RAG Query Retrieval Using Feedback-Tuned Knowledge Fragment Ranking
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Solution Overview
Problem
Current retrieval-augmented generation (RAG) methods fail to capture user preferences and usage habits due to the limitations of pre-trained embedding models, leading to inconsistent and inaccurate retrieval results.
Innovation Solution
Implement a retrieval-augmented generation method that utilizes a trained embedding model to convert query statements into knowledge fragments, followed by a recommendation model based on Bi-LSTM networks to sequence and optimize results based on user interactions, thereby aligning with user preferences and habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a pre-trained embedding model is used to convert query statements into knowledge fragments, then the retrieval process can be automated and scaled, but the system cannot capture user preferences and usage habits
Solution Approach 1:
The system implements feedback by recording user selections from the retrieved knowledge fragments and using this feedback to retrain the embedding model. This allows the model to learn from actual user behavior patterns and adapt to individual preferences over time, resolving the contradiction between automated retrieval and adaptability to user needs.
Solution Approach 2:
The embedding model transitions from a static pre-trained model to a dynamic model that continuously updates its parameters based on user feedback. This dynamic adaptation enables the system to maintain automation while developing the ability to capture and respond to evolving user preferences and usage habits.
2Device complexity
If the same embedding model is used for all queries, then the system maintains consistency and simplicity, but it cannot provide customized results for different users
Solution Approach 1:
The system segments the embedding process into two distinct stages: an initial pre-trained embedding phase that maintains simplicity and consistency, and a subsequent fine-tuning phase that incorporates user-specific feedback. This segmentation allows the system to preserve the simplicity of the base model while adding customization capability through targeted updates.
Solution Approach 2:
Instead of creating completely different embedding models for each user, the system applies local quality by making targeted adjustments to the embedding model parameters based on individual user feedback patterns. This allows customization for specific users without redesigning the entire embedding system, maintaining overall simplicity while enabling personalized results.
3Stability of the object's composition
If pre-trained models with fixed parameters are used, then the model parameters remain stable and reproducible, but the retrieval results cannot adapt to user-specific needs
Solution Approach 1:
The system performs preliminary action by using a pre-trained embedding model with stable, fixed parameters to handle the majority of retrieval tasks. This pre-trained model provides consistent and reproducible results. Only when user feedback indicates a need for customization does the system proceed to fine-tune the model, thereby maintaining parameter stability for most cases while achieving high precision when needed.
Solution Approach 2:
The system applies parameter changes by modifying the embedding model parameters only after incorporating user feedback data. The pre-trained parameters remain stable during normal operation, and changes are made selectively based on user behavior patterns. This approach maintains the stability and reproducibility of the base model while enabling the precision needed for user-specific retrieval requirements.
Data Source
AI summary
A retrieval-augmented generation method, system, device, and medium and a question-answering method are provided, belonging to the field of data processing technology. The retrieval-augmented generation method includes: acquiring a query statement to be retrieved; converting the query statement to be retrieved into K knowledge fragments to be augmented based on a trained embedding model; and inputting the K knowledge fragments to be augmented into the trained recommendation model to obtain several augmented knowledge fragments and their sequencing. The method, system, device, and medium can perform data interaction based on user preferences and usage habits.