The database URL should look something like this: https://demo-uni3q8.eu-west-1.xata.sh/db/langchain.import getpassapi_key = getpass.getpass("Xata API key: ")db_url = input("Xata database URL (copy it from your DB settings):")Create a simple memory store‚ÄãTo test the memory store functionality in isolation, let's use the following code snippet:from langchain.memory import XataChatMessageHistoryhistory = XataChatMessageHistory(    session_id="session-1",    api_key=api_key,    db_url=db_url,    table_name="memory")history.add_user_message("hi!")history.add_ai_message("whats up?")The above code creates a session with the ID session-1 and stores two messages in it. After running the above, if you visit the Xata UI, you should see a table named memory and the two messages added to it.You can retrieve the message history for a particular session with the following code:history.messagesConversational Q&A chain on your data with memory‚ÄãLet's now see a more complex example in which we combine OpenAI, the Xata Vector Store integration, and the Xata memory store integration to create a Q&A chat bot on your data, with follow-up questions and history.We're going to need to access the OpenAI API, so let's configure the API key:import osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")To store the documents that the chatbot will search for answers, add a table named docs to your langchain database using the Xata UI, and add the following columns:content of type "Text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use. In this notebook we use OpenAI embeddings, which have 1536 dimensions.Let's create the vector store and add some sample docs to it:from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores.xata import XataVectorStoreembeddings = OpenAIEmbeddings()texts = [    "Xata is a Serverless Data platform based on PostgreSQL",    "Xata offers a
Xata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions.
Xata is a serverless data platform, based on PostgreSQL and Elasticsearch. It provides a Python SDK for interacting with your database, and a UI for managing your data. With the XataChatMessageHistory class, you can use Xata databases for longer-term persistence of chat sessions. ->: The database URL should look something like this: https://demo-uni3q8.eu-west-1.xata.sh/db/langchain.import getpassapi_key = getpass.getpass("Xata API key: ")db_url = input("Xata database URL (copy it from your DB settings):")Create a simple memory store‚ÄãTo test the memory store functionality in isolation, let's use the following code snippet:from langchain.memory import XataChatMessageHistoryhistory = XataChatMessageHistory(    session_id="session-1",    api_key=api_key,    db_url=db_url,    table_name="memory")history.add_user_message("hi!")history.add_ai_message("whats up?")The above code creates a session with the ID session-1 and stores two messages in it. After running the above, if you visit the Xata UI, you should see a table named memory and the two messages added to it.You can retrieve the message history for a particular session with the following code:history.messagesConversational Q&A chain on your data with memory‚ÄãLet's now see a more complex example in which we combine OpenAI, the Xata Vector Store integration, and the Xata memory store integration to create a Q&A chat bot on your data, with follow-up questions and history.We're going to need to access the OpenAI API, so let's configure the API key:import osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")To store the documents that the chatbot will search for answers, add a table named docs to your langchain database using the Xata UI, and add the following columns:content of type "Text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use. In this notebook we use OpenAI embeddings, which have 1536 dimensions.Let's create the vector store and add some sample docs to it:from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores.xata import XataVectorStoreembeddings = OpenAIEmbeddings()texts = [    "Xata is a Serverless Data platform based on PostgreSQL",    "Xata offers a