PostgreSQL & Airflow RAG Pipeline Development
Budget: ₹1,500 – ₹12,500 INR
Develop a RAG Mechanism using PostgreSQL and Airflow
Objective:
Implement a RAG pipeline that processes data, labels it using OpenAI, stores the labeled data in a PostgreSQL vector database, and enables vector-based search.
Steps and Implementation Details
Read Data
Description: Read data from the source (e.g., CSV file, API, or database).
Airflow Operator: Use a PythonOperator or SQLSensorOperator to read data based on the source.
Output: Data fetched in a structured format (list of records or a DataFrame).
Split and Chunk
Description: Split and chunk the data into smaller, manageable pieces for better processing and labeling.
Airflow Operator: Use a PythonOperator to split and chunk the data. Implement chunking logic to ensure consistency in labeling.
Output: Chunks of data ready for labeling.
Use OpenAI to Label Data
Description: Send each chunk to OpenAI's API for labeling. Ensure each data piece receives at least three labels.
Airflow Operator: Implement a PythonOperator that integrates OpenAI's API for labeling. Add retry logic and rate limiting.
Output: Labeled data with a minimum of three labels per chunk.
Store in PostgreSQL Vector Database
Description: Store labeled data in PostgreSQL, using vector indexing for efficient vector search.
Airflow Operator: Use PostgresOperator for database insertion and ensure vector columns are indexed properly.
Output: Labeled data stored in PostgreSQL with vector embeddings.
Enable Vector Search
Description: Implement vector search functionality to retrieve similar records based on embeddings.
Airflow Operator: Use a custom PythonOperator to handle vector search queries within the database.
Output: A vector search API that returns relevant records based on similarity to input embeddings.
Final Step:
Documentation: Document the pipeline steps and setup instructions.
Testing: Verify each step, especially data retrieval and vector search accuracy.
Access: Ensure data is accessible via vector search (e.g., REST API endpoint).
Objective:
Implement a RAG pipeline that processes data, labels it using OpenAI, stores the labeled data in a PostgreSQL vector database, and enables vector-based search.
Steps and Implementation Details
Read Data
Description: Read data from the source (e.g., CSV file, API, or database).
Airflow Operator: Use a PythonOperator or SQLSensorOperator to read data based on the source.
Output: Data fetched in a structured format (list of records or a DataFrame).
Split and Chunk
Description: Split and chunk the data into smaller, manageable pieces for better processing and labeling.
Airflow Operator: Use a PythonOperator to split and chunk the data. Implement chunking logic to ensure consistency in labeling.
Output: Chunks of data ready for labeling.
Use OpenAI to Label Data
Description: Send each chunk to OpenAI's API for labeling. Ensure each data piece receives at least three labels.
Airflow Operator: Implement a PythonOperator that integrates OpenAI's API for labeling. Add retry logic and rate limiting.
Output: Labeled data with a minimum of three labels per chunk.
Store in PostgreSQL Vector Database
Description: Store labeled data in PostgreSQL, using vector indexing for efficient vector search.
Airflow Operator: Use PostgresOperator for database insertion and ensure vector columns are indexed properly.
Output: Labeled data stored in PostgreSQL with vector embeddings.
Enable Vector Search
Description: Implement vector search functionality to retrieve similar records based on embeddings.
Airflow Operator: Use a custom PythonOperator to handle vector search queries within the database.
Output: A vector search API that returns relevant records based on similarity to input embeddings.
Final Step:
Documentation: Document the pipeline steps and setup instructions.
Testing: Verify each step, especially data retrieval and vector search accuracy.
Access: Ensure data is accessible via vector search (e.g., REST API endpoint).