Merge branch 'main' into postgres-improve-entities-relation-save-process

Dockerfile

@@ -1,13 +1,23 @@
 # Build stage
-FROM python:3.11-slim as builder
+FROM python:3.11-slim AS builder
 
 WORKDIR /app
 
+# Install Rust and required build dependencies
+RUN apt-get update && apt-get install -y \
+    curl \
+    build-essential \
+    pkg-config \
+    && rm -rf /var/lib/apt/lists/* \
+    && curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \
+    && . $HOME/.cargo/env
+
 # Copy only requirements files first to leverage Docker cache
 COPY requirements.txt .
 COPY lightrag/api/requirements.txt ./lightrag/api/
 
 # Install dependencies
+ENV PATH="/root/.cargo/bin:${PATH}"
 RUN pip install --user --no-cache-dir -r requirements.txt
 RUN pip install --user --no-cache-dir -r lightrag/api/requirements.txt
 
@@ -28,6 +38,10 @@ ENV PATH=/root/.local/bin:$PATH
 # Create necessary directories
 RUN mkdir -p /app/data/rag_storage /app/data/inputs
 
+# Docker data directories
+ENV WORKING_DIR=/app/data/rag_storage
+ENV INPUT_DIR=/app/data/inputs
+
 # Expose the default port
 EXPOSE 9621
 

README.md

@@ -37,28 +37,30 @@ This repository hosts the code of LightRAG. The structure of this code is based
 </br>
 
 
+
+
+
 <details>
   <summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
     🎉 News
   </summary>
 
-
+- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
-- [x] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
+- [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
-- [x] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
+- [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
-- [x] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
+- [X] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
-- [x] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
+- [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
-- [x] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
+- [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
-- [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
+- [X] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py).
-- [x] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py).
+- [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
-- [x] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
+- [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
-- [x] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
+- [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
-- [x] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
+- [X] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
-- [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
+- [X] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
-- [x] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
+- [X] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
-- [x] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
+- [X] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
-- [x] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
+- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
-- [x] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
+- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
-- [x] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
 
 </details>
 
@@ -82,16 +84,20 @@ This repository hosts the code of LightRAG. The structure of this code is based
 cd LightRAG
 pip install -e .
 ```
+
 * Install from PyPI
+
 ```bash
 pip install lightrag-hku
 ```
 
 ## Quick Start
+
 * [Video demo](https://www.youtube.com/watch?v=g21royNJ4fw) of running LightRAG locally.
 * All the code can be found in the `examples`.
 * Set OpenAI API key in environment if using OpenAI models: `export OPENAI_API_KEY="sk-...".`
 * Download the demo text "A Christmas Carol by Charles Dickens":
+
 ```bash
 curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
 ```
@@ -187,6 +193,7 @@ class QueryParam:
 <summary> <b>Using Open AI-like APIs</b> </summary>
 
 * LightRAG also supports Open AI-like chat/embeddings APIs:
+
 ```python
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
@@ -225,6 +232,7 @@ async def initialize_rag():
 
     return rag
 ```
+
 </details>
 
 <details>
@@ -252,12 +260,14 @@ rag = LightRAG(
     ),
 )
 ```
+
 </details>
 
 <details>
 <summary> <b>Using Ollama Models</b> </summary>
 
 ### Overview
+
 If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example `nomic-embed-text`.
 
 Then you only need to set LightRAG as follows:
@@ -281,31 +291,37 @@ rag = LightRAG(
 ```
 
 ### Increasing context size
+
 In order for LightRAG to work context should be at least 32k tokens. By default Ollama models have context size of 8k. You can achieve this using one of two ways:
 
 #### Increasing the `num_ctx` parameter in Modelfile.
 
 1. Pull the model:
+
 ```bash
 ollama pull qwen2
 ```
 
 2. Display the model file:
+
 ```bash
 ollama show --modelfile qwen2 > Modelfile
 ```
 
 3. Edit the Modelfile by adding the following line:
+
 ```bash
 PARAMETER num_ctx 32768
 ```
 
 4. Create the modified model:
+
 ```bash
 ollama create -f Modelfile qwen2m
 ```
 
 #### Setup `num_ctx` via Ollama API.
+
 Tiy can use `llm_model_kwargs` param to configure ollama:
 
 ```python
@@ -325,6 +341,7 @@ rag = LightRAG(
     ),
 )
 ```
+
 #### Low RAM GPUs
 
 In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using `gemma2:2b`. It was able to find 197 entities and 19 relations on `book.txt`.
@@ -402,6 +419,7 @@ if __name__ == "__main__":
 ```
 
 #### For detailed documentation and examples, see:
+
 - [LlamaIndex Documentation](lightrag/llm/Readme.md)
 - [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py)
 - [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py)
@@ -483,13 +501,16 @@ print(response_custom)
 We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords.
 
 ##### How It Works?
+
 The function operates by dividing the input into two parts:
+
 - `User Query`
 - `Prompt`
 
 It then performs keyword extraction exclusively on the `user query`. This separation ensures that the extraction process is focused and relevant, unaffected by any additional language in the `prompt`. It also allows the `prompt` to serve purely for response formatting, maintaining the intent and clarity of the user's original question.
 
 ##### Usage Example
+
 This `example` shows how to tailor the function for educational content, focusing on detailed explanations for older students.
 
 ```python
@@ -563,6 +584,7 @@ custom_kg = {
 
 rag.insert_custom_kg(custom_kg)
 ```
+
 </details>
 
 ## Insert
@@ -593,6 +615,7 @@ rag.insert(["TEXT1", "TEXT2", "TEXT3", ...])  # Documents will be processed in b
 ```
 
 The `insert_batch_size` parameter in `addon_params` controls how many documents are processed in each batch during insertion. This is useful for:
+
 - Managing memory usage with large document collections
 - Optimizing processing speed
 - Providing better progress tracking
@@ -647,6 +670,7 @@ text_content = textract.process(file_path)
 
 rag.insert(text_content.decode('utf-8'))
 ```
+
 </details>
 
 ## Storage
@@ -685,6 +709,7 @@ async def initialize_rag():
 
     return rag
 ```
+
 see test_neo4j.py for a working example.
 
 </details>
@@ -693,6 +718,7 @@ see test_neo4j.py for a working example.
 <summary> <b>Using PostgreSQL for Storage</b> </summary>
 
 For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
+
 * PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
 * If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
 * How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
@@ -735,6 +761,7 @@ For production level scenarios you will most likely want to leverage an enterpri
   > It is a known issue of the release version: https://github.com/apache/age/pull/1721
   >
   > You can Compile the AGE from source code and fix it.
+  >
 
 </details>
 
@@ -742,9 +769,11 @@ For production level scenarios you will most likely want to leverage an enterpri
 <summary> <b>Using Faiss for Storage</b> </summary>
 
 - Install the required dependencies:
+
 ```
 pip install faiss-cpu
 ```
+
 You can also install `faiss-gpu` if you have GPU support.
 
 - Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
@@ -810,6 +839,7 @@ relation = rag.create_relation("Google", "Gmail", {
     "weight": 2.0
 })
 ```
+
 </details>
 
 <details>
@@ -835,6 +865,7 @@ updated_relation = rag.edit_relation("Google", "Google Mail", {
     "weight": 3.0
 })
 ```
+
 </details>
 
 All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`).
@@ -851,6 +882,55 @@ All operations are available in both synchronous and asynchronous versions. The
 
 These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent.
 
+## Data Export Functions
+
+## Overview
+
+LightRAG allows you to export your knowledge graph data in various formats for analysis, sharing, and backup purposes. The system supports exporting entities, relations, and relationship data.
+
+## Export Functions
+
+### Basic Usage
+
+```python
+# Basic CSV export (default format)
+rag.export_data("knowledge_graph.csv")
+
+# Specify any format
+rag.export_data("output.xlsx", file_format="excel")
+```
+
+### Different File Formats supported
+
+```python
+#Export data in CSV format
+rag.export_data("graph_data.csv", file_format="csv")
+
+# Export data in Excel sheet
+rag.export_data("graph_data.xlsx", file_format="excel")
+
+# Export data in markdown format
+rag.export_data("graph_data.md", file_format="md")
+
+# Export data in Text
+rag.export_data("graph_data.txt", file_format="txt")
+```
+## Additional Options
+
+Include vector embeddings in the export (optional):
+
+```python
+rag.export_data("complete_data.csv", include_vector_data=True)
+```
+## Data Included in Export
+
+All exports include:
+
+* Entity information (names, IDs, metadata)
+* Relation data (connections between entities)
+* Relationship information from vector database
+
+
 ## Entity Merging
 
 <details>
@@ -913,6 +993,7 @@ rag.merge_entities(
 ```
 
