Unleashing the Power of Large Language Models: Building an AI Chatbot for Private Knowledge Base Queries
Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling powerful AI chatbots that can provide accurate and context-aware responses. In this article, we’ll explore the step-by-step process of building an AI chatbot that leverages a private knowledge base to deliver precise answers to user queries. Let’s dive in and unlock the potential of LLM-based chatbots.
Let’s delve into the process:
Step 1: Structure your internal documents
Begin by breaking down your entire knowledge base into smaller, manageable chunks. Each chunk should represent a distinct piece of information that can be queried. This data can come from various sources, such as Confluence documentation or supplemented PDF reports.
Step 2: Embed the text corpus
Utilize an embedding model to transform each chunk of text into a vector representation. This embedding process captures the essence of the information and encodes it into a numerical format suitable for querying.
Step 3: Store vector embeddings
Save all the vector embeddings obtained from the embedding model in a Vector Database. This database will serve as the repository for your encoded knowledge base.
Step 4: Save text representations
Ensure you save the original text that corresponds to each vector embedding. This text will be necessary to retrieve the relevant information during the querying process.
Now, let’s construct the answer to a question or query:
Step 5: Embed the question
Use the same embedding model employed earlier to transform the question you want to ask into a vector representation.
Step 6: Run a query
Query the Vector Database using the vector embedding generated from the question. Determine the number of context vectors you want to retrieve, which will represent the relevant chunks of information to aid in answering the query.
Step 7: Retrieve similar vectors
Perform an Approximate Nearest Neighbor (ANN) search in the Vector Database to find the most similar vectors to the query embedding. Retrieve the previously selected amount of context vectors, which will contain the most relevant information.
Step 8: Map vectors to text chunks
Associate the retrieved vectors with their corresponding text chunks. This mapping will link the numerical representations to the actual content they represent.
Step 9: Generate the answer
Pass the question and the retrieved context text chunks to the LLM via a prompt. Instruct the LLM to utilize only the provided context for generating the answer. It is important to perform prompt engineering to ensure the generated answers align with the expected boundaries. For instance, if the retrieved context does not contain relevant information, the LLM should avoid fabricating answers.
To create a functional chatbot, you can present a web user interface (UI) that offers a text input box for users to interact with. After going through steps 1 to 9, display the generated answer on the UI. This approach is commonly used in chatbots that rely on a private knowledge base.
Vector databases and Large Language Models are revolutionizing the way we handle and retrieve complex data structures. These powerful tools allow for efficient storage, retrieval, and manipulation of vector embeddings, enabling advanced search capabilities and context-based information retrieval. To delve deeper into the fascinating world of vector databases and their applications, follow my articles “Exploring the Power of Vector Databases: Unleashing the Potential Beyond Large Language Models” and “Unleashing the Power of Vector Databases: A Step-by-Step Guide to Retrieval and Storage of Vector Embeddings.”
By harnessing the power of LLMs, we can construct AI chatbots that tap into private knowledge bases, delivering precise and context-aware responses. Through careful preparation of the knowledge base, effective embedding techniques, and thoughtful prompt engineering, we can create chatbots that provide valuable insights and assistance to users. Embrace the possibilities of LLM-based chatbots and unlock the full potential of your organization’s internal knowledge base.
