4 Methods of Question Answering in LangChain

📚 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.

🔑 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.

💡 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.

🔍 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.

🔑 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.

📝 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.

🔑 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.