Roadmap for your LinkedIn AI Commenter project, including the use of machine learning (ML) for feed classification and automation with Celery and FastAPI:

Phase 1: Project Setup

1. Define Project Requirements

  • Retrieve the top 10 LinkedIn feed posts every 2 hours.
  • Classify the posts into one of the following categories: potential client, feed news, job post, or personal news.
  • Generate relevant comments based on the post type using ML-generated prompts.
  • Post the generated comments back to LinkedIn.
  • Ignore duplicate posts by subtracting old posts from the new ones.

2. Select Technology Stack

  • Backend Framework: FastAPI for the API framework.
  • Task Queue: Celery for scheduling tasks (fetching posts every 2 hours).
  • Database: PostgreSQL or Redis (for caching/identifying duplicates).
  • ML Model: Pre-trained transformer-based models for classification (e.g., BERT) and text generation (e.g., GPT-3 or OpenAI’s API).
  • LinkedIn API: Use LinkedIn’s API for retrieving and posting comments (ensure proper authorization through OAuth).

Phase 2: Data Collection and Processing

1. LinkedIn Feed Retrieval

  • Objective: Create a task to fetch the top 10 posts every 2 hours.
  • Steps:
    • Use LinkedIn API to retrieve posts.
    • Parse response to extract relevant data (e.g., post content, user data, timestamps).
  • Celery Task:
    • Schedule a Celery worker that runs every 2 hours.
    • Store retrieved posts in the database to track which posts are already processed.

2. Duplicate Post Filtering

  • Objective: Ensure no duplicate posts are processed.
  • Steps:
    • Compare new posts with already processed posts in the database.
    • Subtract duplicates from the current batch.
  • Implementation: Use a database to keep track of post IDs and timestamps.

Phase 3: Feed Classification

1. Training/Selecting the Classification Model

  • Objective: Classify posts into four categories (potential client, feed news, job post, personal news).
  • Steps:
    • Fine-tune a pre-trained BERT model (or similar) on labeled LinkedIn post data, or use an off-the-shelf model for text classification.
    • For each post, predict its category.
  • Output: A classification tag for each post.

2. Model Integration

  • Integrate the classification model into the Celery task.
  • For each retrieved post, classify it into one of the four categories.

Phase 4: Comment Generation

1. Prompt Creation

  • Objective: Prepare a set of prompts for each category.
  • Steps:
    • Define relevant prompts for the post categories (e.g., “For a job post, generate an enthusiastic comment about the opportunity”).
    • Store these prompts in a structured format.

2. Text Generation

  • Objective: Generate a comment for each post.
  • Steps:
    • Inject the classified post's text into the relevant prompt.
    • Use a pre-trained language model (e.g., GPT-3, ChatGPT) to generate a comment based on the prompt and post content.

Phase 5: Automation with Celery and FastAPI

1. FastAPI Backend

  • Objective: Set up FastAPI for managing API requests and tasks.
  • Steps:
    • Create API endpoints for starting, stopping, or managing the LinkedIn commenter bot.
    • Define Celery tasks to handle the entire process pipeline (from retrieval to classification, comment generation, and posting).

2. Celery Task Workflow

  • Objective: Automate the workflow using Celery tasks.
  • Task Flow:
    1. Fetch the latest 10 LinkedIn posts.
    2. Filter out duplicate posts.
    3. Classify each post using the ML model.
    4. Retrieve relevant prompts based on classification.
    5. Generate comments using the text generation model.
    6. Post the generated comment back to LinkedIn.
  • Celery Scheduler:
    • Schedule Celery to trigger the retrieval task every 2 hours.

3. Database Setup

  • Objective: Track post IDs and timestamps to filter duplicates and manage state.
  • Steps:
    • Use PostgreSQL/Redis for storing post metadata and managing the list of processed posts.

Phase 6: Posting Comments

1. LinkedIn API Integration

  • Objective: Automate posting the generated comments back to LinkedIn.
  • Steps:
    • Authenticate with LinkedIn API using OAuth.
    • Use LinkedIn’s post/comment API to submit the generated comment for each post.

Phase 7: Testing and Monitoring

1. Unit and Integration Testing

  • Test individual modules (post retrieval, classification, comment generation).
  • Test the entire pipeline using mock data and LinkedIn API responses.

2. Monitoring and Logging

  • Implement logging (using FastAPI’s built-in logger or external tools like Sentry) to track errors and process completion.
  • Set up alerts for task failures or API issues.

Phase 8: Deployment and Maintenance

1. Deployment

  • Deploy FastAPI and Celery on a cloud platform (e.g., AWS, Google Cloud).
  • Set up Celery workers and task queues to ensure scalability.

2. Scaling

  • If traffic increases, scale Celery workers to handle more requests.
  • Optimize API rate limits with LinkedIn.

3. Routine Maintenance

  • Monitor model performance and retrain if necessary.
  • Regularly update LinkedIn API keys and authentication tokens.

Tools & Technologies

  • Backend: FastAPI
  • Task Queue: Celery
  • Database: PostgreSQL/Redis
  • ML Framework: HuggingFace Transformers, OpenAI GPT-3 for comment generation
  • Scheduling: Celery Beat (for recurring tasks)
  • Deployment: Docker, AWS/GCP, Kubernetes (for scaling if needed)
  • Logging/Monitoring: Sentry, Prometheus

This roadmap will guide you in creating a LinkedIn AI commenter that automates classification, text generation, and interaction with LinkedIn in an efficient, scalable manner.