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The following commit(s) were added to refs/heads/main by this push:
new 52a666e feat(llm): support semi-automated prompt generation (#281)
52a666e is described below
commit 52a666efabc879d704edc6aad3837358b2d486c1
Author: LRriver <[email protected]>
AuthorDate: Tue Jul 1 21:01:31 2025 +0800
feat(llm): support semi-automated prompt generation (#281)
### Overview
This PR aims to introduce a semi-automated Prompt generation feature
#253. This functionality leverages the capabilities of large language
models (LLMs), allowing users to generate a highly customized Prompt for
knowledge graph extraction by simply providing the original text, a
description of the desired scenario, and selecting one example from a
few system-provided examples that best matches their scenario. The
generated Prompt serves as a reference for users, reducing the
difficulty for them to get started.
### Main Changes
* **New Operator: `prompt_generate.py`**
* Adds the `PromptGenerate` operator, which is responsible for
constructing the meta-prompt by combining user inputs with a selected
few-shot example, invoking the LLM, and returning the generated prompt.
* **Built-in Prompt Examples & Configuration:**
* Adds `prompt_examples.json`, a centralized file to store multiple,
high-quality, structured few-shot examples for different domains.
* Updates `prompt_config.py` with a new meta-prompt template
(`generate_extract_prompt_template`) designed to guide the LLM in this
generation task.
* **UI & Workflow Updates:**
* Updates `vector_graph_block.py` to add a collapsible "Assist in
generating graph extraction prompts" section in the “Build RAG Index”
tab.
* This new UI section includes a dropdown for selecting few-shot
examples and a dynamic preview area that updates on selection.
* Fixes a UI "cold start" bug by implementing a `demo.load()` event to
ensure the preview for the default example is displayed on initial page
load.
### New UI interface features
**New interface location**
**Expected scenario/direction and few-shot**
**prompt generate by LLM**
---------
Co-authored-by: imbajin <[email protected]>
---
.../config/models/base_prompt_config.py | 11 +-
.../src/hugegraph_llm/config/prompt_config.py | 38 +++
.../demo/rag_demo/vector_graph_block.py | 313 ++++++++++++++-------
.../operators/llm_op/prompt_generate.py | 65 +++++
.../resources/prompt_examples/prompt_examples.json | 26 ++
5 files changed, 351 insertions(+), 102 deletions(-)
diff --git
a/hugegraph-llm/src/hugegraph_llm/config/models/base_prompt_config.py
b/hugegraph-llm/src/hugegraph_llm/config/models/base_prompt_config.py
index 563845d..691247b 100644
--- a/hugegraph-llm/src/hugegraph_llm/config/models/base_prompt_config.py
+++ b/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 @@ class BasePromptConfig:
"\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 @@ gremlin_generate_prompt: |
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)
diff --git a/hugegraph-llm/src/hugegraph_llm/config/prompt_config.py
b/hugegraph-llm/src/hugegraph_llm/config/prompt_config.py
index 01b92b7..547cda9 100644
--- a/hugegraph-llm/src/hugegraph_llm/config/prompt_config.py
+++ b/hugegraph-llm/src/hugegraph_llm/config/prompt_config.py
@@ -386,4 +386,42 @@ MAX_KEYWORDS: {max_keywords}
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":
"""
diff --git
a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
index 51af045..c353303 100644
--- a/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
+++ b/hugegraph-llm/src/hugegraph_llm/demo/rag_demo/vector_graph_block.py
@@ -18,11 +18,16 @@
# pylint: disable=E1101
import asyncio
+import json
+import os
import gradio as gr
from hugegraph_llm.config import huge_settings
from hugegraph_llm.config import prompt
+from hugegraph_llm.config import resource_path
+from hugegraph_llm.models.llms.init_llm import LLMs
+from hugegraph_llm.operators.llm_op.prompt_generate import PromptGenerate
from hugegraph_llm.utils.graph_index_utils import (
get_graph_index_info,
clean_all_graph_index,
@@ -45,115 +50,225 @@ def store_prompt(doc, schema, example_prompt):
prompt.update_yaml_file()
+def generate_prompt_for_ui(source_text, scenario, example_name):
+ """
+ Handles the UI logic for generating a new prompt. It calls the
PromptGenerate operator.
+ """
+ if not all([source_text, scenario, example_name]):
+ gr.Warning("Please provide original text, expected scenario, and
select an example!")
