Chatbots with RAG: LangChain Full Walkthrough

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

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

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

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

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

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

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

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

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

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

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

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

💬 LM chain in the context of line chain refers to a specific type of chain within the line chain framework.

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

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

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

🔗 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.

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

🛠️ 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.

⚡ 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.