 When merging entities:
+
 * All relationships from source entities are redirected to the target entity
 * Duplicate relationships are intelligently merged
 * Self-relationships (loops) are prevented
@@ -946,6 +1027,7 @@ rag.clear_cache(modes=["local"])
 ```
 
 Valid modes are:
+
 - `"default"`: Extraction cache
 - `"naive"`: Naive search cache
 - `"local"`: Local search cache
@@ -961,7 +1043,7 @@ Valid modes are:
 <summary> Parameters </summary>
 
 | **Parameter**                                | **Type**    | **Explanation**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | **Default**                                                                                             |
-|----------------------------------------------| --- |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
+| -------------------------------------------------- | ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
 | **working\_dir**                             | `str`           | Directory where the cache will be stored                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `lightrag_cache+timestamp`                                                                                  |
 | **kv\_storage**                              | `str`           | Storage type for documents and text chunks. Supported types:`JsonKVStorage`, `OracleKVStorage`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | `JsonKVStorage`                                                                                             |
 | **vector\_storage**                          | `str`           | Storage type for embedding vectors. Supported types:`NanoVectorDBStorage`, `OracleVectorDBStorage`                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | `NanoVectorDBStorage`                                                                                       |
@@ -979,14 +1061,14 @@ Valid modes are:
 | **llm\_model\_func**                         | `callable`      | Function for LLM generation                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | `gpt_4o_mini_complete`                                                                                      |
 | **llm\_model\_name**                         | `str`           | LLM model name for generation                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | `meta-llama/Llama-3.2-1B-Instruct`                                                                          |
 | **llm\_model\_max\_token\_size**             | `int`           | Maximum token size for LLM generation (affects entity relation summaries)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `32768`（default value changed by  env var MAX_TOKENS)                                                      |
-| **llm\_model\_max\_async**                   | `int` | Maximum number of concurrent asynchronous LLM processes                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | `16`（default value changed by  env var MAX_ASYNC)                                           |
+| **llm\_model\_max\_async**                   | `int`           | Maximum number of concurrent asynchronous LLM processes                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | `4`（default value changed by  env var MAX_ASYNC)                                                          |
 | **llm\_model\_kwargs**                       | `dict`          | Additional parameters for LLM generation                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |                                                                                                               |
 | **vector\_db\_storage\_cls\_kwargs**         | `dict`          | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | cosine_better_than_threshold: 0.2（default value changed by  env var COSINE_THRESHOLD)                        |
 | **enable\_llm\_cache**                       | `bool`          | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    | `TRUE`                                                                                                      |
 | **enable\_llm\_cache\_for\_entity\_extract** | `bool`          | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | `TRUE`                                                                                                      |
 | **addon\_params**                            | `dict`          | Additional parameters, e.g.,`{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing                                                                                                                                                                                                                                                                                                                                                                                                                                             | `example_number: all examples, language: English, insert_batch_size: 10`                                    |
 | **convert\_response\_to\_json\_func**        | `callable`      | Not used                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | `convert_response_to_json`                                                                                  |
-| **embedding\_cache\_config**                 | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}`                         |
+| **embedding\_cache\_config**                 | `dict`          | Configuration for question-answer caching. Contains three parameters:`<br>`- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.`<br>`- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.`<br>`- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default:`{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}`                          |
 
 </details>
 
@@ -996,12 +1078,15 @@ Valid modes are:
 <summary>Click to view error handling details</summary>
 
 The API includes comprehensive error handling:
+
 - File not found errors (404)
 - Processing errors (500)
 - Supports multiple file encodings (UTF-8 and GBK)
+
 </details>
 
 ## API
+
 LightRag can be installed with API support to serve a Fast api interface to perform data upload and indexing/Rag operations/Rescan of the input folder etc..
 
 [LightRag API](lightrag/api/README.md)
@@ -1035,7 +1120,6 @@ net.show('knowledge_graph.html')
 <details>
 <summary> <b>Graph visualization with Neo4</b> </summary>
 
-
 * The following code can be found in `examples/graph_visual_with_neo4j.py`
 
 ```python
@@ -1171,10 +1255,13 @@ LightRag can be installed with Tools support to add extra tools like the graphml
 </details>
 
 ## Evaluation
+
 ### Dataset
+
 The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
 
 ### Generate Query
+
 LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`.
 
 <details>
@@ -1203,9 +1290,11 @@ Output the results in the following structure:
 - User 5: [user description]
     ...
 ```
+
 </details>
 
 ### Batch Eval
+
 To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
 
 <details>
@@ -1253,12 +1342,13 @@ Output your evaluation in the following JSON format:
     }}
 }}
 ```
+
 </details>
 
 ### Overall Performance Table
 
 |                             | **Agriculture** |                    | **CS** |                    | **Legal** |                    | **Mix**   |                    |
-|----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
+| --------------------------- | --------------------- | ------------------ | ------------ | ------------------ | --------------- | ------------------ | --------------- | ------------------ |
 |                             | NaiveRAG              | **LightRAG** | NaiveRAG     | **LightRAG** | NaiveRAG        | **LightRAG** | NaiveRAG        | **LightRAG** |
 | **Comprehensiveness** | 32.4%                 | **67.6%**    | 38.4%        | **61.6%**    | 16.4%           | **83.6%**    | 38.8%           | **61.2%**    |
 | **Diversity**         | 23.6%                 | **76.4%**    | 38.0%        | **62.0%**    | 13.6%           | **86.4%**    | 32.4%           | **67.6%**    |
@@ -1281,9 +1371,11 @@ Output your evaluation in the following JSON format:
 | **Overall**           | 45.2%                 | **54.8%**    | 48.0%        | **52.0%**    | 47.2%           | **52.8%**    | **50.4%** | 49.6%              |
 
 ## Reproduce
+
 All the code can be found in the `./reproduce` directory.
 
 ### Step-0 Extract Unique Contexts
+
 First, we need to extract unique contexts in the datasets.
 
 <details>
@@ -1340,9 +1432,11 @@ def extract_unique_contexts(input_directory, output_directory):
     print("All files have been processed.")
 
 ```
+
 </details>
 
 ### Step-1 Insert Contexts
+
 For the extracted contexts, we insert them into the LightRAG system.
 
 <details>
@@ -1366,6 +1460,7 @@ def insert_text(rag, file_path):
     if retries == max_retries:
         print("Insertion failed after exceeding the maximum number of retries")
 ```
+
 </details>
 
 ### Step-2 Generate Queries
@@ -1390,9 +1485,11 @@ def get_summary(context, tot_tokens=2000):
 
     return summary
 ```
+
 </details>
 
 ### Step-3 Query
+
 For the queries generated in Step-2, we will extract them and query LightRAG.
 
 <details>
@@ -1409,6 +1506,7 @@ def extract_queries(file_path):
 
     return queries
 ```
+
 </details>
 
 ## Star History
@@ -1441,4 +1539,5 @@ archivePrefix={arXiv},
 primaryClass={cs.IR}
 }
 ```
+
 **Thank you for your interest in our work!**

env.example

@@ -50,7 +50,8 @@
 # MAX_TOKEN_SUMMARY=500        # Max tokens for entity or relations summary
 # SUMMARY_LANGUAGE=English
 # MAX_EMBED_TOKENS=8192
-# ENABLE_LLM_CACHE_FOR_EXTRACT=false  # Enable LLM cache for entity extraction, defaults to false
+# ENABLE_LLM_CACHE_FOR_EXTRACT=true  # Enable LLM cache for entity extraction
+# MAX_PARALLEL_INSERT=2        # Maximum number of parallel processing documents in pipeline
 
 ### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
 LLM_BINDING=ollama

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "1.2.5"
+__version__ = "1.2.6"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/api/README.md

@@ -224,7 +224,7 @@ LightRAG supports binding to various LLM/Embedding backends:
 Use environment variables  `LLM_BINDING` or CLI argument `--llm-binding` to select LLM backend type. Use environment variables  `EMBEDDING_BINDING` or CLI argument `--embedding-binding` to select LLM backend type.
 
 ### Entity Extraction Configuration
-* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: false)
+* ENABLE_LLM_CACHE_FOR_EXTRACT: Enable LLM cache for entity extraction (default: true)
 
 It's very common to set `ENABLE_LLM_CACHE_FOR_EXTRACT` to true for test environment to reduce the cost of LLM calls.
 

lightrag/api/docs/LightRagWithPostGRESQL.md

@@ -141,7 +141,7 @@ Start the LightRAG server using specified options:
 lightrag-server --port 9621 --key sk-somepassword --kv-storage PGKVStorage --graph-storage PGGraphStorage --vector-storage PGVectorStorage --doc-status-storage PGDocStatusStorage
 ```
 
-Replace `the-port-number` with your desired port number (default is 9621) and `your-secret-key` with a secure key.
+Replace the `port` number with your desired port number (default is 9621) and `your-secret-key` with a secure key.
 
