-
 .../test_vectorstores_integration.py}         |   107 +-
 ...torstores.py => test_vectorstores_unit.py} |     0
 6 files changed, 23121 insertions(+), 7756 deletions(-)
 delete mode 100644 tests/integration_tests/fake_docs.json
 create mode 100644 tests/unit_tests/fake_docs.json
 rename tests/{integration_tests => unit_tests}/fake_embeddings.py (97%)
 rename tests/{integration_tests/test_vectorstores.py => unit_tests/test_vectorstores_integration.py} (88%)
 rename tests/unit_tests/{test_vectorstores.py => test_vectorstores_unit.py} (100%)

diff --git a/.github/workflows/prebuild.yml b/.github/workflows/prebuild.yml
index 861c529..4e1c46f 100644
--- a/.github/workflows/prebuild.yml
+++ b/.github/workflows/prebuild.yml
@@ -44,6 +44,4 @@ jobs:
           poetry run pytest -n `nproc` \
             --cov=langchain_weaviate \
             --cov-report term-missing \
-            --cov-fail-under=94.59
-        env:
-          OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY_FROM_WEAVIATE }}
\ No newline at end of file
+            --cov-fail-under=96.44
\ No newline at end of file
diff --git a/tests/integration_tests/fake_docs.json b/tests/integration_tests/fake_docs.json
deleted file mode 100644
index e298849..0000000
--- a/tests/integration_tests/fake_docs.json
+++ /dev/null
@@ -1,7692 +0,0 @@
-{
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\ No newline at end of file
diff --git a/tests/unit_tests/fake_docs.json b/tests/unit_tests/fake_docs.json
new file mode 100644
index 0000000..b4115e9
--- /dev/null
+++ b/tests/unit_tests/fake_docs.json
@@ -0,0 +1,23072 @@
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+        0.020543205450220363,
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+        0.0051159590686401905,
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+        0.0023942821229214145,
+        -0.01638958855605736,
+        -0.0050663540323449,
+        -0.00605184562733302
+    ]
+}
\ No newline at end of file
diff --git a/tests/integration_tests/fake_embeddings.py b/tests/unit_tests/fake_embeddings.py
similarity index 97%
rename from tests/integration_tests/fake_embeddings.py
rename to tests/unit_tests/fake_embeddings.py
index 243e2f8..7906b7a 100644
--- a/tests/integration_tests/fake_embeddings.py
+++ b/tests/unit_tests/fake_embeddings.py
@@ -34,7 +34,7 @@ class ConsistentFakeEmbeddings(FakeEmbeddings):
     vectors for the same texts."""
 
     def __init__(self) -> None:
-        with open("./tests/integration_tests/fake_docs.json", "r") as f:
+        with open("./tests/unit_tests/fake_docs.json", "r") as f:
             self.known_texts: Dict[str, List[float]] = json.load(f)
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
diff --git a/tests/integration_tests/test_vectorstores.py b/tests/unit_tests/test_vectorstores_integration.py
similarity index 88%
rename from tests/integration_tests/test_vectorstores.py
rename to tests/unit_tests/test_vectorstores_integration.py
index 64e46ca..008f620 100644
--- a/tests/integration_tests/test_vectorstores.py
+++ b/tests/unit_tests/test_vectorstores_integration.py
@@ -1,7 +1,6 @@
 """Test Weaviate functionality."""
 
 import logging
-import os
 import re
 import uuid
 from typing import List, Union
@@ -10,7 +9,6 @@
 import requests
 import weaviate
 from langchain_core.documents import Document
-from langchain_openai import OpenAIEmbeddings
 
 from langchain_weaviate.vectorstores import WeaviateVectorStore
 
@@ -20,8 +18,7 @@
 
 
 def setup_module(module):
-    if "OPENAI_API_KEY" not in os.environ:
-        raise EnvironmentError("OPENAI_API_KEY must be defined before running tests")
+    pass
 
 
 def is_ready(url):
@@ -60,30 +57,6 @@ def weaviate_client(docker_ip, docker_services):
     client.close()
 
