doc: update llm readme (#43)

* docs: update llm readme * Update hugegraph-llm/README.md Co-authored-by: imbajin <jin@apache.org> * Update hugegraph-llm/README.md --------- Co-authored-by: imbajin <jin@apache.org>

Author: ChenZiHong-Gavin

hugegraph-llm/README.md

@@ -15,69 +15,120 @@ graph systems and large language models.
 2. Use natural language to operate graph databases (gremlin)
 3. 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](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
+ # ${PROJECT_ROOT_DIR} is the root directory of hugegraph-ai, which needs to be configured by yourself
+ export PYTHONPATH=${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
+ ```bash
+ python3 ./hugegraph_llm/operators/common_op/nltk_helper.py
+ ```
+
 ## Examples
 
-### Examples (knowledge graph construction by llm)
+### 1.Build a knowledge graph in HugeGraph through LLM
 
-1. Start the HugeGraph database, you can do it via Docker. Refer to this [link](https://hub.docker.com/r/hugegraph/hugegraph) for guidance
-2. Run example like `python hugegraph-llm/examples/build_kg_test.py`
- 
-> Note: If you need a proxy to access OpenAI's API, please set your HTTP proxy in `build_kg_test.py`.
+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.
 
-```python
-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()
-)
-```
+ ```python
+ 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()
+ )
+ ```
 
 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()
-```
+ ```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()
-```
+ ```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()
-```
+ ```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()
-```
+ ```python
+ commit_to_hugegraph().print_result()
+ ```
 
 6. **Run**: The `run` method is used to execute the chained operations.
 
-```python
-run()
-```
+ ```python
+ 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:
+
+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.
+
+ ```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**: The `run` method is used to execute the above operations.
+
+ ```python
+ graph_rag.run(verbose=True)
+ ```