+ return gr.update()
+ try:
+ prompt_generator = PromptGenerate(llm=LLMs().get_chat_llm())
+ context = {
+ "source_text": source_text,
+ "scenario": scenario,
+ "example_name": example_name
+ }
+ result_context = prompt_generator.run(context)
+ # Presents the result of generating prompt
+ generated_prompt = result_context.get("generated_extract_prompt",
"Generation failed. Please check the logs.")
+ gr.Info("Prompt generated successfully!")
+ return generated_prompt
+ except Exception as e:
+ log.error("Error generating Prompt: %s", e, exc_info=True)
+ raise gr.Error(f"Error generating Prompt: {e}") from e
+
+
+def load_example_names():
+ """Load all candidate examples"""
+ try:
+ examples_path = os.path.join(resource_path, "prompt_examples",
"prompt_examples.json")
+ with open(examples_path, 'r', encoding='utf-8') as f:
+ examples = json.load(f)
+ return [example.get("name", "Unnamed example") for example in examples]
+ except (FileNotFoundError, json.JSONDecodeError):
+ return ["No available examples"]
+
+
+def update_example_preview(example_name):
+ """Update the display content based on the selected example name."""
+ try:
+ examples_path = os.path.join(resource_path, "prompt_examples",
"prompt_examples.json")
+ with open(examples_path, 'r', encoding='utf-8') as f:
+ all_examples = json.load(f)
+ selected_example = next((ex for ex in all_examples if ex.get("name")
== example_name), None)
+
+ if selected_example:
+ return (
+ selected_example.get('description', ''),
+ selected_example.get('text', ''),
+ selected_example.get('prompt', ''),
+ )
+ except (FileNotFoundError, json.JSONDecodeError) as e:
+ log.warning("Could not update example preview: %s", e)
+ return "", "", ""
+
+
+def _create_prompt_helper_block(demo, input_text, info_extract_template):
+ with gr.Accordion("Assist in generating graph extraction prompts",
open=True):
+ gr.Markdown(
+ "Provide your **original text** and **expected scenario**, "
+ "then select a reference example to generate a high-quality graph
extraction prompt."
+ )
+ user_scenario_text = gr.Textbox(
+ label="Expected scenario/direction",
+ info="For example: social relationships, financial knowledge
graphs, etc.",
+ lines=2
+ )
+ example_names = load_example_names()
+ few_shot_dropdown = gr.Dropdown(
+ choices=example_names,
+ label="Select a Few-shot example as a reference",
+ value=example_names[0] if example_names and example_names[0] !=
"No available examples" else None
+ )
+ with gr.Accordion("View example details", open=False):
+ example_desc_preview = gr.Markdown(label="Example description")
+ example_text_preview = gr.Textbox(label="Example input text",
lines=5, interactive=False)
+ example_prompt_preview = gr.Code(label="Example Graph Extract
Prompt", language="markdown",
+ interactive=False)
+
+ generate_prompt_btn = gr.Button("🚀 Auto-generate Graph Extract
Prompt", variant="primary")
+ # Bind the change event of the dropdown menu
+ few_shot_dropdown.change(
+ fn=update_example_preview,
+ inputs=[few_shot_dropdown],
+ outputs=[example_desc_preview, example_text_preview,
example_prompt_preview]
+ )
+ # Bind the click event of the generated button.
+ generate_prompt_btn.click(
+ fn=generate_prompt_for_ui,
+ inputs=[input_text, user_scenario_text, few_shot_dropdown],
+ outputs=[info_extract_template]
+ )
+
+ # Preload the page on the first load.
