LangChain Document Question-Answering Webinar: Affordable Strategies for Effective Question Answering
This webinar discusses question answering strategies to achieve specific goals, focusing on affordable approaches like vector search and keyword search.
00:00:00 Experts in question-answering over documents introduce themselves. They discuss their projects and tools, highlighting the challenges of working with scientific papers and the need for efficient document retrieval and summarization.
📝 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.
00:08:09 LangChain Document Question-Answering Webinar: The speaker discusses the use of agents in answering questions by gathering evidence from research papers.
💡 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.
00:16:17 The video discusses the challenge of retrieving relevant documents for question answering and proposes two methods: generating a standalone question and doing classification on previous chat messages. The importance of selectively building the corpus is highlighted to prevent distractions.
⚙️ 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.
00:24:27 Summary: This webinar discusses different question answering strategies to achieve specific goals, focusing on affordable approaches like vector search and keyword search. The speaker also talks about customizing chatbots on Chatbase and the types of documents it supports.
🔑 Different objectives in question answering strategies.
💲 Importance of cost-efficiency in scaling.
💡 Customization options for chat bot interface.
00:32:35 Learn about the strategies used in LangChain Document Question-Answering Webinar to deal with hallucinations, generate code, and combine vector search with keyword search.
🔑 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.
00:40:43 LangChain Document Question-Answering Webinar: Strategies for evaluating and improving classification and generation prompts. Discussing hybrid approach, manual checking of document relevance, and experimentation with chatbot prompts.
📝 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.
00:48:52 LangChain Document Question-Answering Webinar: Discussion on the use of chat models and system prompts for better performance and customer satisfaction.
📚 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.
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