 
-@pytest.fixture(scope="session")
-def embedding_openai():
-    class MemoizedOpenAIEmbeddings(OpenAIEmbeddings):
-        def __init__(self):
-            super().__init__()
-            object.__setattr__(self, "cache", {})
-
-        def embed_query(self, query):
-            if query not in self.cache:
-                # Call the base class method if result not cached
-                self.cache[query] = super().embed_query(query)
-            return self.cache[query]
-
-        def embed_documents(self, documents):
-            # Use tuple to allow documents list to be hashable
-            hashable_docs = tuple(documents)
-            if hashable_docs not in self.cache:
-                # Call the base class method if result not cached
-                self.cache[hashable_docs] = super().embed_documents(documents)
-            return self.cache[hashable_docs]
-
-    yield MemoizedOpenAIEmbeddings()
-
-
 @pytest.fixture
 def texts():
     return ["foo", "bar", "baz"]
@@ -94,14 +67,20 @@ def embedding():
     return FakeEmbeddings()
 
 
+@pytest.fixture
+def consistent_embedding():
+    return ConsistentFakeEmbeddings()
+
+
 def test_similarity_search_without_metadata(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search without metadata."""
     texts = ["foo", "bar", "baz"]
     docsearch = WeaviateVectorStore.from_texts(
         texts,
-        embedding_openai,
+        consistent_embedding,
         client=weaviate_client,
     )
 
@@ -110,26 +89,28 @@ def test_similarity_search_without_metadata(
 
 
 def test_similarity_search_with_metadata(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search with metadata."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
     output = docsearch.similarity_search("foo", k=1)
     assert output == [Document(page_content="foo", metadata={"page": 0})]
 
 
 def test_similarity_search_with_metadata_and_filter(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search with metadata."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
     output = docsearch.similarity_search(
         "foo", k=2, filters=weaviate.classes.query.Filter.by_property("page").equal(0)
@@ -138,13 +119,14 @@ def test_similarity_search_with_metadata_and_filter(
 
 
 def test_similarity_search_with_metadata_and_additional(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search with metadata and additional."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
     output = docsearch.similarity_search(
         "foo",
@@ -159,7 +141,8 @@ def test_similarity_search_with_metadata_and_additional(
 
 
 def test_similarity_search_with_uuids(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search with uuids."""
     texts = ["foo", "bar", "baz"]
@@ -169,7 +152,7 @@ def test_similarity_search_with_uuids(
     metadatas = [{"page": i} for i in range(len(texts))]
     docsearch = WeaviateVectorStore.from_texts(
         texts,
-        embedding_openai,
+        consistent_embedding,
         metadatas=metadatas,
         client=weaviate_client,
         uuids=uuids,
@@ -181,13 +164,13 @@ def test_similarity_search_with_uuids(
 def test_similarity_search_by_text(
     weaviate_client: weaviate.WeaviateClient,
     texts: List[str],
-    embedding_openai: OpenAIEmbeddings,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and search by text."""
 
     docsearch = WeaviateVectorStore.from_texts(
         texts,
-        embedding_openai,
+        consistent_embedding,
         client=weaviate_client,
     )
 
@@ -197,14 +180,15 @@ def test_similarity_search_by_text(
 
 
 def test_max_marginal_relevance_search(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and MRR search."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
 
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
     # if lambda=1 the algorithm should be equivalent to standard ranking
     standard_ranking = docsearch.similarity_search("foo", k=2)
@@ -224,16 +208,17 @@ def test_max_marginal_relevance_search(
 
 
 def test_max_marginal_relevance_search_by_vector(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and MRR search by vector."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
 
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
-    foo_embedding = embedding_openai.embed_query("foo")
+    foo_embedding = consistent_embedding.embed_query("foo")
 
     # if lambda=1 the algorithm should be equivalent to standard ranking
     standard_ranking = docsearch.similarity_search("foo", k=2)
@@ -253,14 +238,15 @@ def test_max_marginal_relevance_search_by_vector(
 
 
 def test_max_marginal_relevance_search_with_filter(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     """Test end to end construction and MRR search."""
     texts = ["foo", "bar", "baz"]
     metadatas = [{"page": i} for i in range(len(texts))]
 
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, metadatas=metadatas, client=weaviate_client
+        texts, consistent_embedding, metadatas=metadatas, client=weaviate_client
     )
 
     is_page_0_filter = weaviate.classes.query.Filter.by_property("page").equal(0)
@@ -368,7 +354,8 @@ def test_add_text_with_given_id(
 
 
 def test_similarity_search_with_score(
-    weaviate_client: weaviate.WeaviateClient, embedding_openai: OpenAIEmbeddings
+    weaviate_client: weaviate.WeaviateClient,
+    consistent_embedding: ConsistentFakeEmbeddings,
 ) -> None:
     texts = ["cat", "dog"]
 
