Irrelevant or unexpected outcomes from Atlas Vector searches occur most often because of the following reasons:
Sanitization issues: Failing to properly sanitize data (e.g., filtering out empty content or unnecessary information) can lead to inefficiencies in embeddings and reduced accuracy. Review and clean your data to ensure only valuable content is processed.
Poor-quality embeddings: Poor embeddings can lead to inaccurate semantic representation of data. Ensure your embedding model is well-trained, up-to-date, and produces embeddings aligned to your use case.
Outlier data points: Extreme or unusual embeddings may distort approximate nearest neighbor search results. Remove or analyze outlier data to minimize its impact.
Indexing anomalies: Problems during the creation or updating of the vector index could affect search results. Validate your index configuration, ensuring it’s properly set up.
Similarity metric mismatch: The distance metric (cosine, dotProduct, and euclidean) chosen for comparing vectors may not be appropriate for the application or data type. Review and tune similarity settings to align with your use case.
Mismatch between query and data embeddings: Ensure query embeddings are consistent with the embeddings in your database to avoid irrelevant or unexpected results. See MongoDB documentation for how to measure the accuracy of your query results.
If you’re implementing Retrieval-Augmented Generation (RAG) with Atlas Vector Search, such as for a large language model (LLM) application, poor results can often be traced back to ingestion, retrieval, or generation issues:
Ingestion: Clean and preprocess data, use optimal embeddings, chunk data thoughtfully, and enrich metadata for higher relevance.
Retrieval: Align embeddings between queries and indexed data, and tune similarity metrics to enhance retrieval accuracy.
Generation: Provide effective prompts and sufficient context to your LLM to improve the final output quality.
Explore the MongoDB documentation for guidance on improving vector search performance.
To enhance your proficiency, consider taking MongoDB’s Vector Search Fundamentals Skill Badge and RAG with MongoDB Skill Badge.