RAG with LlamaIndex - Upstash Documentation

In this tutorial, we’ll demonstrate how to use Upstash Vector with LlamaIndex to perform RAG (Retrieval-Augmented Generation). We will upload a document about global warming and generate responses to our questions based on the contents of the document.

Installation and Setup

First, we need to create a Vector Index in the Upstash Console. Make sure to set the index dimensions to 1536 and the distance metric to Cosine. Once we have our index, we will copy the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN and paste them into our .env file. To learn more about index creation, you can check out our getting started page. Add the following content to your .env file (replace with your actual URL, token and API key):

UPSTASH_VECTOR_REST_URL=your_upstash_url
UPSTASH_VECTOR_REST_TOKEN=your_upstash_token
OPENAI_API_KEY=your_openai_api_key

We now need to install the following libraries via PyPI:

pip install llama-index upstash-vector llama-index-vector-stores-upstash python-dotenv

Code

We will load our environment variables, initialize the index, and configure it to use the specified dimensions and distance metric.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.upstash import UpstashVectorStore
from llama_index.core import StorageContext
import openai
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()
openai.api_key = os.environ["OPENAI_API_KEY"]

# Setup the Upstash vector store 
upstash_vector_store = UpstashVectorStore(
    url=os.environ["UPSTASH_VECTOR_REST_URL"],
    token=os.environ["UPSTASH_VECTOR_REST_TOKEN"],
)

# Read the document about global warming from the documents directory
documents = SimpleDirectoryReader("./documents/").load_data()

# Initialize the storage context with the Upstash vector store
storage_context = StorageContext.from_defaults(vector_store=upstash_vector_store)

# Create the index from the loaded document with 1536 dimensions and cosine distance
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

Next, we will query the document:

# Initialize the query engine
query_engine = index.as_query_engine()

# Query the document about global warming
res1 = query_engine.query("What is global warming?")
print(res1)

res2 = query_engine.query("How should we modify our diets to reduce our carbon footprint?")
print(res2)

Sample Output

Here is the output of the queries:

Global warming refers to the long-term increase in Earth’s average temperature due to the accumulation of greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in the atmosphere. The primary drivers of GHG emissions include the burning of fossil fuels for energy, industrial processes, deforestation, and unsustainable agricultural practices. As these gases trap heat, they create a “greenhouse effect,” leading to rising temperatures, melting polar ice caps, rising sea...

Shifting towards plant-based diets, which emphasize the consumption of vegetables, fruits, legumes, grains, and nuts, can significantly reduce our carbon footprint. Plant-based diets have a much lower environmental footprint compared to animal-based foods, particularly red meat, which is highly resource-intensive and contributes disproportionately to greenhouse gas emissions. Studies have shown that reducing the consumption of animal-based foods and increasing the intake of plant-based foods can help red...

Notes

Namespaces can be used to separate different types of documents. You can specify a namespace when creating the UpstashVectorStore instance:

vector_store = UpstashVectorStore(
    url=your_upstash_url, 
    token=your_upstash_token, 
    namespace=your_namespace,
    )

To learn more about LlamaIndex and its integration with Upstash Vector, you can visit the LlamaIndex documentation.

​Installation and Setup

​Code

​Sample Output

​Notes

Installation and Setup

Code

Sample Output

Notes