If you are interested in building your very own personal assistant using artificial intelligence from scratch you might be interested in learning more about how you can use AI combined with retrieval augmented generation (RAG) and Langchain. The complete process is kindly demonstrated by James Briggs who takes you through each process step-by-step.
The tutorial involves the use of OpenAI’s GPT 3.5 model and the Lang chain library, with the primary objective of this endeavor is to construct an AI chatbot capable of answering questions about recent events or internal documentation within an organization or personal documentation allowing it to answer questions related to a specific topic or topics subset. A task that conventional models like ChatGPT 3.5 or ChatGPT4 may struggle with.
The RAG pipeline is a critical component in this process. It functions by taking a query, feeding it into the language model (LM), and generating an output. This pipeline is particularly beneficial for addressing questions about recent developments or specific internal information that the LM has not been trained on. The Lang chain library, compatible with GPT 3.5 and GPT4 models from OpenAI, is a valuable tool in building such a chatbot.
How to build a personal AI assistant
The construction of the chatbot involves initializing a chat model, adding user queries and system prompts, and then feeding these into the chat model to generate responses. However, this process is not without its challenges. One such challenge is the occurrence of ‘hallucinations’ in LMs, where the model generates incorrect or nonsensical responses because it is relying solely on the knowledge it learned during training.
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To counteract this limitation, the RAG pipeline is employed to connect the LM to an external knowledge base. This knowledge base can be updated and managed independently of the LM, providing a dynamic and adaptable source of information for the chatbot. While the video demonstrates how to manually insert context into the chatbot’s prompts to improve its responses, it acknowledges that this is not a practical solution for large-scale applications.
Instead, the RAG pipeline is suggested as a more efficient method to automatically retrieve and insert relevant context from a large knowledge base. This process is made possible through the use of Pinecone, a vector search engine. By setting up a vector database using Pinecone, this database can serve as a knowledge base for the chatbot, providing a rich source of information for the chatbot to draw from.
The tutorial demonstrates the significant improvements that the RAG pipeline can bring to the chatbot’s ability to answer complex or specific questions. By retrieving and incorporating relevant context from the knowledge base, the chatbot can provide more accurate and relevant responses, enhancing its utility and effectiveness.
What is Retrieval Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a machine learning architecture that combines the strengths of both retrieval-based and generative models. In natural language processing, retrieval-based models are good at selecting relevant information from a large dataset but may lack the ability to generate coherent and contextually relevant responses. On the other hand, generative models can produce fluent text but might struggle to incorporate factual or specific information that exists in external databases or text corpora.
In RAG, the process typically involves two key steps:
- Retrieval Step: When given a question or prompt, the model queries a database or corpus to retrieve a set of documents or passages that are likely to contain relevant information. This is often done using an information retrieval algorithm like BM25 or even a neural retrieval mechanism.
- Generation Step: The retrieved documents are then fed as additional context to a generative model, which synthesizes the information to produce a coherent and contextually relevant answer or text.
The integration of these two steps can happen in various ways. For example, in a “RAG-Token” setup, each token generated during the decoding phase can trigger a new retrieval action, while in a “RAG-Sequence” setup, a single initial retrieval might be used for generating the entire sequence.
The advantage of RAG is that it allows the model to pull in real-time information, which can be particularly useful for question-answering tasks that require external knowledge. It also facilitates a more modular approach to NLP, separating the concerns of information retrieval and text generation.
The creation of a chatbot using RAG, OpenAI’s GPT 3.5 model, and the Lang chain library is a complex but rewarding process. Despite the challenges posed by hallucinations in LLMs and the limitations of manual context insertion, the use of the RAG pipeline and an external knowledge base can significantly enhance the chatbot’s performance. This process, as demonstrated by James Briggs, provides a valuable guide for those interested in the field of AI and chatbot creation.
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