Analyzing Vulnerability Assessment Reports using LangChain
As powerful as ChatGPT and the OpenAI API are, they currently have a significant limitation—the token window. This window determines how many characters can be exchanged in a complete message between the user and ChatGPT. Once the token count exceeds this limitation, ChatGPT may lose track of the original context, making the analysis of large bodies of text or documents challenging.
Enter LangChain—a framework designed to navigate around this very hurdle. LangChain allows us to embed and vectorize large groups of text.
Important note
Embedding refers to the process of transforming text into numerical vectors that an ML model can understand and process. Vectorizing, on the other hand, is a technique to encode non-numeric features as numbers. By converting large bodies of text into vectors, we can enable ChatGPT to access and analyze vast amounts of information, effectively turning the text into a knowledgebase...