 ## Conclusion
 

lightrag/api/gunicorn_config.py

@@ -59,7 +59,7 @@ logconfig_dict = {
     },
     "filters": {
         "path_filter": {
-            "()": "lightrag.api.lightrag_server.LightragPathFilter",
+            "()": "lightrag.utils.LightragPathFilter",
         },
     },
     "loggers": {

lightrag/api/lightrag_server.py

@@ -55,41 +55,6 @@ config = configparser.ConfigParser()
 config.read("config.ini")
 
 
-class LightragPathFilter(logging.Filter):
-    """Filter for lightrag logger to filter out frequent path access logs"""
-
-    def __init__(self):
-        super().__init__()
-        # Define paths to be filtered
-        self.filtered_paths = ["/documents", "/health", "/webui/"]
-
-    def filter(self, record):
-        try:
-            # Check if record has the required attributes for an access log
-            if not hasattr(record, "args") or not isinstance(record.args, tuple):
-                return True
-            if len(record.args) < 5:
-                return True
-
-            # Extract method, path and status from the record args
-            method = record.args[1]
-            path = record.args[2]
-            status = record.args[4]
-
-            # Filter out successful GET requests to filtered paths
-            if (
-                method == "GET"
-                and (status == 200 or status == 304)
-                and path in self.filtered_paths
-            ):
-                return False
-
-            return True
-        except Exception:
-            # In case of any error, let the message through
-            return True
-
-
 def create_app(args):
     # Setup logging
     logger.setLevel(args.log_level)
@@ -177,6 +142,9 @@ def create_app(args):
         if api_key
         else "",
         version=__api_version__,
+        openapi_url="/openapi.json",  # Explicitly set OpenAPI schema URL
+        docs_url="/docs",  # Explicitly set docs URL
+        redoc_url="/redoc",  # Explicitly set redoc URL
         openapi_tags=[{"name": "api"}],
         lifespan=lifespan,
     )
@@ -423,12 +391,24 @@ def create_app(args):
             "update_status": update_status,
         }
 
+    # Custom StaticFiles class to prevent caching of HTML files
+    class NoCacheStaticFiles(StaticFiles):
+        async def get_response(self, path: str, scope):
+            response = await super().get_response(path, scope)
+            if path.endswith(".html"):
+                response.headers["Cache-Control"] = (
+                    "no-cache, no-store, must-revalidate"
+                )
+                response.headers["Pragma"] = "no-cache"
+                response.headers["Expires"] = "0"
+            return response
+
     # Webui mount webui/index.html
     static_dir = Path(__file__).parent / "webui"
     static_dir.mkdir(exist_ok=True)
     app.mount(
         "/webui",
-        StaticFiles(directory=static_dir, html=True, check_dir=True),
+        NoCacheStaticFiles(directory=static_dir, html=True, check_dir=True),
         name="webui",
     )
 
@@ -516,7 +496,7 @@ def configure_logging():
             },
             "filters": {
                 "path_filter": {
-                    "()": "lightrag.api.lightrag_server.LightragPathFilter",
+                    "()": "lightrag.utils.LightragPathFilter",
                 },
             },
         }

lightrag/api/requirements.txt

@@ -3,6 +3,7 @@ ascii_colors
 asyncpg
 distro
 fastapi
+graspologic>=3.4.1
 httpcore
 httpx
 jiter

lightrag/api/utils_api.py

@@ -364,7 +364,7 @@ def parse_args(is_uvicorn_mode: bool = False) -> argparse.Namespace:
 
     # Inject LLM cache configuration
     args.enable_llm_cache_for_extract = get_env_value(
-        "ENABLE_LLM_CACHE_FOR_EXTRACT", False, bool
+        "ENABLE_LLM_CACHE_FOR_EXTRACT", True, bool
     )
 
     # Select Document loading tool (DOCLING, DEFAULT)

lightrag/api/webui/index.html

@@ -2,11 +2,14 @@
 <html lang="en">
   <head>
     <meta charset="UTF-8" />
+    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate" />
+    <meta http-equiv="Pragma" content="no-cache" />
+    <meta http-equiv="Expires" content="0" />
     <link rel="icon" type="image/svg+xml" href="./logo.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>
-    <script type="module" crossorigin src="./assets/index-BlVvSIic.js"></script>
+    <script type="module" crossorigin src="./assets/index-DwcJE583.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-CH-3l4_Z.css">
+    <link rel="stylesheet" crossorigin href="./assets/index-BV5s8k-a.css">
   </head>
   <body>
     <div id="root"></div>

lightrag/kg/postgres_impl.py

@@ -767,7 +767,7 @@ class PGDocStatusStorage(DocStatusStorage):
         result = await self.db.query(sql, params, True)
         docs_by_status = {
             element["id"]: DocProcessingStatus(
-                content=result[0]["content"],
+                content=element["content"],
                 content_summary=element["content_summary"],
                 content_length=element["content_length"],
                 status=element["status"],
@@ -1572,7 +1572,7 @@ TABLES = {
                     content_vector VECTOR,
                     create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     update_time TIMESTAMP,
-                    chunk_ids VARCHAR(255)[] NULL,
+                    chunk_id TEXT NULL,
 	                CONSTRAINT LIGHTRAG_VDB_ENTITY_PK PRIMARY KEY (workspace, id)
                     )"""
     },
@@ -1586,7 +1586,7 @@ TABLES = {
                     content_vector VECTOR,
                     create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
                     update_time TIMESTAMP,
-                    chunk_ids VARCHAR(255)[] NULL,
+                    chunk_id TEXT NULL,
 	                CONSTRAINT LIGHTRAG_VDB_RELATION_PK PRIMARY KEY (workspace, id)
                     )"""
     },

lightrag/lightrag.py

@@ -3,11 +3,14 @@ from __future__ import annotations
 import asyncio
 import configparser
 import os
+import csv
 import warnings
 from dataclasses import asdict, dataclass, field
 from datetime import datetime
 from functools import partial
-from typing import Any, AsyncIterator, Callable, Iterator, cast, final
+from typing import Any, AsyncIterator, Callable, Iterator, cast, final, Literal
+import pandas as pd
+
 
 from lightrag.kg import (
     STORAGE_ENV_REQUIREMENTS,
@@ -211,7 +214,7 @@ class LightRAG:
     llm_model_max_token_size: int = field(default=int(os.getenv("MAX_TOKENS", 32768)))
     """Maximum number of tokens allowed per LLM response."""
 
-    llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 16)))
+    llm_model_max_async: int = field(default=int(os.getenv("MAX_ASYNC", 4)))
     """Maximum number of concurrent LLM calls."""
 
     llm_model_kwargs: dict[str, Any] = field(default_factory=dict)
@@ -235,7 +238,7 @@ class LightRAG:
     # Extensions
     # ---
 
-    max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 20)))
+    max_parallel_insert: int = field(default=int(os.getenv("MAX_PARALLEL_INSERT", 2)))
     """Maximum number of parallel insert operations."""
 
     addon_params: dict[str, Any] = field(
@@ -550,6 +553,7 @@ class LightRAG:
         Args:
             input: Single document string or list of document strings
             split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
+            chunk_token_size, it will be split again by token size.
             split_by_character_only: if split_by_character_only is True, split the string by character only, when
             split_by_character is None, this parameter is ignored.
             ids: single string of the document ID or list of unique document IDs, if not provided, MD5 hash IDs will be generated
@@ -571,6 +575,7 @@ class LightRAG:
         Args:
             input: Single document string or list of document strings
             split_by_character: if split_by_character is not None, split the string by character, if chunk longer than
+            chunk_token_size, it will be split again by token size.
             split_by_character_only: if split_by_character_only is True, split the string by character only, when
             split_by_character is None, this parameter is ignored.
             ids: list of unique document IDs, if not provided, MD5 hash IDs will be generated
@@ -764,7 +769,6 @@ class LightRAG:
         async with pipeline_status_lock:
             # Ensure only one worker is processing documents
             if not pipeline_status.get("busy", False):
-                # 先检查是否有需要处理的文档
                 processing_docs, failed_docs, pending_docs = await asyncio.gather(
                     self.doc_status.get_docs_by_status(DocStatus.PROCESSING),
                     self.doc_status.get_docs_by_status(DocStatus.FAILED),
@@ -776,12 +780,10 @@ class LightRAG:
                 to_process_docs.update(failed_docs)
                 to_process_docs.update(pending_docs)
 
-                # 如果没有需要处理的文档，直接返回，保留 pipeline_status 中的内容不变
                 if not to_process_docs:
                     logger.info("No documents to process")
                     return
 
-                # 有文档需要处理，更新 pipeline_status
                 pipeline_status.update(
                     {
                         "busy": True,
@@ -820,7 +822,7 @@ class LightRAG:
                     for i in range(0, len(to_process_docs), self.max_parallel_insert)
                 ]
 
-                log_message = f"Number of batches to process: {len(docs_batches)}."
+                log_message = f"Processing {len(to_process_docs)} document(s) in {len(docs_batches)} batches"
                 logger.info(log_message)
 