+ def warm_up_preview(example_name):
+ if not example_name:
+ return "", "", ""
+ return update_example_preview(example_name)
+
+ demo.load(
+ fn=warm_up_preview,
+ inputs=[few_shot_dropdown],
+ outputs=[example_desc_preview, example_text_preview,
example_prompt_preview]
+ )
+
+
def create_vector_graph_block():
# pylint: disable=no-member
# pylint: disable=C0301
# pylint: disable=unexpected-keyword-arg
- gr.Markdown(
- """## Build Vector/Graph Index & Extract Knowledge Graph
-- Docs:
- - text: Build rag index from plain text
- - file: Upload file(s) which should be <u>TXT</u> or <u>.docx</u> (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 Prompt Header: The user-defined prompt of graph extracting
-- If already exist the graph data, you should click "**Rebuild vid Index**" to
update the index
-"""
- )
-
- with gr.Row():
- with gr.Column():
- with gr.Tab("text") as tab_upload_text:
- input_text = gr.Textbox(
- value=prompt.doc_input_text,
- label="Input Doc(s)",
- lines=20,
- show_copy_button=True
- )
- with gr.Tab("file") as tab_upload_file:
- input_file = gr.File(
- value=None,
- label="Docs (multi-files can be selected together)",
- file_count="multiple",
- )
- input_schema = gr.Code(value=prompt.graph_schema, label="Graph
Schema", language="json", lines=15, max_lines=29)
- info_extract_template = gr.Code(
- value=prompt.extract_graph_prompt, label="Graph Extract Prompt
Header", language="markdown", lines=15,
- max_lines=29
+ with gr.Blocks() as demo:
+
+ gr.Markdown(
+ """## Build Vector/Graph Index & Extract Knowledge Graph
+ - Docs:
+ - text: Build rag index from plain text
+ - file: Upload file(s) which should be <u>TXT</u> or <u>.docx</u>
(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 Prompt Header: The user-defined prompt of graph extracting
+ - If already exist the graph data, you should click "**Rebuild vid
Index**" to update the index
+ """
)
- out = gr.Code(label="Output Info", language="json",
elem_classes="code-container-edit")
- with gr.Row():
- with gr.Accordion("Get RAG Info", open=False):
+ with gr.Row():
with gr.Column():
- vector_index_btn0 = gr.Button("Get Vector Index Info",
size="sm")
- graph_index_btn0 = gr.Button("Get Graph Index Info", size="sm")
- with gr.Accordion("Clear RAG Data", open=False):
- with gr.Column():
- vector_index_btn1 = gr.Button("Clear Chunks Vector Index",
size="sm")
- graph_index_btn1 = gr.Button("Clear Graph Vid Vector Index",
size="sm")
- graph_data_btn0 = gr.Button("Clear Graph Data", size="sm")
-
- vector_import_bt = gr.Button("Import into Vector", variant="primary")
- graph_extract_bt = gr.Button("Extract Graph Data (1)",
variant="primary")
- graph_loading_bt = gr.Button("Load into GraphDB (2)", interactive=True)
- graph_index_rebuild_bt = gr.Button("Update Vid Embedding")
-
- vector_index_btn0.click(get_vector_index_info, outputs=out).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- vector_index_btn1.click(clean_vector_index).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- vector_import_bt.click(build_vector_index, inputs=[input_file,
input_text], outputs=out).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- graph_index_btn0.click(get_graph_index_info, outputs=out).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- graph_index_btn1.click(clean_all_graph_index).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- graph_data_btn0.click(clean_all_graph_data).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
- graph_index_rebuild_bt.click(update_vid_embedding, outputs=out).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
-
- # origin_out = gr.Textbox(visible=False)
- graph_extract_bt.click(
- extract_graph, inputs=[input_file, input_text, input_schema,
info_extract_template], outputs=[out]
- ).then(store_prompt, inputs=[input_text, input_schema,
info_extract_template], )
-
- graph_loading_bt.click(import_graph_data, inputs=[out, input_schema],
outputs=[out]).then(update_vid_embedding).then(
- store_prompt,
- inputs=[input_text, input_schema, info_extract_template],
- )
-
- def on_tab_select(input_f, input_t, evt: gr.SelectData):
- print(f"You selected {evt.value} at {evt.index} from {evt.target}")
- if evt.value == "file":
- return input_f, ""
- if evt.value == "text":
- return [], input_t
- return [], ""
-
- tab_upload_file.select(fn=on_tab_select, inputs=[input_file, input_text],
outputs=[input_file, input_text])
- tab_upload_text.select(fn=on_tab_select, inputs=[input_file, input_text],
outputs=[input_file, input_text])
+ with gr.Tab("text") as tab_upload_text:
+ input_text = gr.Textbox(
+ value=prompt.doc_input_text,
+ label="Input Doc(s)",
+ lines=20,
+ show_copy_button=True
+ )
+ with gr.Tab("file") as tab_upload_file:
+ input_file = gr.File(
+ value=None,
+ label="Docs (multi-files can be selected together)",
+ file_count="multiple",
