mirror of
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104 lines
2.0 KiB
Plaintext
104 lines
2.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1c0cf975",
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"metadata": {},
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"source": [
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"# Jina\n",
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"\n",
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"Let's load the Jina 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": 2,
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"id": "d94c62b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import JinaEmbeddings"
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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": "523a09e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = JinaEmbeddings(\n",
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" jina_auth_token=jina_auth_token, model_name=\"ViT-B-32::openai\"\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": "b212bd5a",
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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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"id": "57db66bd",
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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": null,
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"id": "b790fd09",
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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])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6f3607a0",
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"metadata": {},
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"source": [
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"In the above example, `ViT-B-32::openai`, OpenAI's pretrained `ViT-B-32` model is used. For a full list of models, see [here](https://cloud.jina.ai/user/inference/model/63dca9df5a0da83009d519cd)."
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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": "cd5f148e",
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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": "3.9.1"
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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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