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+# YouTube transcript QA bot with NodeJS
+
+## use LanceDB's Javascript API and OpenAI to build a QA bot for YouTube transcripts
+
+<img id="splash" width="400" alt="nodejs" src="https://github.com/lancedb/lancedb/assets/917119/3a140e75-bf8e-438a-a1e4-af14a72bcf98">
+
+This Q&A bot will allow you to search through youtube transcripts using natural language! We'll introduce how you can use LanceDB's Javascript API to store and manage your data easily.
+
+For this example we're using a HuggingFace dataset that contains YouTube transcriptions: `jamescalam/youtube-transcriptions`, to make it easier, we've converted it to a LanceDB `db` already, which you can download and put in a working directory:
+
+```wget -c https://eto-public.s3.us-west-2.amazonaws.com/lancedb_demo.tar.gz -O - | tar -xz -C .```
+
+Now, we'll create a simple app that can:
+1. Take a text based query and search for contexts in our corpus, using embeddings generated from the OpenAI Embedding API.
+2. Create a prompt with the contexts, and call the OpenAI Completion API to answer the text based query.
+
+Dependencies and setup of OpenAI API:
+
+```javascript
+const lancedb = require("vectordb");
+const { Configuration, OpenAIApi } = require("openai");
+
+const configuration = new Configuration({
+    apiKey: process.env.OPENAI_API_KEY,
+    });
+const openai = new OpenAIApi(configuration);
+```
+
+First, let's set our question and the context amount. The context amount will be used to query similar documents in our corpus.
+
+```javascript
+const QUESTION = "who was the 12th person on the moon and when did they land?";
+const CONTEXT_AMOUNT = 3;
+```
+
+Now, let's generate an embedding from this question:
+
+```javascript
+const embeddingResponse = await openai.createEmbedding({
+    model: "text-embedding-ada-002",
+    input: QUESTION,
+});
+
+const embedding = embeddingResponse.data["data"][0]["embedding"];
+```
+
+Once we have the embedding, we can connect to LanceDB (using the database we downloaded earlier), and search through the chatbot table.
+We'll extract 3 similar documents found.
+
+```javascript
+const db = await lancedb.connect('./lancedb');
+const tbl = await db.openTable('chatbot');
+const query = tbl.search(embedding);
+query.limit = CONTEXT_AMOUNT;
+const context = await query.execute();
+```
+
+Let's combine the context together so we can pass it into our prompt:
+
+```javascript
+for (let i = 1; i < context.length; i++) {
+    context[0]["text"] += " " + context[i]["text"];
+}
+```
+
+Lastly, let's construct the prompt. You could play around with this to create more accurate/better prompts to yield results.
+
+```javascript
+const prompt = "Answer the question based on the context below.\n\n" +
+    "Context:\n" +
+    `${context[0]["text"]}\n` +
+    `\n\nQuestion: ${QUESTION}\nAnswer:`;
+```
+
+We pass the prompt, along with the context, to the completion API.
+
+```javascript
+const completion = await openai.createCompletion({
+    model: "text-davinci-003",
+    prompt,
+    temperature: 0,
+    max_tokens: 400,
+    top_p: 1,
+    frequency_penalty: 0,
+    presence_penalty: 0,
+});
+```
+
+And that's it!
+
+```javascript
+console.log(completion.data.choices[0].text);
+```
+
+The response is (which is non deterministic):
+
+```
+The 12th person on the moon was Harrison Schmitt and he landed on December 11, 1972.
+```