š” Build a chatbot using retrieval augmented generation (RAG) with OpenAI's GPT 3.5 model and the LangChain library.
š RAG pipeline allows the chatbot to answer questions about recent events or internal documentation that other language models cannot.
āļø LLMs may not have knowledge about specific topics, leading to incorrect or made-up answers. RAG eliminates this limitation.
š§ LangChain is a useful tool for building complex AI systems, like chatbots, as it provides additional components that can easily be integrated.
š The chat log structure of LangChain is similar to that of openai chat models, with a system prompt and user queries.
š¤ By appending the AI message to the chat log, the conversation can be continued. LangChain relies on the conversational history to generate responses.
š” A language model (LM) like LangChain can experience hallucinations because it can only rely on the knowledge from its training data.
š§© The purpose of RAG is to address the limitations of LMs by providing access to external knowledge sources.
š¦ The middle box represents a connection to the external world, allowing access to various functions.
š§ Parametric knowledge refers to the knowledge stored within the model parameters, while source knowledge is any information inserted into the model via the prompt.
š” Source knowledge can be added to the language model by inserting it into the prompt, providing additional context and improving model performance.
š The video discusses using the source knowledge approach to gather information about LangChain and LM chain.
š¬ LM chain in the context of line chain refers to a specific type of chain within the line chain framework.
š The video explores the retrieval component of using the RAG model to automatically gather information from a large dataset.
š We need to align the dimensions of the vectors with the model we're using for embedding.
š After initializing the index, we can connect to it and check that the vector count is zero.
š We create embeddings for documents and add them to the Pinecone index, extracting key information about each record.
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.
š The video discusses the use of RAG in chatbots and how it enhances retrieval performance and provides accurate answers.
š 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.
ā” 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.
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