GenerativeAI with Redis LangChain & OpenAI

LangChain, makes it easy to develop applications that interact with a language model and external sources of data or computation. It does this by focusing on clear and modular abstractions for all the building blocks necessary to build; then it constructs commonly used “chains,” which are combinations of the building blocks. For example, the Conversational Retrieval Chain enables users to have a “conversation” with their data in an external store.

OpenAI language models were not trained on your company’s specific data, or certainly not tuned for it. If you want the chatbot to rely on it, you need to provide OpenAI with your data at runtime. The retrieval step fetches relevant data to the user’s query from Redis using Vector Similarity Search (VSS) and then pipes the data into the language model along with the original question. It asks the model to use only the provided sources – what we in AI circles call “context” – to answer the question.

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