                 # Update pipeline status with current batch information
@@ -829,26 +831,16 @@ class LightRAG:
                 pipeline_status["latest_message"] = log_message
                 pipeline_status["history_messages"].append(log_message)
 
-                batches: list[Any] = []
+                async def process_document(
-                # 3. iterate over batches
+                    doc_id: str,
-                for batch_idx, docs_batch in enumerate(docs_batches):
+                    status_doc: DocProcessingStatus,
-                    # Update current batch in pipeline status (directly, as it's atomic)
+                    split_by_character: str | None,
-                    pipeline_status["cur_batch"] += 1
+                    split_by_character_only: bool,
-
+                    pipeline_status: dict,
-                    async def batch(
+                    pipeline_status_lock: asyncio.Lock,
-                        batch_idx: int,
-                        docs_batch: list[tuple[str, DocProcessingStatus]],
-                        size_batch: int,
                 ) -> None:
-                        log_message = (
+                    """Process single document"""
-                            f"Start processing batch {batch_idx + 1} of {size_batch}."
+                    try:
-                        )
-                        logger.info(log_message)
-                        pipeline_status["latest_message"] = log_message
-                        pipeline_status["history_messages"].append(log_message)
-                        # 4. iterate over batch
-                        for doc_id_processing_status in docs_batch:
-                            doc_id, status_doc = doc_id_processing_status
                         # Generate chunks from document
                         chunks: dict[str, Any] = {
                             compute_mdhash_id(dp["content"], prefix="chunk-"): {
@@ -903,7 +895,6 @@ class LightRAG:
                             full_docs_task,
                             text_chunks_task,
                         ]
-                            try:
                         await asyncio.gather(*tasks)
                         await self.doc_status.upsert(
                             {
@@ -920,10 +911,9 @@ class LightRAG:
                         )
                     except Exception as e:
                         # Log error and update pipeline status
-                                error_msg = (
+                        error_msg = f"Failed to process document {doc_id}: {str(e)}"
-                                    f"Failed to process document {doc_id}: {str(e)}"
-                                )
                         logger.error(error_msg)
+                        async with pipeline_status_lock:
                             pipeline_status["latest_message"] = error_msg
                             pipeline_status["history_messages"].append(error_msg)
 
@@ -936,7 +926,6 @@ class LightRAG:
                             ]:
                                 if not task.done():
                                     task.cancel()
-
                         # Update document status to failed
                         await self.doc_status.upsert(
                             {
@@ -951,19 +940,41 @@ class LightRAG:
                                 }
                             }
                         )
-                                continue
+
+                # 3. iterate over batches
+                total_batches = len(docs_batches)
+                for batch_idx, docs_batch in enumerate(docs_batches):
+                    current_batch = batch_idx + 1
                     log_message = (
-                            f"Completed batch {batch_idx + 1} of {len(docs_batches)}."
+                        f"Start processing batch {current_batch} of {total_batches}."
                     )
                     logger.info(log_message)
+                    pipeline_status["cur_batch"] = current_batch
                     pipeline_status["latest_message"] = log_message
                     pipeline_status["history_messages"].append(log_message)
 
-                    batches.append(batch(batch_idx, docs_batch, len(docs_batches)))
+                    doc_tasks = []
+                    for doc_id, status_doc in docs_batch:
+                        doc_tasks.append(
+                            process_document(
+                                doc_id,
+                                status_doc,
+                                split_by_character,
+                                split_by_character_only,
+                                pipeline_status,
+                                pipeline_status_lock,
+                            )
+                        )
 
-                await asyncio.gather(*batches)
+                    # Process documents in one batch parallelly
+                    await asyncio.gather(*doc_tasks)
                     await self._insert_done()
 
+                    log_message = f"Completed batch {current_batch} of {total_batches}."
+                    logger.info(log_message)
+                    pipeline_status["latest_message"] = log_message
+                    pipeline_status["history_messages"].append(log_message)
+
                 # Check if there's a pending request to process more documents (with lock)
                 has_pending_request = False
                 async with pipeline_status_lock:
@@ -1037,7 +1048,7 @@ class LightRAG:
         ]
         await asyncio.gather(*tasks)
 
-        log_message = "All Insert done"
+        log_message = "In memory DB persist to disk"
         logger.info(log_message)
 
         if pipeline_status is not None and pipeline_status_lock is not None:
@@ -1111,6 +1122,7 @@ class LightRAG:
 
                 # Prepare node data
                 node_data: dict[str, str] = {
+                    "entity_id": entity_name,
                     "entity_type": entity_type,
                     "description": description,
                     "source_id": source_id,
@@ -1148,6 +1160,7 @@ class LightRAG:
                         await self.chunk_entity_relation_graph.upsert_node(
                             need_insert_id,
                             node_data={
+                                "entity_id": need_insert_id,
                                 "source_id": source_id,
                                 "description": "UNKNOWN",
                                 "entity_type": "UNKNOWN",
@@ -1838,9 +1851,10 @@ class LightRAG:
         """
         try:
             # 1. Get current entity information
-            node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
+            node_exists = await self.chunk_entity_relation_graph.has_node(entity_name)
-            if not node_data:
+            if not node_exists:
                 raise ValueError(f"Entity '{entity_name}' does not exist")
+            node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
 
             # Check if entity is being renamed
             new_entity_name = updated_data.get("entity_name", entity_name)
@@ -1853,7 +1867,7 @@ class LightRAG:
                         "Entity renaming is not allowed. Set allow_rename=True to enable this feature"
                     )
 
-                existing_node = await self.chunk_entity_relation_graph.get_node(
+                existing_node = await self.chunk_entity_relation_graph.has_node(
                     new_entity_name
                 )
                 if existing_node:
@@ -2035,14 +2049,16 @@ class LightRAG:
         """
         try:
             # 1. Get current relation information
-            edge_data = await self.chunk_entity_relation_graph.get_edge(
+            edge_exists = await self.chunk_entity_relation_graph.has_edge(
                 source_entity, target_entity
             )
-            if not edge_data:
+            if not edge_exists:
                 raise ValueError(
                     f"Relation from '{source_entity}' to '{target_entity}' does not exist"
                 )
-
+            edge_data = await self.chunk_entity_relation_graph.get_edge(
+                source_entity, target_entity
+            )
             # Important: First delete the old relation record from the vector database
             old_relation_id = compute_mdhash_id(
                 source_entity + target_entity, prefix="rel-"
@@ -2151,12 +2167,13 @@ class LightRAG:
         """
         try:
             # Check if entity already exists
-            existing_node = await self.chunk_entity_relation_graph.get_node(entity_name)
+            existing_node = await self.chunk_entity_relation_graph.has_node(entity_name)
             if existing_node:
                 raise ValueError(f"Entity '{entity_name}' already exists")
 
             # Prepare node data with defaults if missing
             node_data = {
+                "entity_id": entity_name,
                 "entity_type": entity_data.get("entity_type", "UNKNOWN"),
                 "description": entity_data.get("description", ""),
                 "source_id": entity_data.get("source_id", "manual"),
@@ -2244,7 +2261,7 @@ class LightRAG:
                 raise ValueError(f"Target entity '{target_entity}' does not exist")
 
             # Check if relation already exists
-            existing_edge = await self.chunk_entity_relation_graph.get_edge(
+            existing_edge = await self.chunk_entity_relation_graph.has_edge(
                 source_entity, target_entity
             )
             if existing_edge:
@@ -2377,19 +2394,22 @@ class LightRAG:
             # 1. Check if all source entities exist
             source_entities_data = {}
             for entity_name in source_entities:
-                node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
+                node_exists = await self.chunk_entity_relation_graph.has_node(
-                if not node_data:
+                    entity_name
+                )
+                if not node_exists:
                     raise ValueError(f"Source entity '{entity_name}' does not exist")
+                node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
                 source_entities_data[entity_name] = node_data
 
             # 2. Check if target entity exists and get its data if it does
             target_exists = await self.chunk_entity_relation_graph.has_node(
                 target_entity
             )
-            target_entity_data = {}
+            existing_target_entity_data = {}
             if target_exists:
-                target_entity_data = await self.chunk_entity_relation_graph.get_node(
+                existing_target_entity_data = (
-                    target_entity
+                    await self.chunk_entity_relation_graph.get_node(target_entity)
                 )
                 logger.info(
                     f"Target entity '{target_entity}' already exists, will merge data"
@@ -2398,7 +2418,7 @@ class LightRAG:
             # 3. Merge entity data
             merged_entity_data = self._merge_entity_attributes(
                 list(source_entities_data.values())
-                + ([target_entity_data] if target_exists else []),
+                + ([existing_target_entity_data] if target_exists else []),
                 merge_strategy,
             )
 
@@ -2592,6 +2612,322 @@ class LightRAG:
             logger.error(f"Error merging entities: {e}")
             raise
 
