LangChain Document Question-Answering Webinar: Affordable Strategies for Effective Question Answering

📝 The webinar is about question answering over documents.

💡 The speakers introduce themselves and their projects related to language models and question answering.

🔍 One speaker shares their approach of using language models to answer questions by embedding and summarizing documents.

💡 LangChain has effective ways to split text and summarize it to remove the need for clean chunking.

🔍 The agent created using LangChain is capable of gathering evidence by searching and reading papers from various sources.

💰 The cost of using the agent-based model with GPT4 is high, but it allows for high-quality answers at a low price.

⚙️ The problem with using a chatbot for question-answering is that it retrieves irrelevant sources. GPT 3.5 and GPT4 are smart enough to ignore irrelevant sources and provide simpler explanations.

📚 One challenge in maintaining context is the length of the conversation. Currently, only the last 10 messages are considered, which may result in incomplete responses.

🔎 To improve question-answering, embedding the whole conversation and using selective retrieval based on relevant chat messages or generating standalone questions are possible approaches.

🌳 Tree-based and LLM-based search approaches can be used to organize documents and improve the retrieval of relevant information.

🔑 Different objectives in question answering strategies.

💲 Importance of cost-efficiency in scaling.

💡 Customization options for chat bot interface.

🔑 Preventing hallucinations in language models can make them more matter-of-fact, but it can also hinder code generation.

🔍 Combining vector search and keyword search can improve document retrieval in language models.

🧪 Evaluation is a crucial but challenging aspect of language model development, with the need to assess both retrieval and generation performance.

📝 Creating a classification prompt to evaluate the generated answer.

🔍 Using a hybrid approach for retrieval and evaluating the relevance of documents.

💡 Discussing the evaluation of complex q&a models and the importance of balancing cost and user experience.

🔒 Exploring the demand for local models for privacy concerns.

📚 There are no good open source models available for hosting locally.

💬 Using system prompts in chat models helps maintain character and prevent prompt hacking.

💡 The ideal text chunk size for question answering is around 100-150 words.