4 Methods of Question Answering in LangChain
Learn four different ways to do question answering in LangChain, including the load qha method.
00:00:00 Learn four different ways to do question answering in LangChain, including the load qha method. Build a PDF chatbot to ask questions about your PDFs.
📚 There are at least four ways to do question answering in LangChain.
💻 In this video, four different methods of question answering in LangChain are demonstrated.
📝 The first method showcased is called 'load qha' and it provides a generic interface for answering questions.
00:01:44 A tutorial on question answering in LangChain, covering four different methods. Includes an example with multiple documents and batch processing.
🔑 Question answering in LangChain allows for answering questions over a set of documents.
⚙️ By changing the chain type to map reduce, large language models can process document batches separately and return answers individually.
📊 In 2021, there were nearly 500,000 AI publications according to the LangChain question answering model.
00:03:28 Learn 4 methods of question answering in LangChain to process long PDF docs. Batch size matters and answers get refined in sequential batches. MapReduce chain returns answers at a higher score.
💡 Question answering in LangChain can be done using batch processing.
✨ The refine chain and memory rank chain are two methods used for question answering in LangChain.
⚡ The refine chain refines answers along the sequence of batches, while the memory rank chain assigns scores to each answer.
00:05:12 This video discusses four methods for question answering in LangChain, focusing on the retrieval QA method that retrieves relevant text chunks to improve efficiency and accuracy.
🔍 Retrieving relevant text chunks from a large document to improve language model efficiency.
🔀 Using retrieval QA chain to find the most similar text chunks to a given question.
💡 Creating a chain of language models to answer questions based on the retrieved text chunks.
00:06:54 Four methods to do question answering in LangChain, including different embedding methods, text splitters, vector stores, and retrievers.
🔑 Different options for embedding methods, text splitters, vector stores, and retrievers in LangChain.
🛠️ You can choose different models, character or token-based text splitters, and different vector stores and retrievers.
🔍 The different search types include similarity search and MMR which optimizes for diversity in vectors.
00:08:39 Learn 4 methods of question answering in LangChain, including using the Vector store index wrapper and the conversational retrieval chain.
📝 The functionalities of LangChain are accessible through a simple interface using just three lines of code.
🔧 Users can customize the parameters of LangChain, such as the text splitter, embedding, and Vector store index creator.
💬 LangChain offers a conversational retrieval chain that combines chat history with the retrieval QA method.
00:10:23 4 methods of question answering in LangChain using conversational retrieval chain and language model, demonstrated with an example.
🔑 Conversational retrieval chain is used for question answering in LangChain.
💡 The chat history can be used as context for the language model to answer questions.
🔄 The answer to a previous query can be passed along with the current query.
Want to deep dive into this video?
