Build your next RAG app using Gemini Pro API, LlamaIndex, and Pinecone [Updated for v0.10+]

Why LlamaIndex, Gemini, and Pinecone? Why not! Some blog members requested that I ditch OpenAI and write about other models, so there you have it.

What we're going to build

For this tutorial, I will show you how to build a simple Python app that uses Gemini Pro, LlamaIndex, and Pinecone to do the following:

• Scrape the contents of this post using BeautifulSoup
• Convert them to Vector Embeddings using models-embedding-001
• Store the embeddings on a Pinecone index
• Load the Pinecone index and perform a similarity search
• Use LlamaIndex to query the Gemini Pro model

But wait! Before we get our hands dirty, let's go over the following essential definitions so you know what I'm talking about.

Not a fan of reading? 🤔

Here's a complete video version of this tutorial:

A quick roundup of the basics

What is Google Gemini?

Google Gemini is a multimodal large language model (or LLM) developed by Google DeepMind. It comes in three flavors and understands information across various data types: text, code, audio, image, and video. It's the GPT killer from Google.

What is LlamaIndex?

LlamaIndex is a data framework that helps integrate private data with LLMs to build RAG apps. It offers tools such as data ingestion, indexing, and querying. The framework is available in Python and TypeScript.

What is Pinecone?

Pinecone is a high-performance vector database for machine learning, natural language processing, and computer vision. It offers low-latency vector search and is known for its scalability and ability to handle high-dimensional data.

What is RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances large language models by adding external knowledge, such as information from PDFs or websites (like we're doing today).

The architecture retrieves relevant facts from a data source outside the model's training data, expanding the model's knowledge.

Here's my best attempt at designing a diagram that shows the RAG architecture:

From theory into practice

Great! Now that the boring stuff is out of the way, it's time to get our toolkit and write some code.

We're going to start by creating a free Pinecone account.

How to create a Pinecone index

Let's set up our Pinecone vector database and index. We'll use the index to store and retrieve the data.

Step 1: Create a free Pinecone account

Head over to the Pinecone site and create a free account. Pinecone offers a free tier which is limited to one project and one index.

Step 2: Create a Pinecone project

Pinecone automatically creates a starter project for you. We're going to use it to create our index.

To create the index, click on the Indexes tab on the sidebar then click on the Create Index button as shown in the image below:

Next, choose any name for the index and make sure to enter 768 as the value for the Dimensions property then choose cosine from the Metric dropdown. You can leave everything else as-is.

Here's what your index configuration should look like:

Now, click on the Create Index button to create your first Pinecone index.

Step 3: Grab your Pinecone API key

Okay, with our Index set up and ready to go we'll need to grab our API key from the Pinecone console.

Go to the API Keys tab from the sidebar as shown below and copy your key:

How to get a Google Gemini API key

To use Gemini Pro, we need to get an API key from the Google AI Studio. To grab yours, navigate to the Google AI Studio page and log in using your Google account.

Once inside Google AI Studio, click on Get API key from the sidebar and copy your API key as shown below:

Good! Now we have our Google Gemini API and Pinecone API keys which is all we need to jump to the next part and write some code.

Building a RAG pipeline using LlamaIndex

I'm using a macOS Ventura 13.6.4 but the steps below should be more or less the same for you. In case you run into problems, drop a comment below and I'll try to help.

Step 1: Prepare the environment

Let's create our project directory and our main.py file. In your terminal and from your working directory, type the following commands:

mkdir llamaindex-gemini-pinecone-demo 
cd llamaindex-gemini-pinecone-demo

touch main.py

Now, let's create and activate a virtual environment. This step is optional, but I highly recommend it. To do so, type the following commands in your terminal:

python -m venv venv
source venv/bin/activate

Next, we'll need to download and install the following packages:

• llama-index
• BeautifulSoup4
• google-generativeai
• pinecone-client

Using pip, type the following command in your terminal:

pip install llama-index BeautifulSoup4 google-generativeai pinecone-client

For LlamaIndex v0.10+ we need to install the following packages:

• llama-index-llms-gemini
• llama-index-embeddings-gemini
• llama-index-vector-stores-pinecone

pip install llama-index-llms-gemini llama-index-embeddings-gemini llama-index-vector-stores-pinecone

It may take a few seconds until pip downloads the packages and sets everything up. If all goes well, you should see a success message.

Step 2: Import libraries and define API keys

We'll need to import a few libraries and take care of some basics.

Copy the code below into your main.py file:

For LlamaIndex v0.9.48 or earlier:

import os
from pinecone import Pinecone
from llama_index.llms import Gemini
from llama_index.vector_stores import PineconeVectorStore
from llama_index.storage.storage_context import StorageContext
from llama_index.embeddings import GeminiEmbedding
from llama_index import ServiceContext, VectorStoreIndex, download_loader, set_global_service_context

For LlamaIndex v0.10 or later:

import os
from pinecone import Pinecone
from llama_index.llms.gemini import Gemini
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.core import StorageContext, VectorStoreIndex, download_loader

from llama_index.core import Settings

Set API keys and set Gemini as llm

GOOGLE_API_KEY = "YOUR_GOOGLE_API_KEY"
PINECONE_API_KEY = "YOUR_PINECONE_API_KEY"

os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
os.environ["PINECONE_API_KEY"] = PINECONE_API_KEY

DATA_URL = "https://www.gettingstarted.ai/how-to-use-gemini-pro-api-llamaindex-pinecone-index-to-build-rag-app"

llm = Gemini()

Make sure to replace YOUR_GOOGLE_API_KEY and YOUR_PINECONE_API_KEY with your respective API keys that you copied earlier.

