From 839ff6eb9a9535f4b76b21404ce86197a768857e Mon Sep 17 00:00:00 2001
From: Avleen Kaur <99690842+avleen-k@users.noreply.github.com>
Date: Mon, 13 Apr 2026 14:16:08 +0200
Subject: [PATCH 1/2] Removed Document Joiner component

---
 tutorials/33_Hybrid_Retrieval.ipynb | 38 +++++------------------------
 1 file changed, 6 insertions(+), 32 deletions(-)

diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index 95ec913..e4247bf 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -224,38 +224,14 @@
     "embedding_retriever = InMemoryEmbeddingRetriever(document_store)\n",
     "bm25_retriever = InMemoryBM25Retriever(document_store)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FC81c8RBrRFf"
-   },
-   "source": [
-    "### 2) Join Retrieval Results\n",
-    "\n",
-    "Haystack offers several joining methods in [`DocumentJoiner`](https://docs.haystack.deepset.ai/docs/documentjoiner) to be used for different use cases such as `merge` and `reciprocal_rank_fusion`. In this example, you will use the default `concatenate` mode to join the documents coming from two Retrievers as the [Ranker](https://docs.haystack.deepset.ai/docs/rankers) will be the main component to rank the documents for relevancy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "id": "GYso6_8BrhY8"
-   },
-   "outputs": [],
-   "source": [
-    "from haystack.components.joiners import DocumentJoiner\n",
-    "\n",
-    "document_joiner = DocumentJoiner()"
-   ]
-  },
+  },    
   {
    "cell_type": "markdown",
    "metadata": {
     "id": "r8_jHzmosbC_"
    },
    "source": [
-    "### 3) Rank the Results\n",
+    "### 2) Rank the Results\n",
     "\n",
     "Use the [TransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/transformerssimilarityranker) that scores the relevancy of all retrieved documents for the given search query by using a cross encoder model. In this example, you will use [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) model to rank the retrieved documents but you can replace this model with other cross-encoder models on Hugging Face."
    ]
@@ -279,9 +255,9 @@
     "id": "Y5jzzvUIstQ4"
    },
    "source": [
-    "### 4) Create the Hybrid Retrieval Pipeline\n",
+    "### 3) Create the Hybrid Retrieval Pipeline\n",
     "\n",
-    "Add all initialized components to your pipeline and connect them."
+    "Add all initialized components to your pipeline. Both the BM25 and Embedding retrievers are connected to the same ranker input."
    ]
   },
   {
@@ -298,13 +274,11 @@
     "hybrid_retrieval.add_component(\"text_embedder\", text_embedder)\n",
     "hybrid_retrieval.add_component(\"embedding_retriever\", embedding_retriever)\n",
     "hybrid_retrieval.add_component(\"bm25_retriever\", bm25_retriever)\n",
-    "hybrid_retrieval.add_component(\"document_joiner\", document_joiner)\n",
     "hybrid_retrieval.add_component(\"ranker\", ranker)\n",
     "\n",
     "hybrid_retrieval.connect(\"text_embedder\", \"embedding_retriever\")\n",
-    "hybrid_retrieval.connect(\"bm25_retriever\", \"document_joiner\")\n",
-    "hybrid_retrieval.connect(\"embedding_retriever\", \"document_joiner\")\n",
-    "hybrid_retrieval.connect(\"document_joiner\", \"ranker\")"
+    "hybrid_retrieval.connect(\"bm25_retriever\", \"ranker\")\n",
+    "hybrid_retrieval.connect(\"embedding_retriever\", \"ranker\")
    ]
   },
   {

From de33840ef83e5e159e7e1d48ee2bdb3afb5aefc6 Mon Sep 17 00:00:00 2001
From: Avleen Kaur <99690842+avleen-k@users.noreply.github.com>
Date: Mon, 13 Apr 2026 14:22:30 +0200
Subject: [PATCH 2/2] JSON format error correction

---
 tutorials/33_Hybrid_Retrieval.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/33_Hybrid_Retrieval.ipynb b/tutorials/33_Hybrid_Retrieval.ipynb
index e4247bf..9bfc9ed 100644
--- a/tutorials/33_Hybrid_Retrieval.ipynb
+++ b/tutorials/33_Hybrid_Retrieval.ipynb
@@ -278,7 +278,7 @@
     "\n",
     "hybrid_retrieval.connect(\"text_embedder\", \"embedding_retriever\")\n",
     "hybrid_retrieval.connect(\"bm25_retriever\", \"ranker\")\n",
-    "hybrid_retrieval.connect(\"embedding_retriever\", \"ranker\")
+    "hybrid_retrieval.connect(\"embedding_retriever\", \"ranker\")"
    ]
   },
   {