diff --git a/docs/pydoc/config/ranker.yml b/docs/pydoc/config/ranker.yml
index ff33cc3cf..e0776e7f0 100644
--- a/docs/pydoc/config/ranker.yml
+++ b/docs/pydoc/config/ranker.yml
@@ -1,7 +1,7 @@
 loaders:
   - type: python
     search_path: [../../../haystack/nodes/ranker]
-    modules: ["base", "sentence_transformers", "recentness_ranker"]
+    modules: ["base", "sentence_transformers", "recentness_ranker", "diversity"]
     ignore_when_discovered: ["__init__"]
 processors:
   - type: filter
@@ -24,4 +24,3 @@ renderer:
     add_method_class_prefix: true
     add_member_class_prefix: false
     filename: ranker_api.md
-
diff --git a/examples/web_lfqa_improved.py b/examples/web_lfqa_improved.py
new file mode 100644
index 000000000..c46852617
--- /dev/null
+++ b/examples/web_lfqa_improved.py
@@ -0,0 +1,49 @@
+import logging
+import os
+
+from haystack import Pipeline
+from haystack.nodes import PromptNode, PromptTemplate, TopPSampler, DocumentMerger
+from haystack.nodes.ranker.diversity import DiversityRanker
+from haystack.nodes.retriever.web import WebRetriever
+
+search_key = os.environ.get("SERPERDEV_API_KEY")
+if not search_key:
+    raise ValueError("Please set the SERPERDEV_API_KEY environment variable")
+
+openai_key = os.environ.get("OPENAI_API_KEY")
+if not openai_key:
+    raise ValueError("Please set the OPENAI_API_KEY environment variable")
+
+prompt_text = """
+Synthesize a comprehensive answer from the following most relevant paragraphs and the given question.
+Provide a clear and concise response that summarizes the key points and information presented in the paragraphs.
+Your answer should be in your own words and be no longer than 50 words.
+\n\n Paragraphs: {documents} \n\n Question: {query} \n\n Answer:
+"""
+
+prompt_node = PromptNode(
+    "gpt-3.5-turbo", default_prompt_template=PromptTemplate(prompt_text), api_key=openai_key, max_length=256
+)
+
+web_retriever = WebRetriever(api_key=search_key, top_search_results=10, mode="preprocessed_documents", top_k=25)
+
+sampler = TopPSampler(top_p=0.95)
+ranker = DiversityRanker()
+merger = DocumentMerger(separator="\n\n")
+
+pipeline = Pipeline()
+pipeline.add_node(component=web_retriever, name="Retriever", inputs=["Query"])
+pipeline.add_node(component=sampler, name="Sampler", inputs=["Retriever"])
+pipeline.add_node(component=ranker, name="Ranker", inputs=["Sampler"])
+pipeline.add_node(component=merger, name="Merger", inputs=["Ranker"])
+pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Merger"])
+
+logger = logging.getLogger("boilerpy3")
+logger.setLevel(logging.CRITICAL)
+
+questions = ["What are the reasons for long-standing animosities between Russia and Poland?"]
+
+for q in questions:
+    print(f"Question: {q}")
+    response = pipeline.run(query=q)
+    print(f"Answer: {response['results'][0]}")
diff --git a/haystack/nodes/ranker/diversity.py b/haystack/nodes/ranker/diversity.py
new file mode 100644
index 000000000..893c751d1
--- /dev/null
+++ b/haystack/nodes/ranker/diversity.py
@@ -0,0 +1,149 @@
+import logging
+from pathlib import Path
+from typing import List, Literal, Optional, Union
+
+from haystack.nodes import BaseRanker
+from haystack.schema import Document
+from haystack.lazy_imports import LazyImport
+
+logger = logging.getLogger(__name__)
+
+with LazyImport(message="Run 'pip install farm-haystack[inference]'") as torch_and_transformers_import:
+    import torch
+    from sentence_transformers import SentenceTransformer
+    from haystack.modeling.utils import initialize_device_settings  # pylint: disable=ungrouped-imports
+
+
+class DiversityRanker(BaseRanker):
+    """
+    Implements a document ranking algorithm that orders documents in such a way as to maximize the overall diversity
+    of the documents.
