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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "b02n_zJ_hl3d"
},
"source": [
"## Cookbook for using Cohere with Embedchain"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gyJ6ui2vhtMY"
},
"source": [
"### Step-1: Install embedchain package"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-NbXjAdlh0vJ",
"outputId": "fae77912-4e6a-4c78-fcb7-fbbe46f7a9c7"
},
"outputs": [],
"source": [
"!pip install embedchain[together]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nGnpSYAAh2bQ"
},
"source": [
"### Step-2: Set Cohere related environment variables\n",
"\n",
"You can find `OPENAI_API_KEY` on your [OpenAI dashboard](https://platform.openai.com/account/api-keys) and `TOGETHER_API_KEY` key on your [Together dashboard](https://api.together.xyz/settings/api-keys)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "0fBdQ9GAiRvK"
},
"outputs": [],
"source": [
"import os\n",
"from embedchain import App\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"os.environ[\"TOGETHER_API_KEY\"] = \"\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PGt6uPLIi1CS"
},
"source": [
"### Step-3 Create embedchain app and define your config"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 321
},
"id": "Amzxk3m-i3tD",
"outputId": "afe8afde-5cb8-46bc-c541-3ad26cc3fa6e"
},
"outputs": [],
"source": [
"app = App.from_config(config={\n",
" \"provider\": \"together\",\n",
" \"config\": {\n",
" \"model\": \"mistralai/Mixtral-8x7B-Instruct-v0.1\",\n",
" \"temperature\": 0.5,\n",
" \"max_tokens\": 1000\n",
" }\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XNXv4yZwi7ef"
},
"source": [
"### Step-4: Add data sources to your app"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 176
},
"id": "Sn_0rx9QjIY9",
"outputId": "2f2718a4-3b7e-4844-fd46-3e0857653ca0"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Inserting batches in chromadb: 100%|██████████| 1/1 [00:01<00:00, 1.16s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully saved https://www.forbes.com/profile/elon-musk (DataType.WEB_PAGE). New chunks count: 4\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
"'8cf46026cabf9b05394a2658bd1fe890'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"app.add(\"https://www.forbes.com/profile/elon-musk\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_7W6fDeAjMAP"
},
"source": [
"### Step-5: All set. Now start asking questions related to your data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cvIK7dWRjN_f",
"outputId": "79e873c8-9594-45da-f5a3-0a893511267f"
},
"outputs": [],
"source": [
"while(True):\n",
" question = input(\"Enter question: \")\n",
" if question in ['q', 'exit', 'quit']:\n",
" break\n",
" answer = app.query(question)\n",
" print(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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