"How to give GPT my business knowledge?" - Knowledge embedding 101
Science & Technology
Introduction
In this article, we’ll explore how to use knowledge embedding to create a knowledge base for your company. This method is akin to building an AI brain that understands the best practices that drive your company's operations. Leveraging this AI brain can significantly enhance day-to-day decision-making processes.
Understanding Knowledge Embedding
A couple of days ago, I discussed the ins and outs of fine-tuning a large language model (LLM). While fine-tuning is a valuable method, it's only one of several ways you can use LLMs to cater to specific needs, such as responding in a certain voice or style. In contrast, knowledge embedding serves a different purpose—it’s about retrieving specific data effectively.
When you fine-tune a large language model, it modifies its behavior based on examples you provide. For instance, if you want the model to emulate a specific individual, such as Donald Trump, fine-tuning would be an effective approach. However, fine-tuning is not suited for retrieving specific domain knowledge, which is where knowledge embedding shines.
The Process of Knowledge-Based Embedding
Knowledge-based embedding works like this: when a user poses a question, rather than sending their query directly to a general LLM that lacks your company-specific data, the system first searches for relevant documents related to the user’s question.
For example, if a user asks about pricing for a team of three, the system will gather all data from your pricing page and input that information along with the user’s question into the large language model. This enables the model to produce more accurate and contextually relevant responses based on the real data supplied.
What Are Embeddings?
Let’s break down the terms—what are embeddings? In simple terms, embeddings represent data points in a multi-dimensional space. For instance, imagine four images: two of trees and two of animals. You can categorize these images along two dimensions—whether they are trees or animals and whether they are large or small—thus determining their relationship based on proximity within those dimensions.
Embeddings allow us to represent data across hundreds or thousands of dimensions. Pre-trained embedding models, such as OpenAI’s embedding or various open-source alternatives, can convert text, images, audio, and video into vectors—essentially lists of numbers that signify how similar or different data points are in relation to one another.
Vector Databases
To effectively manage these high-dimensional vectors, we need a special type of storage solution called a vector database (e.g., Pinecone, Chroma). After converting user inquiries into vector representations, we can leverage a vector database to perform similarity searches, determining which stored data points are closest to the user's query, thus allowing us to retrieve relevant information efficiently.
Why Is Knowledge-Based Embedding Critical for Business?
In today's business landscape, a large percentage of crucial knowledge resides within individuals rather than being documented and shared across teams. This can cause significant knowledge gaps, especially when key personnel leave and take valuable insights with them. Traditional solutions often involve creating lengthy Standard Operating Procedures (SOPs) that become outdated quickly and are rarely referenced.
With knowledge-based embedding, however, we can aggregate past communications and customer interactions to create a dynamic knowledge base. This not only standardizes responses for various scenarios but also empowers junior employees to learn from experienced colleagues by providing them access to best practice examples.
A Case Study: Automating Customer Response Emails
Let's delve into a practical application of this concept: automating customer emails based on your company's best practices. Here’s a brief overview of the steps involved:
- Prepare your knowledge base data by organizing communications and responses.
- Vectorize the data using a model like OpenAI’s text embedding.
- Create a similarity search function to find the best practice responses.
- Use a large language model to convert the retrieved best practices into personalized responses for customer inquiries.
You can implement this using Python and libraries like Streamlit, enabling the entire process to be accessible through a simple user interface.
With just a few more steps, one can deploy this application online, enhancing the responsiveness and effectiveness of customer communications.
Conclusion
Knowledge-based embedding offers a robust way to integrate and leverage organizational knowledge in day-to-day operations. This technology not only streamlines communications but also bridges knowledge gaps within teams, allowing businesses to operate more efficiently and effectively.
Keywords
- Knowledge embedding
- Large language model (LLM)
- Fine-tuning
- Embeddings
- Vector database
- Customer response automation
- Business knowledge management
FAQ
1. What is knowledge embedding? Knowledge embedding is a method allowing organizations to structure and retrieve specific domain knowledge efficiently by using vector representations of data.
2. How does knowledge embedding differ from fine-tuning? Fine-tuning customizes a large language model's behavior, while knowledge embedding focuses on enhancing data retrieval from existing knowledge bases.
3. What are embeddings? Embeddings are high-dimensional representations of data points that capture their relationships based on proximity in a multi-dimensional space.
4. Why are vector databases important? Vector databases store and retrieve vector representations of data efficiently, enabling quick similarity searches to find relevant results for user queries.
5. Can knowledge-based embedding improve customer support? Yes, knowledge-based embedding can automatically generate responses to customer inquiries by referencing a database of best practices, improving efficiency and consistency in support.