+    """
+
+    def __init__(
+        self,
+        model_name_or_path: Union[str, Path] = "all-MiniLM-L6-v2",
+        use_gpu: Optional[bool] = True,
+        devices: Optional[List[Union[str, "torch.device"]]] = None,
+        similarity: Literal["dot_product", "cosine"] = "dot_product",
+    ):
+        """
+        Initialize a DiversityRanker.
+
+        :param model_name_or_path: Path to a pretrained sentence-transformers model.
+        :param use_gpu: Whether to use GPU (if available). If no GPUs are available, it falls back on a CPU.
+        :param devices: List of torch devices (for example, cuda:0, cpu, mps) to limit inference to specific devices.
+        :param similarity: Whether to use dot product or cosine similarity. Can be set to "dot_product" (default) or "cosine".
+        """
+        torch_and_transformers_import.check()
+        super().__init__()
+        self.devices, _ = initialize_device_settings(devices=devices, use_cuda=use_gpu, multi_gpu=True)
+        self.model = SentenceTransformer(model_name_or_path, device=str(self.devices[0]))
+        self.similarity = similarity
+
+    def predict(self, query: str, documents: List[Document], top_k: Optional[int] = None) -> List[Document]:
+        """
+        Rank the documents based on their diversity and return the top_k documents.
+
+        :param query: The query.
+        :param documents: A list of Document objects that should be ranked.
+        :param top_k: The maximum number of documents to return.
+
+        :return: A list of top_k documents ranked based on diversity.
+        """
+        if query is None or len(query) == 0:
+            raise ValueError("Query is empty")
+        if documents is None or len(documents) == 0:
+            raise ValueError("No documents to choose from")
+
+        diversity_sorted = self.greedy_diversity_order(query=query, documents=documents)
+        return diversity_sorted[:top_k]
+
+    def greedy_diversity_order(self, query: str, documents: List[Document]) -> List[Document]:
+        """
+        Orders the given list of documents to maximize diversity. The algorithm first calculates embeddings for
+        each document and the query. It starts by selecting the document that is semantically closest to the query.
+        Then, for each remaining document, it selects the one that, on average, is least similar to the already
+        selected documents. This process continues until all documents are selected, resulting in a list where
+        each subsequent document contributes the most to the overall diversity of the selected set.
+
+        :param query: The search query.
+        :param documents: The list of Document objects to be ranked.
+
+        :return: A list of documents ordered to maximize diversity.
+        """
+
+        # Calculate embeddings
+        doc_embeddings: torch.Tensor = self.model.encode([d.content for d in documents], convert_to_tensor=True)
+        query_embedding: torch.Tensor = self.model.encode([query], convert_to_tensor=True)
+
+        if self.similarity == "dot_product":
+            doc_embeddings /= torch.norm(doc_embeddings, p=2, dim=-1).unsqueeze(-1)
+            query_embedding /= torch.norm(query_embedding, p=2, dim=-1).unsqueeze(-1)
+
+        n = len(documents)
+        selected: List[int] = []
+
+        # Compute the similarity vector between the query and documents
+        query_doc_sim: torch.Tensor = query_embedding @ doc_embeddings.T
+
+        # Start with the document with the highest similarity to the query
+        selected.append(int(torch.argmax(query_doc_sim).item()))
+
+        selected_sum = doc_embeddings[selected[0]] / n
+
+        while len(selected) < n:
+            # Compute mean of dot products of all selected documents and all other documents
+            similarities = selected_sum @ doc_embeddings.T
+            # Mask documents that are already selected
+            similarities[selected] = torch.inf
+            # Select the document with the lowest total similarity score
+            index_unselected = int(torch.argmin(similarities).item())
+
+            selected.append(index_unselected)
+            # It's enough just to add to the selected vectors because dot product is distributive
+            # It's divided by n for numerical stability
+            selected_sum += doc_embeddings[index_unselected] / n
+
+        ranked_docs: List[Document] = [documents[i] for i in selected]
+
+        return ranked_docs
+
+    def predict_batch(
+        self,
+        queries: List[str],
+        documents: Union[List[Document], List[List[Document]]],
+        top_k: Optional[float] = None,
+        batch_size: Optional[int] = None,
+    ) -> Union[List[Document], List[List[Document]]]:
+        """
+        Rank the documents based on their diversity and return the top_k documents.