+ )
+ input_schema = gr.Code(value=prompt.graph_schema, label="Graph
Schema", language="json", lines=15,
+ max_lines=29)
+ info_extract_template = gr.Code(
+ value=prompt.extract_graph_prompt, label="Graph Extract Prompt
Header", language="markdown", lines=15,
+ max_lines=29
+ )
+ out = gr.Code(label="Output Info", language="json",
elem_classes="code-container-edit")
+
+ with gr.Row():
+ with gr.Accordion("Get RAG Info", open=False):
+ with gr.Column():
+ vector_index_btn0 = gr.Button("Get Vector Index Info",
size="sm")
+ graph_index_btn0 = gr.Button("Get Graph Index Info",
size="sm")
+ with gr.Accordion("Clear RAG Data", open=False):
+ with gr.Column():
+ vector_index_btn1 = gr.Button("Clear Chunks Vector Index",
size="sm")
+ graph_index_btn1 = gr.Button("Clear Graph Vid Vector
Index", size="sm")
+ graph_data_btn0 = gr.Button("Clear Graph Data", size="sm")
+
+ vector_import_bt = gr.Button("Import into Vector",
variant="primary")
+ graph_extract_bt = gr.Button("Extract Graph Data (1)",
variant="primary")
+ graph_loading_bt = gr.Button("Load into GraphDB (2)",
interactive=True)
+ graph_index_rebuild_bt = gr.Button("Update Vid Embedding")
+ gr.Markdown("---")
+ _create_prompt_helper_block(demo, input_text, info_extract_template)
+ vector_index_btn0.click(get_vector_index_info, outputs=out).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ vector_index_btn1.click(clean_vector_index).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ vector_import_bt.click(build_vector_index, inputs=[input_file,
input_text], outputs=out).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ graph_index_btn0.click(get_graph_index_info, outputs=out).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ graph_index_btn1.click(clean_all_graph_index).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ graph_data_btn0.click(clean_all_graph_data).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+ graph_index_rebuild_bt.click(update_vid_embedding, outputs=out).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+
+ # origin_out = gr.Textbox(visible=False)
+ graph_extract_bt.click(
+ extract_graph, inputs=[input_file, input_text, input_schema,
info_extract_template], outputs=[out]
+ ).then(store_prompt, inputs=[input_text, input_schema,
info_extract_template], )
+
+ graph_loading_bt.click(import_graph_data, inputs=[out, input_schema],
outputs=[out]).then(
+ update_vid_embedding).then(
+ store_prompt,
+ inputs=[input_text, input_schema, info_extract_template],
+ )
+
+ def on_tab_select(input_f, input_t, evt: gr.SelectData):
+ print(f"You selected {evt.value} at {evt.index} from {evt.target}")
+ if evt.value == "file":
+ return input_f, ""
+ if evt.value == "text":
+ return [], input_t
+ return [], ""
+
+ tab_upload_file.select(fn=on_tab_select, inputs=[input_file,
input_text], outputs=[input_file, input_text])
+ tab_upload_text.select(fn=on_tab_select, inputs=[input_file,
input_text], outputs=[input_file, input_text])
return input_text, input_schema, info_extract_template
+
async def timely_update_vid_embedding(interval_seconds: int = 3600):
"""
Periodically updates vertex embeddings in the graph database.
diff --git
a/hugegraph-llm/src/hugegraph_llm/operators/llm_op/prompt_generate.py
b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/prompt_generate.py
new file mode 100644
index 0000000..a7ea1e3
--- /dev/null
+++ b/hugegraph-llm/src/hugegraph_llm/operators/llm_op/prompt_generate.py
@@ -0,0 +1,65 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import json
+import os
+from typing import Dict, Any
+
+from hugegraph_llm.config import resource_path, prompt as prompt_tpl
+from hugegraph_llm.models.llms.base import BaseLLM
+from hugegraph_llm.utils.log import log
+
+
+class PromptGenerate:
+ def __init__(self, llm: BaseLLM):
+ self.llm = llm
+
+ def _load_few_shot_example(self, example_name: str) -> Dict[str, Any]:
+ """Loads and finds the specified few-shot example from the unified
JSON file."""
+ examples_path = os.path.join(resource_path, "prompt_examples",
"prompt_examples.json")
+ if not os.path.exists(examples_path):
+ raise FileNotFoundError(f"Examples file not found:
{examples_path}")
+ with open(examples_path, "r", encoding="utf-8") as f:
+ all_examples = json.load(f)
+ for example in all_examples:
+ if example.get("name") == example_name:
+ return example
+ raise ValueError(f"Example with name '{example_name}' not found in
prompt_examples.json")
+
+ def run(self, context: Dict[str, Any]) -> Dict[str, Any]:
+ """Executes the core logic of prompt generation."""
+ source_text = context.get("source_text")
+ scenario = context.get("scenario")
+ example_name = context.get("example_name")
+
+ if not all([source_text, scenario, example_name]):
+ raise ValueError("Missing required context: source_text, scenario,
or example_name.")
