Featured Vector Tech Topic: Long Context RAG Performance of LLMs
In the Databricks blog on “Long Context RAG Performance of LLMs,” the discussion centers around the effectiveness of Retrieval Augmented Generation (RAG) when paired with long-context large language models (LLMs). As LLMs like GPT-4, Claude, and Gemini extend their context lengths, the blog explores how these capabilities impact the performance of RAG systems. The authors conducted over 2,000 experiments across 13 models to evaluate how longer contexts affect information retrieval and generation. They found that while longer contexts can improve performance by allowing more documents to be retrieved, this is not universally true; performance can decline past a certain context length due to issues like the “lost in the middle” problem and ineffective utilization of extended contexts.
The blog also delves into the unique failure modes of these models, revealing that LLMs may fail in various ways depending on the length of the context and the specific tasks. The experiments included datasets such as Databricks DocsQA, FinanceBench, Natural Questions, and HotPotQA, offering insights into how different models perform under varying conditions. The findings emphasize the importance of carefully selecting the number of documents and the context length in RAG systems to optimize performance.
My take
Is RAG still required? Or can LLMs with long context windows replace RAG pipelines as all documents can be supplied?
While longer context windows in LLMs hold the promise of improved performance, they are not a universal solution. The performance gains are inconsistent, as models may sometimes overlook or undervalue critical information within extended contexts. Simply supplying LLMs with vast amounts of data, including irrelevant information, is not a sustainable or efficient strategy, leading to unnecessary computational costs and resource waste.
RAG remains a vital component in many applications, particularly where precision and efficiency are crucial. By selectively retrieving and presenting only the most relevant documents, RAG systems ensure that the model focuses on high-value content, improving both performance and cost-effectiveness. Until LLMs can reliably handle vast context windows without diminishing returns, RAG will continue to play an essential role in optimizing language model performance.
Looking ahead, a hybrid approach that combines the strengths of both RAG and long-context LLMs might offer the best of both worlds, particularly as these technologies evolve.
Additional Vector Tech 08/2024 resources
Summary of selected articles that caught my attention.
Introducing sqlite-vec v0.1.0: a vector search SQLite extension that runs everywhere
The blog post introduces sqlite-vec v0.1.0, a new SQLite extension for vector search, written in C without dependencies. The extension focuses on fast brute-force vector search. While the current release lacks approximate nearest neighbors (ANN) indexing, future updates are planned to enhance capabilities. The project is sponsored by Mozilla and will be integrated into cloud services like Turso and SQLite Cloud.
Source: Introducing sqlite-vec v0.1.0: a vector search SQLite extension that runs everywhere by Alex Garcia, 01-AUG-2024
and GitHub sqlite-vec
Database startups: AI and Vectors
Transactional.blog list a variety of databases for different categories like AI and vectors, OLAP SQL, Timeseries. Criteria for a database company to be included in the list are:
- Develop a proprietary database or storage product, or
- Be the primary developer of an open-source database or storage project, with the company primarily focused on that project.
The list for AI and vectors include amongst others DeployQL, Vespa, Qdrant, Pinecone, …
Source: Database Startups by transactional.blog, 13-AUG-2024
Where does Postgres fit in a world of GenAI and vector databases?
In “The Stack Overflow Podcast” episode “Where Does Postgres Fit in a World of GenAI and Vector Databases?” he discussion centers on the evolving role of PostgreSQL in the rapidly expanding landscape of generative AI and vector databases. Avthar Sewrathan, the AI Lead at Timescale, shares insights on how Postgres, traditionally a relational database, is adapting to meet the demands of AI-driven applications through innovations like vector storage and search.
Sewrathan highlights the three critical factors developers consider when choosing between general-purpose technologies like Postgres and specialized vector databases: performance, ease of use, and ecosystem familiarity. He emphasizes that PostgreSQL , with its robust extension ecosystem, is increasingly capable of handling AI workloads, bridging the performance gaps that once led developers to seek out specialized solutions.
Timescale is know as a PostgreSQL extension for timeseries data. The database offers two interesting AI features. A key innovation discussed in the episode is the pgvectorscale extension, which enhances Postgres’s ability to handle large-scale vector searches, a necessity in AI applications. This extension is built on the advanced StreamingDiskANN algorithm, which optimizes vector search by allowing parts of the vector index to reside on disk, leveraging solid-state drives for cost-effective scalability. Additionally, the PGai extension is introduced as an open-source tool under the PostgreSQL license, reinforcing the importance of open-source solutions in the AI domain.
The conversation also touches on the broader implications of these advancements, suggesting that vector storage and search capabilities will soon become standard features across databases. Sewrathan envisions a future where PostgreSQL becomes the go-to database for AI applications, supported by its thriving ecosystem of extensions and the broader open-source community.
Source: Where does Postgres fit in a world of GenAI and vector databases? by The Stack Overflow Podcast, 27-AUG-2024
Looking Ahead: Vector tech conferences or events
A selection of conferences or events containing vector tech sessions:
- Big Data Conference Europe: AI, Cloud and Data Conference, 19-NOV-2024 until 22-NOV-2024, Vilnius and online
- DOAG K&A, 19-NOV-2024 until 22-NOV-2024, Nuremberg
- KI Navigator, 20-NOV-2024 until 21-NOV-2024, Nuremberg
For more articles around Vector Tech see on my blog.