Update notebooks readme

Author: demjened

notebooks/generative-ai/README.md

@@ -5,7 +5,7 @@ This folder contains notebooks that demonstrate various use cases for Elasticsea
 The following notebooks are available:
 
 - [Question answering](#question-answering)
-<!-- - [Chatbot](#chatbot) -->
+- [Chatbot](#chatbot)
 
 ## Notebooks
 
@@ -15,12 +15,20 @@ In the [`question-answering.ipynb`](./question-answering.ipynb) notebook you'll
 
 - Retrieve sample workplace documents from a given URL.
 - Set up an Elasticsearch client.
-- Chunk documents into 800-token passages with an overlap of 400 tokens using the `CharacterTextSplitter` from `langchain`.
+- Chunk documents into 800-character passages with an overlap of 400 characters using the `CharacterTextSplitter` from `langchain`.
 - Use `OpenAIEmbeddings` from `langchain` to create embeddings for the content.
 - Retrieve embeddings for the chunked passages using OpenAI.
 - Persist the passage documents along with their embeddings into Elasticsearch.
 - Set up a question-answering system using `OpenAI` and `ElasticKnnSearch` from `langchain` to retrieve answers along with their source documents.
 
-<!-- ### Chatbot
+### Chatbot
 
-In the [`chatbot.ipynb`](./chatbot.ipynb) notebook you'll learn how to: -->
+In the [`chatbot.ipynb`](./chatbot.ipynb) notebook you'll learn how to:
+
+- Retrieve sample workplace documents from a given URL.
+- Set up an Elasticsearch client.
+- Chunk documents into 800-character passages with an overlap of 400 characters using the `CharacterTextSplitter` from `langchain`.
+- Use `OpenAIEmbeddings` from `langchain` to create embeddings for the content.
+- Retrieve embeddings for the chunked passages using OpenAI.
+- Run hybrid search in Elasticsearch to find documents that answers asked questions.
+- Maintain conversational memory for follow-up questions.