JSON agents with Ollama & LangChain

The text discusses the implementation of an open-source Mixtral agent that interacts with a graph database Neo4j through a semantic layer. It explains how LLMs can be enhanced by providing them with additional tools, such as Bing Search and Python interpreter in ChatGPT's paid version. The author has previously implemented a project allowing an LLM to interact with a graph database through a set of predefined tools, which he called a semantic layer. These tools augment the LLM by providing dynamic, real-time access to information, personalization through memory, and a sophisticated understanding of relationships through the knowledge graph. The text also covers the implementation of a JSON-based LLM agent using Mixtral 8x7b LLM and provides an example input for a recommender tool. It highlights the use of a system prompt that instructs the LLM on what the output should look like, including the JSON structure to define its input when it needs to use any available tools. The author also discusses defining tool inputs in the system prompt using Pydantic tool input definition and rendering them into a JSON object recognized by Mixtral. Finally, the text mentions that most of the work to implement the JSON-based agent was done by Harrison Chase and the LangChain team, and it is looking forward to more open source LLMs being fine-tuned as agents.