If you prefer, you can use the export command to assign your keys to the environment variables like so:

export GOOGLE_API_KEY="YOUR_KEY"
export PINECONE_API_KEY="YOUR_KEY"

Step 3: Create a Pinecone client

To send data back and forth between the app and Pinecone, we'll need to instantiate a Pinecone client. It's a one-liner:

pinecone_client = Pinecone(api_key=os.environ["PINECONE_API_KEY"])

Step 4: Select the Pinecone index

Using our Pinecone client, we can select the Index that we previously created and assign it to the variable pinecone_index:

pinecone_index = pinecone_client.Index("demo")

Step 5: Download webpage contents

Next, we'll need to download the contents of this post. To do this, we will use the LlamaIndex BeautifulSoupWebReader data loader:

BeautifulSoupWebReader = download_loader("BeautifulSoupWebReader")

loader = BeautifulSoupWebReader()
documents = loader.load_data(urls=[DATA_URL])

Step 6: Generate embeddings using GeminiEmbedding

By default, LlamaIndex assumes you're using OpenAI to generate embeddings. To configure it to use Gemini instead, we need to set up the service context which lets LlamaIndex know which llm and which embedding model to use.

Here's how:

For LlamaIndex v0.9.48 and older:

# Define which embedding model to use "models/embedding-001"
gemini_embed_model = GeminiEmbedding(model_name="models/embedding-001")

# Create a service context using the Gemini LLM and the model
service_context = ServiceContext.from_defaults(llm=llm, embed_model=gemini_embed_model)

# Set the global service context to the created service_context
set_global_service_context(service_context)

For LlamaIndex v0.10 and later:

embed_model = GeminiEmbedding(model_name="models/embedding-001")

Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512

Step 7: Generate and store embeddings in the Pinecone index

Using the VectorStoreIndex class, LlamaIndex takes care of sending the data chunks to the embedding model and then handles storing the vectorized data into the Pinecone index.

Here's how:

# Create a PineconeVectorStore using the specified pinecone_index
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)

# Create a StorageContext using the created PineconeVectorStore
storage_context = StorageContext.from_defaults(
    vector_store=vector_store
)

# Use the chunks of documents and the storage_context to create the index
index = VectorStoreIndex.from_documents(
    documents, 
    storage_context=storage_context
)

Alternatively, if you want to load your existing index you can use the from_vector_store method of the VectorStoreIndex class as follows:

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

Step 8: Query Pinecone vector store

Now the contents of the URL are converted to embeddings and stored in the Pinecone index.

Let's perform a similarity search by querying the index:

query_engine = index.as_query_engine()

# Query the index, send the context to Gemini, and wait for the response
gemini_response = query_engine.query("What does the author think about LlamaIndex?")

The query_engine uses the query to find similar information from the index. Once it does, the information is passed to the Gemini Pro model, which generates an answer.

Now you've officially built a RAG pipeline using LlamaIndex. Congrats!

Step 9: Print the response

print(gemini_response)

And that's all! Now let's run our Python app. In your terminal window type the following command:

python3 main.py

Here's what Gemini Pro has to say about what I think of LlamaIndex:

The author thinks that LlamaIndex is a powerful data framework that supports a wide range of large language models. It is incredibly simple to extract and store custom data in a Pinecone vector store. Moreover, LlamaIndex simplifies querying the index and makes communicating with Google's Gemini Pro model easy.

Psst...

Join a community of people learning about AI for free and stay ahead.

Subscribe

Email sent! Check your inbox to complete your signup.

No spam. Unsubscribe anytime.

Final thoughts

Beautiful! You made it this far which means you like the content which in turn means that you MUST subscribe to the blog. It's free and you can unsubscribe anytime.

LlamaIndex is a powerful data framework that supports a wide range of large language models. As you've seen, it is incredibly simple to extract and store your custom data in a Pinecone vector store.

Moreover, LlamaIndex simplifies querying your index and makes communicating with Google's Gemini Pro model easy.

If you have any questions, suggestions, or anything else please drop a comment below. Also, follow me on X (Twitter) for more updates.

Further readings

More from Getting Started with AI

• Build Your Own RAG AI Agent with LlamaIndex
• An introduction to RAG tools and frameworks: Haystack, LangChain, and LlamaIndex
• The beginner's guide to start using the new Gemini Pro models in Google AI Studio
• Big news: Gemini the GPT rival from Google DeepMind is finally here
• LlamaIndex: A closer look into storage customization, persisting and loading data
• LlamaIndex: Using data connectors to build a custom ChatGPT for private documents
• Introduction to augmenting LLMs with private data using LlamaIndex
• From traditional SQL to vector databases in the age of artificial intelligence

Source code for LlamaIndex v0.9.48 or earlier

Source code for LlamaIndex v0.10+