Quick Fix: Use text splitting and embedding techniques, such as those provided by LangChain’s RecursiveCharacterTextSplitter and OpenAIEmbeddings, to break down the large text into chunks that fit within ChatGPT’s context size. Store these chunks and their embeddings in a vector database and use them to retrieve the most relevant context for each query.
The Problem:
How does LangChain overcome the limited context size of GPT-3, enabling it to process and understand longer texts for tasks like question answering and summarization?
The Solutions:
Solution 1: LangChain’s Architecture
LangChain enables ChatGPT to handle long documents by employing the following process:
Text Chunking: LangChain divides the document into smaller chunks using techniques like RecursiveCharacterTextSplitter, ensuring that each chunk fits within ChatGPT’s context size.
Text Embedding: Each chunk is then converted into a numerical representation called an embedding using methods like OpenAIEmbeddings. These embeddings are stored in a vector database.
Relevant Chunk Retrieval: For a given query, LangChain searches the vector database to identify the most relevant chunks based on their embeddings.
Prompt Generation: The relevant chunks are then inserted into a prompt template, such as the one used by LangChain’s RetrievalQA. This template guides ChatGPT to use the provided chunks to answer the user’s question.
LLM Response: ChatGPT then generates an answer based on the information provided in the prompts, leveraging its language generation capabilities.
Q&A
How does LangChain handle the limited context size of ChatGPT?
—
LangChain splits documents into smaller chunks, creates embeddings, and finds the most relevant chunks for the query, using only those chunks for the context passed to the LLM.
What is a key component of LangChain’s solution?
—
LangChain uses vectorstores to store embeddings of text chunks, enabling efficient retrieval of relevant chunks for a given query.
How does LangChain incorporate ChatGPT’s NLP abilities?
—
LangChain uses automated prompting to pass relevant text chunks to ChatGPT for various NLP tasks, such as question answering and summarization.
Video Explanation:
The following video, titled "Workaround OpenAI's Token Limit With Chain Types - YouTube", provides additional insights and in-depth exploration related to the topics discussed in this post.
Twitter: https://twitter.com/GregKamradt Newsletter: https://mail.gregkamradt.com/signup Longer Prompts w/ LangChain - Get past your model's ...
