diff --git a/example-apps/chatbot-rag-app/README.md b/example-apps/chatbot-rag-app/README.md
index c8940731..a3a1e4da 100644
--- a/example-apps/chatbot-rag-app/README.md
+++ b/example-apps/chatbot-rag-app/README.md
@@ -51,14 +51,14 @@ ES_INDEX_CHAT_HISTORY=workplace-app-docs-chat-history
 
 ## Connecting to LLM
 
-We support three LLM providers: Azure, OpenAI and Bedrock.
-
-To use one of them, you need to set the `LLM_TYPE` environment variable:
+We support several LLM providers. To use one of them, you need to set the `LLM_TYPE` environment variable. For example:
 
 ```sh
 export LLM_TYPE=azure
 ```
 
+The following sub-sections define the configuration requirements of each supported LLM.
+
 ### OpenAI
 
 To use OpenAI LLM, you will need to provide the OpenAI key via `OPENAI_API_KEY` environment variable:
@@ -72,7 +72,7 @@ You can get your OpenAI key from the [OpenAI dashboard](https://platform.openai.
 
 ### Azure OpenAI
 
-If you are using Azure LLM, you will need to set the following environment variables:
+If you want to use Azure LLM, you will need to set the following environment variables:
 
 ```sh
 export LLM_TYPE=azure
@@ -84,7 +84,7 @@ export OPENAI_ENGINE=... # deployment name in Azure
 
 ### Bedrock LLM
 
-To use Bedrock LLM you need to set the following environment variables in order to AWS.
+To use Bedrock LLM you need to set the following environment variables in order to authenticate to AWS.
 
 ```sh
 export LLM_TYPE=bedrock
@@ -108,7 +108,7 @@ region=...
 
 ### Vertex AI
 
-To use Vertex AI you need to set the following environment variables. More infos [here](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm).
+To use Vertex AI you need to set the following environment variables. More information [here](https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm).
 
 ```sh
 export LLM_TYPE=vertex
@@ -117,6 +117,17 @@ export VERTEX_REGION=<gcp-region> # Default is us-central1
 export GOOGLE_APPLICATION_CREDENTIALS=<path-json-service-account>
 ```
 
+### Mistral AI
+
+To use Mistral AI you need to set the following environment variables:
+
+```
+export LLM_TYPE=mistral
+export MISTRAL_API_KEY=...
+export MISTRAL_API_ENDPOINT=...  # optional
+export MISTRAL_MODEL=...  # optional
+```
+
 ## Running the App
 
 Once you have indexed data into the Elasticsearch index, there are two ways to run the app: via Docker or locally. Docker is advised for testing & production use. Locally is advised for development.
diff --git a/example-apps/chatbot-rag-app/api/llm_integrations.py b/example-apps/chatbot-rag-app/api/llm_integrations.py
index 8c20fe27..d6f4bb46 100644
--- a/example-apps/chatbot-rag-app/api/llm_integrations.py
+++ b/example-apps/chatbot-rag-app/api/llm_integrations.py
@@ -1,4 +1,11 @@
-from langchain.chat_models import ChatOpenAI, ChatVertexAI, AzureChatOpenAI, BedrockChat
+from langchain_community.chat_models import (
+    ChatOpenAI,
+    ChatVertexAI,
+    AzureChatOpenAI,
+    BedrockChat,
+)
+from langchain_core.messages import HumanMessage
+from langchain_mistralai.chat_models import ChatMistralAI
 import os
 import vertexai
 import boto3
@@ -54,11 +61,27 @@ def init_bedrock(temperature):
     )
 
 
+def init_mistral_chat(temperature):
+    MISTRAL_API_ENDPOINT = os.getenv("MISTRAL_API_ENDPOINT")
+    MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")
+    MISTRAL_MODEL = os.getenv("MISTRAL_MODEL")
+    kwargs = {
+        "mistral_api_key": MISTRAL_API_KEY,
+        "temperature": temperature,
+    }
+    if MISTRAL_API_ENDPOINT:
+        kwargs["endpoint"] = MISTRAL_API_ENDPOINT
+    if MISTRAL_MODEL:
+        kwargs["model"] = MISTRAL_MODEL
+    return ChatMistralAI(**kwargs)
+
+
 MAP_LLM_TYPE_TO_CHAT_MODEL = {
     "azure": init_azure_chat,
     "bedrock": init_bedrock,
     "openai": init_openai_chat,
     "vertex": init_vertex_chat,
+    "mistral": init_mistral_chat,
 }
 
 
diff --git a/example-apps/chatbot-rag-app/env.example b/example-apps/chatbot-rag-app/env.example
index 037740f9..cb8081c1 100644
--- a/example-apps/chatbot-rag-app/env.example
+++ b/example-apps/chatbot-rag-app/env.example
@@ -34,3 +34,9 @@ ES_INDEX_CHAT_HISTORY=workplace-app-docs-chat-history
 # VERTEX_PROJECT_ID=
 # VERTEX_REGION=
 # GOOGLE_APPLICATION_CREDENTIALS=
+
+# Uncomment and complete if you want to use Mistral AI
+# LLM_TYPE=mistral
+# MISTRAL_API_KEY=
+# MISTRAL_API_ENDPOINT=
+# MISTRAL_MODEL=
diff --git a/example-apps/chatbot-rag-app/requirements.in b/example-apps/chatbot-rag-app/requirements.in
index b7a615bf..840c43ae 100644
--- a/example-apps/chatbot-rag-app/requirements.in
+++ b/example-apps/chatbot-rag-app/requirements.in
@@ -20,6 +20,9 @@ grpcio-status
 # BedRock dependencies
 boto3
 
+# Mistral dependencies
+langchain-mistralai
+
 # TBD if these are still needed
 exceptiongroup
 importlib-metadata
diff --git a/example-apps/chatbot-rag-app/requirements.txt b/example-apps/chatbot-rag-app/requirements.txt
index feedebbb..d5129714 100644
--- a/example-apps/chatbot-rag-app/requirements.txt
+++ b/example-apps/chatbot-rag-app/requirements.txt
@@ -133,13 +133,13 @@ jsonpatch==1.33
     #   langchain-core
 jsonpointer==2.4
     # via jsonpatch
-langchain==0.0.333
+langchain==0.1.9
     # via -r requirements.in
 langchain-core==0.1.23
     # via langchain-elasticsearch
 langchain-elasticsearch==0.1.0
     # via -r requirements.in
-langsmith==0.0.87
+langsmith==0.1.10
     # via
     #   langchain
     #   langchain-core
@@ -195,12 +195,12 @@ pyasn1==0.5.0
     #   rsa
 pyasn1-modules==0.3.0
     # via google-auth
-pydantic==2.3.0
+pydantic==2.5.2
     # via
     #   langchain
     #   langchain-core
     #   langsmith
-pydantic-core==2.6.3
+pydantic-core==2.14.5
     # via pydantic
 pyproject-hooks==1.0.0
     # via build
@@ -268,6 +268,8 @@ yarl==1.9.2
 zipp==3.17.0
     # via importlib-metadata
 
+langchain-mistralai==0.0.5
+    # via -r requirements.in
 # The following packages are considered to be unsafe in a requirements file:
 # pip
 # setuptools