By

Episode 8: How to retrieve context using RAG & Chroma DB

In today’s fast-evolving tech landscape, effective data retrieval is essential, especially with the growing need for context-rich outputs. This article will dive into Vector Database-based Retrieval-Augmented Generation (RAG) and how tools like Chroma DB can help streamline your data processes.

I. Why choose a vector database?

Speed and efficiency are critical in modern data retrieval. Vector databases offer a significant edge, especially when compared to traditional methods like Cypher queries in Neo4j graph databases. In similarity searches, a well-optimized vector database can be up to three times faster, especially for complex queries.

But speed isn’t the only advantage. Vector databases are particularly effective in handling large-scale applications and deep contextual retrieval. They are designed for high-dimensional similarity searches, making them the ideal solution when context involves complex semantic relationships.

II. The vector database advantage

Graph databases, through algorithms like GraphSAGE or Node2Vec, can also be used for similar tasks. However, linking your large language model (LLM) to your memory system remains a challenge. To solve this, you need to align the embeddings generated by your LLM with your vector database. This ensures that the vectors produced by the LLM match across environments, maintaining consistency in retrieval.

III. Technical insight

The key issue arises from the LLM’s internal memory, stored in the form of vectors. If the vectors in your database don’t align with the LLM’s, retrieval becomes inaccurate, as the vectors won’t represent the same semantic space. By using the embeddings from your LLM, you can effectively index the node properties, labels, and relationships. This is where algorithms like FAISS come into play.

IV. FAISS algorithms for vector indexing

FAISS offers various indexing techniques to optimize your data retrieval, including:

  • Flat (Brute Force)
    • Advantage: Guarantees the most accurate results by comparing all data points.
    • Downside: Slow for large datasets due to exhaustive search.
  • IVF (Inverted File Index)
    • Advantage: Faster by partitioning data into clusters.
    • Downside: Less accurate, as it skips some data points.
  • HNSW (Hierarchical Navigable Small World)
    • Advantage: Extremely fast with high accuracy for large datasets.
    • Downside: Requires significant memory to maintain graph structures.
  • LSH (Locality-Sensitive Hashing)
    • Advantage: Efficient for high-dimensional data by hashing similar points into the same bucket.
    • Downside: Lower accuracy compared to other methods, especially for detailed similarity.

V. Best practices for indexing

After extensive testing, our Golden Rule is: index for complex retrievals or large databases, not just for easy retrievals. In large-scale operations, efficient indexing makes a huge difference in performance and relevance.

VI. Why we use Chroma DB

At our organization, we use Chroma DB, which integrates seamlessly with tools like FAISS, NumPy, scikit-learn, LangChain, and LlamaIndex. Other vector databases like Pinecone and Weaviate are cloud-based solutions, but we prefer Chroma DB for its compatibility with our existing technologies and flexible deployment.

VII. Conclusion

Whether you’re building a deep contextual retrieval system or scaling up to handle massive datasets, understanding the advantages of vector databases and the right indexing techniques is key. With tools like RAG and Chroma DB, you can ensure fast, accurate, and scalable data retrieval solutions.

Scroll to Top