Dify Version and Environment

Oct 13, 2025 by miodeya.com 29 views

Why Does the Embeddings Table Still Exist in the PostgreSQL Database and Store Vector Data?

Introduction

When working with vector databases, it's essential to understand how data is stored and managed. In the context of the Dify project, we've encountered an issue where the embeddings table still exists in the PostgreSQL database and stores vector data, even when a dedicated database for vector storage is specified. In this article, we'll delve into the reasons behind this behavior and explore possible solutions to optimize database performance.

Background

The Dify project utilizes a vector database, QDrant, to store and manage vector data. QDrant is designed to provide efficient storage and querying capabilities for high-dimensional vectors. However, when we specify the dedicated database for vector storage, VECTOR_STORE=qdrant, we expect the vector data to be stored solely in QDrant, without any additional storage in the PostgreSQL database.

Self Checks

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We've searched for existing issues, including closed ones, to confirm that this is a new problem.
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Dify Version and Environment

We're using the following Dify version and environment:

Dify version: db
Cloud or Self Hosted: Self Hosted (Docker)

Steps to Reproduce

To reproduce this issue, we've followed these steps:

Modify .env: We've updated the .env file to specify the dedicated database for vector storage, VECTOR_STORE=qdrant.
Docker startup: We've started the Docker container.
Create a knowledge base and input the first document: We've created a knowledge base and input the first document.
Connect to the PostgreSQL database and check if there is any added data in the embeddings table: We've connected to the PostgreSQL database and checked if there is any added data in the embeddings table.
Check the QDrant vector database to see if vector data is stored: We've checked the QDrant vector database to see if vector data is stored.

Expected Behavior

Based on our configuration, we expect the vector data to be stored solely in QDrant, without any additional storage in the PostgreSQL database.

Actual Behavior

However, our actual behavior is that vector data exists in both the PostgreSQL database and the QDrant vector database. According to statistics, the amount of hard disk data stored in PostgreSQL is 8 times that of QDrant, which seriously consumes hard disk resources and leads to a decrease in database performance.

Possible Causes

There are several possible causes for this behavior:

Misconfigured environment variables: It's possible that the environment variables are not correctly configured, leading to the vector data being stored in both databases.
Incorrect database connections: The database connections might be incorrect, causing the vector data to be stored in both databases.
Database schema issues: There might be issues with the database schema, leading to the vector data being stored in both databases.

Solutions

To resolve this issue, we can try the following solutions:

Verify environment variables: We can verify that the environment variables are correctly configured to ensure that the vector data is stored solely in QDrant.
Check database connections: We can check the database connections to ensure that they are correct and that the vector data is not being stored in both databases.
Review database schema: We can review the database schema to ensure that it is correct and that the vector data is not being stored in both databases.

Conclusion

In conclusion, the embeddings table still exists in the PostgreSQL database and stores vector data, even when a dedicated database for vector storage is specified. This behavior can lead to a decrease in database performance and consume hard disk resources. By verifying environment variables, checking database connections, and reviewing database schema, we can resolve this issue and ensure that the vector data is stored solely in QDrant.

Future Work

In the future, we can explore the following:

Implementing a more efficient storage solution: We can explore implementing a more efficient storage solution that reduces the amount of hard disk data stored in PostgreSQL.
Optimizing database performance: We can optimize database performance by reducing the amount of data stored in PostgreSQL and improving query performance.
Improving database schema: We can improve the database schema to ensure that it is correct and that the vector data is not being stored in both databases.
Q&A: Why Does the Embeddings Table Still Exist in the PostgreSQL Database and Store Vector Data?

Introduction

In our previous article, we explored the issue of the embeddings table still existing in the PostgreSQL database and storing vector data, even when a dedicated database for vector storage is specified. In this article, we'll provide a Q&A section to address some of the common questions and concerns related to this issue.

Q: What is the embeddings table, and why is it still existing in the PostgreSQL database?

A: The embeddings table is a database table that stores vector data. In the context of the Dify project, the embeddings table is used to store the vector representations of documents. Even when a dedicated database for vector storage is specified, the embeddings table still exists in the PostgreSQL database, which can lead to a decrease in database performance and consume hard disk resources.

Q: Why is the vector data being stored in both the PostgreSQL database and the QDrant vector database?

A: There are several possible causes for this behavior, including misconfigured environment variables, incorrect database connections, and database schema issues. To resolve this issue, we can try verifying environment variables, checking database connections, and reviewing database schema.

Q: How can I verify environment variables to ensure that the vector data is stored solely in QDrant?

A: To verify environment variables, you can check the .env file to ensure that the VECTOR_STORE variable is correctly set to qdrant. You can also check the environment variables in the Docker container to ensure that they are correctly configured.

Q: How can I check database connections to ensure that the vector data is not being stored in both databases?

A: To check database connections, you can use the psql command to connect to the PostgreSQL database and check the database schema. You can also use the qdrant command to connect to the QDrant vector database and check the vector data.

Q: How can I review database schema to ensure that it is correct and that the vector data is not being stored in both databases?

A: To review database schema, you can use the psql command to connect to the PostgreSQL database and check the database schema. You can also use the qdrant command to connect to the QDrant vector database and check the vector data.

Q: What are some possible solutions to resolve this issue?

A: Some possible solutions to resolve this issue include:

Verifying environment variables: Ensure that the environment variables are correctly configured to store the vector data solely in QDrant.
Checking database connections: Ensure that the database connections are correct and that the vector data is not being stored in both databases.
Reviewing database schema: Ensure that the database schema is correct and that the vector data is not being stored in both databases.

Q: How can I optimize database performance to reduce the amount of hard disk data stored in PostgreSQL?

A: To optimize database performance, you can try the following:

Reducing the amount of data stored in PostgreSQL: Consider reducing the amount of data stored in PostgreSQL by using a more efficient storage solution.
Improving query performance: Consider improving query performance by optimizing database schema and indexing.
Using a more efficient database: Consider using a more efficient database that is optimized for vector data storage.

Q: How can I improve database schema to ensure that it is correct and that the vector data is not being stored in both databases?

A: To improve database schema, you can try the following:

Reviewing database schema: Review the database schema to ensure that it is correct and that the vector data is not being stored in both databases.
Optimizing database schema: Optimize the database schema to improve query performance and reduce the amount of data stored in PostgreSQL.
Using a more efficient database schema: Consider using a more efficient database schema that is optimized for vector data storage.

Conclusion

In conclusion, the embeddings table still existing in the PostgreSQL database and storing vector data, even when a dedicated database for vector storage is specified, can lead to a decrease in database performance and consume hard disk resources. By verifying environment variables, checking database connections, and reviewing database schema, we can resolve this issue and ensure that the vector data is stored solely in QDrant.