191 lines
4.3 KiB
Plaintext
191 lines
4.3 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 GPT4All 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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},
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"id": "-NbXjAdlh0vJ",
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"outputId": "077fa470-b51f-4c29-8c22-9c5f0a9cef47"
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},
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"outputs": [],
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"source": [
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"!pip install embedchain[dataloaders,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 GPT4ALL related environment variables\n",
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"\n",
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"GPT4All is free for all and doesn't require any API Key to use it. So you can use it for free!"
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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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"from embedchain import Pipeline as App"
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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: gpt4all\n",
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" config:\n",
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" model: 'orca-mini-3b-gguf2-q4_0.gguf'\n",
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" temperature: 0.5\n",
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" max_tokens: 1000\n",
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" top_p: 1\n",
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" stream: false\n",
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"\n",
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"embedder:\n",
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" provider: gpt4all\n",
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" config:\n",
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" model: 'all-MiniLM-L6-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('gpt4all.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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Amzxk3m-i3tD",
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"outputId": "775db99b-e217-47db-f87f-788495d86f26"
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},
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"outputs": [],
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"source": [
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"app = App.from_config(config_path=\"gpt4all.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": 52
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},
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"id": "Sn_0rx9QjIY9",
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"outputId": "c6514f17-3cb2-4fbc-c80d-79b3a311ff30"
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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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"height": 480
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},
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"id": "cvIK7dWRjN_f",
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"outputId": "c74f356a-d2fb-426d-b36c-d84911397338"
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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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