Develop RAG applications and don’t share your private data with anyone! — In the spirit of Hacktoberfest, I decided to write a blog post using a vector database for change. The main reason for that is that in spirit of open source love, I have to give something back to Philip Vollet in exchange for all the significant exposure he provided me…

Seamlessy implement information extraction pipeline with LangChain and Neo4j — Extracting structured information from unstructured data like text has been around for some time and is nothing new. However, LLMs brought a significant shift to the field of information extraction. If before you needed a team of machine learning experts to curate datasets and train custom models, you only need…

Leveraging knowledge graphs to power LangChain Applications — RAG applications are all the rage at the moment. Everyone is building their company documentation chatbot or similar. Mostly, they all have in common that their source of knowledge is unstructured text, which gets chunked and embedded in one way or another. However, not all information arrives as unstructured text. …

Learn how to customize LangChain’s wrapper of Neo4j vector index — Neo4j was and is an excellent fit for handling structured information, but it struggled a bit with semantic search due to its brute-force approach. However, the struggle is in the past as Neo4j has introduced a new vector index in version 5.11 designed to efficiently perform semantic search over unstructured…

Streamlining data ingestion and querying in Retrieval-Augmented Generation Applications — If you have been on vacation for the past six months, first of all, congratulations. Secondly, you should know that since the introduction of ChatGPT-like large language models (LLM), the technology ecosystem has changed dramatically. Nowadays, it’s all about retrieval-augmented generation (RAG) applications. …

Integrate the newly added vector index into LangChain to enhance your RAG applications — Since the advent of ChatGPT six months ago, the technology landscape has undergone a transformative shift. ChatGPT’s exceptional capacity for generalization has diminished the requirement for specialized deep learning teams and extensive training datasets to create custom NLP models. This has democratized access to a range of NLP tasks, such…

LLMs occasionally mess up the relationship direction in Cypher statements, but we can fix that — In the time of LLMs, it is becoming increasingly popular to implement Retrieval-Augmented Generation (RAG) applications, where you feed additional information to an LLM at query time to increase its ability to generate valid, accurate, and up-to-date answers. …

Understanding data points through the context of their relationships — This is the fourth blog post of Neo4j’s NaLLM project. We started this project to explore, develop, and showcase practical uses of these LLMs in conjunction with Neo4j. As part of this project, we will construct and publicly display demonstrations in a GitHub repository, providing an open space for our…

Retrieve information that spans across multiple documents — This is the third blog post of Neo4j’s NaLLM project. We started this project to explore, develop, and showcase practical uses of these LLMs in conjunction with Neo4j. As part of this project, we will construct and publicly display demonstrations in a GitHub repository, providing an open space for our…