Discover the basics of building a Retrieval-Augmented Generation (RAG) application using the LangChain framework and Node.js …
Here are highlights from article Building AI Applications with LangChain and Node.js
1. AI-driven querying using generative AI and large language models (LLMs) allows users to get answers directly from detailed data.
– Users can pose questions about specific content and receive accurate answers.
– This technique is gaining traction in software development and can change the way we use and think about digital content.
2. Generative AI enables a conversational approach to exploring data and obtaining detailed results.
– This capability is usually limited by the LLM provider’s training data.
– Retrieval-augmented generation (RAG) combines retrieval-based and generative methods in AI to improve the accuracy and relevance of AI responses.
3. RAG consists of three stages: Index, Retrieve, and Augment.
– In the index stage, relevant documents are loaded, broken down into smaller chunks, transformed into vectors, and stored in a vector store.
– In the retrieval stage, the user’s question is converted into a vector, and relevant text chunks are fetched from the vector store.
– In the augment stage, the retrieved context is added to the initial prompt and forwarded to the LLM to obtain a context-relevant answer.
4. Text embeddings and vectors are used to represent textual information in a format that machines can process.
– Text embeddings convert words and sentences into numerical representations.
– Vectors allow for efficient similarity search algorithms to retrieve relevant information.
5. Open-source tools like LangChain and Node.js can be used to build tools for asking technical questions about the content of YouTube videos.
– Video transcripts can be used as a source of context for generating accurate answers.
– Crafted prompts that hold relevant context can unlock the power of generative AI without the need to train an LLM using the video content.
You can read it here: https://sfdc.blog/eBZie
Source from developer(dot)salesforce(dot)com