mirror of
https://github.com/hwchase17/langchain
synced 2024-10-31 15:20:26 +00:00
40096c73cd
- Description: [BGE-large](https://huggingface.co/BAAI/bge-large-en) embeddings from BAAI are at the top of [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Hence adding support for it. - Tag maintainer: @baskaryan - Twitter handle: @ManabChetia3 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
85 lines
1.7 KiB
Plaintext
85 lines
1.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "719619d3",
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"metadata": {},
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"source": [
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"# BGE Hugging Face Embeddings\n",
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"\n",
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"This notebook shows how to use BGE Embeddings through Hugging Face"
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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": 8,
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"id": "f7a54279",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# !pip install sentence_transformers"
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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": "9e1d5b6b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
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"\n",
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"model_name = \"BAAI/bge-small-en\"\n",
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"model_kwargs = {'device': 'cpu'}\n",
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"encode_kwargs = {'normalize_embeddings': False}\n",
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"hf = HuggingFaceBgeEmbeddings(\n",
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" model_name=model_name,\n",
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" model_kwargs=model_kwargs,\n",
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" encode_kwargs=encode_kwargs\n",
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")"
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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": 7,
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"id": "e59d1a89",
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding = hf.embed_query(\"hi this is harrison\")"
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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": "e596315f",
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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": "3.10.1"
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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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