Why?
Monitoring the infrastructure, devices, etc is not enough. The application performance for its task is important to monitor.
From Wikipedia: "LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis"
What?
Use Langchain in the context of retrieval-augmented generation
References
- "LangChain RAG Pipeline Design and Feasibility Test" -- COnflience page in the AD Space under the "Large Language Models/Reports" section
- AIAD-1935 LLM - Prompt Engineering & RAG -- Jira epic
Why?
Monitoring the infrastructure, devices, etc is not enough. The application performance for its task is important to monitor.
From Wikipedia: "LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis"
What?
References
- "LangChain RAG Pipeline Design and Feasibility Test" -- COnflience page in the AD Space under the "Large Language Models/Reports" section
- AIAD-1935 LLM - Prompt Engineering & RAG -- Jira epic