8b8a2f2949
## Problem Description
Multiple files in the RAGFlow project contain closure trap issues when
using lambda functions with `trio.open_nursery()`. This problem causes
concurrent tasks created in loops to reference the same variable,
resulting in all tasks processing the same data (the data from the last
iteration) rather than each task processing its corresponding data from
the loop.
## Issue Details
When using a `lambda` to create a closure function and passing it to
`nursery.start_soon()` within a loop, the lambda function captures a
reference to the loop variable rather than its value. For example:
```python
# Problematic code
async with trio.open_nursery() as nursery:
for d in docs:
nursery.start_soon(lambda: doc_keyword_extraction(chat_mdl, d, topn))
```
In this pattern, when concurrent tasks begin execution, `d` has already
become the value after the loop ends (typically the last element),
causing all tasks to use the same data.
## Fix Solution
Changed the way concurrent tasks are created with `nursery.start_soon()`
by leveraging Trio's API design to directly pass the function and its
arguments separately:
```python
# Fixed code
async with trio.open_nursery() as nursery:
for d in docs:
nursery.start_soon(doc_keyword_extraction, chat_mdl, d, topn)
```
This way, each task uses the parameter values at the time of the
function call, rather than references captured through closures.
## Fixed Files
Fixed closure traps in the following files:
1. `rag/svr/task_executor.py`: 3 fixes, involving document keyword
extraction, question generation, and tag processing
2. `rag/raptor.py`: 1 fix, involving document summarization
3. `graphrag/utils.py`: 2 fixes, involving graph node and edge
processing
4. `graphrag/entity_resolution.py`: 2 fixes, involving entity resolution
and graph node merging
5. `graphrag/general/mind_map_extractor.py`: 2 fixes, involving document
processing
6. `graphrag/general/extractor.py`: 3 fixes, involving content
processing and graph node/edge merging
7. `graphrag/general/community_reports_extractor.py`: 1 fix, involving
community report extraction
## Potential Impact
This fix resolves a serious concurrency issue that could have caused:
- Data processing errors (processing duplicate data)
- Performance degradation (all tasks working on the same data)
- Inconsistent results (some data not being processed)
After the fix, all concurrent tasks should correctly process their
respective data, improving system correctness and reliability.
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📕 Table of Contents
- 💡 What is RAGFlow?
- 🎮 Demo
- 📌 Latest Updates
- 🌟 Key Features
- 🔎 System Architecture
- 🎬 Get Started
- 🔧 Configurations
- 🔧 Build a docker image without embedding models
- 🔧 Build a docker image including embedding models
- 🔨 Launch service from source for development
- 📚 Documentation
- 📜 Roadmap
- 🏄 Community
- 🙌 Contributing
💡 What is RAGFlow?
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
🎮 Demo
Try our demo at https://demo.ragflow.io.
🔥 Latest Updates
- 2025-03-19 Supports using a multi-modal model to make sense of images within PDF or DOCX files.
- 2025-02-28 Combined with Internet search (Tavily), supports reasoning like Deep Research for any LLMs.
- 2025-01-26 Optimizes knowledge graph extraction and application, offering various configuration options.
- 2024-12-18 Upgrades Document Layout Analysis model in DeepDoc.
- 2024-11-01 Adds keyword extraction and related question generation to the parsed chunks to improve the accuracy of retrieval.
- 2024-08-22 Support text to SQL statements through RAG.
🎉 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
🎬 Get Started
📝 Prerequisites
- CPU >= 4 cores
- RAM >= 16 GB
- Disk >= 50 GB
- Docker >= 24.0.0 & Docker Compose >= v2.26.1
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
v0.17.2-slimedition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different fromv0.17.2-slim, update theRAGFLOW_IMAGEvariable accordingly in docker/.env before usingdocker composeto start the server. For example: setRAGFLOW_IMAGE=infiniflow/ragflow:v0.17.2for the full editionv0.17.2.
$ cd ragflow/docker
# Use CPU for embedding and DeepDoc tasks:
$ docker compose -f docker-compose.yml up -d
# To use GPU to accelerate embedding and DeepDoc tasks:
# docker compose -f docker-compose-gpu.yml up -d
| RAGFlow image tag | Image size (GB) | Has embedding models? | Stable? |
|---|---|---|---|
| v0.17.2 | ≈9 | ✔️ | Stable release |
| v0.17.2-slim | ≈2 | ❌ | Stable release |
| nightly | ≈9 | ✔️ | Unstable nightly build |
| nightly-slim | ≈2 | ❌ | Unstable nightly build |
-
Check the server status after having the server up and running:
$ docker logs -f ragflow-serverThe 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 anormalerror 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 without embedding models
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 --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
🔧 Build a Docker image including embedding models
This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
git clone https://github.com/infiniflow/ragflow.git
cd ragflow/
docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly .
🔨 Launch service from source for development
-
Install uv, or skip this step if it is already installed:
pipx install uv -
Clone the source code and install Python dependencies:
git clone https://github.com/infiniflow/ragflow.git cd ragflow/ uv sync --python 3.10 --all-extras # install RAGFlow dependent python modules -
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 -
If you cannot access HuggingFace, set the
HF_ENDPOINTenvironment variable to use a mirror site:export HF_ENDPOINT=https://hf-mirror.com -
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:
📚 Documentation
📜 Roadmap
See the RAGFlow Roadmap 2025
🏄 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.