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 PineconeDB 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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"id": "-NbXjAdlh0vJ"
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},
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"outputs": [],
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"source": [
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"!pip install embedchain"
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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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"outputs": [],
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"source": [
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"!pip install embedchain[dataloaders]"
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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 environment variables needed for Pinecone and install the dependencies.\n",
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"\n",
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"You can find this env variable on your [OpenAI dashboard](https://platform.openai.com/account/api-keys) and [Pinecone dashboard](https://app.pinecone.io/). Now lets install the dependencies needed for Pinecone."
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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": "-MUFRfxV7Jk7"
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},
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"outputs": [],
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"source": [
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"!pip install --upgrade 'embedchain[pinecone]'"
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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[\"OPENAI_API_KEY\"] = \"sk-xxx\"\n",
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"os.environ[\"PINECONE_API_KEY\"] = \"xxx\"\n",
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"os.environ[\"PINECONE_ENV\"] = \"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 Vector Database 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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"vectordb:\n",
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" provider: pinecone\n",
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" config:\n",
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" metric: cosine\n",
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" vector_dimension: 768\n",
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" collection_name: pc-index\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('pinecone.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(yaml_path=\"pinecone.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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"id": "Sn_0rx9QjIY9"
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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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"id": "cvIK7dWRjN_f"
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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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