RAG Retrieval Gotchas at Scale: Insights and Solutions

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import faiss
import numpy as np
# Create a FAISS index
index = faiss.IndexFlatL2(embedding_dimension)
index.add(np.array(embeddings)) # Add your document embeddings

# Search for the top-k closest documents
D, I = index.search(np.array([query_embedding]), k)

Gotcha 2: Data Quality and Relevance

Problem

The effectiveness of a RAG system heavily relies on the quality and relevance of the data being retrieved. Low-quality data can lead to incorrect or nonsensical answers.

Solution

Curate Your Dataset: Regularly update and curate your dataset to ensure the information is accurate and relevant. For example, The Hive Collective offers a curated dataset available at the-hive-corpus that can serve as a good starting point.
Use Relevance Feedback: Implement a feedback loop mechanism where user interactions can help refine and improve the dataset. This can be achieved through active learning techniques.

Gotcha 3: Handling Ambiguity in Queries

Problem

Ambiguous queries can lead to poor retrieval performance. For instance, the term