HugeGraph-AI Quick Start

1 HugeGraph-AI Overview

hugegraph-ai aims to explore the integration of HugeGraph and artificial intelligence (AI), including applications combined with large models, integration with graph machine learning components, etc., to provide comprehensive support for developers to use HugeGraph’s AI capabilities in projects.

2 Environment Requirements

• python 3.9+
• hugegraph-server 1.2+

3 Preparation

1. Start the HugeGraph database, you can run it via Docker/Binary Package.
   Refer to detailed doc for more guidance

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 at the path hugegraph-llm/.env. Additionally, a prompt-related configuration file config_prompt.yaml will also be generated at the path hugegraph-llm/src/hugegraph_llm/resources/demo/config_prompt.yaml.

   You can modify the content on the web page, and it will be automatically saved to the configuration file after the corresponding feature is triggered. You can also modify the file directly without restarting the web application; simply refresh the page to load your latest changes.

   (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).

9. (Optional) offline download NLTK stopwords

   python ./hugegraph_llm/operators/common_op/nltk_helper.py
   

4 Examples

4.1 Build a knowledge graph in HugeGraph through LLM

4.1.1 Build a knowledge graph through the gradio interactive interface

Parameter description:

• Docs:
  • text: Build rag index from plain text
  • file: Upload file(s) which should be TXT or .docx (Multiple files can be selected together)
• Schema: (Accept 2 types)
  • User-defined Schema (JSON format, follow the template to modify it)
  • Specify the name of the HugeGraph graph instance, it will automatically get the schema from it (like “hugegraph”)
• Graph extract head: The user-defined prompt of graph extracting
• If already exist the graph data, you should click “Rebuild vid Index” to update the index

4.1.2 Build a knowledge graph through code

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.
   Initialize the LLMs instance, get the LLM, and then create a task instance KgBuilder for graph construction. KgBuilder defines multiple operators, and users can freely combine them according to their needs. (tip: print_result() can print the result of each step in the console, without affecting the overall execution logic)

   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()
       .chunk_split(TEXT).print_result()
       .extract_info(extract_type="property_graph").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
   builder.import_schema(from_hugegraph="xxx").print_result()
   # Import schema from an extraction result
   builder.import_schema(from_extraction="xxx").print_result()
   # Import schema from user-defined schema
   builder.import_schema(from_user_defined="xxx").print_result()
   

3. Chunk Split: The chunk_split method is used to split the input text into chunks. The text should be passed as a string argument to the method.

   # Split the input text into documents
   builder.chunk_split(TEXT, split_type="document").print_result()
   # Split the input text into paragraphs
   builder.chunk_split(TEXT, split_type="paragraph").print_result()
   # Split the input text into sentences
   builder.chunk_split(TEXT, split_type="sentence").print_result()
   

4. Extract Info: The extract_info method is used to extract info 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 property graph from the input text
   builder.extract_info(extract_type="property_graph").print_result()
   # extract triples from the input text
   builder.extract_info(extract_type="property_graph").print_result()
   

5. Commit to HugeGraph: The commit_to_hugegraph method is used to commit the constructed knowledge graph to a HugeGraph instance.

   builder.commit_to_hugegraph().print_result()
   

6. Run: The run method is used to execute the chained operations.

   builder.run()
   

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

4.2 Retrieval augmented generation (RAG) based on HugeGraph

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.

   from hugegraph_llm.operators.graph_rag_task import RAGPipeline
   graph_rag = RAGPipeline()
   graph_rag.extract_keywords(text="Tell me about Al Pacino.").print_result()
   

2. Match Vid from Keywords: Match the nodes with the keywords in the graph.

   graph_rag.keywords_to_vid().print_result()
   

3. 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()
   

4. Rerank Searched Result: Rerank the searched results based on the similarity between the question and the results.

   graph_rag.merge_dedup_rerank().print_result()
   

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

   graph_rag.synthesize_answer(vector_only_answer=False, graph_only_answer=True).print_result()
   

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

   graph_rag.run(verbose=True)