+
+        :param queries: The queries.
+        :param documents: A list (or a list of lists) of Document objects that should be ranked.
+        :param top_k: The maximum number of documents to return.
+        :param batch_size: The number of documents to process in one batch.
+
+        :return: A list (or a list of lists) of top_k documents ranked based on diversity.
+        """
+        if queries is None or len(queries) == 0:
+            raise ValueError("No queries to choose from")
+        if documents is None or len(documents) == 0:
+            raise ValueError("No documents to choose from")
+        if len(documents) > 0 and isinstance(documents[0], Document):
+            # Docs case 1: single list of Documents -> rerank single list of Documents based on single query
+            if len(queries) != 1:
+                raise ValueError("Number of queries must be 1 if a single list of Documents is provided.")
+            return self.predict(query=queries[0], documents=documents, top_k=top_k)  # type: ignore
+        else:
+            # Docs case 2: list of lists of Documents -> rerank each list of Documents based on corresponding query
+            # If queries contains a single query, apply it to each list of Documents
+            if len(queries) == 1:
+                queries = queries * len(documents)
+            if len(queries) != len(documents):
+                raise ValueError("Number of queries must be equal to number of provided Document lists.")
+
+            results = []
+            for query, cur_docs in zip(queries, documents):
+                results.append(self.predict(query=query, documents=cur_docs, top_k=top_k))  # type: ignore
+            return results
diff --git a/releasenotes/notes/add-most-diverse-ranker-21cf310be4554551.yaml b/releasenotes/notes/add-most-diverse-ranker-21cf310be4554551.yaml
new file mode 100644
index 000000000..8f475dd60
--- /dev/null
+++ b/releasenotes/notes/add-most-diverse-ranker-21cf310be4554551.yaml
@@ -0,0 +1,17 @@
+---
+prelude: >
+    We're introducing a new ranker to Haystack - DiversityRanker. This
+    ranker aims to maximize the overall diversity of the given documents.
+    It leverages sentence-transformer models to calculate semantic embeddings
+    for each document. It orders documents so that the next one, on average,
+    is least similar to the already selected documents. Such ranking results in a
+    list where each subsequent document contributes the most to the overall
+    diversity of the selected document set.
+features:
+  - |
+    The DiversityRanker can be used like other rankers in Haystack and
+    it can be particularly helpful in cases where you have highly relevant
+    yet similar sets of documents. By ensuring a diversity of documents,
+    this new ranker facilitates a more comprehensive utilization of the
+    documents and, particularly in RAG pipelines, potentially contributes
+    to more accurate and rich model responses.
diff --git a/test/nodes/test_diversity_ranker.py b/test/nodes/test_diversity_ranker.py
new file mode 100644
index 000000000..857f8a904
--- /dev/null
+++ b/test/nodes/test_diversity_ranker.py
@@ -0,0 +1,233 @@
+from typing import List
+
+import pytest
+
+from haystack import Document
+from haystack.nodes.ranker.diversity import DiversityRanker
+
+
+# Tests that predict method returns a list of Document objects
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_returns_list_of_documents(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "test query"
+    documents = [Document(content="doc1"), Document(content="doc2")]
+    result = ranker.predict(query=query, documents=documents)
+    assert isinstance(result, list)
+    assert len(result) == 2
+    assert all(isinstance(doc, Document) for doc in result)
+
+
+#  Tests that predict method returns the correct number of documents
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_returns_correct_number_of_documents(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "test query"
+    documents = [Document(content="doc1"), Document(content="doc2")]
+    result = ranker.predict(query=query, documents=documents, top_k=1)
+    assert len(result) == 1
+
+
+#  Tests that predict method returns documents in the correct order
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_returns_documents_in_correct_order(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "city"
+    documents = [
+        Document("France"),
+        Document("Germany"),
+        Document("Eiffel Tower"),
+        Document("Berlin"),
+        Document("bananas"),
+        Document("Silicon Valley"),
+        Document("Brandenburg Gate"),
+    ]
+    result = ranker.predict(query=query, documents=documents)
