Fix/issue 1213 (#2932)

### Background

Continuing to implement all the interface methods defined by the
`VectorStore` class. This PR pertains to implementation of the
`max_marginal_relevance_search` method.

### Changes

- a `max_marginal_relevance_search` method implementation has been added
in `weaviate.py`
- tests have been added to the the new method
- vcr cassettes have been added for the weaviate tests

### Test Plan

Added tests for the `max_marginal_relevance_search` implementation

### Change Safety

- [x] I have added tests to cover my changes

tests/integration_tests/vectorstores/cassettes/test_weaviate/TestWeaviate.test_max_marginal_relevance_search.yaml

@@ -0,0 +1,729 @@
+interactions:
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+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '65'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - User-Agent-DUMMY
+      X-OpenAI-Client-User-Agent:
+      - X-OpenAI-Client-User-Agent-DUMMY
+      authorization:
+      - authorization-DUMMY
+    method: POST
+    uri: https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings
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tests/integration_tests/vectorstores/docker-compose/weaviate.yml

@@ -17,6 +17,7 @@ services:
       QUERY_DEFAULTS_LIMIT: 25
       AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
       PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
-      DEFAULT_VECTORIZER_MODULE: 'none'
-      ENABLE_MODULES: ''
+      DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
+      ENABLE_MODULES: 'text2vec-openai'
+      OPENAI_APIKEY: '${OPENAI_API_KEY}'
       CLUSTER_HOSTNAME: 'node1'

tests/integration_tests/vectorstores/test_weaviate.py

@@ -1,5 +1,6 @@
 """Test Weaviate functionality."""
 import logging
+import os
 from typing import Generator, Union
 
 import pytest
@@ -18,6 +19,11 @@ docker compose -f weaviate.yml up
 
 
 class TestWeaviate:
+    @classmethod
+    def setup_class(cls) -> None:
+        if not os.getenv("OPENAI_API_KEY"):
+            raise ValueError("OPENAI_API_KEY environment variable is not set")
+
     @pytest.fixture(scope="class", autouse=True)
     def weaviate_url(self) -> Union[str, Generator[str, None, None]]:
         """Return the weaviate url."""
@@ -28,24 +34,57 @@ class TestWeaviate:
         client = Client(url)
         client.schema.delete_all()
 
-    def test_similarity_search_without_metadata(self, weaviate_url: str) -> None:
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_similarity_search_without_metadata(
+        self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
+    ) -> None:
         """Test end to end construction and search without metadata."""
         texts = ["foo", "bar", "baz"]
         docsearch = Weaviate.from_texts(
             texts,
-            OpenAIEmbeddings(),
+            embedding_openai,
             weaviate_url=weaviate_url,
         )
 
         output = docsearch.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo")]
 
-    def test_similarity_search_with_metadata(self, weaviate_url: str) -> None:
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_similarity_search_with_metadata(
+        self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
+    ) -> None:
         """Test end to end construction and search with metadata."""
         texts = ["foo", "bar", "baz"]
         metadatas = [{"page": i} for i in range(len(texts))]
         docsearch = Weaviate.from_texts(
-            texts, OpenAIEmbeddings(), metadatas=metadatas, weaviate_url=weaviate_url
+            texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
         )
         output = docsearch.similarity_search("foo", k=1)
         assert output == [Document(page_content="foo", metadata={"page": 0})]
+
+    @pytest.mark.vcr(ignore_localhost=True)
+    def test_max_marginal_relevance_search(
+        self, weaviate_url: str, embedding_openai: OpenAIEmbeddings
+    ) -> None:
+        """Test end to end construction and MRR search."""
+        texts = ["foo", "bar", "baz"]
+        metadatas = [{"page": i} for i in range(len(texts))]
+
+        docsearch = Weaviate.from_texts(
+            texts, embedding_openai, metadatas=metadatas, weaviate_url=weaviate_url
+        )
+        # if lambda=1 the algorithm should be equivalent to standard ranking
+        standard_ranking = docsearch.similarity_search("foo", k=2)
+        output = docsearch.max_marginal_relevance_search(
+            "foo", k=2, fetch_k=3, lambda_mult=1.0
+        )
+        assert output == standard_ranking
+
+        # if lambda=0 the algorithm should favour maximal diversity
+        output = docsearch.max_marginal_relevance_search(
+            "foo", k=2, fetch_k=3, lambda_mult=0.0
+        )
+        assert output == [
+            Document(page_content="foo", metadata={"page": 0}),
+            Document(page_content="bar", metadata={"page": 1}),
+        ]