From f180a72bf6d3217bfa184816dff17cb3605d9911 Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Wed, 30 Oct 2024 06:14:00 +0800 Subject: [PATCH 1/6] refactor(hg-ai): Update the hg-ai documentation --- content/cn/docs/quickstart/hugegraph-ai.md | 291 +++++++++++--------- content/en/docs/images/gradio-kg.png | Bin 246455 -> 3931356 bytes content/en/docs/quickstart/hugegraph-ai.md | 306 ++++++++++++--------- 3 files changed, 329 insertions(+), 268 deletions(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index 705b4b148..528543837 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -1,148 +1,177 @@ ---- title: "HugeGraph-AI Quick Start" linkTitle: "使用 HugeGraph-AI" weight: 4 ---- ### 1 HugeGraph-AI 概述 hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与大模型结合的应用,与图机器学习组件的集成等,为开发者在项目中利用 HugeGraph 的 AI 能力提供全面支持。 ### 2 环境要求 -- python 3.8+ -- hugegraph 1.0.0+ +- python 3.9+ +- hugegraph-server 1.2+ ### 3 准备工作 -- 启动 HugeGraph 数据库,你可以通过 Docker 来实现。请参考这个[链接](https://hub.docker.com/r/hugegraph/hugegraph)获取指引。 -- 启动 gradio 交互式 demo,你可以通过以下命令启动,启动后打开 [http://127.0.0.1:8001](http://127.0.0.1:8001) -```bash -# ${PROJECT_ROOT_DIR} 为 hugegraph-ai 的根目录,需要自行配置 -export PYTHONPATH=${PROJECT_ROOT_DIR}/hugegraph-llm/src:${PROJECT_ROOT_DIR}/hugegraph-python-client/src -python3 ./hugegraph-llm/src/hugegraph_llm/utils/gradio_demo.py -``` -- 配置 HugeGraph 数据库连接信息和 LLM 模型信息,可以通过两种方式配置: - 1. 配置 `./hugegraph-llm/src/config/config.ini` 文件 - 2. 在 gradio 中,分别完成 LLM 和 HugeGraph 的配置后,点击 `Initialize configs`,将返回初始化后的完整配置文件。如图所示: - ![gradio 配置](/docs/images/gradio-config.png) -- 离线下载 NLTK stopwords -```bash -python3 ./hugegraph_llm/operators/common_op/nltk_helper.py -``` - - -### 4 使用说明 -#### 4.1 通过 LLM 在 HugeGraph 中构建知识图谱 -##### 4.1.1 通过 gradio 交互式界面构建知识图谱 -- 参数说明: - - Text: 输入的文本。 - - Schema:接受以下两种类型的文本: - - 用户定义的 JSON 格式模式。 - - 指定 HugeGraph 图实例的名称,它将自动提取图的模式。 - - Disambiguate word sense:是否进行词义消除歧义。 - - Commit to hugegraph:是否将构建的知识图谱提交到 HugeGraph 服务器 + +1. 启动HugeGraph数据库,可以通过[Docker](https://hub.docker.com/r/hugegraph/hugegraph) / [Binary Package](https://hugegraph.apache.org/docs/download/download/)运行它。 + 请参阅详细[文档](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev)以获取更多指导 + +2. 克隆项目 + ```bash + git clone https://github.com/apache/incubator-hugegraph-ai.git + ``` + +3. 安装 [hugegraph-python-client](../hugegraph-python-client) 和 [hugegraph_llm](src/hugegraph_llm) + ```bash + 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. 进入项目目录 + ```bash + cd ./hugegraph-llm/src + ``` + +5. 启动 **Graph RAG** 的 gradio 交互 demo,可以使用以下命令运行,启动后打开 http://127.0.0.1:8001 + ```bash + python3 -m hugegraph_llm.demo.rag_demo.app + ``` + 默认主机为 `0.0.0.0` ,端口为 `8001` 。您可以通过传递命令行参数 `--host` 和 `--port` 来更改它们。 + ```bash + python3 -m hugegraph_llm.demo.rag_demo.app --host 127.0.0.1 --port 18001 + ``` + +6. 启动 **Text2Gremlin** 的 gradio 交互演示,可以使用以下命令运行,启动后打开 http://127.0.0.1:8002。您还可以按上述方式更改默认主机 `0.0.0.0` 和端口 `8002` 。(🚧ing) + ```bash + python3 -m hugegraph_llm.demo.gremlin_generate_web_demo + ``` + +7. 在运行演示程序后,配置文件文件将被删除。`.env ` 将自动生成在 `hugegraph-llm/.env` 路径下。此外,还有一个与 prompt 相关的配置文件 `config_prompt.yaml` 。也会在`hugegraph-llm/src/hugegraph_llm/resources/demo/config_prompt.yaml`路径下生成。 + + 您可以在页面上修改内容,触发相应功能后会自动保存到配置文件中。你也可以直接修改文件而无需重启应用程序;只需刷新页面即可加载最新的更改。 + + (可选)要重新生成配置文件,您可以将 `config.generate` 与 `-u` 或 `--update` 一起使用。 + ```bash + python3 -m hugegraph_llm.config.generate --update + ``` + +9. (**可选**)您可以使用 [hugegraph-hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble/#21-use-docker-convenient-for-testdev) 来访问图形数据,可以通过 [Docker/Docker-Compose](https://hub.docker.com/r/hugegraph/hubble) 运行它以获得指导。 (Hubble 是一个图形分析仪表板,包括数据加载/模式管理/图形遍历/显示)。 + +10. (__可选__) 离线下载NLTK停用词 + + ```bash + python ./hugegraph_llm/operators/common_op/nltk_helper.py + ``` + +## 4 示例 +### 4.1 通过 LLM 在 HugeGraph 中构建知识图谱 +#### 4.1.1 通过 gradio 交互式界面构建知识图谱 + +**参数描述:** + +- Docs: + - text: 从纯文本建立 rag 索引 + - file: 上传文件:TXT.docx(可同时选择多个文件) +- [Schema](https://hugegraph.apache.org/docs/clients/restful-api/schema/):(接受**2种类型**) + - 用户定义模式( JSON 格式,遵循[模板](https://github.com/apache/incubator-hugegraph-ai/blob/aff3bbe25fa91c3414947a196131be812c20ef11/hugegraph-llm/src/hugegraph_llm/config/config_data.py#L125)来修改它) + - 指定 HugeGraph 图实例的名称,它将自动从中获取模式(如 **“hugegraph”**) +- Graph extract head: 用户自定义的图提取提示 +- 如果已经存在图数据,你应该点击 "**Rebuild vid Index**" 来更新索引 + ![gradio 配置](/docs/images/gradio-kg.png) ##### 4.1.2 通过代码构建知识图谱 -- 完整代码 -```python -from hugegraph_llm.llms.init_llm import LLMs -from hugegraph_llm.operators.kg_construction_task import KgBuilder - -llm = LLMs().get_llm() -builder = KgBuilder(llm) -( - builder - .import_schema(from_hugegraph="test_graph").print_result() - .extract_triples(TEXT).print_result() - .disambiguate_word_sense().print_result() - .commit_to_hugegraph() - .run() -) -``` -- 时序图 -![gradio 配置](/docs/images/kg-uml.png) - -1. 初始化:初始化 LLMs 实例,获取 LLM,然后创建图谱构建的任务实例 `KgBuilder`,KgBuilder 中定义了多个 operator,用户可以根据需求自由组合达到目的。(tip: `print_result()` 可以在控制台打印每一步输出的结果,不影响整体执行逻辑) - -```python -llm = LLMs().get_llm() -builder = KgBuilder(llm) -``` - -2. 导入 Schema:使用 `import_schema` 方法导入,支持三种模式: - - 从 HugeGraph 实例导入,指定 HugeGraph 图实例的名称,它将自动提取图的模式。 - - 从用户定义的模式导入,接受用户定义的 JSON 格式模式。 - - 从提取结果导入(即将发布) -```python -# Import schema from a HugeGraph instance -builder.import_schema(from_hugegraph="test_graph").print_result() -# Import schema from user-defined schema -builder.import_schema(from_user_defined="xxx").print_result() -# Import schema from an extraction result -builder.import_schema(from_extraction="xxx").print_result() -``` -3. 提取三元组:使用 `extract_triples` 方法从文本中提取三元组。 - -```python -TEXT = "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom she's shared a home with since 2010." -builder.extract_triples(TEXT).print_result() -``` -4. 消除词义歧义:使用 `disambiguate_word_sense` 方法消除词义歧义。 - -```python -builder.disambiguate_word_sense().print_result() -``` -5. 提交到 HugeGraph:使用 `commit_to_hugegraph` 方法提交构建的知识图谱到 HugeGraph 实例。 - -```python -builder.commit_to_hugegraph().print_result() -``` - -6. 运行:使用 `run` 方法执行上述操作。 -```python -builder.run() -``` + +运行示例 `python3 ./hugegraph_llm/examples/build_kg_test.py` + +`KgBuilder` 类用于构建知识图谱。下面是使用过程: + +1. **初始化**: `KgBuilder` 类使用语言模型的实例进行初始化。这可以从 `LLMs` 类中获得。 + + 初始化 `LLMs`实例,获取 `LLM`,然后创建一个任务实例 `KgBuilder` 用于图的构建。`KgBuilder` 定义了多个运算符,用户可以根据需要自由组合它们。(提示:`print_result()` 可以在控制台中打印出每一步的结果,而不会影响整个执行逻辑) + + ```python + 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() + ) + ``` + + ![gradio-config](https://hugegraph.apache.org/docs/images/kg-uml.png) + +2. **导入架构**: `import_schema` 方法用于从源导入架构。源可以是 HugeGraph 实例、用户定义的模式或提取结果。可以链接 `print_result` 方法来打印结果。 + + ```python + # 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** : `extract_triples` 方法用于从文本中提取三元组。文本应作为字符串参数传递给该方法。 + + ```python + 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. **消除词义歧义**: `disambiguate_word_sense` 方法用于消除提取的三元组中词义的歧义。 + + ```python + disambiguate_word_sense().print_result() + ``` + +5. **Commit to HugeGraph** : `commit_to_hugegraph` 方法用于将构建的知识图谱提交到 HugeGraph 实例。 + + ```python + commit_to_hugegraph().print_result() + ``` + +6. **Run** : `run` 方法用于执行链式操作。 + + ```python + run() + ``` + +`KgBuilder` 类的方法可以链接在一起以执行一系列操作。 #### 4.2 基于 HugeGraph 的检索增强生成(RAG) -##### 4.1.1 通过 gradio 交互问答 -1. 首先点击 `Initialize HugeGraph test data` 按钮,初始化 HugeGraph 数据。 - ![gradio 配置](/docs/images/gradio-rag-1.png) -2. 然后点击 `Retrieval augmented generation` 按钮,生成问题的答案。 - ![gradio 配置](/docs/images/gradio-rag-2.png) - -##### 4.1.2 通过代码构建 Graph RAG -- 完整代码 -```python -graph_rag = GraphRAG() -result = ( - graph_rag.extract_keyword(text="Tell me about Al Pacino.").print_result() - .query_graph_for_rag( - max_deep=2, - max_items=30 - ).print_result() - .synthesize_answer().print_result() - .run(verbose=True) -) -``` -1. extract_keyword: 提取关键词, 并进行近义词扩展 -```python -graph_rag.extract_keyword(text="Tell me about Al Pacino.").print_result() -``` -2. query_graph_for_rag: 从 HugeGraph 中检索对应的关键词,及其多度的关联关系 - - max_deep: hugegraph 检索的最大深度 - - max_items: hugegraph 最大返回结果数 -```python -graph_rag.query_graph_for_rag( - max_deep=2, - max_items=30 -).print_result() -``` -3. synthesize_answer: 针对提问,汇总结果,组织语言回答问题。 -```python -graph_rag.synthesize_answer().print_result() -``` -4. run: 执行上述操作。 -```python -graph_rag.run(verbose=True) -``` + +运行示例 `python3 ./hugegraph_llm/examples/graph_rag_test.py` + +`RAGPipeline` 类用于将 HugeGraph 与大型语言模型集成,以提供检索增强生成功能。下面是使用过程: + +1. **提取关键字:**提取关键字并扩展同义词。 + + ```python + graph_rag.extract_keywords(text="Tell me about Al Pacino.").print_result() + ``` + +2. **Query Graph for Rag**:从 HugeGraph 中检索对应的关键词及其多度关联关系。 + + ```python + graph_rag.query_graphdb(max_deep=2, max_items=30).print_result() + ``` +3. **综合答案**:总结结果并组织语言来回答问题。 + + ```python + graph_rag.synthesize_answer().print_result() + ``` + +4. **Run**: `run` 方法用于执行上述操作。 + + ```python + graph_rag.run(verbose=True) + ``` diff --git a/content/en/docs/images/gradio-kg.png b/content/en/docs/images/gradio-kg.png index 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z!%b(h8v>uC3vc}Oh`Z^^(YUYL2Muwa+yV{kj$@tlxb(UA_W;AMPHi5(%d+OZ?+iIF zZeqAwXiW`W^FD2FZ5F(^{46!BG?Q-^QA7$S?ly;(gtq%g$=lP%D+JF~gGG%LM|Ja3 zvS#a3?O5JA4({RSy%1AYEMDDz(9MESP89reIX^Y5B$I8nIDbb9)CiQnSuA<1Jo?Yh z>(a;OTGQf+>7OpZus0HDO`z^&TRm`N=0w0%a0|^t$)p>r@`F%wI>S+)wjLKo9~Ni$ zpF47KWln7vF1J6j%Fab|`_lo2y)BztB)L20y$CBeY4#gtuzKt)3qQXDbkyp$HkvTZ zI+uJ=%`l9fu-%Y*UTXT or .docx (Multiple files can be selected together) +- [Schema](https://hugegraph.apache.org/docs/clients/restful-api/schema/): (Accept **2 types**) + - User-defined Schema (JSON format, follow the [template](https://github.com/apache/incubator-hugegraph-ai/blob/aff3bbe25fa91c3414947a196131be812c20ef11/hugegraph-llm/src/hugegraph_llm/config/config_data.py#L125) + 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 ![gradio-config](/docs/images/gradio-kg.png) ##### 4.1.2 Build a knowledge graph through code -- Complete code -```python -from hugegraph_llm.llms.init_llm import LLMs -from hugegraph_llm.operators.kg_construction_task import KgBuilder - -llm = LLMs().get_llm() -builder = KgBuilder(llm) -( - builder - .import_schema(from_hugegraph="test_graph").print_result() - .extract_triples(TEXT).print_result() - .disambiguate_word_sense().print_result() - .commit_to_hugegraph() - .run() -) -``` -- Sequence Diagram - ![gradio-config](/docs/images/kg-uml.png) - -1. Initialize: 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) - -```python -llm = LLMs().get_llm() -builder = KgBuilder(llm) -``` -2. Import Schema: Import using the `import_schema` method, which supports three modes: - - Import from a HugeGraph instance, specify the name of the HugeGraph graph instance, and it will automatically extract the schema of the graph. - - Import from a user-defined schema, accept user-defined JSON format schema. - - Import from the extraction result (release soon) - -```python -# Import schema from a HugeGraph instance -builder.import_schema(from_hugegraph="test_graph").print_result() -# Import schema from user-defined schema -builder.import_schema(from_user_defined="xxx").print_result() -# Import schema from an extraction result -builder.import_schema(from_extraction="xxx").print_result() -``` -3. Extract triples: Use the `extract_triples` method