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#### What I do Adding embedding api for [DashScope](https://help.aliyun.com/product/610100.html), which is the DAMO Academy's multilingual text unified vector model based on the LLM base. It caters to multiple mainstream languages worldwide and offers high-quality vector services, helping developers quickly transform text data into high-quality vector data. Currently supported languages include Chinese, English, Spanish, French, Portuguese, Indonesian, and more. #### Who can review? Models - @hwchase17 - @agola11 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
84 lines
1.6 KiB
Plaintext
84 lines
1.6 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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"metadata": {},
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"source": [
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"# DashScope\n",
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"\n",
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"Let's load the DashScope Embedding class."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import DashScopeEmbeddings"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = DashScopeEmbeddings(model='text-embedding-v1', dashscope_api_key='your-dashscope-api-key')"
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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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"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": null,
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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)\n",
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"print(query_result)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"doc_results = embeddings.embed_documents([\"foo\"])\n",
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"print(doc_results)"
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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": "chatgpt",
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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": "3.10.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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