Assessing RAG and HyDE on 1B vs. 4B-Parameter Gemma LLMs for Personal Assistants Integretion (2506.21568v1)

Abstract: Resource efficiency is a critical barrier to deploying LLMs in edge and privacy-sensitive applications. This study evaluates the efficacy of two augmentation strategies--Retrieval-Augmented Generation (RAG) and Hypothetical Document Embeddings (HyDE)--on compact Gemma LLMs of 1 billion and 4 billion parameters, within the context of a privacy-first personal assistant. We implement short-term memory via MongoDB and long-term semantic storage via Qdrant, orchestrated through FastAPI and LangChain, and expose the system through a React.js frontend. Across both model scales, RAG consistently reduces latency by up to 17\% and eliminates factual hallucinations when responding to user-specific and domain-specific queries. HyDE, by contrast, enhances semantic relevance--particularly for complex physics prompts--but incurs a 25--40\% increase in response time and a non-negligible hallucination rate in personal-data retrieval. Comparing 1 B to 4 B models, we observe that scaling yields marginal throughput gains for baseline and RAG pipelines, but magnifies HyDE's computational overhead and variability. Our findings position RAG as the pragmatic choice for on-device personal assistants powered by small-scale LLMs.

• The paper demonstrates that RAG significantly reduces hallucination and query latency in 1B Gemma LLMs.
• It compares augmentation strategies, revealing HyDE's computational overhead and scalability challenges in 4B models.
• Integrating MongoDB and Qdrant, the research highlights robust memory management and semantic retrieval for personal assistants.

Evaluation of RAG and HyDE Augmentation Strategies in Compact Gemma LLMs

Introduction

The paper "Assessing RAG and HyDE on 1B vs. 4B-Parameter Gemma LLMs for Personal Assistants Integration" investigates augmentation strategies for compact LLMs, specifically focusing on 1 billion (1B) and 4 billion (4B) parameter variants. The paper aims to address the resource constraints faced by LLM deployment in privacy-sensitive environments by using Retrieval-Augmented Generation (RAG) and Hypothetical Document Embeddings (HyDE). With a structured memory architecture employing MongoDB for short-term data and Qdrant for long-term semantic storage, the research explores the performance and factual reliability improvements across user-specific and scientific queries.

Methodology

To effectively assess RAG and HyDE methodologies, the paper developed a sophisticated personal assistant system using a combination of technologies. The architecture leverages Docker Compose for container orchestration, LM Studio for model hosting, FastAPI for backend management, and React.js for frontend interfacing. MongoDB serves to store structured personal information, while Qdrant facilitates semantic vector search for the physics corpus. The paper design incorporates rule-based operation modes to dynamically switch between personal, physics, and standard contexts based on user input.

The integration of RAG and HyDE is pivotal in enhancing interaction quality. RAG employs an external knowledge retrieval approach during inference, effectively minimizing hallucination risks and improving factual grounding. Conversely, HyDE generates hypothetical embeddings to enrich semantic retrieval, albeit with increased computational demands and variability.

Results

Physics Data Set Analysis

All model configurations—Standard, RAG, and HyDE—demonstrate competent problem-solving capability regarding physics queries. However, semantic and qualitative improvements varied markedly among augmentation strategies.

• Reduction of Hallucination Risk: RAG consistently grounds responses in factual data, eliminating black-box tendentiousness typical in LLMs.
• Latency Analysis for 1B LLM (Figure 1):
  • Standard Mode averages 9.25 seconds per query.
  • RAG significantly reduces latency to 7.70 seconds.
  • HyDE incurs higher overhead, averaging 13.24 seconds.
  • 

  • Figure 1: Response Time Distribution for 1B LLM Variant.

Scaling Effects on 4B LLM (Figure 2)

Scaling models from 1B to 4B yields marginal improvements across Standard variants but exacerbates latency and variability in HyDE due to increased computational complexity.

• Latency Analysis for 4B LLM:
  • Standard Mode improves slightly to 8.66 seconds.
  • RAG maintains consistent speed with a slight decrease.
  • HyDE latency increases, evidencing inefficiency at larger scales.
  • 

  • Figure 2: Response Time Distribution for 4B LLM Variant.

Personal Data Retrieval

In personal data contexts, RAG showcases zero hallucination rates by precisely echoing stored user information. HyDE, however, introduces factual inaccuracies across all test questions, likely due to its generative nature.

Conclusion

The paper concludes that RAG provides a practical, efficient enhancement method for compact Gemma LLMs, significantly mitigating hallucination and latency issues while grounding model outputs in factual context. HyDE's computational overhead limits its applicability, highlighting the need for optimized retrieval strategies. The synergy of MongoDB and Qdrant offers robust, scalable memory integration suitable for personal assistant applications, although future work should emphasize real-user data evaluations and domain expansion for broader applicability.

Implications and Future Work

This research establishes a foundation for deploying compact LLMs in privacy-first environments, offering a blueprint for further technological integration and memory optimization. Future studies should focus on enhancing HyDE's retrieval efficiency, expanding domain coverage, and conducting real-user trials to fully assess practical deployment readiness. Hybrid augmentation strategies combining RAG and HyDE may offer balanced benefits, fostering deeper semantic retrieval without compromising computational feasibility.