+    expected_order = "Berlin, bananas, Eiffel Tower, Silicon Valley, France, Brandenburg Gate, Germany"
+    assert ", ".join([doc.content for doc in result]) == expected_order
+
+
+#  Tests that predict_batch method returns a list of lists of Document objects
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_batch_returns_list_of_lists_of_documents(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    queries = ["test query 1", "test query 2"]
+    documents = [
+        [Document(content="doc1"), Document(content="doc2")],
+        [Document(content="doc3"), Document(content="doc4")],
+    ]
+    result: List[List[Document]] = ranker.predict_batch(queries=queries, documents=documents)
+    assert isinstance(result, list)
+    assert all(isinstance(docs, list) for docs in result)
+    assert all(isinstance(doc, Document) for docs in result for doc in docs)
+
+
+#  Tests that predict_batch method returns the correct number of documents
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_batch_returns_correct_number_of_documents(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    queries = ["test query 1", "test query 2"]
+    documents = [
+        [Document(content="doc1"), Document(content="doc2")],
+        [Document(content="doc3"), Document(content="doc4")],
+    ]
+    result: List[List[Document]] = ranker.predict_batch(queries=queries, documents=documents, top_k=1)
+    assert len(result) == 2
+    assert len(result[0]) == 1
+    assert len(result[1]) == 1
+
+
+#  Tests that predict_batch method returns documents in the correct order
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_batch_returns_documents_in_correct_order(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    queries = ["Berlin", "Paris"]
+    documents = [
+        [Document(content="Germany"), Document(content="Munich"), Document(content="agriculture")],
+        [Document(content="France"), Document(content="Space exploration"), Document(content="Eiffel Tower")],
+    ]
+    result: List[List[Document]] = ranker.predict_batch(queries=queries, documents=documents)
+    assert len(result) == 2
+
+    # check the correct most diverse order are in batches
+    expected_order_0 = "Germany, agriculture, Munich"
+    expected_order_1 = "France, Space exploration, Eiffel Tower"
+    assert ", ".join([doc.content for doc in result[0]]) == expected_order_0
+    assert ", ".join([doc.content for doc in result[1]]) == expected_order_1
+
+
+# Tests that predict method returns the correct number of documents for a single document
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_single_document_corner_case(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "test"
+    documents = [Document(content="doc1")]
+    result = ranker.predict(query=query, documents=documents)
+    assert len(result) == 1
+
+
+#  Tests that predict method raises ValueError if query is empty
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_raises_value_error_if_query_is_empty(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = ""
+    documents = [Document(content="doc1"), Document(content="doc2")]
+    with pytest.raises(ValueError):
+        ranker.predict(query=query, documents=documents)
+
+
+#  Tests that predict method raises ValueError if documents is empty
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_raises_value_error_if_documents_is_empty(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "test query"
+    documents = []
+    with pytest.raises(ValueError):
+        ranker.predict(query=query, documents=documents)
+
+
+#  Tests that predict_batch method raises ValueError if queries is empty
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_batch_raises_value_error_if_queries_is_empty(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    queries = []
+    documents = [
+        [Document(content="doc1"), Document(content="doc2")],
+        [Document(content="doc3"), Document(content="doc4")],
+    ]
+    with pytest.raises(ValueError):
+        ranker.predict_batch(queries=queries, documents=documents)
+
+
+#  Tests that predict_batch method raises ValueError if documents is empty
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_batch_raises_value_error_if_documents_is_empty(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    queries = ["test query 1", "test query 2"]
+    documents = []
+    with pytest.raises(ValueError):
+        ranker.predict_batch(queries=queries, documents=documents)
+
+
+@pytest.mark.integration
+@pytest.mark.parametrize("similarity", ["dot_product", "cosine"])
+def test_predict_real_world_use_case(similarity: str):
+    ranker = DiversityRanker(similarity=similarity)  # type: ignore
+    query = "What are the reasons for long-standing animosities between Russia and Poland?"
