hugegraph-llm

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 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 graph systems and large language models.

1. Construct knowledge graph by LLM + HugeGraph
2. Use natural language to operate graph databases (Gremlin/Cypher)
3. Knowledge graph supplements answer context (GraphRAG)

Environment Requirements

• python 3.9+
• hugegraph-server 1.2+

Preparation

1. Start the HugeGraph database, you can run it via Docker/Binary Package. Refer to detailed doc for more guidance (PS: Graph visualization in step8)

2. Clone this project

   git clone https://github.com/apache/incubator-hugegraph-ai.git

3. Install hugegraph-python-client and hugegraph_llm

   cd ./incubator-hugegraph-ai # better to use virtualenv (source venv/bin/activate) 
   pip install ./hugegraph-python-client
   pip install -r ./hugegraph-llm/requirements.txt

4. Enter the project directory

   cd ./hugegraph-llm/src

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

   python3 -m hugegraph_llm.demo.rag_demo.app

   The default host is 0.0.0.0 and the port is 8001. You can change them by passing command line arguments--host and --port.

   python3 -m hugegraph_llm.demo.rag_demo.app --host 127.0.0.1 --port 18001

6. Or start the gradio interactive demo of Text2Gremlin, you can run with the following command, and open http://127.0.0.1:8002 after starting. You can also change the default host 0.0.0.0 and port 8002 as above. (🚧ing)

   python3 -m hugegraph_llm.demo.gremlin_generate_web_demo

7. After running the web demo, the config file .env will be automatically generated. You can modify its content on the web page. Or modify the file directly and restart the web application.

   (Optional)To regenerate the config file, you can use config.generate with -u or --update.

   python3 -m hugegraph_llm.config.generate --update

8. (Optional) You could use hugegraph-hubble to visit the graph data, could run it via Docker/Docker-Compose for guidance. (Hubble is a graph-analysis dashboard include data loading/schema management/graph traverser/display).

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:

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

   from hugegraph_llm.models.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()
   )

2. 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()

3. 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()

4. 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()

5. 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()

6. 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 RAGPipeline 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.

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

2. Query Graph for Rag: Retrieve the corresponding keywords and their multi-degree associated relationships from HugeGraph.

   graph_rag.query_graphdb(max_deep=2, max_items=30).print_result()

3. Synthesize Answer: Summarize the results and organize the language to answer the question.

   graph_rag.synthesize_answer().print_result()

4. Run: The run method is used to execute the above operations.

   graph_rag.run(verbose=True)