+    async def aexport_data(
+        self,
+        output_path: str,
+        file_format: Literal["csv", "excel", "md", "txt"] = "csv",
+        include_vector_data: bool = False,
+    ) -> None:
+        """
+        Asynchronously exports all entities, relations, and relationships to various formats.
+        Args:
+            output_path: The path to the output file (including extension).
+            file_format: Output format - "csv", "excel", "md", "txt".
+                - csv: Comma-separated values file
+                - excel: Microsoft Excel file with multiple sheets
+                - md: Markdown tables
+                - txt: Plain text formatted output
+                - table: Print formatted tables to console
+            include_vector_data: Whether to include data from the vector database.
+        """
+        # Collect data
+        entities_data = []
+        relations_data = []
+        relationships_data = []
+
+        # --- Entities ---
+        all_entities = await self.chunk_entity_relation_graph.get_all_labels()
+        for entity_name in all_entities:
+            entity_info = await self.get_entity_info(
+                entity_name, include_vector_data=include_vector_data
+            )
+            entity_row = {
+                "entity_name": entity_name,
+                "source_id": entity_info["source_id"],
+                "graph_data": str(
+                    entity_info["graph_data"]
+                ),  # Convert to string to ensure compatibility
+            }
+            if include_vector_data and "vector_data" in entity_info:
+                entity_row["vector_data"] = str(entity_info["vector_data"])
+            entities_data.append(entity_row)
+
+        # --- Relations ---
+        for src_entity in all_entities:
+            for tgt_entity in all_entities:
+                if src_entity == tgt_entity:
+                    continue
+
+                edge_exists = await self.chunk_entity_relation_graph.has_edge(
+                    src_entity, tgt_entity
+                )
+                if edge_exists:
+                    relation_info = await self.get_relation_info(
+                        src_entity, tgt_entity, include_vector_data=include_vector_data
+                    )
+                    relation_row = {
+                        "src_entity": src_entity,
+                        "tgt_entity": tgt_entity,
+                        "source_id": relation_info["source_id"],
+                        "graph_data": str(
+                            relation_info["graph_data"]
+                        ),  # Convert to string
+                    }
+                    if include_vector_data and "vector_data" in relation_info:
+                        relation_row["vector_data"] = str(relation_info["vector_data"])
+                    relations_data.append(relation_row)
+
+        # --- Relationships (from VectorDB) ---
+        all_relationships = await self.relationships_vdb.client_storage
+        for rel in all_relationships["data"]:
+            relationships_data.append(
+                {
+                    "relationship_id": rel["__id__"],
+                    "data": str(rel),  # Convert to string for compatibility
+                }
+            )
+
+        # Export based on format
+        if file_format == "csv":
+            # CSV export
+            with open(output_path, "w", newline="", encoding="utf-8") as csvfile:
+                # Entities
+                if entities_data:
+                    csvfile.write("# ENTITIES\n")
+                    writer = csv.DictWriter(csvfile, fieldnames=entities_data[0].keys())
+                    writer.writeheader()
+                    writer.writerows(entities_data)
+                    csvfile.write("\n\n")
+
+                # Relations
+                if relations_data:
+                    csvfile.write("# RELATIONS\n")
+                    writer = csv.DictWriter(
+                        csvfile, fieldnames=relations_data[0].keys()
+                    )
+                    writer.writeheader()
+                    writer.writerows(relations_data)
+                    csvfile.write("\n\n")
+
+                # Relationships
+                if relationships_data:
+                    csvfile.write("# RELATIONSHIPS\n")
+                    writer = csv.DictWriter(
+                        csvfile, fieldnames=relationships_data[0].keys()
+                    )
+                    writer.writeheader()
+                    writer.writerows(relationships_data)
+
+        elif file_format == "excel":
+            # Excel export
+            entities_df = (
+                pd.DataFrame(entities_data) if entities_data else pd.DataFrame()
+            )
+            relations_df = (
+                pd.DataFrame(relations_data) if relations_data else pd.DataFrame()
+            )
+            relationships_df = (
+                pd.DataFrame(relationships_data)
+                if relationships_data
+                else pd.DataFrame()
+            )
+
+            with pd.ExcelWriter(output_path, engine="xlsxwriter") as writer:
+                if not entities_df.empty:
+                    entities_df.to_excel(writer, sheet_name="Entities", index=False)
+                if not relations_df.empty:
+                    relations_df.to_excel(writer, sheet_name="Relations", index=False)
+                if not relationships_df.empty:
+                    relationships_df.to_excel(
+                        writer, sheet_name="Relationships", index=False
+                    )
+
+        elif file_format == "md":
+            # Markdown export
+            with open(output_path, "w", encoding="utf-8") as mdfile:
+                mdfile.write("# LightRAG Data Export\n\n")
+
+                # Entities
+                mdfile.write("## Entities\n\n")
+                if entities_data:
+                    # Write header
+                    mdfile.write("| " + " | ".join(entities_data[0].keys()) + " |\n")
+                    mdfile.write(
+                        "| "
+                        + " | ".join(["---"] * len(entities_data[0].keys()))
+                        + " |\n"
+                    )
+
+                    # Write rows
+                    for entity in entities_data:
+                        mdfile.write(
+                            "| " + " | ".join(str(v) for v in entity.values()) + " |\n"
+                        )
+                    mdfile.write("\n\n")
+                else:
+                    mdfile.write("*No entity data available*\n\n")
+
+                # Relations
+                mdfile.write("## Relations\n\n")
+                if relations_data:
+                    # Write header
+                    mdfile.write("| " + " | ".join(relations_data[0].keys()) + " |\n")
+                    mdfile.write(
+                        "| "
+                        + " | ".join(["---"] * len(relations_data[0].keys()))
+                        + " |\n"
+                    )
+
+                    # Write rows
+                    for relation in relations_data:
+                        mdfile.write(
+                            "| "
+                            + " | ".join(str(v) for v in relation.values())
+                            + " |\n"
+                        )
+                    mdfile.write("\n\n")
+                else:
+                    mdfile.write("*No relation data available*\n\n")
+
+                # Relationships
+                mdfile.write("## Relationships\n\n")
+                if relationships_data:
+                    # Write header
+                    mdfile.write(
+                        "| " + " | ".join(relationships_data[0].keys()) + " |\n"
+                    )
+                    mdfile.write(
+                        "| "
+                        + " | ".join(["---"] * len(relationships_data[0].keys()))
+                        + " |\n"
+                    )
+
+                    # Write rows
+                    for relationship in relationships_data:
+                        mdfile.write(
+                            "| "
+                            + " | ".join(str(v) for v in relationship.values())
+                            + " |\n"
+                        )
+                else:
+                    mdfile.write("*No relationship data available*\n\n")
+
+        elif file_format == "txt":
+            # Plain text export
+            with open(output_path, "w", encoding="utf-8") as txtfile:
+                txtfile.write("LIGHTRAG DATA EXPORT\n")
+                txtfile.write("=" * 80 + "\n\n")
+
+                # Entities
+                txtfile.write("ENTITIES\n")
+                txtfile.write("-" * 80 + "\n")
+                if entities_data:
+                    # Create fixed width columns
+                    col_widths = {
+                        k: max(len(k), max(len(str(e[k])) for e in entities_data))
+                        for k in entities_data[0]
+                    }
+                    header = "  ".join(k.ljust(col_widths[k]) for k in entities_data[0])
+                    txtfile.write(header + "\n")
+                    txtfile.write("-" * len(header) + "\n")
+
+                    # Write rows
+                    for entity in entities_data:
+                        row = "  ".join(
+                            str(v).ljust(col_widths[k]) for k, v in entity.items()
+                        )
+                        txtfile.write(row + "\n")
+                    txtfile.write("\n\n")
+                else:
+                    txtfile.write("No entity data available\n\n")
+
+                # Relations
+                txtfile.write("RELATIONS\n")
+                txtfile.write("-" * 80 + "\n")
+                if relations_data:
+                    # Create fixed width columns
+                    col_widths = {
+                        k: max(len(k), max(len(str(r[k])) for r in relations_data))
+                        for k in relations_data[0]
+                    }
+                    header = "  ".join(
+                        k.ljust(col_widths[k]) for k in relations_data[0]
+                    )
+                    txtfile.write(header + "\n")
+                    txtfile.write("-" * len(header) + "\n")
+
+                    # Write rows
+                    for relation in relations_data:
+                        row = "  ".join(
+                            str(v).ljust(col_widths[k]) for k, v in relation.items()
+                        )
+                        txtfile.write(row + "\n")
+                    txtfile.write("\n\n")
+                else:
+                    txtfile.write("No relation data available\n\n")
+
+                # Relationships
+                txtfile.write("RELATIONSHIPS\n")
+                txtfile.write("-" * 80 + "\n")
+                if relationships_data:
+                    # Create fixed width columns
+                    col_widths = {
+                        k: max(len(k), max(len(str(r[k])) for r in relationships_data))
+                        for k in relationships_data[0]
+                    }
+                    header = "  ".join(
+                        k.ljust(col_widths[k]) for k in relationships_data[0]
+                    )
+                    txtfile.write(header + "\n")
+                    txtfile.write("-" * len(header) + "\n")
+
+                    # Write rows
+                    for relationship in relationships_data:
+                        row = "  ".join(
+                            str(v).ljust(col_widths[k]) for k, v in relationship.items()
+                        )
+                        txtfile.write(row + "\n")
+                else:
+                    txtfile.write("No relationship data available\n\n")
+
+        else:
+            raise ValueError(
+                f"Unsupported file format: {file_format}. "
+                f"Choose from: csv, excel, md, txt"
+            )
+        if file_format is not None:
+            print(f"Data exported to: {output_path} with format: {file_format}")
+        else:
+            print("Data displayed as table format")
+
+    def export_data(
+        self,
+        output_path: str,
+        file_format: Literal["csv", "excel", "md", "txt"] = "csv",
+        include_vector_data: bool = False,
+    ) -> None:
+        """
+        Synchronously exports all entities, relations, and relationships to various formats.
+        Args:
+            output_path: The path to the output file (including extension).
+            file_format: Output format - "csv", "excel", "md", "txt".
+                - csv: Comma-separated values file
+                - excel: Microsoft Excel file with multiple sheets
+                - md: Markdown tables
+                - txt: Plain text formatted output
+                - table: Print formatted tables to console
+            include_vector_data: Whether to include data from the vector database.
+        """
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        loop.run_until_complete(
+            self.aexport_data(output_path, file_format, include_vector_data)
+        )
+
     def merge_entities(
         self,
         source_entities: list[str],