@@ -384,7 +371,7 @@ def test_similarity_search_with_score(
 
     # now create an instance with an embedding
     docsearch = WeaviateVectorStore.from_texts(
-        texts, embedding_openai, client=weaviate_client
+        texts, consistent_embedding, client=weaviate_client
     )
 
     results = docsearch.similarity_search_with_score("kitty", k=1)
@@ -568,14 +555,14 @@ def test_simple_from_texts(
 
 
 def test_search_with_multi_tenancy(
-    weaviate_client: weaviate.WeaviateClient, texts: List[str], embedding_openai
+    weaviate_client: weaviate.WeaviateClient, texts: List[str], consistent_embedding
 ) -> None:
     index_name = f"Index_{uuid.uuid4().hex}"
     tenant_name = "Foo"
 
     docsearch = WeaviateVectorStore.from_texts(
         texts,
-        embedding=embedding_openai,
+        embedding=consistent_embedding,
         client=weaviate_client,
         index_name=index_name,
         tenant=tenant_name,
@@ -631,7 +618,7 @@ def test_invalid_client_type():
     )
 
 
-def test_embedding_property(weaviate_client, embedding_openai):
+def test_embedding_property(weaviate_client, consistent_embedding):
     index_name = "test_index"
     text_key = "text"
 
@@ -639,13 +626,13 @@ def test_embedding_property(weaviate_client, embedding_openai):
         client=weaviate_client,
         index_name=index_name,
         text_key=text_key,
-        embedding=embedding_openai,
+        embedding=consistent_embedding,
     )
 
-    assert type(docsearch.embeddings) == type(embedding_openai)
+    assert type(docsearch.embeddings) == type(consistent_embedding)
 
 
-def test_documents_with_many_properties(weaviate_client, embedding_openai):
+def test_documents_with_many_properties(weaviate_client, consistent_embedding):
     data = [
         {
             "aliases": ["Big Tech Co", "Tech Giant"],
@@ -693,7 +680,7 @@ def test_documents_with_many_properties(weaviate_client, embedding_openai):
         index_name=index_name,
         # in default schema, "page_content" is stored in "text" property
         text_key="text",
-        embedding=embedding_openai,
+        embedding=consistent_embedding,
     )
 
     texts = [doc["page_content"] for doc in data]
@@ -717,7 +704,7 @@ def test_documents_with_many_properties(weaviate_client, embedding_openai):
     assert set(doc.metadata.keys()) == {"uuid", "ticker", "categoryid"}
 
 
-def test_ingest_bad_documents(weaviate_client, embedding_openai, caplog):
+def test_ingest_bad_documents(weaviate_client, consistent_embedding, caplog):
     # try to ingest 2 documents
     docs = [
         Document(page_content="foo", metadata={"page": 0}),
@@ -731,7 +718,7 @@ def test_ingest_bad_documents(weaviate_client, embedding_openai, caplog):
     with caplog.at_level(logging.ERROR):
         _ = WeaviateVectorStore.from_documents(
             documents=docs,
-            embedding=embedding_openai,
+            embedding=consistent_embedding,
             client=weaviate_client,
             index_name=index_name,
             text_key=text_key,
@@ -804,11 +791,11 @@ def run_similarity_test(search_method):
     run_similarity_test("similarity_search_with_score")
 
 
-def test_invalid_search_param(weaviate_client, embedding_openai):
+def test_invalid_search_param(weaviate_client, consistent_embedding):
     index_name = f"TestIndex_{uuid.uuid4().hex}"
     text_key = "page"
     weaviate_vector_store = WeaviateVectorStore(
-        weaviate_client, index_name, text_key, embedding_openai
+        weaviate_client, index_name, text_key, consistent_embedding
     )
 
     with pytest.raises(ValueError) as excinfo:
diff --git a/tests/unit_tests/test_vectorstores.py b/tests/unit_tests/test_vectorstores_unit.py
similarity index 100%
rename from tests/unit_tests/test_vectorstores.py
rename to tests/unit_tests/test_vectorstores_unit.py