6ce014c23b
### What problem does this PR solve? Addresses event-loop blocking under high concurrency reported in #13825. When multiple requests hit the API simultaneously, synchronous DB/Redis calls block the async event loop, preventing Quart from handling other requests and causing cascading 502/504 timeouts. This PR wraps all remaining blocking DB/Redis calls in `canvas_app.py`, `chat_api.py`, `session.py`, and `canvas_service.py` with `await thread_pool_exec()` - Offload all synchronous `Service.*`, `REDIS_CONN.*`, and `APIToken.query` calls to the thread pool - Convert sync endpoint handlers (`list_chats`, `get_chat`, `templates`, `sessions`, etc.) to `async def` - Convert sync helper functions (`_ensure_owned_chat`, `_validate_llm_id`, `_validate_dataset_ids`, etc.) to async - no duplicate sync/async pairs - Wrap `CanvasReplicaService` Redis IO calls (`bootstrap`, `replace_for_set`, `commit_after_run`) - Use `asyncio.gather()` for concurrent file uploads and chat response building **Note:** This fixes the code-level event-loop blocking, which is a prerequisite for handling concurrent requests. For the full "30 concurrent requests without 502/504" goal described in the issue, users should also tune deployment config: - `WS=4` or higher (HTTP worker processes, default 1) - `MAX_CONCURRENT_CHATS=50` (default 10) - `SANDBOX_EXECUTOR_MANAGER_POOL_SIZE` for workflow-heavy workloads ### Performance verification Reviewer asked for a before-vs-after comparison ([comment](https://github.com/infiniflow/ragflow/pull/13941#issuecomment-4393667231)). I built a self-contained microbenchmark that reproduces the exact failure mode this PR targets: an async handler that performs blocking DB/Redis-style calls (50 ms each, 3 per request, 30 concurrent requests) is run twice — once with the pre-PR pattern (sync call directly inside the async handler) and once with the post-PR pattern (`await thread_pool_exec(...)`). The benchmark imports nothing from RAGFlow except `thread_pool_exec` itself, so it is hermetic and reproducible (`THREAD_POOL_MAX_WORKERS=128`, Python 3.13.12). **Throughput — wall-clock for 30 concurrent requests (lower is better)** | flavour | wall(s) | p50(s) | p95(s) | max(s) | |---|---:|---:|---:|---:| | before | 4.986 | 0.158 | 0.207 | 0.269 | | after | 0.248 | 0.181 | 0.230 | 0.231 | The pre-PR handler serializes the entire load on the event-loop thread, so 30 × 3 × 50 ms ≈ 4.5 s shows up as the wall time. The post-PR handler parallelizes the blocking work across the thread pool and finishes the same load in 248 ms — a **~20× speedup** on this workload. **Event-loop responsiveness — latency of an unrelated probe coroutine while the 30 slow requests are running (lower is better)** | flavour | samples | probe p50 (ms) | probe p95 (ms) | probe max (ms) | |---|---:|---:|---:|---:| | before | 1 | 5442.26 | 5442.26 | 5442.26 | | after | 28 | 0.88 | 11.53 | 98.02 | This is the metric that maps directly to "the API still answers other requests while one is busy". A 5 ms-interval probe was scheduled while the 30 slow handlers ran. With the pre-PR code the event loop was frozen for the entire duration of the blocking work, so only one probe sample was ever picked up and it waited **5,442 ms**. After the PR, 28 probe samples landed with **p50 0.88 ms / p95 11.53 ms**, meaning unrelated requests are no longer starved by the slow ones. That is the regression mode behind the cascading 502/504s reported in #13825. <details> <summary>Raw benchmark output</summary> ``` config: 30 concurrent requests, 3 blocking calls of 50ms each per request, THREAD_POOL_MAX_WORKERS=128 === Throughput (lower wall is better) === flavour wall(s) p50(s) p95(s) max(s) before 4.986 0.158 0.207 0.269 after 0.248 0.181 0.230 0.231 === Event-loop responsiveness (lower probe latency is better) === flavour samples probe p50(ms) probe p95(ms) probe max(ms) before 1 5442.26 5442.26 5442.26 after 28 0.88 11.53 98.02 ``` </details> The benchmark script is included as a comment on the PR for reproducibility. ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue) - [x] Performance Improvement Closes [#13825](https://github.com/infiniflow/ragflow/issues/13825) --------- Co-authored-by: tmimmanuel <tmimmanuel@users.noreply.github.com> Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
Cloud | Document | Roadmap | Discord
📕 Table of Contents
💡 What is RAGFlow?
