From: aidotengineer
Monitoring, tracing, and evaluation are crucial aspects for ensuring the performance and reliability of Retrieval Augmented Generation (RAG) solutions, especially when deployed in production environments [02:40:00].
Monitoring and Tracing
Monitoring and tracing RAG solutions helps in troubleshooting and understanding the system’s behavior [02:44:00]. It allows users to identify where the majority of time is spent within the pipeline, such as in the large language model or other components [02:47:00].
Tools for Monitoring and Tracing
- Prototyping: For prototyping, Langsmith Phoenix is a suitable solution [02:56:00].
- Production: In production, Arise Phoenix is often used, especially because it can be easily deployed in a Docker container [03:00:00]. These tools allow for tracking and troubleshooting the RAG pipeline [17:08:00].
Evaluation
Evaluating the RAG solution is essential to assess its quality and effectiveness [03:24:00]. While a single question might be used for initial testing, a comprehensive RAG evaluation framework is needed to test across a much larger set of documents and queries [16:34:00].
Frameworks for Evaluation
- Ragas: Ragas is highlighted as an excellent framework for RAG evaluation [03:29:00]. It helps check the quality of RAG solutions in various ways and integrates well with large language models, making the evaluation task relatively straightforward [17:33:00].
Components in a Production Docker Environment
A typical production environment leveraging Docker Compose would include specific Docker images for different RAG components, facilitating monitoring and evaluation:
- Data Ingestion: An image for ingesting data, connected to the knowledge base to pull in HTML files [17:57:00].
- Vector Database: An image for Quadrant, serving as the vector database, which can be pulled from Docker Hub [18:03:00].
- Front-end Application: A front-end application for the solution [18:12:00].
- Model Serving: Olama or Hugging Face’s text generation inference engine to serve models [18:12:00].
- Tracing: Phoenix for tracing [18:19:00].
- Evaluation: Ragas for evaluating models [18:22:00].
These images can be run as containers within Docker Compose, with configurations for embedding, reranking, and large language models [18:26:00].