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Add warning for ComponentDevice argument #495

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bilgeyucel merged 1 commit into from
Nov 2, 2026
Merged

Add warning for ComponentDevice argument #495

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36 changes: 29 additions & 7 deletions tutorials/33_Hybrid_Retrieval.ipynb
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Expand Up @@ -5,7 +5,15 @@
"metadata": {
"id": "kTas9ZQ7lXP7"
},
"source": "# Tutorial: Creating a Hybrid Retrieval Pipeline\n\n- **Level**: Intermediate\n- **Time to complete**: 15 minutes\n- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`SentenceTransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker)\n- **Prerequisites**: None\n- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful."
"source": [
"# Tutorial: Creating a Hybrid Retrieval Pipeline\n",
"\n",
"- **Level**: Intermediate\n",
"- **Time to complete**: 15 minutes\n",
"- **Components Used**: [`DocumentSplitter`](https://docs.haystack.deepset.ai/docs/documentsplitter), [`SentenceTransformersDocumentEmbedder`](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [`InMemoryDocumentStore`](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [`InMemoryBM25Retriever`](https://docs.haystack.deepset.ai/docs/inmemorybm25retriever), [`InMemoryEmbeddingRetriever`](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), and [`SentenceTransformersSimilarityRanker`](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker)\n",
"- **Prerequisites**: None\n",
"- **Goal**: After completing this tutorial, you will have learned about creating a hybrid retrieval and when it's useful."
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -38,7 +46,13 @@
"id": "L40ZxZW8lXQh"
},
"outputs": [],
"source": "%%bash\n\npip install haystack-ai\npip install \"datasets>=2.6.1\"\npip install sentence-transformers-haystack"
"source": [
"%%bash\n",
"\n",
"pip install haystack-ai\n",
"pip install \"datasets>=2.6.1\"\n",
"pip install sentence-transformers-haystack"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -144,7 +158,7 @@
"\n",
"document_splitter = DocumentSplitter(split_by=\"word\", split_length=512, split_overlap=32)\n",
"document_embedder = SentenceTransformersDocumentEmbedder(\n",
" model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
" model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\") # remove the device argument if you are not using a GPU\n",
")\n",
"document_writer = DocumentWriter(document_store)\n",
"\n",
Expand Down Expand Up @@ -194,7 +208,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {
"id": "DVfQvnWYrMWr"
},
Expand All @@ -204,7 +218,7 @@
"from haystack_integrations.components.embedders.sentence_transformers import SentenceTransformersTextEmbedder\n",
"\n",
"text_embedder = SentenceTransformersTextEmbedder(\n",
" model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\")\n",
" model=\"BAAI/bge-small-en-v1.5\", device=ComponentDevice.from_str(\"cuda:0\") # remove the device argument if you are not using a GPU\n",
")\n",
"embedding_retriever = InMemoryEmbeddingRetriever(document_store)\n",
"bm25_retriever = InMemoryBM25Retriever(document_store)"
Expand All @@ -215,7 +229,11 @@
"metadata": {
"id": "r8_jHzmosbC_"
},
"source": "### 2) Rank the Results\n\nUse the [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker) 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."
"source": [
"### 2) Rank the Results\n",
"\n",
"Use the [SentenceTransformersSimilarityRanker](https://docs.haystack.deepset.ai/docs/sentencetransformerssimilarityranker) 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."
]
},
{
"cell_type": "code",
Expand All @@ -224,7 +242,11 @@
"id": "cN0woIxHs4Ng"
},
"outputs": [],
"source": "from haystack_integrations.components.rankers.sentence_transformers import SentenceTransformersSimilarityRanker\n\nranker = SentenceTransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
"source": [
"from haystack_integrations.components.rankers.sentence_transformers import SentenceTransformersSimilarityRanker\n",
"\n",
"ranker = SentenceTransformersSimilarityRanker(model=\"BAAI/bge-reranker-base\")"
]
},
{
"cell_type": "markdown",
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10 changes: 6 additions & 4 deletions tutorials/39_Embedding_Metadata_for_Improved_Retrieval.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,11 @@
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "%%bash\n\npip install haystack-ai wikipedia sentence-transformers-haystack"
"source": [
"%%bash\n",
"\n",
"pip install haystack-ai wikipedia sentence-transformers-haystack"
]
},
{
"cell_type": "markdown",
Expand Down Expand Up @@ -83,7 +87,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -92,8 +96,6 @@
"from haystack_integrations.components.embedders.sentence_transformers import SentenceTransformersDocumentEmbedder\n",
"from haystack.components.writers import DocumentWriter\n",
"from haystack.document_stores.types import DuplicatePolicy\n",
"from haystack.utils import ComponentDevice\n",
"\n",
"\n",
"def create_indexing_pipeline(document_store, metadata_fields_to_embed=None):\n",
" document_cleaner = DocumentCleaner()\n",
Expand Down
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