The article discusses Retrieval-Augmented Generation (RAG), which is a concept that provides additional information from an external knowledge source to large language models (LLMs). The article explains the problem of factual inaccuracies that can occur when prompting LLMs and presents RAG as a solution. It also provides an implementation example using LangChain for orchestration, OpenAI language models, and a Weaviate vector database.
Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation
Learn how to bridge the gap between large language models (LLMs) and your proprietary data using Retrieval-Augmented Generation (RAG). RAG provides LLMs with additional information from an external knowledge source, allowing them to generate more accurate and contextual answers while reducing factual inaccuracies.
The Problem
LLMs are trained on large amounts of data to achieve general knowledge, but they may lack specific or domain-specific information. Prompting an LLM to generate a completion that requires knowledge not included in its training data can lead to factual inaccuracies.
The Solution
RAG is a technique that combines a generative model with a retriever module to provide additional information from an external knowledge source. This technique separates factual knowledge from the LLM’s reasoning capability, allowing for more accurate and contextual completions.
Implementation using LangChain
LangChain is a Python library that can be used to implement a RAG pipeline. This pipeline combines an OpenAI LLM, a Weaviate vector database, and an OpenAI embedding model for retrieval-augmented generation.
To implement the RAG pipeline, follow these steps:
- Install the required Python packages: langchain, openai, and weaviate-client.
- Define your relevant environment variables in a .env file.
- Prepare a vector database as an external knowledge source by collecting, loading, chunking, embedding, and storing your data.
- Define the retriever component to fetch additional context based on semantic similarity.
- Augment the prompt with the additional context using a prompt template.
- Build a chain for the RAG pipeline, chaining together the retriever, the prompt template, and the LLM.
- Invoke the RAG chain with a user query to generate a retrieval-augmented completion.
By implementing the RAG pipeline, you can enhance the accuracy and contextual understanding of your LLMs, making them more effective in generating responses.
Practical AI Solutions for Middle Managers
If you want to evolve your company with AI and stay competitive, consider implementing Retrieval-Augmented Generation (RAG). This AI technique can redefine your way of work by providing more accurate and contextual answers while reducing factual inaccuracies.
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