Integrating Vector Databases into Fine-Tuning Workflows for Knowledge Augmentation in Large Language Models
Keywords:
vector databases, large language modelsAbstract
Vector databases enhance LLM customization and reasoning. Traditional fine-tuning datasets may lose domain-specific data. Real-time domain-relevant data retrieval from vector databases like Pinecone improves LLM performance in this investigation. In training and inference, vector databases store and retrieve high-dimensional embeddings to enhance and contextualize LLMs.
This article discusses vector database design, similarity search, indexing, and scaling. Low latency and high throughput make these databases excellent for embedded storage and retrieval. Next, vector database integration with LLM fine-tuning aligns embedding spaces, permits many input modalities, and reduces real-time data retrieval computational cost.
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