"How to give GPT my business knowledge?" - Knowledge embedding 101

Summary

This video script explores the concept of knowledge base embedding, a technique that enables AI to understand and utilize a company's specific domain knowledge for informed decision-making. It contrasts with fine-tuning, which is more about modifying AI behavior rather than retrieving specific data. The script details how to implement this using vector databases and embedding models to create a searchable knowledge base that can be leveraged by AI to generate responses mimicking top performers' best practices, thus enhancing business automation and workflow.

Takeaways

• 🤖 Embedding is used to create a company's knowledge base, acting like an AI brain that understands the company's operations and uses this knowledge to inform decisions.
• 🔍 Fine-tuning large language models is different from knowledge-based embedding; the former is for specific behaviors, while the latter is for retrieving specific data and knowledge.
• 📚 Knowledge-based embedding involves searching relevant documents related to a user's question and feeding this content to a large language model to generate more accurate answers.
• 📈 Embedding represents how close each data point is to each other in a multi-dimensional space, typically using hundreds or thousands of dimensions.
• 🌐 Embedding models like Open AI's can turn data into vector dimensions, which are lists of numbers that represent similarities between data points.
• 🗂️ Vector databases like Pinecone or Chroma are specialized in storing and retrieving vector data, which is crucial for performing similarity searches.
• 💼 Knowledge base embedding is beneficial for business automation and workflow, as it can help standardize best practices and reduce reliance on individual team members' knowledge.
• 📧 The process involves vectorizing data, such as customer interactions, and using it to train a large language model to mimic the responses of top performers.
• 🛠️ A practical example given is automatically drafting customer emails based on company best practices by vectorizing email chains and using them to guide the language model.
• 💡 The video also demonstrates how to implement this concept using Python, with steps including loading and vectorizing CSV data, creating a similarity search function, and generating responses based on the search results.

Q & A

What is the main purpose of using embedding to create a company's knowledge base?

The main purpose of using embedding to create a company's knowledge base is to have an AI system that understands the company's best practices and domain-specific knowledge, which can then be used to power day-to-day decisions and improve operational efficiency.

What is the difference between fine-tuning a large language model and using knowledge base embedding?

Fine-tuning a large language model is useful when you want the model to behave in a certain way, such as mimicking a specific person's speech style. In contrast, knowledge base embedding is particularly useful for retrieving specific data and knowledge within a domain, such as a company's standard operating procedures or private data.

How does a knowledge base embedding help in responding to user inquiries?

Knowledge base embedding helps by searching for relevant documents related to the user's questions within the company's data, and then feeding both the user's inquiry and the relevant content to the large language model to generate an informed and contextually accurate response.

What is embedding in the context of AI and data representation?

In AI, embedding is a type of vector representation that indicates how close each data point is to each other in a multi-dimensional space. It is a way to represent data points in hundreds or thousands of dimensions, capturing complex relationships and similarities between data points.

Can you explain the role of a Vector database in the context of knowledge base embedding?

A Vector database is used to store and retrieve vector representations of data efficiently. It is essential for performing similarity searches and finding relevant information in a company's documents based on a user's inquiry when using knowledge base embedding.

How does the process of vectorizing data work in the context of knowledge base embedding?

Vectorizing data involves using an embedding model to convert data into a vector format, which is a list of numbers representing the data in a multi-dimensional space. This process allows for the efficient storage and retrieval of data in a Vector database and enables similarity searches.

What is the significance of creating a knowledge base embedding for business automation and workflow?

Creating a knowledge base embedding is significant for business automation and workflow as it helps to centralize and share knowledge that might otherwise be trapped in individual's heads. It enables the AI to mimic the behavior of top performers, thus improving consistency and efficiency across the team.

Can you provide an example of how knowledge base embedding can be used to draft customer emails based on company best practices?

Knowledge base embedding can be used to draft customer emails by taking past email chains where top salespeople responded to customer inquiries. By vectorizing these emails and performing a similarity search when a new customer message is received, the system can generate a response that mimics the best practices of top performers.

What are the steps involved in implementing a knowledge base embedding system for responding to customer emails?

The steps include loading and vectorizing the CSV data, creating a function for similarity search, setting up the large language model with appropriate prompts, and generating a response by feeding the search results into the model.

How can a company share the knowledge base embedding system with other team members?

A company can share the knowledge base embedding system by creating a user interface, such as a web app using Streamlit, where team members can input customer messages and receive generated responses based on the company's best practices.

What are some alternative platforms to build knowledge base embedding workflows other than custom coding?

Alternative platforms to build knowledge base embedding workflows include no-code platforms like Relevance AI, which allow users to upload data, vectorize it, and create AI applications without the need for custom coding.