ARTIFICIAL INTELLIGENCE • MACHINE LEARNING
Real-Time RAG
For developers working on AI applications, Retrieval-Augmented Generation (RAG) is a fundamental tool that integrates the capabilities of large language models (LLMs) with real-time information retrieval to improve response accuracy. Although LLMs are capable of generating answers independently, they may occasionally lack precision or up-to-date information. By incorporating Timeplus, which conducts real-time search and provides relevant sources, RAG ensures that the responses generated are both accurate and well-cited, thereby serving as a robust solution for tasks that demand reliable and current information.
ARCHITECTURE
WHY TIMEPLUS?
Fast and Accurate
Enhance AI model performance by delivering real-time context to improve both inference latency and accuracy.
By integrating fresh, relevant data into the inference process, Timeplus ensures that your AI applications make decisions based on the most current information, reducing response times and boosting precision in dynamic environments.
Enriched Context and Relevance
Enable real-time access to distributed and heterogeneous data sources, making them readily available for Large Language Models (LLMs).
This capability ensures that your AI applications can draw from diverse, up-to-date data during generation tasks, enriching the context and relevance of outputs.
This integration allows for more sophisticated and contextually aware AI solutions.
Cost-Efficient
Real-time vector search capabilities, optimizing the retrieval of relevant data for LLMs.
By providing a single platform that handles data ingestion, transformation, and feature generation, Timeplus reduces the need for multiple tools and integrations.
This streamlined approach not only saves time and resources but also minimizes potential points of failure, making your ML pipelines more efficient and reliable.
Discover End-to-End Capabilities
Remote/Python UDFs
Maintain point-in-time correctness in your ML models, allowing you to accurately associate events and features with the right temporal context during both training and inference.
Vector Store, Vector Search
Leverage mutable streams in Timeplus to correct and update labels in real-time as new information becomes available. Enable your models to remain accurate and up-to-date with the latest data.
Data Ingestion
Timeplus supports ingestion from various sources, including log files, Apache Kafka streams, and API pushes.
ETL Data Pipelines
With real-time ETL data pipelines and data transformation capabilities, process and transform raw data to prepare it for immediate use by LLMs and other AI models.
Resources
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