Next, now that we have small chunks of text we need to create embeddings for each piece of text and store them in a vectorstore. If you want to change how to the text is split, you should change these lines text_splitter = RecursiveCharacterTextSplitter()ĭocuments = text_splitter.split_documents(raw_documents) Create embeddings and store in vectorstore The lines below are responsible for this. In order to split up the text, we will need to initialize a text splitter and then call it on the raw documents. This is necessary in order to make sure we only pass the smallest, most relevant pieces of text to the language model. In addition to just loading the text, we also need to make sure to chunk it up into small pieces. loader = UnstructuredFileLoader("state_of_the_union.txt") If you want to change the logic for how the documents are loading, this is the line of code you should change. The line below contains the line of code responsible for loading the relevant documents. If you want to contribute, feel free to open a PR directly or open a GitHub issue with a snippet of your work. See here for existing example notebooks, and see here for the underlying code. Ideally, we will add the loading logic into the core library. At the very least, we hope to get a lot of example notebooks on how to load data from sources. This object is pretty simple and consists of (1) the text itself, (2) any metadata associated with that text (where it came from, etc).īecause there are so many potential places to load data from, this is one area we hope will be driven a lot by the community. Again, because this tutorial is focused on text data, the common format will be a LangChain Document object. This section dives into more detail on the steps necessary to ingest data.įirst, we need to load data into a standard format. We will also briefly touch on deployment of this chatbot, though not spend too much time on that (future post!) Given the standalone question and the relevant documents, we can use a language model to generate a response Using the embeddings and vectorstore created during ingestion, we can look up relevant documents for the answer This is necessary because we want to allow for the ability to ask follow up questions (an important UX consideration). Combine chat history and a new question into a single standalone question.Again, these steps are highly modular, and mostly rely on prompts that can be substituted out. This can also be broken into a few steps. Querying of Data Diagram of query process Vecstorstores help us find the most similar chunks in the embedding space quickly and efficiently. Load embeddings to vectorstore: this involves putting embeddings and documents into a vectorstore.This is necessary because we only want to select the most relevant chunks of text for a given question, and we will do this by finding the most similar chunks in the embedding space. Embed text: this involves creating a numerical embedding for each chunk of text.This is necessary because language models generally have a limit to the amount of text they can deal with, so creating as small chunks of text as possible is necessary. Chunk text: this involves chunking the loaded text into smaller chunks.This is one place where we hope the community will help out! Load data sources to text: this involves loading your data from arbitrary sources to text in a form that it can be used downstream.All of these steps are highly modular and as part of this tutorial we will go over how to substitute steps out. Walking through the steps of each at a high level here: Ingestion of data Diagram of ingestion process For how to interact with other sources of data with a natural language layer, see the below tutorials:Īt a high level, there are two components to setting up ChatGPT over your own data: (1) ingestion of the data, (2) chatbot over the data. There is an accompanying GitHub repo that has the relevant code referenced in this post. This blog post is a tutorial on how to set up your own version of ChatGPT over a specific corpus of data. It doesn’t know about your private data, it doesn’t know about recent sources of data. But while it’s great for general purpose knowledge, it only knows information about what it has been trained on, which is pre-2021 generally available internet data. Note: See the accompanying GitHub repo for this blogpost here.ĬhatGPT has taken the world by storm.
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