Enterprise AI: From large language models to Neo4j

What is RAG (Retrieval-Augmented Generation)?

This term refers to a software system that combines information retrieval with large language models. It allows documents to be retrieved and texts to be generated by LLMs. The generated texts are accurate because the queries submitted to the system access information from various data sources and databases. This gives chatbots access to internal company data and enables them to provide factual information.

RAG systems combine document search and response generation using LLMs. Modern LLMs can also solve different tasks. Large language models are used with RAG without fine-tuning for chatting with your own data. Retrieval augmented generation can be combined with all LLMs (programmatically or via API). Various databases and searches are used for RAG to display search results as text sections. To use knowledge graphs and SQL databases, an LLM generates the database query in the required query language (SQL, SPARQL, Cypher). With the help of open source libraries (LlamaIndex and LangChain), the databases can be connected to LLM and the vector database with embedding model and document importer. The latter implement the standard interfaces for large language models and related technologies and can be integrated with many providers.

RAG technology offers several advantages:

1. The quality of the generated responses is high. They are up-to-date and contextually accurate.
2. Introducing new data into the LLM is cost-effective.
3. User confidence improves because the information is presented with real source references.
4. RAG improves control of AI applications for developers. They can monitor and modify the LLM's information source to adapt it to changing requirements.
5. The model parameters are not adjusted, so the model can be moved across many use cases.

In addition to the advantages, there are also disadvantages:

1. RAG requires high computational effort and presents difficulties in validating responses.
2. Text generation takes longer due to the multi-stage processing of information.
3. RAG systems are only effective if the data is current and relevant.
4. When updating a knowledge base, developers should initiate the conversion of new data into vectors and update the vector database.
5. When comparing RAG vs. fine-tuning, the former is lightweight. The LLM is used in its original form. Since it can process natural language, costs and time are saved. It can be easily kept up to date with the latest knowledge.