feat(llm): support semi-automated prompt generation

hugegraph-llm/src/hugegraph_llm/config/models/base_prompt_config.py

@@ -15,14 +15,14 @@
 # specific language governing permissions and limitations
 # under the License.
 
-import sys
 import os
+import sys
 from pathlib import Path
 
 import yaml
 
-from hugegraph_llm.utils.log import log
 from hugegraph_llm.utils.anchor import get_project_root
+from hugegraph_llm.utils.log import log
 
 dir_name = os.path.dirname
 F_NAME = "config_prompt.yaml"
@@ -39,6 +39,7 @@ class BasePromptConfig:
     text2gql_graph_schema: str = ''
     gremlin_generate_prompt: str = ''
     doc_input_text: str = ''
+    generate_extract_prompt_template: str = ''
 
     def ensure_yaml_file_exists(self):
         current_dir = Path.cwd().resolve()
@@ -78,7 +79,9 @@ def save_to_yaml(self):
             "\n".join([f"    {line}" for line in self.keywords_extract_prompt.splitlines()])
         )
         indented_doc_input_text = "\n".join([f"  {line}" for line in self.doc_input_text.splitlines()])
-
+        indented_generate_extract_prompt = "\n".join(
+            [f"  {line}" for line in self.generate_extract_prompt_template.splitlines()]
+        ) + "\n"
         # This can be extended to add storage fields according to the data needs to be stored
         yaml_content = f"""graph_schema: |
 {indented_schema}
@@ -107,6 +110,8 @@ def save_to_yaml(self):
 doc_input_text: |
 {indented_doc_input_text}
 
+generate_extract_prompt_template: |
+{indented_generate_extract_prompt}
 """
         with open(yaml_file_path, "w", encoding="utf-8") as file:
             file.write(yaml_content)

hugegraph-llm/src/hugegraph_llm/config/prompt_config.py

@@ -386,4 +386,42 @@ class PromptConfig(BasePromptConfig):
     doc_input_text_CN: str = """介绍一下Sarah，她是一位30岁的律师，还有她的室友James，他们从2010年开始一起合租。James是一名记者，
 职业道路也很出色。另外，Sarah拥有一个个人网站www.sarahsplace.com，而James也经营着自己的网页，不过这里没有提到具体的网址。这两个人，
 Sarah和James，不仅建立起了深厚的室友情谊，还各自在网络上开辟了自己的一片天地，展示着他们各自丰富多彩的兴趣和经历。
+"""
+
+    generate_extract_prompt_template: str = """## Your Role
+You are an expert in crafting high-quality prompts for Large Language Models (LLMs), specializing in extracting graph structures from text.
+
+## Core Task
+Your goal is to generate a new, tailored "Graph Extract Prompt Header" based on user requirements and a provided example. This new prompt will be used to guide another LLM.
+
+## Input Information
+1.  **User's Source Text**: A sample of the text for extraction.
+2.  **User's Desired Scenario/Direction**: A description of the user's goal.
+3.  **A High-Quality Few-shot Example**: A complete, working example including a sample text and the corresponding full "Graph Extract Prompt".
+
+## Generation Rules
+1.  **Analyze**: Carefully analyze the user's source text and desired scenario.
+2.  **Adapt**: From the provided Few-shot Example's "Graph Extract Prompt", you must learn its structure, rules, and especially the format of the `graph schema example` and `Output example` sections.
+3.  **Create New Content**:
+    - **Infer a New Schema**: Based on the user's scenario and text, create a new `graph schema example` block.
+    - **Synthesize a New Output**: Based on the user's text and your new schema, create a new `Output example` block.
+4.  **Construct the Final Prompt**: Combine the general instructions from the Few-shot Example with your newly created `graph schema example` and `Output example` to form a complete, new "Graph Extract Prompt Header".
+
+---
+## Provided Few-shot Example (For Your Reference)
+### Example Text:
+{few_shot_text}
+
+### Corresponding "Graph Extract Prompt":
+{few_shot_prompt}
+---
+
+## User's Request (Generate a new prompt based on this)
+### User's Source Text:
+{user_text}
+
+### User's Desired Scenario/Direction:
+{user_scenario}
+
+## Your Generated "Graph Extract Prompt Header":
 """