to extract triples from the text. - -```python -TEXT = "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom she's shared a home with since 2010." -builder.extract_triples(TEXT).print_result() -``` -4. Disambiguate word sense: Use the `disambiguate_word_sense` method to disambiguate word sense. - -```python -builder.disambiguate_word_sense().print_result() -``` -5. Commit to HugeGraph: Use the `commit_to_hugegraph` method to submit the constructed knowledge graph to the HugeGraph instance. - -```python -builder.commit_to_hugegraph().print_result() -``` - -6. Run: Use the `run` method to execute the above operations. - -```python -builder.run() -``` +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. + 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) + + ```python + 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() + ) + ``` + + ![gradio-config](https://hugegraph.apache.org/docs/images/kg-uml.png) + +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. + + ```python + # 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. + + ```python + 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. + + ```python + 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. + + ```python + commit_to_hugegraph().print_result() + ``` + +6. **Run**: The `run` method is used to execute the chained operations. + + ```python + 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 -##### 4.1.1 Interactive Q&A through gradio -1. First click the `Initialize HugeGraph test data` button to initialize the HugeGraph data. - ![gradio-config](/docs/images/gradio-rag-1.png) -2. Then click the `Retrieval augmented generation` button to generate the answer to the question. - ![gradio-config](/docs/images/gradio-rag-2.png) - -##### 4.1.2 Build Graph RAG through code -- code -```python -graph_rag = GraphRAG() -result = ( - graph_rag.extract_keyword(text="Tell me about Al Pacino.").print_result() - .query_graph_for_rag( - max_deep=2, - max_items=30 - ).print_result() - .synthesize_answer().print_result() - .run(verbose=True) -) -``` -1. extract_keyword: Extract keywords and expand synonyms. - -```python -graph_rag.extract_keyword(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. - - max_deep: The maximum depth of hugegraph retrieval. - - max_items: The maximum number of results returned by hugegraph. - -```python -graph_rag.query_graph_for_rag( - max_deep=2, - max_items=30 -).print_result() -``` -3. synthesize_answer: Summarize the results and organize the language to answer the question. -```python -graph_rag.synthesize_answer().print_result() -``` -4. run: Execute the above operations. - -```python -graph_rag.run(verbose=True) -``` + +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. + + ```python + 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. + + ```python + 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. + + ```python + graph_rag.synthesize_answer().print_result() + ``` + +4. **Run**: The `run` method is used to execute the above operations. + + ```python + graph_rag.run(verbose=True) + ``` \ No newline at end of file From 359675f1c854bd5c719dad7cec1512010f36876a Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Wed, 30 Oct 2024 06:24:35 +0800 Subject: [PATCH 2/6] fix(hg-ai): img error --- content/cn/docs/quickstart/hugegraph-ai.md | 4 ++-- content/en/docs/quickstart/hugegraph-ai.md | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index 528543837..48a78cf3c 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -79,7 +79,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 - 如果已经存在图数据,你应该点击 "**Rebuild vid Index**" 来更新索引 -![gradio 配置](/docs/images/gradio-kg.png) +![gradio 配置](https://github.com/apache/incubator-hugegraph-doc/blob/master/content/cn/docs/images/gradio-kg.png?raw=true) ##### 4.1.2 通过代码构建知识图谱 @@ -145,7 +145,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 run() ``` -`KgBuilder` 类的方法可以链接在一起以执行一系列操作。 + `KgBuilder` 类的方法可以链接在一起以执行一系列操作。 #### 4.2 基于 HugeGraph 的检索增强生成(RAG) diff --git a/content/en/docs/quickstart/hugegraph-ai.md b/content/en/docs/quickstart/hugegraph-ai.md index 60d4c4235..d7f81f770 100644 --- a/content/en/docs/quickstart/hugegraph-ai.md +++ b/content/en/docs/quickstart/hugegraph-ai.md @@ -85,7 +85,7 @@ with large models, integration with graph machine learning components, etc., to - 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 -![gradio-config](/docs/images/gradio-kg.png) +![gradio-config](https://github.com/apache/incubator-hugegraph-doc/blob/master/content/cn/docs/images/gradio-kg.png?raw=true) ##### 4.1.2 Build a knowledge graph through code Run example like `python3 ./hugegraph_llm/examples/build_kg_test.py` @@ -149,8 +149,8 @@ This can be obtained from the `LLMs` class. ```python run() ``` - -The methods of the `KgBuilder` class can be chained together to perform a sequence of operations. + + 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 From 784d5fbed3b85ca575e84c2fdb1fc799fed0b383 Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Wed, 30 Oct 2024 06:26:34 +0800 Subject: [PATCH 3/6] fefactor(hg-ai): tiny fix --- content/cn/docs/quickstart/hugegraph-ai.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index 48a78cf3c..e7b01bcfe 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -57,7 +57,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 9. (**可选**)您可以使用 [hugegraph-hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble/#21-use-docker-convenient-for-testdev) 来访问图形数据,可以通过 [Docker/Docker-Compose](https://hub.docker.com/r/hugegraph/hubble) 运行它以获得指导。 (Hubble 是一个图形分析仪表板,包括数据加载/模式管理/图形遍历/显示)。 -10. (__可选__) 离线下载NLTK停用词 +10. (__可选__)离线下载NLTK停用词 ```bash python ./hugegraph_llm/operators/common_op/nltk_helper.py From c5f0b0df780cab4e29ba4fed7f2ae10d9395dc12 Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:05:03 +0800 Subject: [PATCH 4/6] doc(hg-ai): Revise according to review --- content/cn/docs/quickstart/hugegraph-ai.md | 21 ++++++++------------- 1 file changed, 8 insertions(+), 13 deletions(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index e7b01bcfe..d88958b84 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -7,26 +7,23 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 的 AI 能力提供全面支持。 ### 2 环境要求 -- python 3.9+ -- hugegraph-server 1.2+ +- python 3.9+ (better to use `3.10`) +- hugegraph-server 1.3+ ### 3 准备工作 -1. 启动HugeGraph数据库,可以通过[Docker](https://hub.docker.com/r/hugegraph/hugegraph) / [Binary Package](https://hugegraph.apache.org/docs/download/download/)运行它。 +1. 启动HugeGraph数据库,可以通过 [Docker](https://hub.docker.com/r/hugegraph/hugegraph)/[Binary Package](https://hugegraph.apache.org/docs/download/download/) 运行它。 请参阅详细[文档](https://hugegraph.apache.org/docs/quickstart/hugegraph-server/#31-use-docker-container-convenient-for-testdev)以获取更多指导 - 2. 克隆项目 ```bash git clone https://github.com/apache/incubator-hugegraph-ai.git ``` - 3. 安装 [hugegraph-python-client](../hugegraph-python-client) 和 [hugegraph_llm](src/hugegraph_llm) ```bash 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. 进入项目目录 ```bash cd ./hugegraph-llm/src @@ -83,8 +80,6 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ##### 4.1.2 通过代码构建知识图谱 -运行示例 `python3 ./hugegraph_llm/examples/build_kg_test.py` - `KgBuilder` 类用于构建知识图谱。下面是使用过程: 1. **初始化**: `KgBuilder` 类使用语言模型的实例进行初始化。这可以从 `LLMs` 类中获得。 @@ -100,8 +95,8 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ( builder .import_schema(from_hugegraph="talent_graph").print_result() - .extract_triples(TEXT).print_result() - .disambiguate_word_sense().print_result() + .chunk_split(TEXT).print_result() + .extract_info(extract_type="property_graph").print_result() .commit_to_hugegraph() .run() ) @@ -113,11 +108,11 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ```python # Import schema from a HugeGraph instance - import_schema(from_hugegraph="xxx").print_result() + builder.import_schema(from_hugegraph="xxx").print_result() # Import schema from an extraction result - import_schema(from_extraction="xxx").print_result() + builder.import_schema(from_extraction="xxx").print_result() # Import schema from user-defined schema - import_schema(from_user_defined="xxx").print_result() + builder.import_schema(from_user_defined="xxx").print_result() ``` 3. **Extract Triples** : `extract_triples` 方法用于从文本中提取三元组。文本应作为字符串参数传递给该方法。 From 836534c13304d3d1a28ebc5043c9a3a6d2a0e5d3 Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Sun, 3 Nov 2024 10:32:44 +0800 Subject: [PATCH 5/6] docs(hg-ai): sync with the hg-llm repository readme --- content/cn/docs/quickstart/hugegraph-ai.md | 78 +++++++++----------- content/en/docs/quickstart/hugegraph-ai.md | 83 ++++++++++------------ 2 files changed, 73 insertions(+), 88 deletions(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index d88958b84..e4dcedb63 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -1,6 +1,8 @@ +--- title: "HugeGraph-AI Quick Start" linkTitle: "使用 HugeGraph-AI" weight: 4 +--- ### 1 HugeGraph-AI 概述 hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与大模型结合的应用,与图机器学习组件的集成等,为开发者在项目中利用 HugeGraph @@ -28,7 +30,6 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ```bash cd ./hugegraph-llm/src ``` - 5. 启动 **Graph RAG** 的 gradio 交互 demo,可以使用以下命令运行,启动后打开 http://127.0.0.1:8001 ```bash python3 -m hugegraph_llm.demo.rag_demo.app @@ -37,25 +38,18 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ```bash python3 -m hugegraph_llm.demo.rag_demo.app --host 127.0.0.1 --port 18001 ``` - 6. 启动 **Text2Gremlin** 的 gradio 交互演示,可以使用以下命令运行,启动后打开 http://127.0.0.1:8002。您还可以按上述方式更改默认主机 `0.0.0.0` 和端口 `8002` 。(🚧ing) ```bash python3 -m hugegraph_llm.demo.gremlin_generate_web_demo ``` - 7. 在运行演示程序后,配置文件文件将被删除。`.env ` 将自动生成在 `hugegraph-llm/.env` 路径下。此外,还有一个与 prompt 相关的配置文件 `config_prompt.yaml` 。也会在`hugegraph-llm/src/hugegraph_llm/resources/demo/config_prompt.yaml`路径下生成。 - 您可以在页面上修改内容,触发相应功能后会自动保存到配置文件中。你也可以直接修改文件而无需重启应用程序;只需刷新页面即可加载最新的更改。 - (可选)要重新生成配置文件,您可以将 `config.generate` 与 `-u` 或 `--update` 一起使用。 ```bash python3 -m hugegraph_llm.config.generate --update ``` - -9. (**可选**)您可以使用 [hugegraph-hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble/#21-use-docker-convenient-for-testdev) 来访问图形数据,可以通过 [Docker/Docker-Compose](https://hub.docker.com/r/hugegraph/hubble) 运行它以获得指导。 (Hubble 是一个图形分析仪表板,包括数据加载/模式管理/图形遍历/显示)。 - -10. (__可选__)离线下载NLTK停用词 - +8. (**可选**)您可以使用 [hugegraph-hubble](https://hugegraph.apache.org/docs/quickstart/hugegraph-hubble/#21-use-docker-convenient-for-testdev) 来访问图形数据,可以通过 [Docker/Docker-Compose](https://hub.docker.com/r/hugegraph/hubble) 运行它以获得指导。 (Hubble 是一个图形分析仪表板,包括数据加载/模式管理/图形遍历/显示)。 +9. (__可选__)离线下载 NLTK 停用词 ```bash python ./hugegraph_llm/operators/common_op/nltk_helper.py ``` @@ -83,9 +77,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 `KgBuilder` 类用于构建知识图谱。下面是使用过程: 1. **初始化**: `KgBuilder` 类使用语言模型的实例进行初始化。这可以从 `LLMs` 类中获得。 - 初始化 `LLMs`实例,获取 `LLM`,然后创建一个任务实例 `KgBuilder` 用于图的构建。`KgBuilder` 定义了多个运算符,用户可以根据需要自由组合它们。(提示:`print_result()` 可以在控制台中打印出每一步的结果,而不会影响整个执行逻辑) - ```python from hugegraph_llm.models.llms.init_llm import LLMs from hugegraph_llm.operators.kg_construction_task import KgBuilder @@ -101,11 +93,8 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 .run() ) ``` - ![gradio-config](https://hugegraph.apache.org/docs/images/kg-uml.png) - 2. **导入架构**: `import_schema` 方法用于从源导入架构。