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135 lines
2.8 KiB
Plaintext
135 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Together AI\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"metadata": {},
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"source": [
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"# TogetherEmbeddings\n",
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"\n",
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"This notebook covers how to get started with open source embedding models hosted in the Together AI API.\n",
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"\n",
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"## Installation"
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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": "4c3bef91",
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"metadata": {},
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"outputs": [],
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"source": [
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"# install package\n",
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"%pip install --upgrade --quiet langchain-together"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"\n",
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"Make sure to set the following environment variables:\n",
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"\n",
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"- `TOGETHER_API_KEY`\n",
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"\n",
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"## Usage\n",
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"\n",
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"First, select a supported model from [this list](https://docs.together.ai/docs/embedding-models). In the following example, we will use `togethercomputer/m2-bert-80M-8k-retrieval`."
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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": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain_together.embeddings import TogetherEmbeddings\n",
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"\n",
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"embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")"
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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": "12fcfb4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_query(\"My query to look up\")"
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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": "1f2e6104",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\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": null,
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"id": "46739f68",
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"metadata": {},
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"outputs": [],
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"source": [
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"# async embed query\n",
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"await embeddings.aembed_query(\"My query to look up\")"
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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": "e48632ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"# async embed documents\n",
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"await embeddings.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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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": "3.10.5"
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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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