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Google is still a strong contender in the LLM race, recently launching its most powerful and accurate multimodal model, Gemini 2.0. In this tutorial, we will explore Gemini 2.0 Flash, learn how to access it using the Python API, and build a document Q&A application with the LlamaIndex framework. Finally, we will create a RAG chatbot with memory for enhanced conversational capabilities.
Understanding Gemini 2.0
Gemini 2.0 represents a significant leap in AI technology, introducing the experimental Gemini 2.0 Flash, a high-performance, multimodal model designed for low latency and advanced capabilities. Building on the success of Gemini 1.5 Flash, this new model supports multimodal inputs (like images, video, and audio) and outputs, including text-to-speech and image generation, while also enabling tool integrations such as Google Search and code execution.
The Experimental Gemini 2.0 Flash model is available to the developers via the Gemini API and Google AI Studio and offers enhanced performance and faster response times. It also powers a more capable AI assistant in the Gemini app and explores agentic experiences.
1. Setting Up
For this project, we are using Deepnote as our coding environment to build and run the AI application. To set up the environment, we first have to install all the necessary Python packages using the PIP command.
%%capture
%pip install llama-index-llms-gemini
%pip install llama-index
%pip install llama-index-embeddings-gemini
%pip install pypdf
Then, generate a Gemini API key by going to your Google AI Studio dashboard. Finally, create the alignment variable in Deepnote and provide it with the variable name and the API key.
2. Loading the Language and Embedding Models
Securely load the API key and create the LLM client by providing the model name. In this case, we are using the Gemini 2.0 Flash experimental model.
import os
from llama_index.llms.gemini import Gemini
GoogleAPIKey = os.environ["GEMINI_API_KEY"]
llm = Gemini(
model="models/gemini-2.0-flash-exp",
api_key=GoogleAPIKey,
)
Provide the LLM client with the prompts to generate the response.
response = llm.complete("Write a poem in the style of Rumi.")
print(response)
The generated poem is perfect and has a style similar to Rumi’s poems.
Next, we will load the Embedding model, which we will use to convert the text into the embedding, making it easy for us to run a similar search.
from llama_index.embeddings.gemini import GeminiEmbedding
embed_model = GeminiEmbedding(model_name="models/text-embedding-004")
3. Loading the Documentation
Load the Song Lyrics dataset from Kaggle. It consists of TXT files containing lyrics and poems by top US singers.
We will load all the TXT files using the directory reader.
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader('./data')
doc_txt = documents.load_data()
4. Building the Q&A Application
By using the Settings class, we will set the default configuration for our AI application. We are setting the LLM, embedding model, chunk size, and chunk overlap.
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 800
Settings.chunk_overlap = 20
Convert the TXT document into the meanings and store them into the vector store.
from llama_index.core import VectorStoreIndex
from IPython.display import Markdown, display
index = VectorStoreIndex.from_documents(doc_txt, service_context=Settings)
index.storage_context.persist('./VectorStore')
Convert the index into a query engine and ask it a question. The query engine transforms the questions into embeddings, compares them with the vector store, and retrieves results with the highest similarity scores. These results are then passed through the LLM to provide detailed context.
query_engine = index.as_query_engine()
response = query_engine.query("Which verse do you think is the most thought-provoking by Rihanna?")
display(Markdown(response.response))
The query engine correctly identified the answer.
"Get a space where my heart was, There's a crater, I got feelings but no hard ones, See you later" is a thought-provoking verse.
5. Building the RAG Chatbot with History
Now, let’s create a chatbot that allows back-and-forth conversations. To achieve this, we will first set up a Chat Memory Buffer to store the conversation history. Then, we will convert the index into a retriever and build a RAG (Retrieval-Augmented Generation) chatbot pipeline with memory.
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.chat_engine import CondensePlusContextChatEngine
memory = ChatMemoryBuffer.from_defaults(token_limit=3900)
chat_engine = CondensePlusContextChatEngine.from_defaults(
index.as_retriever(),
memory=memory,
llm=llm,
)
response = chat_engine.chat(
"What do you think about Kanye West songs? "
)
display(Markdown(response.response))
The chatbot provides a context-aware answer using the song lyrics.
Next, let’s ask another question and generate the response as a stream. Streaming displays the response token by token..
response = chat_engine.stream_chat(
"Use one of the songs to write a poem. ",
)
for chunk in response.chat_stream:
print(chunk.delta or "", end="", flush=True)
Final Thoughts
Gemini models and Google AI Studio are rapidly improving and now rival the capabilities of OpenAI and Anthropic APIs. While the platform had a slow start, it now enables you to build applications that are significantly faster than those of its predecessors.
Access to Gemini 2.0 is free, allowing you to integrate it into local chatbot applications or develop full-fledged AI systems that seamlessly fit into your ecosystem. Gemini 2.0 supports text, image, audio, and even video input and offers easy tool integrations.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.