langchain/docs/extras/integrations/text_embedding/gradient.ipynb
Ikko Eltociear Ashimine 33884b2184
Fix typo in gradient.ipynb (#11206)
Enviroment -> Environment

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2023-09-29 11:45:40 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Gradient\n",
"\n",
"`Gradient` allows to create `Embeddings` as well fine tune and get completions on LLMs with a simple web API.\n",
"\n",
"This notebook goes over how to use Langchain with Embeddings of [Gradient](https://gradient.ai/).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.embeddings import GradientEmbeddings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set the Environment API Key\n",
"Make sure to get your API key from Gradient AI. You are given $10 in free credits to test and fine-tune different models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from getpass import getpass\n",
"import os\n",
"\n",
"if not os.environ.get(\"GRADIENT_ACCESS_TOKEN\",None):\n",
" # Access token under https://auth.gradient.ai/select-workspace\n",
" os.environ[\"GRADIENT_ACCESS_TOKEN\"] = getpass(\"gradient.ai access token:\")\n",
"if not os.environ.get(\"GRADIENT_WORKSPACE_ID\",None):\n",
" # `ID` listed in `$ gradient workspace list`\n",
" # also displayed after login at at https://auth.gradient.ai/select-workspace\n",
" os.environ[\"GRADIENT_WORKSPACE_ID\"] = getpass(\"gradient.ai workspace id:\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optional: Validate your Environment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models. Using the `gradientai` Python package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install gradientai"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the Gradient instance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"documents = [\"Pizza is a dish.\",\"Paris is the capital of France\", \"numpy is a lib for linear algebra\"]\n",
"query = \"Where is Paris?\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embeddings = GradientEmbeddings(\n",
" model=\"bge-large\"\n",
")\n",
"\n",
"documents_embedded = embeddings.embed_documents(documents)\n",
"query_result = embeddings.embed_query(query)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# (demo) compute similarity\n",
"import numpy as np\n",
"\n",
"scores = np.array(documents_embedded) @ np.array(query_result).T\n",
"dict(zip(documents, scores))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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