mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
d6d697a41b
The sentence transformers was a dup of the HF one. This is a breaking change (model_name vs. model) for anyone using `SentenceTransformerEmbeddings(model="some/nondefault/model")`, but since it was landed only this week it seems better to do this now rather than doing a wrapper.
123 lines
3.0 KiB
Plaintext
123 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "ed47bb62",
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"metadata": {},
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"source": [
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"# Sentence Transformers Embeddings\n",
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"\n",
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"[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",
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"\n",
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"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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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "06c9f47d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\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",
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"\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"
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]
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}
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],
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"source": [
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"!pip install sentence_transformers > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "861521a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceEmbeddings, SentenceTransformerEmbeddings "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ff9be586",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
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"# Equivalent to SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "d0a98ae9",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5d6c682b",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "bb5e74c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = embeddings.embed_documents([text, \"This is not a test document.\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aaad49f8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.16"
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},
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"vscode": {
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"interpreter": {
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"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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