LRriver opened a new pull request, #281:
URL: https://github.com/apache/incubator-hugegraph-ai/pull/281
### Overview
This PR aims to introduce a semi-automated Prompt generation feature. 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**
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