源可以是 HugeGraph 实例、用户定义的模式或提取结果。可以链接 `print_result` 方法来打印结果。 - ```python # Import schema from a HugeGraph instance builder.import_schema(from_hugegraph="xxx").print_result() @@ -114,59 +103,60 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 # Import schema from user-defined schema builder.import_schema(from_user_defined="xxx").print_result() ``` - -3. **Extract Triples** : `extract_triples` 方法用于从文本中提取三元组。文本应作为字符串参数传递给该方法。 - +3. **Chunk Split** : `chunk_split` 方法用于将输入文本分割为块。文本应该作为字符串参数传递给方法。 ```python - 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() + # 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. **消除词义歧义**: `disambiguate_word_sense` 方法用于消除提取的三元组中词义的歧义。 - +4. **信息抽取**:`extract_info` 方法用于从文本中提取信息。文本应该作为字符串参数传递给方法。 ```python - disambiguate_word_sense().print_result() + 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** : `commit_to_hugegraph` 方法用于将构建的知识图谱提交到 HugeGraph 实例。 - ```python - commit_to_hugegraph().print_result() + builder.commit_to_hugegraph().print_result() ``` - 6. **Run** : `run` 方法用于执行链式操作。 - ```python - run() + builder.run() ``` - `KgBuilder` 类的方法可以链接在一起以执行一系列操作。 #### 4.2 基于 HugeGraph 的检索增强生成(RAG) -运行示例 `python3 ./hugegraph_llm/examples/graph_rag_test.py` - `RAGPipeline` 类用于将 HugeGraph 与大型语言模型集成,以提供检索增强生成功能。下面是使用过程: -1. **提取关键字:**提取关键字并扩展同义词。 - +1. **提取关键字**:提取关键字并扩展同义词。 ```python + 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. **Query Graph for Rag**:从 HugeGraph 中检索对应的关键词及其多度关联关系。 - +2. **根据关键字匹配 Vid**::将节点与图中的关键字匹配。 + ```python + graph_rag.keywords_to_vid().print_result() + ``` +3. **RAG 的查询图**:从 HugeGraph 中检索对应的关键词及其多度关联关系。 ```python graph_rag.query_graphdb(max_deep=2, max_items=30).print_result() ``` -3. **综合答案**:总结结果并组织语言来回答问题。 - +4. **重新排序搜索结果**:根据问题与结果之间的相似性对搜索结果进行重新排序。 ```python - graph_rag.synthesize_answer().print_result() + graph_rag.merge_dedup_rerank().print_result() ``` - -4. **Run**: `run` 方法用于执行上述操作。 - +5. **合成答案**:总结结果并组织语言来回答问题。 + ```python + graph_rag.synthesize_answer(vector_only_answer=False, graph_only_answer=True).print_result() + ``` +6. **运行**:`run` 方法用于执行上述操作。 ```python graph_rag.run(verbose=True) ``` diff --git a/content/en/docs/quickstart/hugegraph-ai.md b/content/en/docs/quickstart/hugegraph-ai.md index d7f81f770..b6efc63ae 100644 --- a/content/en/docs/quickstart/hugegraph-ai.md +++ b/content/en/docs/quickstart/hugegraph-ai.md @@ -87,15 +87,13 @@ with large models, integration with graph machine learning components, etc., to ![gradio-config](https://github.com/apache/incubator-hugegraph-doc/blob/master/content/cn/docs/images/gradio-kg.png?raw=true) -##### 4.1.2 Build a knowledge graph through code -Run example like `python3 ./hugegraph_llm/examples/build_kg_test.py` +#### 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) - ```python from hugegraph_llm.models.llms.init_llm import LLMs from hugegraph_llm.operators.kg_construction_task import KgBuilder @@ -105,80 +103,77 @@ This can be obtained from the `LLMs` class. ( builder .import_schema(from_hugegraph="talent_graph").print_result() - .extract_triples(TEXT).print_result() - .disambiguate_word_sense().print_result() + .chunk_split(TEXT).print_result() + .extract_info(extract_type="property_graph").print_result() .commit_to_hugegraph() .run() ) ``` - ![gradio-config](https://hugegraph.apache.org/docs/images/kg-uml.png) - 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. - ```python # Import schema from a HugeGraph instance - import_schema(from_hugegraph="xxx").print_result() + builder.import_schema(from_hugegraph="xxx").print_result() # Import schema from an extraction result - import_schema(from_extraction="xxx").print_result() + builder.import_schema(from_extraction="xxx").print_result() # Import schema from user-defined schema - import_schema(from_user_defined="xxx").print_result() + builder.