lightrag/utils.py

@@ -76,6 +76,7 @@ class LightragPathFilter(logging.Filter):
         super().__init__()
         # Define paths to be filtered
         self.filtered_paths = ["/documents", "/health", "/webui/"]
+        # self.filtered_paths = ["/health", "/webui/"]
 
     def filter(self, record):
         try:

lightrag_webui/bun.lock

@@ -62,6 +62,7 @@
         "@types/node": "^22.13.5",
         "@types/react": "^19.0.10",
         "@types/react-dom": "^19.0.4",
+        "@types/react-i18next": "^8.1.0",
         "@types/react-syntax-highlighter": "^15.5.13",
         "@types/seedrandom": "^3.0.8",
         "@vitejs/plugin-react-swc": "^3.8.0",
@@ -443,6 +444,8 @@
 
     "@types/react-dom": ["@types/react-dom@19.0.4", "", { "peerDependencies": { "@types/react": "^19.0.0" } }, "sha512-4fSQ8vWFkg+TGhePfUzVmat3eC14TXYSsiiDSLI0dVLsrm9gZFABjPy/Qu6TKgl1tq1Bu1yDsuQgY3A3DOjCcg=="],
 
+    "@types/react-i18next": ["@types/react-i18next@8.1.0", "", { "dependencies": { "react-i18next": "*" } }, "sha512-d4xhcjX5b3roNMObRNMfb1HinHQlQLPo8xlDj60dnHeeAw2bBymR2cy/l1giJpHzo/ZFgSvgVUvIWr4kCrenCg=="],
+
     "@types/react-syntax-highlighter": ["@types/react-syntax-highlighter@15.5.13", "", { "dependencies": { "@types/react": "*" } }, "sha512-uLGJ87j6Sz8UaBAooU0T6lWJ0dBmjZgN1PZTrj05TNql2/XpC6+4HhMT5syIdFUUt+FASfCeLLv4kBygNU+8qA=="],
 
     "@types/react-transition-group": ["@types/react-transition-group@4.4.12", "", { "peerDependencies": { "@types/react": "*" } }, "sha512-8TV6R3h2j7a91c+1DXdJi3Syo69zzIZbz7Lg5tORM5LEJG7X/E6a1V3drRyBRZq7/utz7A+c4OgYLiLcYGHG6w=="],

lightrag_webui/index.html

@@ -2,6 +2,9 @@
 <html lang="en">
   <head>
     <meta charset="UTF-8" />
+    <meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate" />
+    <meta http-equiv="Pragma" content="no-cache" />
+    <meta http-equiv="Expires" content="0" />
     <link rel="icon" type="image/svg+xml" href="/logo.png" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Lightrag</title>

lightrag_webui/package.json

@@ -71,6 +71,7 @@
     "@types/node": "^22.13.5",
     "@types/react": "^19.0.10",
     "@types/react-dom": "^19.0.4",
+    "@types/react-i18next": "^8.1.0",
     "@types/react-syntax-highlighter": "^15.5.13",
     "@types/seedrandom": "^3.0.8",
     "@vitejs/plugin-react-swc": "^3.8.0",

lightrag_webui/src/App.tsx

@@ -1,5 +1,6 @@
 import { useState, useCallback } from 'react'
 import ThemeProvider from '@/components/ThemeProvider'
+import TabVisibilityProvider from '@/contexts/TabVisibilityProvider'
 import MessageAlert from '@/components/MessageAlert'
 import ApiKeyAlert from '@/components/ApiKeyAlert'
 import StatusIndicator from '@/components/graph/StatusIndicator'
@@ -21,7 +22,7 @@ import { Tabs, TabsContent } from '@/components/ui/Tabs'
 function App() {
   const message = useBackendState.use.message()
   const enableHealthCheck = useSettingsStore.use.enableHealthCheck()
-  const [currentTab] = useState(() => useSettingsStore.getState().currentTab)
+  const currentTab = useSettingsStore.use.currentTab()
   const [apiKeyInvalid, setApiKeyInvalid] = useState(false)
 
   // Health check
@@ -54,6 +55,7 @@ function App() {
 
   return (
     <ThemeProvider>
+      <TabVisibilityProvider>
         <main className="flex h-screen w-screen overflow-x-hidden">
           <Tabs
             defaultValue={currentTab}
@@ -81,6 +83,7 @@ function App() {
           {apiKeyInvalid && <ApiKeyAlert />}
           <Toaster />
         </main>
+      </TabVisibilityProvider>
     </ThemeProvider>
   )
 }

lightrag_webui/src/components/AppSettings.tsx

@@ -0,0 +1,66 @@
+import { useState, useCallback } from 'react'
+import { Popover, PopoverContent, PopoverTrigger } from '@/components/ui/Popover'
+import Button from '@/components/ui/Button'
+import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from '@/components/ui/Select'
+import { useSettingsStore } from '@/stores/settings'
+import { PaletteIcon } from 'lucide-react'
+import { useTranslation } from 'react-i18next'
+
+export default function AppSettings() {
+  const [opened, setOpened] = useState<boolean>(false)
+  const { t } = useTranslation()
+
+  const language = useSettingsStore.use.language()
+  const setLanguage = useSettingsStore.use.setLanguage()
+
+  const theme = useSettingsStore.use.theme()
+  const setTheme = useSettingsStore.use.setTheme()
+
+  const handleLanguageChange = useCallback((value: string) => {
+    setLanguage(value as 'en' | 'zh')
+  }, [setLanguage])
+
+  const handleThemeChange = useCallback((value: string) => {
+    setTheme(value as 'light' | 'dark' | 'system')
+  }, [setTheme])
+
+  return (
+    <Popover open={opened} onOpenChange={setOpened}>
+      <PopoverTrigger asChild>
+        <Button variant="outline" size="icon" className="h-9 w-9">
+          <PaletteIcon className="h-5 w-5" />
+        </Button>
+      </PopoverTrigger>
+      <PopoverContent side="bottom" align="end" className="w-56">
+        <div className="flex flex-col gap-4">
+          <div className="flex flex-col gap-2">
+            <label className="text-sm font-medium">{t('settings.language')}</label>
+            <Select value={language} onValueChange={handleLanguageChange}>
+              <SelectTrigger>
+                <SelectValue />
+              </SelectTrigger>
+              <SelectContent>
+                <SelectItem value="en">English</SelectItem>
+                <SelectItem value="zh">中文</SelectItem>
+              </SelectContent>
+            </Select>
+          </div>
+
+          <div className="flex flex-col gap-2">
+            <label className="text-sm font-medium">{t('settings.theme')}</label>
+            <Select value={theme} onValueChange={handleThemeChange}>
+              <SelectTrigger>
+                <SelectValue />
+              </SelectTrigger>
+              <SelectContent>
+                <SelectItem value="light">{t('settings.light')}</SelectItem>
+                <SelectItem value="dark">{t('settings.dark')}</SelectItem>
+                <SelectItem value="system">{t('settings.system')}</SelectItem>
+              </SelectContent>
+            </Select>
+          </div>
+        </div>
+      </PopoverContent>
+    </Popover>
+  )
+}

