190 lines
4.4 KiB
Plaintext
190 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "b02n_zJ_hl3d"
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},
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"source": [
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"## Cookbook for using Hugging Face Hub with Embedchain"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "gyJ6ui2vhtMY"
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},
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"source": [
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"### Step-1: Install embedchain package"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "-NbXjAdlh0vJ",
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"outputId": "35ddc904-8067-44cf-dcc9-3c8b4cd29989"
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},
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"outputs": [],
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"source": [
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"!pip install embedchain[dataloaders,huggingface_hub,opensource]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "nGnpSYAAh2bQ"
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},
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"source": [
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"### Step-2: Set Hugging Face Hub related environment variables\n",
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"\n",
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"You can find your `HUGGINGFACE_ACCESS_TOKEN` key on your [Hugging Face Hub dashboard](https://huggingface.co/settings/tokens)"
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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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"id": "0fBdQ9GAiRvK"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from embedchain import Pipeline as App\n",
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"\n",
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"os.environ[\"HUGGINGFACE_ACCESS_TOKEN\"] = \"hf_xxx\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Ns6RhPfbiitr"
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},
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"source": [
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"### Step-3: Define your llm and embedding model config"
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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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"id": "S9CkxVjriotB"
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},
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"outputs": [],
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"source": [
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"config = \"\"\"\n",
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"llm:\n",
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" provider: huggingface\n",
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" config:\n",
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" model: 'google/flan-t5-xxl'\n",
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" temperature: 0.5\n",
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" max_tokens: 1000\n",
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" top_p: 0.8\n",
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" stream: false\n",
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"\n",
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"embedder:\n",
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" provider: huggingface\n",
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" config:\n",
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" model: 'sentence-transformers/all-mpnet-base-v2'\n",
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"\"\"\"\n",
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"\n",
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"# Write the multi-line string to a YAML file\n",
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"with open('huggingface.yaml', 'w') as file:\n",
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" file.write(config)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "PGt6uPLIi1CS"
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},
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"source": [
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"### Step-4 Create embedchain app based on the config"
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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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"id": "Amzxk3m-i3tD"
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},
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"outputs": [],
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"source": [
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"app = App.from_config(config_path=\"huggingface.yaml\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "XNXv4yZwi7ef"
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},
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"source": [
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"### Step-5: Add data sources to your app"
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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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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 70
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},
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"id": "Sn_0rx9QjIY9",
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"outputId": "3c2a803a-3a93-4b0d-a6ae-17ae3c96c3c2"
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},
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"outputs": [],
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"source": [
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"app.add(\"https://www.forbes.com/profile/elon-musk\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "_7W6fDeAjMAP"
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},
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"source": [
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"### Step-6: All set. Now start asking questions related to your data"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "cvIK7dWRjN_f",
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"outputId": "47a89d1c-b322-495c-822a-6c2ecef894d2"
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},
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"outputs": [],
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"source": [
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"while(True):\n",
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" question = input(\"Enter question: \")\n",
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" if question in ['q', 'exit', 'quit']:\n",
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" break\n",
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" answer = app.query(question)\n",
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" print(answer)"
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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