langchain/docs/modules/models/text_embedding/examples/sentence_transformers.ipynb

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"# Sentence Transformers Embeddings\n",
"\n",
"Let's generate embeddings using the [SentenceTransformers](https://www.sbert.net/) integration. 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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"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
}
],
"source": [
"!pip install sentence_transformers > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "861521a9",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import SentenceTransformerEmbeddings "
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ff9be586",
"metadata": {},
"outputs": [],
"source": [
"embeddings = SentenceTransformerEmbeddings(model=\"all-MiniLM-L6-v2\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d0a98ae9",
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"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5d6c682b",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "bb5e74c0",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
]
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