langchain/docs/extras/integrations/text_embedding/sentence_transformers.ipynb

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"# Sentence Transformers Embeddings\n",
"\n",
"[SentenceTransformers](https://www.sbert.net/) embeddings are called using the `HuggingFaceEmbeddings` integration. We have also added an alias for `SentenceTransformerEmbeddings` for users who are more familiar with directly using that package.\n",
"\n",
"SentenceTransformers is a python package that can generate text and image embeddings, originating from [Sentence-BERT](https://arxiv.org/abs/1908.10084)"
]
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"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.1\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"source": [
"!pip install sentence_transformers > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "861521a9",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff9be586",
"metadata": {},
"outputs": [],
"source": [
"embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
"# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d0a98ae9",
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"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5d6c682b",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bb5e74c0",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
]
},
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