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs. It offers a streamlined RAG workflow adaptable to enterprises of any scale. Powered by a converged context engine and pre-built agent templates, RAGFlow enables developers to transform complex data into high-fidelity, production-ready AI systems with exceptional efficiency and precision.
🎮 Get Started
Try our cloud service at https://cloud.ragflow.io.
🔥 Latest Updates
- 2026-04-24 Supports DeepSeek v4.
- 2026-03-24 RAGFlow Skill on OpenClaw — Provides an official skill for accessing RAGFlow datasets via OpenClaw.
- 2025-12-26 Supports 'Memory' for AI agent.
- 2025-11-19 Supports Gemini 3 Pro.
- 2025-11-12 Supports data synchronization from Confluence, S3, Notion, Discord, Google Drive.
- 2025-10-23 Supports MinerU & Docling as document parsing methods.
- 2025-10-15 Supports orchestrable ingestion pipeline.
- 2025-08-08 Supports OpenAI's latest GPT-5 series models.
- 2025-08-01 Supports agentic workflow and MCP.
- 2025-05-23 Adds a Python/JavaScript code executor component to Agent.
- 2025-05-05 Supports cross-language query.
- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
🎉 Stay Tuned
⭐️ Star our repository to stay up-to-date with exciting new features and improvements! Get instant notifications for new releases! 🌟
🌟 Key Features
🍭 "Quality in, quality out"
- Deep document understanding-based knowledge extraction from unstructured data with complicated formats.
- Finds "needle in a data haystack" of literally unlimited tokens.
🍱 Template-based chunking
- Intelligent and explainable.
- Plenty of template options to choose from.
🌱 Grounded citations with reduced hallucinations
- Visualization of text chunking to allow human intervention.
- Quick view of the key references and traceable citations to support grounded answers.
🍔 Compatibility with heterogeneous data sources
- Supports Word, slides, excel, txt, images, scanned copies, structured data, web pages, and more.
🛀 Automated and effortless RAG workflow
- Streamlined RAG orchestration catered to both personal and large businesses.
- Configurable LLMs as well as embedding models.
- Multiple recall paired with fused re-ranking.
- Intuitive APIs for seamless integration with business.
🔎 System Architecture
🎬 Self-Hosting
📝 Prerequisites
- CPU >= 4 cores
- RAM >= 16 GB
- Disk >= 50 GB
- Docker >= 24.0.0 & Docker Compose >= v2.26.1
- gVisor: Required only if you intend to use the code executor (sandbox) feature of RAGFlow.
Tip
If you have not installed Docker on your local machine (Windows, Mac, or Linux), see Install Docker Engine.
🚀 Start up the server
-
Ensure
vm.max_map_count>= 262144:To check the value of
vm.max_map_count:$ sysctl vm.max_map_countReset
vm.max_map_countto a value at least 262144 if it is not.# In this case, we set it to 262144: $ sudo sysctl -w vm.max_map_count=262144This change will be reset after a system reboot. To ensure your change remains permanent, add or update the
vm.max_map_countvalue in /etc/sysctl.conf accordingly:vm.max_map_count=262144 -
Clone the repo:
$ git clone https://github.com/infiniflow/ragflow.git -
Start up the server using the pre-built Docker images:
Caution
All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64. If you are on an ARM64 platform, follow this guide to build a Docker image compatible with your system.
The command below downloads the
v0.25.2edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different fromv0.25.2, update theRAGFLOW_IMAGEvariable accordingly in docker/.env before usingdocker composeto start the server.
$ cd ragflow/docker
# git checkout v0.25.2
# Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
# This step ensures the **entrypoint.sh** file in the code matches the Docker image version.
# Use CPU for DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
# To use GPU to accelerate DeepDoc tasks:
# sed -i '1i DEVICE=gpu' .env
# docker compose -f docker-compose.yml up -d
Note: Prior to
v0.22.0, we provided both images with embedding models and slim images without embedding models. Details as follows:
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
|---|---|---|---|
| v0.21.1 | ≈9 | ✔️ | Stable release |
| v0.21.1-slim | ≈2 | ❌ | Stable release |
Starting with
v0.22.0, we ship only the slim edition and no longer append the -slim suffix to the image tag.
