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new 9231272 docs: improve README clarity and deployment instructions
(#276)
9231272 is described below
commit 9231272d04954e15578c0edbfe3af065ed3ef334
Author: Linyu <[email protected]>
AuthorDate: Wed Jun 18 20:49:59 2025 +0800
docs: improve README clarity and deployment instructions (#276)
This PR improves the README documentation with the following changes:
1. Added DeepWiki reference for AI-generated documentation
2. Restructured deployment section for better clarity:
- Renamed "Preparation" to "Deployment Options"
- Clarified Docker deployment instructions
- Simplified container startup commands
3. Fixed grammar and formatting:
- Improved sentence structures
4. Enhanced readability:
- Added clear section headers
- Improved command examples formatting
- Better organized deployment options
The changes make the documentation more user-friendly and easier to
follow, especially for new users getting started with HugeGraph-AI.
---------
Co-authored-by: imbajin <[email protected]>
---
.github/workflows/auto-pr-comment.yml | 35 +++++++++++++++++++++++++++++++
hugegraph-llm/README.md | 39 +++++++++++++++++------------------
2 files changed, 54 insertions(+), 20 deletions(-)
diff --git a/.github/workflows/auto-pr-comment.yml
b/.github/workflows/auto-pr-comment.yml
new file mode 100644
index 0000000..6a58535
--- /dev/null
+++ b/.github/workflows/auto-pr-comment.yml
@@ -0,0 +1,35 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+name: "Auto PR Commenter"
+
+on:
+ pull_request_target:
+ types: [opened]
+
+jobs:
+ add-review-comment:
+ runs-on: ubuntu-latest
+ permissions:
+ pull-requests: write
+ steps:
+ - name: Add review comment
+ uses: peter-evans/create-or-update-comment@v4
+ with:
+ issue-number: ${{ github.event.pull_request.number }}
+ body: |
+ @codecov-ai-reviewer review
diff --git a/hugegraph-llm/README.md b/hugegraph-llm/README.md
index 96cacec..8c3e296 100644
--- a/hugegraph-llm/README.md
+++ b/hugegraph-llm/README.md
@@ -15,41 +15,40 @@ graph systems and large language models.
2. Use natural language to operate graph databases (Gremlin/Cypher)
3. Knowledge graph supplements answer context (GraphRAG → Graph Agent)
+> [!NOTE]
+> For the detailed documentation generated by AI, please visit our
[DeepWiki](https://deepwiki.com/apache/incubator-hugegraph-ai) page.
+
## 2. Environment Requirements
> [!IMPORTANT]
> - python 3.10+ (not tested in 3.12)
> - hugegraph-server 1.3+ (better to use 1.5+)
> - uv 0.7+
-## 3. Preparation
+## 3. Deployment Options
+
+You can choose one of the following two deployment methods:
-### 3.1 Docker
+### 3.1 Docker Deployment
**Docker Deployment**
- Alternatively, you can deploy HugeGraph-AI using Docker:
- - Ensure you have Docker installed
- - We provide two container images:
+ Deploy HugeGraph-AI using Docker for quick setup:
+ - Ensure Docker is installed
+ - We provide two container images to choose from:
- **Image 1**:
[hugegraph/rag](https://hub.docker.com/r/hugegraph/rag/tags)
For building and running RAG functionality for rapid deployment and
direct source code modification
- **Image 2**:
[hugegraph/rag-bin](https://hub.docker.com/r/hugegraph/rag-bin/tags)
A binary translation of C compiled with Nuitka, for better performance
and efficiency.
- - Pull the Docker images:
+ - Pull one of the Docker images:
```bash
- docker pull hugegraph/rag:latest # Pull Image 1
+ docker pull hugegraph/rag:latest # Pull Image 1
docker pull hugegraph/rag-bin:latest # Pull Image 2
```
- - Start the Docker container:
- ```bash
- docker run -it --name rag -v /path/to/.env:/home/work/hugegraph-llm/.env
-p 8001:8001 hugegraph/rag bash
- docker run -it --name rag-bin -v
/path/to/.env:/home/work/hugegraph-llm/.env -p 8001:8001 hugegraph/rag-bin bash
- ```
- - Start the Graph RAG demo:
+ - Start one of the Docker containers:
```bash
- # For Image 1
- python ./src/hugegraph_llm/demo/rag_demo/app.py # or run python -m
hugegraph_llm.demo.rag_demo.app
-
- # For Image 2
- ./app.dist/app.bin
+ # Replace '/path/to/.env' with your actual .env file path
+ docker run -itd --name rag -v /path/to/.env:/home/work/hugegraph-llm/.env
-p 8001:8001 hugegraph/rag
+ # or
+ docker run -itd --name rag-bin -v
/path/to/.env:/home/work/hugegraph-llm/.env -p 8001:8001 hugegraph/rag-bin
```
- Access the interface at http://localhost:8001
@@ -103,8 +102,8 @@ graph systems and large language models.
8. (__Optional__) offline download NLTK stopwords
```bash
python ./hugegraph_llm/operators/common_op/nltk_helper.py
- ```
-> [!TIP]
+ ```
+> [!TIP]
> You can also refer to our
> [quick-start](https://github.com/apache/incubator-hugegraph-ai/blob/main/hugegraph-llm/quick_start.md)
> doc to understand how to use it & the basic query logic 🚧
## 4. Examples