+
+    doc1 = Document(
+        "One of the earliest known events in Russian-Polish history dates back to 981, when the Grand Prince of Kiev , "
+        "Vladimir Svyatoslavich , seized the Cherven Cities from the Duchy of Poland . The relationship between two by "
+        "that time was mostly close and cordial, as there had been no serious wars between both. In 966, Poland "
+        "accepted Christianity from Rome while Kievan Rus' —the ancestor of Russia, Ukraine and Belarus—was "
+        "Christianized by Constantinople. In 1054, the internal Christian divide formally split the Church into "
+        "the Catholic and Orthodox branches separating the Poles from the Eastern Slavs."
+    )
+
+    doc2 = Document(
+        "Since the fall of the Soviet Union , with Lithuania , Ukraine and Belarus regaining independence, the "
+        "Polish–Russian border has mostly been replaced by borders with the respective countries, but there still "
+        "is a 210 km long border between Poland and the Kaliningrad Oblast"
+    )
+
+    doc3 = Document(
+        "As part of Poland's plans to become fully energy independent from Russia within the next years, Piotr "
+        "Wozniak, president of state-controlled oil and gas company PGNiG , stated in February 2019: 'The strategy of "
+        "the company is just to forget about Eastern suppliers and especially about Gazprom .'[53] In 2020, the "
+        "Stockholm Arbitral Tribunal ruled that PGNiG's long-term contract gas price with Gazprom linked to oil prices "
+        "should be changed to approximate the Western European gas market price, backdated to 1 November 2014 when "
+        "PGNiG requested a price review under the contract. Gazprom had to refund about $1.5 billion to PGNiG."
+    )
+
+    doc4 = Document(
+        "Both Poland and Russia had accused each other for their historical revisionism . Russia has repeatedly "
+        "accused Poland for not honoring Soviet Red Army soldiers fallen in World War II for Poland, notably in "
+        "2017, in which Poland was thought on 'attempting to impose its own version of history' after Moscow was "
+        "not allowed to join an international effort to renovate a World War II museum at Sobibór , site of a "
+        "notorious Sobibor extermination camp."
+    )
+
+    doc5 = Document(
+        "President of Russia Vladimir Putin and Prime Minister of Poland Leszek Miller in 2002 Modern Polish–Russian "
+        "relations begin with the fall of communism – 1989 in Poland ( Solidarity and the Polish Round Table "
+        "Agreement ) and 1991 in Russia ( dissolution of the Soviet Union ). With a new democratic government after "
+        "the 1989 elections , Poland regained full sovereignty, [2] and what was the Soviet Union, became 15 newly "
+        "independent states , including the Russian Federation . Relations between modern Poland and Russia suffer "
+        "from constant ups and downs."
+    )
+
+    doc6 = Document(
+        "Soviet influence in Poland finally ended with the Round Table Agreement of 1989 guaranteeing free elections "
+        "in Poland, the Revolutions of 1989 against Soviet-sponsored Communist governments in the Eastern Bloc , and "
+        "finally the formal dissolution of the Warsaw Pact."
+    )
+
+    doc7 = Document(
+        "Dmitry Medvedev and then Polish Prime Minister Donald Tusk , 6 December 2010 BBC News reported that one of "
+        "the main effects of the 2010 Polish Air Force Tu-154 crash would be the impact it has on Russian-Polish "
+        "relations. [38] It was thought if the inquiry into the crash were not transparent, it would increase "
+        "suspicions toward Russia in Poland."
+    )
+
+    doc8 = Document(
+        "Soviet control over the Polish People's Republic lessened after Stalin's death and Gomułka's Thaw , and "
+        "ceased completely after the fall of the communist government in Poland in late 1989, although the "
+        "Soviet-Russian Northern Group of Forces did not leave Polish soil until 1993. The continuing Soviet military "
+        "presence allowed the Soviet Union to heavily influence Polish politics."
+    )
+
+    documents = [doc1, doc2, doc3, doc4, doc5, doc6, doc7, doc8]
+    result = ranker.predict(query=query, documents=documents)
+    expected_order = [doc5, doc7, doc3, doc1, doc4, doc2, doc6, doc8]
+    assert result == expected_order