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new 5ca7a1c docs(llm): synchronization with official documentation (#273)
5ca7a1c is described below
commit 5ca7a1cc05c703132f63062820a5aa322d782b51
Author: Linyu <94553312+weijing...@users.noreply.github.com>
AuthorDate: Mon Jun 16 14:27:56 2025 +0800
docs(llm): synchronization with official documentation (#273)
## Key Updates
Synchronization with official documentation.
---------
Co-authored-by: imbajin <j...@apache.org>
---
hugegraph-llm/README.md | 16 ++++++++--------
hugegraph-llm/quick_start.md | 10 ----------
2 files changed, 8 insertions(+), 18 deletions(-)
diff --git a/hugegraph-llm/README.md b/hugegraph-llm/README.md
index ae5e281..21b7172 100644
--- a/hugegraph-llm/README.md
+++ b/hugegraph-llm/README.md
@@ -30,9 +30,9 @@ graph systems and large language models.
- Ensure you have Docker installed
- We provide two container images:
- **Image 1**:
[hugegraph/rag](https://hub.docker.com/r/hugegraph/rag/tags)
- For building and running the RAG functionality, suitable for quick
deployment and development
+ 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)
- Binary version compiled with Nuitka for more stable and efficient
performance in production
+ A binary translation of C compiled with Nuitka, for better performance
and efficiency.
- Pull the Docker images:
```bash
docker pull hugegraph/rag:latest # Pull Image 1
@@ -40,8 +40,8 @@ graph systems and large language models.
```
- Start the Docker container:
```bash
- docker run -it --name rag -p 8001:8001 hugegraph/rag bash
- docker run -it --name rag-bin -p 8001:8001 hugegraph/rag-bin 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:
```bash
@@ -60,7 +60,7 @@ graph systems and large language models.
```bash
docker run -itd --name=server -p 8080:8080 hugegraph/hugegraph
```
- You can refer to the detailed documents
[doc](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev)
for more guidance.
+ You can refer to the detailed documents
[doc](/docs/quickstart/hugegraph/hugegraph-server/#31-use-docker-container-convenient-for-testdev)
for more guidance.
2. Configuring the uv environment, Use the official installer to install uv,
See the [uv documentation](https://docs.astral.sh/uv/configuration/installer/)
for other installation methods
```bash
@@ -80,7 +80,7 @@ graph systems and large language models.
```
If dependency download fails or too slow due to network issues, it is
recommended to modify `hugegraph-llm/pyproject.toml`.
-5. Start the gradio interactive demo of **Graph RAG**, you can run with the
following command and open http://127.0.0.1:8001 after starting
+5. To start the Gradio interactive demo for **Graph RAG**, run the following
command, then open http://127.0.0.1:8001 in your browser.
```bash
python -m hugegraph_llm.demo.rag_demo.app # same as "uv run xxx"
```
@@ -97,7 +97,7 @@ graph systems and large language models.
```
Note: `Litellm` support multi-LLM provider, refer
[litellm.ai](https://docs.litellm.ai/docs/providers) to config it
7. (__Optional__) You could use
-
[hugegraph-hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble/#21-use-docker-convenient-for-testdev)
+
[hugegraph-hubble](/docs/quickstart/toolchain/hugegraph-hubble/#21-use-docker-convenient-for-testdev)
to visit the graph data, could run it via
[Docker/Docker-Compose](https://hub.docker.com/r/hugegraph/hubble)
for guidance. (Hubble is a graph-analysis dashboard that includes data
loading/schema management/graph traverser/display).
8. (__Optional__) offline download NLTK stopwords
@@ -107,7 +107,7 @@ graph systems and large language models.
> [!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
+## 4. Examples
### 4.1 Build a knowledge graph in HugeGraph through LLM
diff --git a/hugegraph-llm/quick_start.md b/hugegraph-llm/quick_start.md
index dbbfe92..dab247c 100644
--- a/hugegraph-llm/quick_start.md
+++ b/hugegraph-llm/quick_start.md
@@ -17,8 +17,6 @@ Construct a knowledge graph, chunk vector, and graph vid
vector from the text.
-
-
```mermaid
graph TD;
A[Raw Text] --> B[Text Segmentation]
@@ -30,11 +28,8 @@ graph TD;
G --> H[Store graph in Graph Database, \nautomatically vectorize vertices
\nand store in Vector Database]
I[Retrieve vertices from Graph Database] --> J[Vectorize vertices and
store in Vector Database \nNote: Incremental update]
-
```
-
-
### Four Input Fields:
- **Doc(s):** Input text
@@ -96,8 +91,6 @@ graph TD;
J --> K[Generate answer]
```
-
-
### Input Fields:
- **Question:** Input the query
@@ -172,11 +165,8 @@ graph TD;
F[Natural Language Query] --> G[Search for the most similar query \nin the
Vector Database \n(If no Gremlin pairs exist in the Vector Database,
\ndefault files will be automatically vectorized) \nand retrieve the
corresponding Gremlin]
G --> H[Add the matched pair to the prompt \nand use LLM to generate the
Gremlin \ncorresponding to the Natural Language Query]
-
```
-
-
### Input Fields for the Second Part:
- **Natural Language** **Query**: Input the natural language text to be
converted into Gremlin.
