You can use LlamaIndex with Upstash Vector to perform Retrieval-Augmented Generation (RAG). LlamaIndex is a powerful tool that integrates seamlessly with vector databases like Upstash Vector, enabling advanced query and response capabilities.
First, create a Vector Index in the Upstash Console. Configure the index with:
Dimensions: 1536
Distance Metric: Cosine
Once the index is created, copy the UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN and add them to your .env file along with your OpenAI API key:
Once the index is created, you can query it to retrieve and generate responses based on document content.
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# Initialize the query enginequery_engine = index.as_query_engine()# Perform queriesresponse_1 = query_engine.query("What is global warming?")print(response_1)response_2 = query_engine.query("How can we reduce our carbon footprint?")print(response_2)