The sharp use of AI in chatbots is itself a big achievement because have you ever noticed that the two or more chatbot types are never the same in terms of functions, interface, purpose, and more. However, the rise of a chatbot is also undeniable. In conclusion, that's all it takes to make a personalized chatbot.The rise of artificial intelligence is bringing up many new developing ideas. Run kill 1 in the Repl's shell to force a reboot. Why isn't my OpenAI API key working in Replit? Storing it in a directory puts less stress on the system to load and cache. It takes a lot of time and processing power to generate the vector store. What is the advantage of creating the vectorStore directory over running the store initialization script?
The entirety of the output GPT gives you is based entirely on previous knowledge. OpenAI LLMs don't have internet access and can't provide information such as the current date and time. No actual fine-tuning or training processes are taking place.Ĭan GPT directly scrape text from a site? What is the advantage of using LangChain over referencing the documentation URLs directly? The data you pass to your chatbot will get compiled into a single vector store file and be used as context when generating responses. Is the process here actually training/fine-tuning the AI model, or just adding documents for context?
CREATE CHATBOT WITH PERSONALITY PDF
Passing a non-utf-8 file like a PDF will either make your chatbot speak to you in non-utf-8, or just be completely useless. Theoretically you can, but try to use utf-8 files. The relevant documents, the prompt, and (optionally) the conversation history is passed to Langchain to predict and generate a response.Ĭan I use other file formats like.A similarity search is conducted through the vector store to find any relevant Documents regarding the prompt.(on first run) We initialize our OpenAI LLM, PromptTemplate, LLMChain, and load our vector store from the vectorStore directory.Here's what the process looks like when a response is generated (see lib/generateResponse.js): Whenever a response is generated, a similarity search is run through the vectorStore/hnswlib.index file. These features are then represented as high-dimensional vectors, where each dimension corresponds to a particular feature. In AI they are often used to represent features or attributes of data points.Ī vector store is typically constructed by taking a large corpus of text or other types of data and extracting features from each data point. Vectors are mathematical objects that have both magnitude and direction. After that, Langchain's omTexts method will populate the training folder.Ī vector store is a data structure used to represent and store large collections of high-dimensional vectors. The initialization script will iterate through and read all files of the desired format in the training folder. Folder names and depth don't matter since all files of the desired format will be iterated through. txt, make sure you specify it in script/initializeStore.js.Īfter specifying your file format, start uploading or creating files and folders in the directory. If you would like to use a different file format such as. At the moment, only markdown files will be used for training. Try to specify what each one does in the base prompt and give them a relevant name to make it easier for your LLM to understand.Įxcluding the base prompt, all the data you will be passing to your chatbot will be through the training directory. Prompt variables can take any name (preferably alphanumeric). Enter fullscreen mode Exit fullscreen mode
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