langchain/docs/modules/models/text_embedding/examples/jina.ipynb

102 lines
2.0 KiB
Plaintext
Raw Normal View History

{
"cells": [
{
"cell_type": "markdown",
"id": "1c0cf975",
"metadata": {},
"source": [
"# Jina\n",
"\n",
"Let's load the Jina Embedding class."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d94c62b4",
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import JinaEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "523a09e3",
"metadata": {},
"outputs": [],
"source": [
"embeddings = JinaEmbeddings(jina_auth_token=jina_auth_token, model_name=\"ViT-B-32::openai\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b212bd5a",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57db66bd",
"metadata": {},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b790fd09",
"metadata": {},
"outputs": [],
"source": [
"doc_result = embeddings.embed_documents([text])"
]
},
{
"cell_type": "markdown",
"id": "6f3607a0",
"metadata": {},
"source": [
"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)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd5f148e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}