Leveraging Documents and Data to Create a Custom LLM Chatbot
Apr 5, 2024
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Calvin Hendryx-Parker, Co-founder and CTO of Six Feet Up, talks about customizing a LLM chatbot for accessing farm research data stored as PDFs spanning 50 years. He discusses tools like LangChain and ChromaDB for vectorizing data, as well as creating a chatbot from a conference website using Django and Python prompt-toolkit.
Customizing LLM chatbots for specific domains involves using tools like Langchain and ChromaDB to process unstructured data effectively.
Parsing PDFs with unstructured data requires strategies like cleaning, extracting, and structuring data to ensure accurate query results.
Augmenting LLMs with retrieval augmented generation (RAG) models improves contextual understanding and response quality by indexing relevant documents and creating personalized interactions.
Deep dives
Using LLMs to Develop AI Chat Interfaces
Developing AI-powered chat interfaces powered by large language models (LLMs) involves customizing models for specific domains, such as a project for a family-owned seed company to provide access to years of farm research stored in PDFs. Tools like Langchain and chromaDB are employed to overcome obstacles in processing unstructured data, achieving more accurate and contextually rich responses.
Challenges of Parsing Unstructured Data
Parsing PDFs with unstructured data poses challenges like hidden text, visual indicators, and conflicting dependencies. Tools like LAMA index initially used for parsing may later be replaced by more robust frameworks like Langchain. Strategies include cleaning, extracting, and structuring data to ensure effective query results.
Enhancing Context and Response Quality
Augmenting LLMs with retrieval augmented generation (RAG) models enables better contextual understanding and response quality. Techniques involve indexing relevant documents, creating context windows for personalized interactions, and on-the-fly rating to refine model responses. Tools like ChromeDB help in storing and querying data effectively.
Conclusion
The podcast delves into the intricacies of developing AI chat interfaces, handling challenges in unstructured data processing, and enhancing response quality through contextual cues. The discussion showcases the evolving landscape of using LLMs and related tools to tailor AI solutions for specific business needs.
The Importance of User Experience in Chat Interfaces and GPT Tools
Chat interfaces and GPT tools provide users with a familiar and easy-to-use experience, resembling a chat box for interactions. Despite the simplicity of the user interface, the underlying technology differs significantly. Chat GPT gained popularity due to its accessible nature, allowing users to interact with GPT-based tools effortlessly. Improved customer service experiences are highlighted, where advanced models enhance responses by understanding queries and retrieving relevant information efficiently, leading to delightful interactions for users.
The Significance of Asking the Right Questions and Understanding Customer Needs
Prompt engineering plays a crucial role in customer interactions, requiring companies to design questions that guide customers effectively. Understanding the audience and tailoring responses based on their expertise level and requirements are essential for a positive user experience. The need to train customers to utilize chat tools effectively is emphasized, with a focus on improving prompt generation and delivering accurate information. Additionally, maintaining a balance between advanced technology deployment and ensuring user-friendly experiences remains a challenge, highlighting the importance of human oversight in critical decision-making processes.
How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.
Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares a recent project for a family-owned seed company that wanted to build a tool for customers to access years of farm research. These documents were stored as brochure-style PDFs and spanned 50 years.
We discuss several of the tools used to augment a LLM. Calvin covers working with LangChain and vectorizing data with ChromaDB. We talk about the obstacles and limitations of capturing documentation.
Calvin also shares a smaller project that you can try out yourself. It takes the information from a conference website and creates a chatbot using Django and Python prompt-toolkit.
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Topics:
00:00:00 – Introduction
00:02:21 – Background on the project
00:03:51 – Complexity of adding documents
00:09:01 – Retrieval-augmented generation and providing links
00:13:46 – Updating information and larger conversation context
00:18:08 – Sponsor: Mailtrap
00:18:43 – Working with context
00:21:02 – Temperature adjustment
00:22:07 – Rally Conference Chatbot Project
00:26:20 – Vectorization using ChromaDB
00:32:49 – Employing Python prompt-toolkit
00:35:07 – Learning libraries on the fly
00:37:38 – Video Course Spotlight
00:39:00 – Problems with tables in documents
00:42:30 – Everything looks like a chat box
00:44:26 – Finding the right fit for a client and customer
00:49:05 – What are questions you ask a new client now?
00:51:54 – Canada Air anecdote
00:56:20 – How do you stay up to date on these topics?
01:01:03 – What are you excited about in the world of Python?
01:03:22 – What do you want to learn next?
01:04:58 – How can people follow your work online?
