This PR introduces UUIDv7 implementations in both Python and SQL to establish the foundation for migrating from UUIDv4 to UUIDv7 as proposed in #19754.
ID generation algorithm of existing models are not changed, and new models should use UUIDv7 for ID generation.
Close#19754.
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.
Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types
Closes#22024.
The `BaseSession` class in the `core/mcp/session` package uses `ThreadPoolExecutor`
to run the receive loop but fails to properly clean up the executor and receiver
future, leading to potential thread leaks.
This PR addresses this issue by:
- Initializing `_executor` and `_receiver_future` attributes to `None` for proper cleanup checks
- Adding graceful shutdown with a 5-second timeout in the `__exit__` method
- Ensuring the ThreadPoolExecutor is properly shut down to prevent resource leaks
This fix prevents memory leaks and hanging threads in long-running scenarios where
multiple MCP sessions are created and destroyed.
Signed-off-by: neatguycoding <15627489+NeatGuyCoding@users.noreply.github.com>
Co-authored-by: QuantumGhost <obelisk.reg+git@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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@ -42,20 +44,22 @@ body:
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📌 <ahref="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Introducing Dify Workflow File Upload: Recreate Google NotebookLM Podcast</a>
📌 <ahref="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Introducing Dify Workflow File Upload: Recreate Google NotebookLM Podcast111</a>
</p>
<palign="center">
@ -54,7 +54,7 @@
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
Dify is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features, and more, allowing you to quickly move from prototype to production.
Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more—allowing you to quickly move from prototype to production.
## Quick start
@ -65,7 +65,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
</br>
The easiest way to start the Dify server is through [docker compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
The easiest way to start the Dify server is through [Docker Compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
```bash
cd dify
@ -205,6 +205,7 @@ If you'd like to configure a highly-available setup, there are community-contrib
- [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
#### Using Terraform for Deployment
@ -261,8 +262,8 @@ At the same time, please consider supporting Dify by sharing it on social media
## Security disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
## License
This repository is available under the [Dify Open Source License](LICENSE), which is essentially Apache 2.0 with a few additional restrictions.
This repository is licensed under the [Dify Open Source License](LICENSE), based on Apache 2.0 with additional conditions.
## Guidelines for Database Connection Management in App Runner and Task Pipeline
Due to the presence of tasks in App Runner that require long execution times, such as LLM generation and external requests, Flask-Sqlalchemy's strategy for database connection pooling is to allocate one connection (transaction) per request. This approach keeps a connection occupied even during non-DB tasks, leading to the inability to acquire new connections during high concurrency requests due to multiple long-running tasks.
Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid detach errors.
Examples:
1. Creating a new record:
```python
app = App(id=1)
db.session.add(app)
db.session.commit()
db.session.refresh(app) # Retrieve table default values, like created_at, cached in the app object, won't affect after close
# Handle non-long-running tasks or store the content of the App instance in memory (via variable assignment).
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