A knowledge base organizes domain information so users or AI assistants can retrieve answers from questions, documents, facts, or other structured content. In web applications, it commonly becomes the source of grounded conversational responses. section 11.1 section 11.4
A production knowledge-base application combines authenticated access, document management, vector representations, search, and a conversational interface. Users can create separate collections, upload PDF or DOCX files, and query the selected collection through an AI assistant. section 11.1 section 11.1.1 section 11.4
A typical retrieval workflow proceeds as follows:
- Store questions, answers, or parsed document content together with their embeddings. section 5.4.3 section 11.1.1
- Embed each user query with the same model used for stored content. section 5.4.3
- Compare the query embedding with stored embeddings and select the closest result or several high-scoring results. section 5.4.3
- Return the matched answer directly or provide retrieved context to an AI model for formatted conversational output. section 5.4.3 section 11.4
IT support knowledge base
Store questions such as “How do I reset my password?” with their answers. For “I forgot my password,” generate a query embedding, compare it using cosine similarity, and return the matching password-reset guidance. section 5.4.3
Listing 5.9 Converting text to embeddings
const embeddingDB = {}; #1
const questions = [
"How do I reset my password?",
"What should I do if my computer won't start?",
]; #2
const answers = [ #3
"To reset your password, go to the login page and click
'Forgot Password'. Follow the instructions to reset
your password.",
"If your computer won't start, check the power cable, try
restarting it, and if the issue persists, contact support.",
];
for (let i = 0; i < questions.length; i++) {
const { embedding } = await embed({
model: google.textEmbeddingModel("text-embedding-004"),
value: questions[i],
});
embeddingDB[questions[i]] = embedding; #4
}
The listing demonstrates a JavaScript embedding database built from question-and-answer pairs, using Google's text-embedding-004 model. section 5.4.3