+ few_shot_example = self._load_few_shot_example(example_name)
+
+ meta_prompt = prompt_tpl.generate_extract_prompt_template.format(
+ few_shot_text=few_shot_example.get('text', ''),
+ few_shot_prompt=few_shot_example.get('prompt', ''),
+ user_text=source_text,
+ user_scenario=scenario
+ )
+ log.debug("Meta-prompt sent to LLM: %s", meta_prompt)
+ generated_prompt = self.llm.generate(prompt=meta_prompt)
+ log.debug("Generated prompt from LLM: %s", generated_prompt)
+
+ context["generated_extract_prompt"] = generated_prompt
+ return context
diff --git
a/hugegraph-llm/src/hugegraph_llm/resources/prompt_examples/prompt_examples.json
b/hugegraph-llm/src/hugegraph_llm/resources/prompt_examples/prompt_examples.json
new file mode 100644
index 0000000..f3bd33c
--- /dev/null
+++
b/hugegraph-llm/src/hugegraph_llm/resources/prompt_examples/prompt_examples.json
@@ -0,0 +1,26 @@
+[
+ {
+ "name": "Official Person-Relationship Extraction",
+ "description": "A standard template for extracting Person and Webpage
entities, along with their relationships (Roommate, Owns), from descriptive
text.",
+ "text": "Meet Sarah, a 30-year-old attorney, and her roommate, James, whom
she's shared a home with since 2010. James, in his professional life, works as
a journalist. Additionally, Sarah is the proud owner of the website
www.sarahsplace.com.",
+ "prompt": "## Main Task\nGiven the following graph schema and a piece of
text, your task is to analyze the text and extract information that fits into
the schema's structure, formatting the information into vertices and edges as
specified.\n\n## Basic Rules:\n### Schema Format:\nGraph Schema:\n-
\"vertices\": [List of vertex labels and their properties]\n- \"edges\": [List
of edge labels, their source and target vertex labels, and properties]\n\n###
Content Rule:\nPlease read the pro [...]
+ },
+ {
+ "name": "Traffic Accident Element Extraction",
+ "description": "Extracts key elements from a traffic accident report,
including persons involved, vehicles, and responsibilities.",
+ "text": "On March 15, 2024, John Smith, driving a red Porsche with license
plate NY-88888, collided with a scooter ridden by Mike Lee at the intersection
of People's Road and Liberation Road. The collision resulted in a fracture in
Mike Lee's right leg. The traffic police determined that John Smith was fully
responsible for running a red light.",
+ "prompt": "## Main Task\nGiven the following graph schema and a piece of
text about a traffic accident, your task is to extract information that fits
into the schema's structure, formatting the information into vertices and edges
as specified.\n\n## Basic Rules:\n### Schema Format:\nGraph Schema:\n-
\"vertices\": [List of vertex labels and their properties]\n- \"edges\": [List
of edge labels, their source and target vertex labels, and properties]\n\n###
Content Rule:\nPlease read the [...]
+ },
+ {
+ "name": "Financial Event Extraction",
+ "description": "Extracts key financial information such as companies,
acquisition events, and amounts from financial news.",
+ "text": "Tech giant Company A announced yesterday that it will fully
acquire startup Company B, which operates in the artificial intelligence
sector, for a price of $2 billion. The acquisition is expected to be completed
by the end of the year.",
+ "prompt": "## Main Task\nGiven the following graph schema and a piece of
financial news, your task is to extract information about corporate mergers and
acquisitions.\n\n## Basic Rules:\n### Schema Format:\nGraph Schema:\n-
\"vertices\": [List of vertex labels and their properties]\n- \"edges\": [List
of edge labels, their source and target vertex labels, and properties]\n\n###
Content Rule:\nPlease read the provided text carefully and identify any
information that corresponds to the [...]
+ },
+ {
+ "name": "Medical Diagnosis Extraction",
+ "description": "Extracts patients, symptoms, diagnosis results, and
recommended drugs from medical record text.",
+ "text": "Patient Li Hua, presents with a headache and fever for three
days. After examination, the diagnosis is a viral cold. It is recommended to
take the drug 'Gankang' for treatment.",
+ "prompt": "## Main Task\nGiven the following graph schema and a piece of
medical record, your task is to extract entities and relationships related to
diagnosis and treatment.\n\n## Basic Rules:\n### Schema Format:\nGraph
Schema:\n- \"vertices\": [List of vertex labels and their properties]\n-
\"edges\": [List of edge labels, their source and target vertex labels, and
properties]\n\n### Content Rule:\nPlease read the provided text carefully and
identify any information that correspon [...]
+ }
+]
