Unleashing the Power of Gemini With LlamaIndex

Large language models (LLMs) like Google's Gemini are revolutionizing how we interact with information, but harnessing their power for your own applications can seem daunting. That's where LlamaIndex comes in, providing a simple yet powerful framework to leverage LLMs like Gemini.

This tutorial will guide you through a practical example, using the Python code snippet as a starting point, to demonstrate how to seamlessly integrate Gemini's LLM and embedding capabilities into your projects using LlamaIndex.

Step 1: Setting the Stage 

1.1 Setup API Keys 

Before diving in, ensure you have the necessary API key for accessing Gemini. Refer to this link to generate the API Key (does not require a credit card or billing enabled for now). This key acts as your authentication token for utilizing Google's powerful LLM. 

Once you have your API key, store it securely as an environment variable:

export GOOGLE_API_KEY="<api_key>"

Or use the following code:

import os
os.environ["GOOGLE_API_KEY"] = "<api_key>"

1.2 Setup the Modules and Imports

First install required modules/dependencies.

pip install -q llama-index google-generativeai
pip install llama-index-llms-gemini
pip install llama-index-embeddings-gemini

Next, import the required modules from LlamaIndex:

from llama_index.core import Settings
from llama_index.core import SimpleDirectoryReader, GPTVectorStoreIndex

from llama_index.llms.gemini import Gemini
from llama_index.embeddings.gemini import GeminiEmbedding

1.3 Listing Available Models

import google.generativeai as genai

for m in genai.list_models():    
	if "generateContent" in m.supported_generation_methods:        
	print(m.name)

This snippet shows a list of available models as below:

• models/gemini-flash
• models/gemini-pro
• models/gemini-pro-vision
• models/gemini-ultra

1.4. Setup Required Models and Framework Settings

# default model
#Settings.llm = Gemini() 

Settings.llm = Gemini(models/gemini-pro)
Settings.embed_model = GeminiEmbedding()

This code snippet sets the foundation for using Gemini within the LlamaIndex framework.

Step 2: Exploring Gemini's Capabilities

The code below showcases two key functionalities:

2.1 Building Basic Chat With Prompting

• chat() function: This function demonstrates a simple chat interaction with Gemini. It sends a prompt to the LLM and prints the generated response.

def chat():
    resp = Settings.llm.complete("Write a poem about a magic backpack")
    print(resp)

2.2 Enabling RAG With Embeddings

• rag() function: This function showcases Retrieval Augmented Generation (RAG) using LlamaIndex. It loads data from a directory, creates a vector index using Gemini embeddings, and allows querying the indexed information.

First, set up some test data to be indexed.

mkdir -p 'data/paul_graham/'
wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

Then, build embedding on top of the data and query on the index using query engine.

def rag():

    path = "data"
    query = " Who is the author ?"
    #query = " Where did author lived and studied ?"


    reader = SimpleDirectoryReader(path, recursive=True)
    documents = reader.load_data()
    index = GPTVectorStoreIndex.from_documents(documents)
    query_engine = index.as_query_engine()
    response = query_engine.query(query)

    print(response)

Step 3: Building Upon the Foundation

The above code serves as a springboard for more complex applications. Let's explore some potential enhancements:

Expanding the Chatbot

Enhance the chat() function to create a more interactive chatbot. Implement conversation history management and allow users to ask follow-up questions for a more engaging experience.

Customizing the RAG Pipeline

The rag() function provides a basic RAG implementation. You can customize this further by experimenting with different indexing strategies, fine-tuning the query engine parameters, and incorporating user feedback to improve retrieval accuracy.

Conclusion

This tutorial provided a practical introduction to using Google's Gemini LLM and embedding models within the LlamaIndex framework. By building upon the provided code snippets and exploring the suggested enhancements, you can unlock the power of Gemini to build sophisticated language-based applications.