Watson Conversation Logs by Date

ghz 昨天 ⋅ 5 views

I wanted to know if there is a way to pull Watson conversation logs for a specific time period using Watson Python SDK? Also is there a way to avoid the pagination added at the end of the logs? I want to pull all the logs for a given time period. Below is the code I am currently using which pulls logs for a specific workspace:

import json
import watson_developer_cloud

conversation = watson_developer_cloud.ConversationV1(
    username='xxxxxxxxxx',
    password- 'xxxxxxxx',
    version='2017-05-26'
)

response = conversation.list_logs(
    workspace_id = 'xxxxxxx'
)

js = json.dumps(response,indent=2)

with open('data.txt', 'w') as outfile:
    json.dump(response, outfile)

Answer

To pull Watson Conversation logs for a specific time period using the Watson Python SDK and avoid pagination, you need to use a combination of the following:

  1. Filtering logs by date range (unfortunately, the Watson Conversation API doesn't directly support filtering by time range, so you'll need to filter the logs locally).
  2. Handling pagination (since the API limits the number of logs returned per call, you'll need to loop through all pages to get all the logs).

Handling Pagination

The Watson Conversation API uses pagination to limit the number of logs returned in a single response. The response includes a next_url field, which is the URL to fetch the next page of logs. You need to keep calling the API and follow the next_url until there are no more pages.

Code to Pull Logs for a Specific Time Period

Below is a modified version of your code that handles pagination and filters logs by a specific time period:

import json
import watson_developer_cloud
from datetime import datetime

# Watson Conversation client setup
conversation = watson_developer_cloud.ConversationV1(
    username='xxxxxxxxxx',
    password='xxxxxxxx',
    version='2017-05-26'
)

# Define the workspace_id
workspace_id = 'xxxxxxx'

# Define the start and end date for filtering logs (modify these as needed)
start_date = '2018-01-01T00:00:00'  # Format: 'YYYY-MM-DDTHH:MM:SS'
end_date = '2018-12-31T23:59:59'  # Format: 'YYYY-MM-DDTHH:MM:SS'

# Convert to datetime objects for comparison
start_date = datetime.strptime(start_date, "%Y-%m-%dT%H:%M:%S")
end_date = datetime.strptime(end_date, "%Y-%m-%dT%H:%M:%S")

# Initialize a list to hold all the logs
all_logs = []

# First API call to get the first page of logs
response = conversation.list_logs(workspace_id=workspace_id)

# Function to check if a log entry is within the date range
def is_log_in_time_range(log, start_date, end_date):
    timestamp = log.get('timestamp')
    if timestamp:
        log_time = datetime.utcfromtimestamp(timestamp / 1000)  # Convert timestamp to datetime
        return start_date <= log_time <= end_date
    return False

# Paginate through all log entries
while response:
    # Filter logs for the specific time range
    filtered_logs = [
        log for log in response.get('logs', []) 
        if is_log_in_time_range(log, start_date, end_date)
    ]
    all_logs.extend(filtered_logs)

    # Check if there are more pages of logs
    next_url = response.get('next_url')
    if next_url:
        response = conversation.request('GET', next_url)  # Follow pagination
    else:
        response = None  # End the loop if there's no next page

# Save the logs to a file
with open('filtered_logs.json', 'w') as outfile:
    json.dump(all_logs, outfile, indent=2)

print(f"Retrieved {len(all_logs)} logs within the specified date range.")

Key Changes:

  1. Handling Pagination: The loop continues to fetch logs as long as there is a next_url. This ensures you get all the logs, even if they span multiple pages.
  2. Filtering by Date: The logs are filtered by checking the timestamp field (in milliseconds) against the start_date and end_date you provide. This is done using the is_log_in_time_range function.
  3. Storing Logs: All the logs that fall within the time range are accumulated in the all_logs list and then saved to a JSON file.

Notes:

  • Date Format: The Watson Conversation logs use a Unix timestamp in milliseconds. The code converts this to a datetime object using datetime.utcfromtimestamp().
  • Performance: If the time period is long and the number of logs is large, this could still take some time to process since you're retrieving all logs across multiple pages.
  • API Rate Limiting: Make sure to handle potential rate-limiting issues if you're retrieving a large number of logs in quick succession.

This approach allows you to retrieve all logs for a specific time period and handle pagination automatically.

#py