Chatbots with RAG: LangChain Full Walkthrough

Learn how to build a chatbot using retrieval augmented generation (RAG) from start to finish.

00:00:00 Learn how to build a chatbot using retrieval augmented generation (RAG) from start to finish. This method allows the chatbot to answer questions about recent events and internal documentation, which traditional language models cannot do.

šŸ’” Build a chatbot using retrieval augmented generation (RAG) with OpenAI's GPT 3.5 model and the LangChain library.

šŸ” RAG pipeline allows the chatbot to answer questions about recent events or internal documentation that other language models cannot.

āš™ļø LLMs may not have knowledge about specific topics, leading to incorrect or made-up answers. RAG eliminates this limitation.

00:05:08 A walkthrough of using the LangChain tool for building chatbots with the RAG pipeline. It demonstrates how to initialize the chat model, format the chat log, and append AI messages to continue the conversation.

šŸ”§ LangChain is a useful tool for building complex AI systems, like chatbots, as it provides additional components that can easily be integrated.

šŸ“ The chat log structure of LangChain is similar to that of openai chat models, with a system prompt and user queries.

šŸ¤– By appending the AI message to the chat log, the conversation can be continued. LangChain relies on the conversational history to generate responses.

šŸ’” A language model (LM) like LangChain can experience hallucinations because it can only rely on the knowledge from its training data.

šŸ§© The purpose of RAG is to address the limitations of LMs by providing access to external knowledge sources.

00:10:20 Chatbots with RAG: LangChain Full Walkthrough. This video explains how the RAG pipeline connects to the external world and enables the addition and modification of long-term memory in language models.

šŸ“¦ The middle box represents a connection to the external world, allowing access to various functions.

šŸ§  Parametric knowledge refers to the knowledge stored within the model parameters, while source knowledge is any information inserted into the model via the prompt.

šŸ’” Source knowledge can be added to the language model by inserting it into the prompt, providing additional context and improving model performance.

00:15:31 This video provides a walkthrough of using the RAG (Retrieval-Augmented Generation) model to generate concise summaries of text data. The speaker demonstrates how to use the RAG model to extract information about the LM chain in the context of Line Chain and discusses the process of setting up a knowledge base using a data set and a vector database. The video also introduces the concept of text embedding and explains how it is used in the RAG model.

šŸ“š The video discusses using the source knowledge approach to gather information about LangChain and LM chain.

šŸ’¬ LM chain in the context of line chain refers to a specific type of chain within the line chain framework.

šŸ” The video explores the retrieval component of using the RAG model to automatically gather information from a large dataset.

00:20:44 This video provides a walkthrough on using RAG to create chatbots. It covers dimension alignment, embedding models, initializing an index, and connecting the knowledge base to the language model.

šŸ”‘ We need to align the dimensions of the vectors with the model we're using for embedding.

šŸš€ After initializing the index, we can connect to it and check that the vector count is zero.

šŸ”— We create embeddings for documents and add them to the Pinecone index, extracting key information about each record.

00:25:56 LangChain Full Walkthrough: Discover the special features of llama2, a collection of pre-trained and fine-tuned language models that outperform open source models on most benchmarks. These models are optimized for dialogue use cases, designed to align with human preferences, and exhibit helpfulness and safety.

Llama2 is a collection of pre-trained and fine-tuned large language models developed and released by the authors of the work.

Llama2 models range in scale from 7 billion to 70 billion parameters and are optimized for dialogue use cases.

Llama2 models align with human preferences, enhancing their usability and safety.

00:31:05 Introduction to Rag-based chatbots and their implementation, including safety measures, iterative evaluations, and alternative approaches.

šŸ” The video discusses the use of RAG in chatbots and how it enhances retrieval performance and provides accurate answers.

šŸ› ļø Safety measures, such as specific data annotation and tuning, red teaming, and iterative evaluations, are implemented to prioritize safety considerations in the development of RAG models.

āš” The implementation of RAG with LangChain involves augmenting the prompt and using a simplified approach, which improves retrieval performance but may not be suitable for all queries.

Summary of a video "Chatbots with RAG: LangChain Full Walkthrough" by James Briggs on YouTube.

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