Vector storage is a crucial component in R2R (RAG to Riches) for efficient similarity searches. This guide focuses on troubleshooting common vector storage issues, particularly with Postgres and pgvector.
Check Postgres Connection:
If this fails, the issue might be with Postgres itself, not specifically vector storage.
Verify Environment Variables: Ensure these are correctly set in your R2R configuration:
POSTGRES_USER
POSTGRES_PASSWORD
POSTGRES_HOST
POSTGRES_PORT
POSTGRES_DBNAME
R2R_PROJECT_NAME
Check Docker Network: If using Docker, ensure the R2R and Postgres containers are on the same network:
Check if pgvector is Installed: Connect to your database and run:
Install pgvector: If not installed, run:
Verify Postgres Version: pgvector requires Postgres 11 or later. Check your version:
Check Vector Dimensions: Verify the dimension of vectors you’re trying to insert matches your schema:
Verify R2R Configuration: Ensure the vector dimension in your R2R configuration matches your database schema.
Recreate Table with Correct Dimensions: If dimensions are mismatched, you may need to recreate the table:
Check Index: Ensure you have an appropriate index:
Analyze Table: Run ANALYZE to update statistics:
Monitor Query Performance:
Use EXPLAIN ANALYZE
to check query execution plans:
Adjust Work Memory: If dealing with large vectors, increase work_mem:
Check Vector Normalization: Ensure vectors are normalized before insertion if using cosine similarity.
Verify Data Insertion: Check if data is being correctly inserted:
Inspect Random Samples: Look at some random entries to ensure data quality:
Check Disk Space:
Monitor Postgres Disk Usage:
Identify Large Tables:
If all else fails, you may need to restore from a backup:
Create a Backup:
Restore from Backup:
If these steps don’t resolve your issue:
Remember to always backup your data before making significant changes to your database or vector storage configuration.
Vector storage is a crucial component in R2R (RAG to Riches) for efficient similarity searches. This guide focuses on troubleshooting common vector storage issues, particularly with Postgres and pgvector.
Check Postgres Connection:
If this fails, the issue might be with Postgres itself, not specifically vector storage.
Verify Environment Variables: Ensure these are correctly set in your R2R configuration:
POSTGRES_USER
POSTGRES_PASSWORD
POSTGRES_HOST
POSTGRES_PORT
POSTGRES_DBNAME
R2R_PROJECT_NAME
Check Docker Network: If using Docker, ensure the R2R and Postgres containers are on the same network:
Check if pgvector is Installed: Connect to your database and run:
Install pgvector: If not installed, run:
Verify Postgres Version: pgvector requires Postgres 11 or later. Check your version:
Check Vector Dimensions: Verify the dimension of vectors you’re trying to insert matches your schema:
Verify R2R Configuration: Ensure the vector dimension in your R2R configuration matches your database schema.
Recreate Table with Correct Dimensions: If dimensions are mismatched, you may need to recreate the table:
Check Index: Ensure you have an appropriate index:
Analyze Table: Run ANALYZE to update statistics:
Monitor Query Performance:
Use EXPLAIN ANALYZE
to check query execution plans:
Adjust Work Memory: If dealing with large vectors, increase work_mem:
Check Vector Normalization: Ensure vectors are normalized before insertion if using cosine similarity.
Verify Data Insertion: Check if data is being correctly inserted:
Inspect Random Samples: Look at some random entries to ensure data quality:
Check Disk Space:
Monitor Postgres Disk Usage:
Identify Large Tables:
If all else fails, you may need to restore from a backup:
Create a Backup:
Restore from Backup:
If these steps don’t resolve your issue:
Remember to always backup your data before making significant changes to your database or vector storage configuration.