How to monitor LlamaIndex apps with Langfuse and PostHog

LlamaIndex is a powerful framework for connecting LLMs with external data sources. By combining PostHog with Langfuse, an open-source LLM observability platform, you can easily monitor your LLM app.

In this tutorial, we show you how to set this up by walking you through a simple RAG chat app.

1. Set up the sample app

For this tutorial, we create an app that answers questions about how to care for hedgehogs. The first step is to vectorize a pdf on how to care for hedgehogs using Mistral's 8x22B model.

To do this, install all the necessary dependencies for the app:

pip install llama-index llama-index-llms-mistralai llama-index-embeddings-mistralai nest_asyncio langfuse --upgrade

Next, create a folder for the project as well as a python file called app.py:

mkdir hedgehog-app
cd hedgehog-app
touch app.py

Then, paste the following code into app.py to set up the app:

# Set the API keys
import os
os.environ["MISTRAL_API_KEY"] = "<your_mistral_api_key>"
 
# Ensures that sync and async code can be used together without issues
import nest_asyncio
nest_asyncio.apply()
 
# Import and set up llama index
from llama_index.llms.mistralai import MistralAI
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.core import Settings
 
# Define your LLM and embedding model
llm = MistralAI(model="open-mixtral-8x22b", temperature=0.1)
embed_model = MistralAIEmbedding(model_name="mistral-embed")
 
# Set the LLM and embedding model in the Settings object
Settings.llm = llm
Settings.embed_model = embed_model

You need to replace <your_mistral_api_key> with your actual key. To do this, you first need to sign up for a Mistral account. Then subscribe to a free trial or billing plan, after which you’ll be able to generate an API key.

2. Set up monitoring with Langfuse

Next, we set up Langfuse to trace our model generations. Sign up for a free Langfuse account if you haven't already. Then create a new project and copy the API keys.

Paste the keys into the top of app.py and instantiate Langfuse:

# Set the API keys
import os
os.environ["MISTRAL_API_KEY"] = "<your_mistral_api_key>"
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" 

# Import and instantiate Langfuse
from langfuse import Langfuse
langfuse = Langfuse()

# Rest of your existing code...

Then we register Langfuse's LlamaIndexCallbackHandler in LlamaIndex's Settings.callback_manager just below the existing code where we import LlamaIndex.

# Existing code...
from llama_index.llms.mistralai import MistralAI
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.core import Settings

# Add these lines of code just below the above existing imports
from llama_index.core import Settings
from llama_index.core.callbacks import CallbackManager
from langfuse.llama_index import LlamaIndexCallbackHandler

langfuse_callback_handler = LlamaIndexCallbackHandler()
Settings.callback_manager = CallbackManager([langfuse_callback_handler])

# Rest of your existing code...

3. Build RAG on the hedgehog pdf

First download the hedgehog care guide pdf by running the following command in your project directory:

!wget 'https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf' -O './hedgehog.pdf'

Next, we load the pdf using the LlamaIndex SimpleDirectoryReader, create vector embeddings using VectorStoreIndex, and then convert it into a queryable engine that we can retrieve information from.

# Add this to the bottom of app.py
from llama_index.core import SimpleDirectoryReader

hedgehog_docs = SimpleDirectoryReader(
    input_files=["./hedgehog.pdf"]
).load_data()

from llama_index.core import VectorStoreIndex

hedgehog_index = VectorStoreIndex.from_documents(hedgehog_docs)
hedgehog_query_engine = hedgehog_index.as_query_engine(similarity_top_k=5)

Finally, we can add code to query the engine and print the answer:

# Add this to the bottom of app.py
response = hedgehog_query_engine.query("What is the best way to care for a hedgehog?")
print(response)

Test the app by running python app.py. You should see a response like the one below 🎉:

The best way to care for a hedgehog is to provide it with a suitable 
habitat that includes nesting possibilities and a food supply located closely 
together within a small area. This can be achieved by creating a garden 
with a variety of natural structures such as hedges, bushes, groundcover 
plants, piles of leaves, and undergrowth.

All steps of the LLM chain are now tracked in Langfuse and you can view them in your Langfuse dashboard.

4. Integrate Langfuse with PostHog

Next we [connect Langfuse to PostHog](/docs/ai-engineering/langfuse-posthog so that you can combine your LLM trace data with your PostHog analytics. This enables you to answer product questions such as:

• What are my LLM costs by customer, model, and in total?
• How many of my users are interacting with my LLM features?
• Does interacting with LLM features correlate with other metrics (retention, usage, revenue, etc.)?

Here's how to connect Langfuse to PostHog:

1. In your Langfuse dashboard, click on your project settings and then on Integrations.
2. On the PostHog integration, click Configure and paste in your PostHog host and project API key (you can find these in your PostHog project settings).
3. Click Enabled and then Save.

Langfuse will now begin sending your LLM analytics to PostHog once a day.

5. Set up a PostHog dashboard

The last step is to set up a PostHog dashboard so that you can view your LLM insights. We've made this easy for you by creating a dashboard template. To create the dashboard template:

1. Go the dashboard tab in PostHog.
2. Click the New dashboard button in the top right.
3. Select LLM metrics – Langfuse from the list of templates.

Further reading

• How to compare AWS Bedrock prompts
• How to set up LLM analytics for ChatGPT
• How to compare AWS Bedrock foundational models

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