lightrag_webui/src/components/Root.tsx

@@ -0,0 +1,24 @@
+import { StrictMode, useEffect, useState } from 'react'
+import { initializeI18n } from '@/i18n'
+import App from '@/App'
+
+export const Root = () => {
+  const [isI18nInitialized, setIsI18nInitialized] = useState(false)
+
+  useEffect(() => {
+    // Initialize i18n immediately with persisted language
+    initializeI18n().then(() => {
+      setIsI18nInitialized(true)
+    })
+  }, [])
+
+  if (!isI18nInitialized) {
+    return null // or a loading spinner
+  }
+
+  return (
+    <StrictMode>
+      <App />
+    </StrictMode>
+  )
+}

lightrag_webui/src/components/ThemeProvider.tsx

@@ -1,4 +1,4 @@
-import { createContext, useEffect, useState } from 'react'
+import { createContext, useEffect } from 'react'
 import { Theme, useSettingsStore } from '@/stores/settings'
 
 type ThemeProviderProps = {
@@ -21,30 +21,32 @@ const ThemeProviderContext = createContext<ThemeProviderState>(initialState)
  * Component that provides the theme state and setter function to its children.
  */
 export default function ThemeProvider({ children, ...props }: ThemeProviderProps) {
-  const [theme, setTheme] = useState<Theme>(useSettingsStore.getState().theme)
+  const theme = useSettingsStore.use.theme()
+  const setTheme = useSettingsStore.use.setTheme()
 
   useEffect(() => {
     const root = window.document.documentElement
     root.classList.remove('light', 'dark')
 
     if (theme === 'system') {
-      const systemTheme = window.matchMedia('(prefers-color-scheme: dark)').matches
+      const mediaQuery = window.matchMedia('(prefers-color-scheme: dark)')
-        ? 'dark'
+      const handleChange = (e: MediaQueryListEvent) => {
-        : 'light'
+        root.classList.remove('light', 'dark')
-      root.classList.add(systemTheme)
+        root.classList.add(e.matches ? 'dark' : 'light')
-      setTheme(systemTheme)
-      return
       }
 
+      root.classList.add(mediaQuery.matches ? 'dark' : 'light')
+      mediaQuery.addEventListener('change', handleChange)
+
+      return () => mediaQuery.removeEventListener('change', handleChange)
+    } else {
       root.classList.add(theme)
+    }
   }, [theme])
 
   const value = {
     theme,
-    setTheme: (theme: Theme) => {
+    setTheme
-      useSettingsStore.getState().setTheme(theme)
-      setTheme(theme)
-    }
   }
 
   return (

lightrag_webui/src/components/graph/FocusOnNode.tsx

@@ -13,16 +13,25 @@ const FocusOnNode = ({ node, move }: { node: string | null; move?: boolean }) =>
    * When the selected item changes, highlighted the node and center the camera on it.
    */
   useEffect(() => {
-    if (!node) return
-    sigma.getGraph().setNodeAttribute(node, 'highlighted', true)
     if (move) {
+      if (node) {
+        sigma.getGraph().setNodeAttribute(node, 'highlighted', true)
         gotoNode(node)
+      } else {
+        // If no node is selected but move is true, reset to default view
+        sigma.setCustomBBox(null)
+        sigma.getCamera().animate({ x: 0.5, y: 0.5, ratio: 1 }, { duration: 0 })
+      }
       useGraphStore.getState().setMoveToSelectedNode(false)
+    } else if (node) {
+      sigma.getGraph().setNodeAttribute(node, 'highlighted', true)
     }
 
     return () => {
+      if (node) {
         sigma.getGraph().setNodeAttribute(node, 'highlighted', false)
       }
+    }
   }, [node, move, sigma, gotoNode])
 
   return null

lightrag_webui/src/components/graph/GraphControl.tsx

@@ -1,10 +1,11 @@
 import { useLoadGraph, useRegisterEvents, useSetSettings, useSigma } from '@react-sigma/core'
+import Graph from 'graphology'
 // import { useLayoutCircular } from '@react-sigma/layout-circular'
 import { useLayoutForceAtlas2 } from '@react-sigma/layout-forceatlas2'
 import { useEffect } from 'react'
 
 // import useRandomGraph, { EdgeType, NodeType } from '@/hooks/useRandomGraph'
-import useLightragGraph, { EdgeType, NodeType } from '@/hooks/useLightragGraph'
+import { EdgeType, NodeType } from '@/hooks/useLightragGraph'
 import useTheme from '@/hooks/useTheme'
 import * as Constants from '@/lib/constants'
 
@@ -21,7 +22,6 @@ const isButtonPressed = (ev: MouseEvent | TouchEvent) => {
 }
 
 const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean }) => {
-  const { lightrageGraph } = useLightragGraph()
   const sigma = useSigma<NodeType, EdgeType>()
   const registerEvents = useRegisterEvents<NodeType, EdgeType>()
   const setSettings = useSetSettings<NodeType, EdgeType>()
@@ -34,21 +34,25 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
 
   const { theme } = useTheme()
   const hideUnselectedEdges = useSettingsStore.use.enableHideUnselectedEdges()
+  const enableEdgeEvents = useSettingsStore.use.enableEdgeEvents()
+  const renderEdgeLabels = useSettingsStore.use.showEdgeLabel()
+  const renderLabels = useSettingsStore.use.showNodeLabel()
   const selectedNode = useGraphStore.use.selectedNode()
   const focusedNode = useGraphStore.use.focusedNode()
   const selectedEdge = useGraphStore.use.selectedEdge()
   const focusedEdge = useGraphStore.use.focusedEdge()
+  const sigmaGraph = useGraphStore.use.sigmaGraph()
 
   /**
    * When component mount or maxIterations changes
    * => load the graph and apply layout
    */
   useEffect(() => {
-    // Create & load the graph
+    if (sigmaGraph) {
-    const graph = lightrageGraph()
+      loadGraph(sigmaGraph as unknown as Graph<NodeType, EdgeType>)
-    loadGraph(graph)
       assignLayout()
-  }, [assignLayout, loadGraph, lightrageGraph, maxIterations])
+    }
+  }, [assignLayout, loadGraph, sigmaGraph, maxIterations])
 
   /**
    * When component mount
@@ -58,39 +62,52 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
     const { setFocusedNode, setSelectedNode, setFocusedEdge, setSelectedEdge, clearSelection } =
       useGraphStore.getState()
 
-    // Register the events
+    // Define event types
-    registerEvents({
+    type NodeEvent = { node: string; event: { original: MouseEvent | TouchEvent } }
-      enterNode: (event) => {
+    type EdgeEvent = { edge: string; event: { original: MouseEvent | TouchEvent } }
+
+    // Register all events, but edge events will only be processed if enableEdgeEvents is true
+    const events: Record<string, any> = {
+      enterNode: (event: NodeEvent) => {
         if (!isButtonPressed(event.event.original)) {
           setFocusedNode(event.node)
         }
       },
-      leaveNode: (event) => {
+      leaveNode: (event: NodeEvent) => {
         if (!isButtonPressed(event.event.original)) {
           setFocusedNode(null)
         }
       },
-      clickNode: (event) => {
+      clickNode: (event: NodeEvent) => {
         setSelectedNode(event.node)
         setSelectedEdge(null)
       },
-      clickEdge: (event) => {
+      clickStage: () => clearSelection()
+    }
+
+    // Only add edge event handlers if enableEdgeEvents is true
+    if (enableEdgeEvents) {
+      events.clickEdge = (event: EdgeEvent) => {
         setSelectedEdge(event.edge)
         setSelectedNode(null)
-      },
+      }
-      enterEdge: (event) => {
+
+      events.enterEdge = (event: EdgeEvent) => {
         if (!isButtonPressed(event.event.original)) {
           setFocusedEdge(event.edge)
         }
-      },
+      }
-      leaveEdge: (event) => {
+
+      events.leaveEdge = (event: EdgeEvent) => {
         if (!isButtonPressed(event.event.original)) {
           setFocusedEdge(null)
         }
-      },
+      }
-      clickStage: () => clearSelection()
+    }
-    })
+
-  }, [registerEvents])
+    // Register the events
+    registerEvents(events)
+  }, [registerEvents, enableEdgeEvents])
 
   /**
    * When component mount or hovered node change
@@ -101,7 +118,14 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
     const labelColor = isDarkTheme ? Constants.labelColorDarkTheme : undefined
     const edgeColor = isDarkTheme ? Constants.edgeColorDarkTheme : undefined
 
