hugegraph-llm

Summary

The hugegraph-llm is a tool for the implementation and research related to large language models. 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.

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 graph systems and large language models.

Construct knowledge graph by LLM + HugeGraph
Use natural language to operate graph databases (gremlin)
Knowledge graph supplements answer context (RAG)

Environment Requirements

python 3.8+
hugegraph 1.0.0+

Preparation

Start the HugeGraph database, you can do it via Docker. Refer to docker-link & deploy-doc for guidance

Start the gradio interactive demo, you can start with the following command, and open http://127.0.0.1:8001 after starting

# ${PROJECT_ROOT_DIR} is the root directory of hugegraph-ai, which needs to be configured by yourself
export PYTHONPATH=${PROJECT_ROOT_DIR}/hugegraph-llm/src:${PROJECT_ROOT_DIR}/hugegraph-python-client/src
python3 ./hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py

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.

offline download NLTK stopwords

python3 ./hugegraph_llm/operators/common_op/nltk_helper.py

Examples

1.Build a knowledge graph in HugeGraph through LLM

Run example like python3 ./hugegraph-llm/examples/build_kg_test.py

The KgBuilder class is used to construct a knowledge graph. Here is a brief usage guide:

Initialization: The KgBuilder class is initialized with an instance of a language model. This can be obtained from the LLMs class.

from hugegraph_llm.llms.init_llm import LLMs
from hugegraph_llm.operators.kg_construction_task import KgBuilder

TEXT = ""
builder = KgBuilder(LLMs().get_llm())
(
    builder
    .import_schema(from_hugegraph="talent_graph").print_result()
    .extract_triples(TEXT).print_result()
    .disambiguate_word_sense().print_result()
    .commit_to_hugegraph()
    .run()
)

Import Schema: The import_schema method is used to import a schema from a source. The source can be a HugeGraph instance, a user-defined schema or an extraction result. The method print_result can be chained to print the result.

# Import schema from a HugeGraph instance
import_schema(from_hugegraph="xxx").print_result()
# Import schema from an extraction result
import_schema(from_extraction="xxx").print_result()
# Import schema from user-defined schema
import_schema(from_user_defined="xxx").print_result()

Extract Triples: The extract_triples method is used to extract triples from a text. The text should be passed as a string argument to the method.

TEXT = "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom she's shared a home with since 2010."
extract_triples(TEXT).print_result()

Disambiguate Word Sense: The disambiguate_word_sense method is used to disambiguate the sense of words in the extracted triples.
```
disambiguate_word_sense().print_result()
```
Commit to HugeGraph: The commit_to_hugegraph method is used to commit the constructed knowledge graph to a HugeGraph instance.
```
commit_to_hugegraph().print_result()
```
Run: The run method is used to execute the chained operations.
```
run()
```

The methods of the KgBuilder class can be chained together to perform a sequence of operations.

2. Retrieval augmented generation (RAG) based on HugeGraph

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:

Extract Keyword:: Extract keywords and expand synonyms.

graph_rag.extract_keyword(text="Tell me about Al Pacino.").print_result()

Query Graph for Rag: Retrieve the corresponding keywords and their multi-degree associated relationships from HugeGraph.
```
graph_rag.query_graph_for_rag(
   max_deep=2,
   max_items=30
).print_result()
```
Synthesize Answer: Summarize the results and organize the language to answer the question.
```
graph_rag.synthesize_answer().print_result()
```
Run: The run method is used to execute the above operations.
```
graph_rag.run(verbose=True)
```