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docs: Add Google Firestore Vectorstore doc (#20078)
- **Description:**Add Google Firestore Vector store docs
- **Issue:** NA
- **Dependencies:** NA
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This commit is contained in:
Averi Kitsch
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@ -486,6 +486,21 @@ See [usage example](/docs/integrations/vectorstores/google_spanner).
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from langchain_google_spanner import SpannerVectorStore
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```
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### Firestore (Native Mode)
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> [Google Cloud Firestore](https://cloud.google.com/firestore/docs/) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
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Install the python package:
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```bash
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pip install langchain-google-firestore
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```
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See [usage example](/docs/integrations/vectorstores/google_firestore).
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```python
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from langchain_google_firestore import FirestoreVectorstore
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```
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### Cloud SQL for MySQL
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> [Google Cloud SQL for MySQL](https://cloud.google.com/sql) is a fully-managed database service that helps you set up, maintain, manage, and administer your MySQL relational databases on Google Cloud.
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399
docs/docs/integrations/vectorstores/google_firestore.ipynb
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399
docs/docs/integrations/vectorstores/google_firestore.ipynb
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@ -0,0 +1,399 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "1957f5cb",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_label: Firestore\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ef1f0986",
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"metadata": {},
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"source": [
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"# Google Firestore (Native Mode)\n",
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"\n",
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"> [Firestore](https://cloud.google.com/firestore) is a serverless document-oriented database that scales to meet any demand. Extend your database application to build AI-powered experiences leveraging Firestore's Langchain integrations.\n",
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"\n",
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"This notebook goes over how to use [Firestore](https://cloud.google.com/firestore) to to store vectors and query them using the `FirestoreVectorStore` class.\n",
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"\n",
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"[](https://colab.research.google.com/github/googleapis/langchain-google-firestore-python/blob/main/docs/vectorstores.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36fdc060",
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"metadata": {},
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"source": [
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"## Before You Begin\n",
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"\n",
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"To run this notebook, you will need to do the following:\n",
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"\n",
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"* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)\n",
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"* [Enable the Firestore API](https://console.cloud.google.com/flows/enableapi?apiid=firestore.googleapis.com)\n",
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"* [Create a Firestore database](https://cloud.google.com/firestore/docs/manage-databases)\n",
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"\n",
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"After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts."
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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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"id": "22e53b34",
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"metadata": {},
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"outputs": [],
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"source": [
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"# @markdown Please specify a source for demo purpose.\n",
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"COLLECTION_NAME = \"test\" # @param {type:\"CollectionReference\"|\"string\"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e5d3d8e4",
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"metadata": {},
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"source": [
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"### 🦜🔗 Library Installation\n",
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"\n",
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"The integration lives in its own `langchain-google-firestore` package, so we need to install it. For this notebook, we will also install `langchain-google-genai` to use Google Generative AI embeddings."
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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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"id": "75510ef7",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -upgrade --quiet langchain-google-firestore langchain-google-vertexai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2664ca45",
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"metadata": {},
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"source": [
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"**Colab only**: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top."
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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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"id": "ddfcd6b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # Automatically restart kernel after installs so that your environment can access the new packages\n",
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"# import IPython\n",
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"\n",
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"# app = IPython.Application.instance()\n",
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"# app.kernel.do_shutdown(True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ab63daa",
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"metadata": {},
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"source": [
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"### ☁ Set Your Google Cloud Project\n",
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"Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.\n",
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"\n",
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"If you don't know your project ID, try the following:\n",
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"\n",
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"* Run `gcloud config list`.\n",
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"* Run `gcloud projects list`.\n",
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"* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)."
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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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"id": "129f1f8d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.\n",
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"\n",
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"PROJECT_ID = \"extensions-testing\" # @param {type:\"string\"}\n",
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"\n",
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"# Set the project id\n",
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"!gcloud config set project {PROJECT_ID}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ccd32ce5",
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"metadata": {},
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"source": [
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"### 🔐 Authentication\n",
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"\n",
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"Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.\n",
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"\n",
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"- If you are using Colab to run this notebook, use the cell below and continue.\n",
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"- If you are using Vertex AI Workbench, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
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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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"id": "4b5793e7",
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.colab import auth\n",
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"\n",
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"auth.authenticate_user()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2cade39f",
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"metadata": {},
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"source": [
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"# Basic Usage"
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]
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},
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{
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"cell_type": "markdown",
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"id": "580e6f96",
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"metadata": {},
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"source": [
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"### Initialize FirestoreVectorStore\n",
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"\n",
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"`FirestoreVectorStore` allows you to store new vectors in a Firestore database. You can use it to store embeddings from any model, including those from Google Generative AI."
