docs: add page for Firestore Chat Message History integration (#15554)

- **Description:** Adds documentation for the
`FirestoreChatMessageHistory` integration and lists integration in
Google's documentation
  - **Issue:** NA
  - **Dependencies:** No

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/docs/integrations/memory/firestore_chat_message_history.ipynb

@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "91c6a7ef",
+   "metadata": {},
+   "source": [
+    "# Google Cloud Firestore\n",
+    "\n",
+    "> [`Cloud Firestore`](https://cloud.google.com/firestore) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.\n",
+    "\n",
+    "This notebook goes over how to use Firestore to store chat message history."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6ed3c8-b70a-498c-bc9e-41b91797d3b7",
+   "metadata": {},
+   "source": [
+    "## Setting up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8eca282",
+   "metadata": {},
+   "source": [
+    "To run this notebook, you will need a Google Cloud Project, a Firestore database instance in Native Mode, and Google credentials, see [Firestore Quickstarts](https://cloud.google.com/firestore/docs/quickstarts)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a7f3b3f-d9b8-4577-a7ef-bdd8ecaedb70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install firebase-admin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8e63850-3e14-46fe-a59e-be6d6bf8fe61",
+   "metadata": {},
+   "source": [
+    "## Basic Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d15e3302",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_message_histories.firestore import (\n",
+    "    FirestoreChatMessageHistory,\n",
+    ")\n",
+    "\n",
+    "message_history = FirestoreChatMessageHistory(\n",
+    "    collection_name=\"langchain-chat-history\",\n",
+    "    session_id=\"user-session-id\",\n",
+    "    user_id=\"user-id\",\n",
+    ")\n",
+    "\n",
+    "message_history.add_user_message(\"hi!\")\n",
+    "message_history.add_ai_message(\"whats up?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64fc465e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='hi!'),\n",
+       " HumanMessage(content='hi!'),\n",
+       " AIMessage(content='whats up?')]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "message_history.messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4be8576e",
+   "metadata": {},
+   "source": [
+    "## Custom Firestore Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12999273",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import firebase_admin\n",
+    "from firebase_admin import credentials, firestore\n",
+    "\n",
+    "# Use a service account.\n",
+    "cred = credentials.Certificate(\"path/to/serviceAccount.json\")\n",
+    "\n",
+    "app = firebase_admin.initialize_app(cred)\n",
+    "client = firestore.client(app=app)\n",
+    "\n",
+    "message_history = FirestoreChatMessageHistory(\n",
+    "    collection_name=\"langchain-chat-history\",\n",
+    "    session_id=\"user-session-id\",\n",
+    "    user_id=\"user-id\",\n",
+    "    firestore_client=client,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/platforms/google.mdx

@@ -186,7 +186,7 @@ from langchain_community.document_loaders import GoogleSpeechToTextLoader
 ### Google Vertex AI Vector Search
 
 > [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview),
-> formerly known as `Vertex AI Matching Engine`, provides the industry's leading high-scale 
+> formerly known as `Vertex AI Matching Engine`, provides the industry's leading high-scale
 > low latency vector database. These vector databases are commonly
 > referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
 
@@ -207,7 +207,7 @@ from langchain_community.vectorstores import MatchingEngine
 > [Google BigQuery](https://cloud.google.com/bigquery),
 > BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
 >
-> Google BigQuery Vector Search 
+> Google BigQuery Vector Search
 > BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
 
 > It can calculate Euclidean or  Cosine distance. With LangChain, we default to use Euclidean distance.
@@ -228,7 +228,7 @@ from langchain.vectorstores import BigQueryVectorSearch
 
 >[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
 > (Scalable Nearest Neighbors) is a python package.
-> 
+>
 >`ScaNN` is a method for efficient vector similarity search at scale.
 
 >`ScaNN` includes search space pruning and quantization for Maximum Inner
@@ -285,9 +285,9 @@ from langchain.retrievers import GoogleVertexAISearchRetriever
 
 ### Document AI Warehouse
 > [Google Cloud Document AI Warehouse](https://cloud.google.com/document-ai-warehouse)
-> allows enterprises to search, store, govern, and manage documents and their AI-extracted 
+> allows enterprises to search, store, govern, and manage documents and their AI-extracted
 > data and metadata in a single platform.
-> 
+>
 
 ```python
 from langchain.retrievers import GoogleDocumentAIWarehouseRetriever
@@ -304,9 +304,9 @@ documents = docai_wh_retriever.get_relevant_documents(
 
 ### Google Cloud Text-to-Speech
 
->[Google Cloud Text-to-Speech](https://cloud.google.com/text-to-speech) enables developers to 
+>[Google Cloud Text-to-Speech](https://cloud.google.com/text-to-speech) enables developers to
-> synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants. 
+> synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants.
-> It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks 
+> It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks
 > to deliver the highest fidelity possible.
 
 We need to install a python package.
@@ -354,7 +354,7 @@ from langchain.tools import GooglePlacesTool
 ### Google Search
 
 - Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
-- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables 
+- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables
 `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively.
 
 ```python
@@ -444,12 +444,12 @@ from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
 
 ### Google Document AI
 
->[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform` 
+>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform`
-> service that transforms unstructured data from documents into structured data, making it easier 
+> service that transforms unstructured data from documents into structured data, making it easier
 > to understand, analyze, and consume.
 
-We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)  
+We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)
-The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`) 
+The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`)
 and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
 We can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
 tab in the Google Cloud Console.
@@ -507,6 +507,23 @@ See a [usage example and authorization instructions](/docs/integrations/toolkits
 from langchain_community.agent_toolkits import GmailToolkit
 ```
 
+## Memory
+
+### Cloud Firestore
+
+> [`Cloud Firestore`](https://cloud.google.com/firestore) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
+
+First, we need to install the python package.
+
+```bash
+pip install firebase-admin
+```
+
+See a [usage example and authorization instructions](/docs/integrations/memory/firestore_chat_message_history).
+
+```python
+from langchain_community.chat_message_histories.firestore import  FirestoreChatMessageHistory
+```
 
 ## Chat Loaders
 
@@ -560,7 +577,7 @@ from langchain_community.utilities import GoogleSerperAPIWrapper
 ### YouTube
 
 >[YouTube Search](https://github.com/joetats/youtube_search) package searches `YouTube` videos avoiding using their heavily rate-limited API.
-> 
+>
 >It uses the form on the YouTube homepage and scrapes the resulting page.
 
 We need to install a python package.