This is an automated email from the ASF dual-hosted git repository.
liuxiaocs pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/incubator-hugegraph-ai.git
The following commit(s) were added to refs/heads/main by this push:
new a805bb2 refact: improve the graph rag experience (#53)
a805bb2 is described below
commit a805bb2817bf13cd89e6058e6e7f3c50fc842b20
Author: imbajin <[email protected]>
AuthorDate: Wed Jul 3 16:09:04 2024 +0800
refact: improve the graph rag experience (#53)
fix: use unified path & SSRF SEC problem
---
README.md | 2 +-
hugegraph-llm/README.md | 25 +++++++++++++++--------
hugegraph-llm/requirements.txt | 4 ++--
hugegraph-llm/src/hugegraph_llm/config/config.ini | 8 +++-----
4 files changed, 23 insertions(+), 16 deletions(-)
diff --git a/README.md b/README.md
index 9328561..705934d 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ in their projects.
- [hugegraph-llm](./hugegraph-llm):The `hugegraph-llm` will house the
implementation and research related to large language models.
It will include runnable demos and can also be used as a third-party library,
reducing the cost of using graph systems
and the complexity of building knowledge graphs. Graph systems can help large
models address challenges like timeliness
-and hallucination, while large models can assist graph systems with
cost-related issues. Therefore, this module will
+and hallucination, while large models can help graph systems with cost-related
issues. Therefore, this module will
explore more applications and integration solutions for graph systems and
large language models.
- [hugegraph-ml](./hugegraph-ml): The `hugegraph-ml` will focus on integrating
HugeGraph with graph machine learning,
graph neural networks, and graph embeddings libraries. It will build an
efficient and versatile intermediate layer
diff --git a/hugegraph-llm/README.md b/hugegraph-llm/README.md
index 3df3e97..2cb40aa 100644
--- a/hugegraph-llm/README.md
+++ b/hugegraph-llm/README.md
@@ -6,7 +6,7 @@ The `hugegraph-llm` is a tool for the implementation and
research related to lar
This project includes runnable demos, it can also be used as a third-party
library.
As we know, graph systems can help large models address challenges like
timeliness and hallucination,
-while large models can assist graph systems with cost-related issues.
+while large models can help graph systems with cost-related issues.
With this project, we aim to reduce the cost of using graph systems, and
decrease the complexity of
building knowledge graphs. This project will offer more applications and
integration solutions for
@@ -25,16 +25,24 @@ graph systems and large language models.
- Start the HugeGraph database, you can do it via Docker. Refer to
[docker-link](https://hub.docker.com/r/hugegraph/hugegraph) &
[deploy-doc](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev)
for guidance
- Start the gradio interactive demo, you can start with the following command,
and open http://127.0.0.1:8001 after starting
```bash
- # ${PROJECT_ROOT_DIR} is the root directory of hugegraph-ai, which needs
to be configured by yourself
+ # 0. clone the hugegraph-ai project & enter the root dir
+ # 1. configure the environment path
+ PROJECT_ROOT_DIR = "/path/to/hugegraph-ai" # root directory of hugegraph-ai
export
PYTHONPATH=${PROJECT_ROOT_DIR}/hugegraph-llm/src:${PROJECT_ROOT_DIR}/hugegraph-python-client/src
- python3 ./hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py
+
+ # 2. install the required packages/deps (better to use virtualenv(venv) to
manage the environment)
+ cd hugegraph-llm
+ pip install -r requirements.txt # ensure the python/pip version is
satisfied
+ # 2.1 set basic configs in the hugegraph-llm/config/config.ini (Optional,
you can also set it in gradio)
+
+ # 3. start the gradio server, wait for some time to initialize
+ python3 ./src/hugegraph_llm/utils/gradio_demo.py
```
-- Configure HugeGraph database connection information and LLM information,
which can be configured in two ways:
- 1. Configure the `./hugegraph-llm/src/config/config.ini` file
- 2. In gradio, after completing the configurations for LLM and HugeGraph,
click on `Initialize configs`, the complete and initialized configuration file
will be outputted.
+- Configure HugeGraph database connection information & LLM information in the
gradio interface,
+ click on `Initialize configs`, the complete and initialized configuration
file will be overwritten.
- offline download NLTK stopwords
```bash
- python3 ./hugegraph_llm/operators/common_op/nltk_helper.py
+ python3 ./src/hugegraph_llm/operators/common_op/nltk_helper.py
```
## Examples
@@ -105,7 +113,8 @@ The methods of the `KgBuilder` class can be chained
together to perform a sequen
Run example like `python3 ./hugegraph-llm/examples/graph_rag_test.py`
-The `GraphRAG` class is used to integrate HugeGraph with large language
models to provide retrieval-augmented generation capabilities. Here is a brief
usage guide:
+The `GraphRAG` class is used to integrate HugeGraph with large language models
to provide retrieval-augmented generation capabilities.
+Here is a brief usage guide:
1. **Extract Keyword:**: Extract keywords and expand synonyms.
diff --git a/hugegraph-llm/requirements.txt b/hugegraph-llm/requirements.txt
index 47a2d23..36bf09e 100644
--- a/hugegraph-llm/requirements.txt
+++ b/hugegraph-llm/requirements.txt
@@ -1,5 +1,5 @@
openai==0.28.1
retry==0.9.2
-tiktoken==0.5.1
+tiktoken==0.7.0
nltk==3.8.1
-gradio==4.19.2
+gradio==4.37.2
diff --git a/hugegraph-llm/src/hugegraph_llm/config/config.ini
b/hugegraph-llm/src/hugegraph_llm/config/config.ini
index d3ca7d3..5043fdd 100644
--- a/hugegraph-llm/src/hugegraph_llm/config/config.ini
+++ b/hugegraph-llm/src/hugegraph_llm/config/config.ini
@@ -28,26 +28,24 @@ graph = hugegraph
# type = local_api
# llm_url = http://localhost:7999/v1/chat/completions
#
-## openai
+## OpenAI
# type = openai
# api_key = xxx
# api_base = xxx
# model_name = gpt-3.5-turbo-16k
# max_token = 4000
#
-## ernie
+## WenXin (ernie)
# type = ernie
# api_key = xxx
# secret_key = xxx
-# llm_url = xxx
+# llm_url =
https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=
# model_name = ernie
#
-# type = openai
type = local_api
api_key = xxx
api_base = https://api.openai.com/v1
secret_key = xxx
-# llm_url =
https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro?access_token=
llm_url = http://localhost:7999/v1/chat/completions
model_name = gpt-3.5-turbo-16k
max_token = 4000
