mirror of
https://github.com/hwchase17/langchain
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fdba711d28
Updated `integrations/embeddings`: fixed titles; added links, descriptions Updated `integrations/providers`.
108 lines
2.4 KiB
Plaintext
108 lines
2.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6802946f",
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"metadata": {},
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"source": [
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"# NLP Cloud\n",
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"\n",
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">[NLP Cloud](https://docs.nlpcloud.com/#introduction) is an artificial intelligence platform that allows you to use the most advanced AI engines, and even train your own engines with your own data. \n",
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"\n",
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"The [embeddings](https://docs.nlpcloud.com/#embeddings) endpoint offers the following model:\n",
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"\n",
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"* `paraphrase-multilingual-mpnet-base-v2`: Paraphrase Multilingual MPNet Base V2 is a very fast model based on Sentence Transformers that is perfectly suited for embeddings extraction in more than 50 languages (see the full list here)."
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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": "490d7923",
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install nlpcloud"
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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": "6a39ed4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import NLPCloudEmbeddings"
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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": "c105d8cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"os.environ[\"NLPCLOUD_API_KEY\"] = \"xxx\"\n",
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"nlpcloud_embd = NLPCloudEmbeddings()"
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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": 3,
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"id": "cca84023",
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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": 4,
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"id": "26868d0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"query_result = nlpcloud_embd.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": 5,
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"id": "0c171c2f",
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"metadata": {},
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"outputs": [],
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"source": [
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"doc_result = nlpcloud_embd.embed_documents([text])"
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]
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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": "3.10.12"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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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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