Question Answering Over Your PDF Files in LangChain
Large Language Models or LLMs such as GPT4 can answer questions about a lot of topics. However this knowledge is limited to the data it was trained on. If you’ve used ChatGPT, you know that LLMs, especially GPT4, are very good at understanding language, following instructions and basic reasoning. However, as the question above suggests, they clearly need help with up-to-date knowledge (this includes your specific/proprietary data). To overcome this, a technique called retrieval augmented generation (RAG) has become popular. In this technique, documents are retrieved and then inserted into the prompt, and the LLM is instructed to only respond based on those documents.
This helps both in giving the language model additional context as well as in keeping it grounded[1]. In this article, we’ll cover how to use LangChain to chat with your data. To keep things focused, we’ll focus on working with PDF files. Once you’ve mastered working with PDF files, you should be able to work with many other types of data. LangChain provides many different types of data loaders (see below 👇🏾).
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