its timestamp. We'll use this function to create a uuid for each git log entry.Important note: If you are working with documents and want the current date and time associated with vector for time-based search, you can skip this step. A uuid will be automatically generated when the documents are ingested by default.from timescale_vector import client# Function to take in a date string in the past and return a uuid v1def create_uuid(date_string: str):    if date_string is None:        return None    time_format = '%a %b %d %H:%M:%S %Y %z'    datetime_obj = datetime.strptime(date_string, time_format)    uuid = client.uuid_from_time(datetime_obj)    return str(uuid)Next, we'll define a metadata function to extract the relevant metadata from the JSON record. We'll pass this function to the JSONLoader. See the JSON document loader docs for more details.# Helper function to split name and email given an author string consisting of Name Lastname <email>def split_name(input_string: str) -> Tuple[str, str]:    if input_string is None:        return None, None    start = input_string.find("<")    end = input_string.find(">")    name = input_string[:start].strip()    email = input_string[start+1:end].strip()    return name, email# Helper function to transform a date string into a timestamp_tz stringdef create_date(input_string: str) -> datetime:    if input_string is None:        return None    # Define a dictionary to map month abbreviations to their numerical equivalents    month_dict = {        "Jan": "01",        "Feb": "02",        "Mar": "03",        "Apr": "04",        "May": "05",        "Jun": "06",        "Jul": "07",        "Aug": "08",        "Sep": "09",        "Oct": "10",        "Nov": "11",        "Dec": "12",    }    # Split the input string into its components    components = input_string.split()    # Extract relevant information    day = components[2]    month = month_dict[components[1]]    year = components[4]    time = components[3]
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries.
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: its timestamp. We'll use this function to create a uuid for each git log entry.Important note: If you are working with documents and want the current date and time associated with vector for time-based search, you can skip this step. A uuid will be automatically generated when the documents are ingested by default.from timescale_vector import client# Function to take in a date string in the past and return a uuid v1def create_uuid(date_string: str):    if date_string is None:        return None    time_format = '%a %b %d %H:%M:%S %Y %z'    datetime_obj = datetime.strptime(date_string, time_format)    uuid = client.uuid_from_time(datetime_obj)    return str(uuid)Next, we'll define a metadata function to extract the relevant metadata from the JSON record. We'll pass this function to the JSONLoader. See the JSON document loader docs for more details.# Helper function to split name and email given an author string consisting of Name Lastname <email>def split_name(input_string: str) -> Tuple[str, str]:    if input_string is None:        return None, None    start = input_string.find("<")    end = input_string.find(">")    name = input_string[:start].strip()    email = input_string[start+1:end].strip()    return name, email# Helper function to transform a date string into a timestamp_tz stringdef create_date(input_string: str) -> datetime:    if input_string is None:        return None    # Define a dictionary to map month abbreviations to their numerical equivalents    month_dict = {        "Jan": "01",        "Feb": "02",        "Mar": "03",        "Apr": "04",        "May": "05",        "Jun": "06",        "Jul": "07",        "Aug": "08",        "Sep": "09",        "Oct": "10",        "Nov": "11",        "Dec": "12",    }    # Split the input string into its components    components = input_string.split()    # Extract relevant information    day = components[2]    month = month_dict[components[1]]    year = components[4]    time = components[3]