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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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"id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain_google_firestore import FirestoreVectorStore\n",
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"from langchain_google_vertexai import VertexAIEmbeddings\n",
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"\n",
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"embedding = VertexAIEmbeddings(\n",
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" model_name=\"textembedding-gecko@latest\",\n",
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" project=PROJECT_ID,\n",
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")\n",
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"\n",
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"# Sample data\n",
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"ids = [\"apple\", \"banana\", \"orange\"]\n",
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"fruits_texts = ['{\"name\": \"apple\"}', '{\"name\": \"banana\"}', '{\"name\": \"orange\"}']\n",
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"\n",
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"# Create a vector store\n",
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"vector_store = FirestoreVectorStore(\n",
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" collection=\"fruits\",\n",
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" embedding=embedding,\n",
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")\n",
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"\n",
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"# Add the fruits to the vector store\n",
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"vector_store.add_texts(fruits_texts, ids=ids)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f8a4d7f7",
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"metadata": {},
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"source": [
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"As a shorthand, you can initilize and add vectors in a single step using the `from_texts` and `from_documents` method."
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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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"id": "0bb6745e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store = FirestoreVectorStore.from_texts(\n",
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" collection=\"fruits\",\n",
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" texts=fruits_texts,\n",
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" embedding=embedding,\n",
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")"
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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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"id": "f86024b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.documents import Document\n",
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"\n",
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"fruits_docs = [Document(page_content=fruit) for fruit in fruits_texts]\n",
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"\n",
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"vector_store = FirestoreVectorStore.from_documents(\n",
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" collection=\"fruits\",\n",
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" documents=fruits_docs,\n",
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" embedding=embedding,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "942911a8",
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"metadata": {},
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"source": [
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"### Delete Vectors"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee1d8090",
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"metadata": {},
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"source": [
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"You can delete documents with vectors from the database using the `delete` method. You'll need to provide the document ID of the vector you want to delete. This will remove the whole document from the database, including any other fields it may have."
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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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"id": "901f2ae7",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store.delete(ids)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc8e555f",
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"metadata": {},
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"source": [
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"### Update Vectors"
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]
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},
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{
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"cell_type": "markdown",
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"id": "af734e8f",
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"metadata": {},
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"source": [
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"Updating vectors is similar to adding them. You can use the `add` method to update the vector of a document by providing the document ID and the new vector."
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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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"id": "cb2aadb7",
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"metadata": {},
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"outputs": [],
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"source": [
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"fruit_to_update = ['{\"name\": \"apple\",\"price\": 12}']\n",
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"apple_id = \"apple\"\n",
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"\n",
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"vector_store.add_texts(fruit_to_update, ids=[apple_id])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16342b7a",
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"metadata": {},
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"source": [
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"## Similarity Search\n",
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"\n",
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"You can use the `FirestoreVectorStore` to perform similarity searches on the vectors you have stored. This is useful for finding similar documents or text."
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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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"id": "44d1b94e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store.similarity_search(\"I like fuji apples\", k=3)"
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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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"id": "acb2f640",
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"metadata": {},
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"outputs": [],
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"source": [
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"vector_store.max_marginal_relevance_search(\"fuji\", 5)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ac1d391",
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"metadata": {},
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"source": [
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"You can add a pre-filter to the search by using the `filters` parameter. This is useful for filtering by a specific field or value."
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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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"id": "cd864d4f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.cloud.firestore_v1.base_query import FieldFilter\n",
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"\n",
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"vector_store.max_marginal_relevance_search(\n",
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" \"fuji\", 5, filters=FieldFilter(\"content\", \"==\", \"apple\")\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9988c71d",
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"metadata": {},
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"source": [
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"### Customize Connection & Authentication"
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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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"id": "6b9dfc65",
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"metadata": {},
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"outputs": [],
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"source": [
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"from google.api_core.client_options import ClientOptions\n",
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"from google.cloud import firestore\n",
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"from langchain_google_firestore import FirestoreVectorStore\n",
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"\n",
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"client_options = ClientOptions()\n",
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"client = firestore.Client(client_options=client_options)\n",
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"\n",
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"# Create a vector store\n",
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"vector_store = FirestoreVectorStore(\n",
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" collection=\"fruits\",\n",
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" embedding=embedding,\n",
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" client=client,\n",
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")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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