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. - +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. ```python - 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() + # 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. **Disambiguate Word Sense**: The `disambiguate_word_sense` method is used to disambiguate the sense of words in the extracted triples. - +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. ```python - disambiguate_word_sense().print_result() + 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. - ```python - commit_to_hugegraph().print_result() + builder.commit_to_hugegraph().print_result() ``` - 6. **Run**: The `run` method is used to execute the chained operations. - ```python - run() + builder.run() ``` - - The methods of the `KgBuilder` class can be chained together to perform a sequence of operations. + 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 - -Run example like `python3 ./hugegraph_llm/examples/graph_rag_test.py` +### 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. - +1. **Extract Keyword**: Extract keywords and expand synonyms. ```python + 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. **Query Graph for Rag**: Retrieve the corresponding keywords and their multi-degree associated relationships from HugeGraph. - +2. **Match Vid from Keywords**: Match the nodes with the keywords in the graph. + ```python + graph_rag.keywords_to_vid().print_result() + ``` +3. **Query Graph for Rag**: Retrieve the corresponding keywords and their multi-degree associated relationships from HugeGraph. ```python 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. - +4. **Rerank Searched Result**: Rerank the searched results based on the similarity between the question and the results. + ```python + graph_rag.merge_dedup_rerank().print_result() + ``` +5. **Synthesize Answer**: Summarize the results and organize the language to answer the question. ```python - graph_rag.synthesize_answer().print_result() + graph_rag.synthesize_answer(vector_only_answer=False, graph_only_answer=True).print_result() ``` - -4. **Run**: The `run` method is used to execute the above operations. - +6. **Run**: The `run` method is used to execute the above operations. ```python graph_rag.run(verbose=True) - ``` \ No newline at end of file + ``` From 3d140dbd9b1d54d4c5560e823651ca63a67b8987 Mon Sep 17 00:00:00 2001 From: Hongjun Li <103014488+returnToInnocence@users.noreply.github.com> Date: Tue, 5 Nov 2024 18:49:03 +0800 Subject: [PATCH 6/6] chore(hg-ai): link style changes --- content/cn/docs/quickstart/hugegraph-ai.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/cn/docs/quickstart/hugegraph-ai.md b/content/cn/docs/quickstart/hugegraph-ai.md index e4dcedb63..ba06fe977 100644 --- a/content/cn/docs/quickstart/hugegraph-ai.md +++ b/content/cn/docs/quickstart/hugegraph-ai.md @@ -38,7 +38,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 ```bash python3 -m hugegraph_llm.demo.rag_demo.app --host 127.0.0.1 --port 18001 ``` -6. 启动 **Text2Gremlin** 的 gradio 交互演示,可以使用以下命令运行,启动后打开 http://127.0.0.1:8002。您还可以按上述方式更改默认主机 `0.0.0.0` 和端口 `8002` 。(🚧ing) +6. 启动 **Text2Gremlin** 的 gradio 交互演示,可以使用以下命令运行,启动后打开 http://127.0.0.1:8002 ,您还可以按上述方式更改默认主机 `0.0.0.0` 和端口 `8002` 。(🚧ing) ```bash python3 -m hugegraph_llm.demo.gremlin_generate_web_demo ``` @@ -143,7 +143,7 @@ hugegraph-ai 旨在探索 HugeGraph 与人工智能(AI)的融合,包括与 2. **根据关键字匹配 Vid**::将节点与图中的关键字匹配。 ```python graph_rag.keywords_to_vid().print_result() - ``` + ``` 3. **RAG 的查询图**:从 HugeGraph 中检索对应的关键词及其多度关联关系。 ```python graph_rag.query_graphdb(max_deep=2, max_items=30).print_result()