-
Check the server status after having the server up and running:
$ docker logs -f docker-ragflow-cpu-1The following output confirms a successful launch of the system:
____ ___ ______ ______ __ / __ \ / | / ____// ____// /____ _ __ / /_/ // /| | / / __ / /_ / // __ \| | /| / / / _, _// ___ |/ /_/ // __/ / // /_/ /| |/ |/ / /_/ |_|/_/ |_|\____//_/ /_/ \____/ |__/|__/ * Running on all addresses (0.0.0.0)If you skip this confirmation step and directly log in to RAGFlow, your browser may prompt a
network abnormalerror because, at that moment, your RAGFlow may not be fully initialized. -
In your web browser, enter the IP address of your server and log in to RAGFlow.
With the default settings, you only need to enter
http://IP_OF_YOUR_MACHINE(sans port number) as the default HTTP serving port80can be omitted when using the default configurations. -
In service_conf.yaml.template, select the desired LLM factory in
user_default_llmand update theAPI_KEYfield with the corresponding API key.See llm_api_key_setup for more information.
The show is on!
🔧 Configurations
When it comes to system configurations, you will need to manage the following files:
- .env: Keeps the fundamental setups for the system, such as
SVR_HTTP_PORT,MYSQL_PASSWORD, andMINIO_PASSWORD. - service_conf.yaml.template: Configures the back-end services. The environment variables in this file will be automatically populated when the Docker container starts. Any environment variables set within the Docker container will be available for use, allowing you to customize service behavior based on the deployment environment.
- docker-compose.yml: The system relies on docker-compose.yml to start up.
The ./docker/README file provides a detailed description of the environment settings and service configurations which can be used as
${ENV_VARS}in the service_conf.yaml.template file.
To update the default HTTP serving port (80), go to docker-compose.yml and change 80:80
to <YOUR_SERVING_PORT>:80.
Updates to the above configurations require a reboot of all containers to take effect:
$ docker compose -f docker-compose.yml up -d
Switch doc engine from Elasticsearch to Infinity
RAGFlow uses Elasticsearch by default for storing full text and vectors. To switch to Infinity, follow these steps:
-
Stop all running containers:
$ docker compose -f docker/docker-compose.yml down -v
Warning
-vwill delete the docker container volumes, and the existing data will be cleared.
-
Set
DOC_ENGINEin docker/.env toinfinity. -
Start the containers:
$ docker compose -f docker-compose.yml up -d
Warning
Switching to Infinity on a Linux/arm64 machine is not yet officially supported.
🔧 Build a Docker image
This image is approximately 2 GB in size and relies on external LLM and embedding services.
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
Or if you are behind a proxy, you can pass proxy arguments:
docker build --platform linux/amd64 \
--build-arg http_proxy=http://YOUR_PROXY:PORT \
--build-arg https_proxy=http://YOUR_PROXY:PORT \
-f Dockerfile -t infiniflow/ragflow:nightly .
🔨 Launch service from source for development
-
Install
uvandpre-commit, or skip this step if they are already installed:pipx install uv pre-commit -
Clone the source code and install Python dependencies:
git clone https://github.com/infiniflow/ragflow.git cd ragflow/ uv sync --python 3.12 # install RAGFlow dependent python modules uv run python3 download_deps.py pre-commit install -
Launch the dependent services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
docker compose -f docker/docker-compose-base.yml up -dAdd the following line to
/etc/hoststo resolve all hosts specified in docker/.env to127.0.0.1:127.0.0.1 es01 infinity mysql minio redis sandbox-executor-manager -
If you cannot access HuggingFace, set the
HF_ENDPOINTenvironment variable to use a mirror site:export HF_ENDPOINT=https://hf-mirror.com -
If your operating system does not have jemalloc, please install it as follows:
# Ubuntu sudo apt-get install libjemalloc-dev # CentOS sudo yum install jemalloc # OpenSUSE sudo zypper install jemalloc # macOS sudo brew install jemalloc -
Launch backend service:
source .venv/bin/activate export PYTHONPATH=$(pwd) bash docker/launch_backend_service.sh -
Install frontend dependencies:
cd web npm install -
Launch frontend service:
npm run devThe following output confirms a successful launch of the system:
-
Stop RAGFlow front-end and back-end service after development is complete:
pkill -f "ragflow_server.py|task_executor.py"
📚 Documentation
📜 Roadmap
See the RAGFlow Roadmap 2026
🏄 Community
🙌 Contributing
RAGFlow flourishes via open-source collaboration. In this spirit, we embrace diverse contributions from the community. If you would like to be a part, review our Contribution Guidelines first.