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Welcome to the blog…
Table Of Contents
- Introduction
- Retrieval Augmented Generation
- How We Can Use LangChain To Chat With Our Data
- Summary
- References
Introduction
In this post, we discuss How We Can Use LangChain To Chat With Our Data. the steps that we have to follow when we are working with chat with our data using LangChain.
Retrieval Augmented Generation
Retrieval augmented generation refers to a powerful approach that combines retrieval-based methods with generative models to enhance the quality and relevance of generated content.
How We Can Use LangChain To Chat With Our Data
To chat with our data we have to follow.
1. Document Loading
- First, we have to use Lang chain document loaders to load data from a variety of existing sources.
- Use document loaders to load data from a source as Documents. A Document is a piece of text and associated metadata. For example, there are document loaders for loading a simple
.txt file, for loading the text contents of any web page, or even for loading a transcript of a YouTube video. - https://galaxyofai.com/documents-loading-for-chat-with-our-data-using-langchain-document-loaders/
2. Document Splitting
- Document splitting is used to split our full documents into semantically meaningful chunks.
- It is helpful when we are working with big documents or big data files.
- When we want to deal with long pieces of text, it is necessary to split up that text into chunks.
- As simple as this sounds, there is a lot of potential complexity here. Ideally, we want to keep the semantically related pieces of text together. What “semantically related” means could depend on the type of text.
- https://galaxyofai.com/documents-splitting-for-chat-with-our-data-using-langchain-text-splitters/
3. Vector stores And Embeddings
- Vector stores and embeddings are techniques that are used in natural language processing.
- One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors
- (NLP) and machine learning to represent and store textual data in a numerical format.
- Both methods play the most important roles in various NLP tasks, including information retrieval, document similarity analysis, and text classification.
4. Retrieval
- This is important at question time when we got a question that comes in and we have to retrieve the most relevant splits.
- A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
- A retriever does not need to be able to store documents, only to return (or retrieve) them.
5. Question Answering
- After retrieval of meaningful data from all the given data, we ask questions and get the answers from that retrieval information.
6. Chat
- In last, we create a fully-fledged chatbot that can chat like a human and give answers from our given data.
And our AI chatbot is ready with our data using LangChain.
To chat with pdf follow this post,
Summary
So this is an overview of the full implementation. we will implement all the above steps as we go further.
Read for more learning,