+    // Update all dynamic settings directly without recreating the sigma container
     setSettings({
+      // Update display settings
+      enableEdgeEvents,
+      renderEdgeLabels,
+      renderLabels,
+
+      // Node reducer for node appearance
       nodeReducer: (node, data) => {
         const graph = sigma.getGraph()
         const newData: NodeType & {
@@ -140,6 +164,8 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
         }
         return newData
       },
+
+      // Edge reducer for edge appearance
       edgeReducer: (edge, data) => {
         const graph = sigma.getGraph()
         const newData = { ...data, hidden: false, labelColor, color: edgeColor }
@@ -181,7 +207,10 @@ const GraphControl = ({ disableHoverEffect }: { disableHoverEffect?: boolean })
     sigma,
     disableHoverEffect,
     theme,
-    hideUnselectedEdges
+    hideUnselectedEdges,
+    enableEdgeEvents,
+    renderEdgeLabels,
+    renderLabels
   ])
 
   return null

lightrag_webui/src/components/graph/GraphLabels.tsx

@@ -1,37 +1,48 @@
-import { useCallback } from 'react'
+import { useCallback, useEffect, useRef } from 'react'
 import { AsyncSelect } from '@/components/ui/AsyncSelect'
-import { getGraphLabels } from '@/api/lightrag'
 import { useSettingsStore } from '@/stores/settings'
 import { useGraphStore } from '@/stores/graph'
 import { labelListLimit } from '@/lib/constants'
 import MiniSearch from 'minisearch'
 import { useTranslation } from 'react-i18next'
 
-const lastGraph: any = {
-  graph: null,
-  searchEngine: null,
-  labels: []
-}
-
 const GraphLabels = () => {
   const { t } = useTranslation()
   const label = useSettingsStore.use.queryLabel()
-  const graph = useGraphStore.use.sigmaGraph()
+  const allDatabaseLabels = useGraphStore.use.allDatabaseLabels()
+  const labelsLoadedRef = useRef(false)
 
-  const getSearchEngine = useCallback(async () => {
+  // Track if a fetch is in progress to prevent multiple simultaneous fetches
-    if (lastGraph.graph == graph) {
+  const fetchInProgressRef = useRef(false)
-      return {
-        labels: lastGraph.labels,
-        searchEngine: lastGraph.searchEngine
-      }
-    }
-    const labels = ['*'].concat(await getGraphLabels())
 
-    // Ensure query label exists
+  // Fetch labels once on component mount, using global flag to prevent duplicates
-    if (!labels.includes(useSettingsStore.getState().queryLabel)) {
+  useEffect(() => {
-      useSettingsStore.getState().setQueryLabel(labels[0])
+    // Check if we've already attempted to fetch labels in this session
-    }
+    const labelsFetchAttempted = useGraphStore.getState().labelsFetchAttempted
 
+    // Only fetch if we haven't attempted in this session and no fetch is in progress
+    if (!labelsFetchAttempted && !fetchInProgressRef.current) {
+      fetchInProgressRef.current = true
+      // Set global flag to indicate we've attempted to fetch in this session
+      useGraphStore.getState().setLabelsFetchAttempted(true)
+
+      console.log('Fetching graph labels (once per session)...')
+
+      useGraphStore.getState().fetchAllDatabaseLabels()
+        .then(() => {
+          labelsLoadedRef.current = true
+          fetchInProgressRef.current = false
+        })
+        .catch((error) => {
+          console.error('Failed to fetch labels:', error)
+          fetchInProgressRef.current = false
+          // Reset global flag to allow retry
+          useGraphStore.getState().setLabelsFetchAttempted(false)
+        })
+    }
+  }, []) // Empty dependency array ensures this only runs once on mount
+
+  const getSearchEngine = useCallback(() => {
     // Create search engine
     const searchEngine = new MiniSearch({
       idField: 'id',
@@ -46,41 +57,32 @@ const GraphLabels = () => {
     })
 
     // Add documents
-    const documents = labels.map((str, index) => ({ id: index, value: str }))
+    const documents = allDatabaseLabels.map((str, index) => ({ id: index, value: str }))
     searchEngine.addAll(documents)
 
-    lastGraph.graph = graph
-    lastGraph.searchEngine = searchEngine
-    lastGraph.labels = labels
-
     return {
-      labels,
+      labels: allDatabaseLabels,
       searchEngine
     }
-  }, [graph])
+  }, [allDatabaseLabels])
 
   const fetchData = useCallback(
     async (query?: string): Promise<string[]> => {
-      const { labels, searchEngine } = await getSearchEngine()
+      const { labels, searchEngine } = getSearchEngine()
 
       let result: string[] = labels
       if (query) {
         // Search labels
-        result = searchEngine.search(query).map((r) => labels[r.id])
+        result = searchEngine.search(query).map((r: { id: number }) => labels[r.id])
       }
 
       return result.length <= labelListLimit
         ? result
-        : [...result.slice(0, labelListLimit), t('graphLabels.andOthers', { count: result.length - labelListLimit })]
+        : [...result.slice(0, labelListLimit), '...']
     },
     [getSearchEngine]
   )
 
-  const setQueryLabel = useCallback((label: string) => {
-    if (label.startsWith('And ') && label.endsWith(' others')) return
-    useSettingsStore.getState().setQueryLabel(label)
-  }, [])
-
   return (
     <AsyncSelect<string>
       className="ml-2"
@@ -94,8 +96,38 @@ const GraphLabels = () => {
       notFound={<div className="py-6 text-center text-sm">No labels found</div>}
       label={t('graphPanel.graphLabels.label')}
       placeholder={t('graphPanel.graphLabels.placeholder')}
-      value={label !== null ? label : ''}
+      value={label !== null ? label : '*'}
-      onChange={setQueryLabel}
+      onChange={(newLabel) => {
+        const currentLabel = useSettingsStore.getState().queryLabel
+
+        // select the last item means query all
+        if (newLabel === '...') {
+          newLabel = '*'
+        }
+
+        // Reset the fetch attempted flag to force a new data fetch
+        useGraphStore.getState().setGraphDataFetchAttempted(false)
+
+        // Clear current graph data to ensure complete reload when label changes
+        if (newLabel !== currentLabel) {
+          const graphStore = useGraphStore.getState();
+          graphStore.clearSelection();
+
+          // Reset the graph state but preserve the instance
+          if (graphStore.sigmaGraph) {
+            const nodes = Array.from(graphStore.sigmaGraph.nodes());
+            nodes.forEach(node => graphStore.sigmaGraph?.dropNode(node));
+          }
+        }
+
+        if (newLabel === currentLabel && newLabel !== '*') {
+          // reselect the same itme means qery all
+          useSettingsStore.getState().setQueryLabel('*')
+        } else {
+          useSettingsStore.getState().setQueryLabel(newLabel)
+        }
+      }}
+      clearable={false}  // Prevent clearing value on reselect
     />
   )
 }

lightrag_webui/src/components/graph/GraphSearch.tsx

@@ -1,4 +1,4 @@
-import { FC, useCallback, useMemo } from 'react'
+import { FC, useCallback, useEffect, useMemo } from 'react'
 import {
   EdgeById,
   NodeById,
@@ -28,6 +28,7 @@ function OptionComponent(item: OptionItem) {
 }
 
 const messageId = '__message_item'
+// Reset this cache when graph changes to ensure fresh search results
 const lastGraph: any = {
   graph: null,
   searchEngine: null
@@ -48,6 +49,15 @@ export const GraphSearchInput = ({
   const { t } = useTranslation()
   const graph = useGraphStore.use.sigmaGraph()
 
+  // Force reset the cache when graph changes
+  useEffect(() => {
+    if (graph) {
+      // Reset cache to ensure fresh search results with new graph data
+      lastGraph.graph = null;
+      lastGraph.searchEngine = null;
+    }
+  }, [graph]);
+
   const searchEngine = useMemo(() => {
     if (lastGraph.graph == graph) {
       return lastGraph.searchEngine
@@ -85,8 +95,19 @@ export const GraphSearchInput = ({
   const loadOptions = useCallback(
     async (query?: string): Promise<OptionItem[]> => {
       if (onFocus) onFocus(null)
-      if (!query || !searchEngine) return []
+      if (!graph || !searchEngine) return []
-      const result: OptionItem[] = searchEngine.search(query).map((r) => ({
+
+      // If no query, return first searchResultLimit nodes
+      if (!query) {
+        const nodeIds = graph.nodes().slice(0, searchResultLimit)
+        return nodeIds.map(id => ({
+          id,
+          type: 'nodes'
+        }))
+      }
+
+      // If has query, search nodes
+      const result: OptionItem[] = searchEngine.search(query).map((r: { id: string }) => ({
         id: r.id,
         type: 'nodes'
       }))
@@ -103,7 +124,7 @@ export const GraphSearchInput = ({
           }
         ]
     },
-    [searchEngine, onFocus]
+    [graph, searchEngine, onFocus, t]
   )
 
   return (

lightrag_webui/src/components/graph/PropertiesView.tsx

@@ -132,14 +132,22 @@ const PropertyRow = ({
   onClick?: () => void
   tooltip?: string
 }) => {
+  const { t } = useTranslation()
+
+  const getPropertyNameTranslation = (name: string) => {
+    const translationKey = `graphPanel.propertiesView.node.propertyNames.${name}`
+    const translation = t(translationKey)
+    return translation === translationKey ? name : translation
+  }
+
   return (
     <div className="flex items-center gap-2">
-      <label className="text-primary/60 tracking-wide">{name}</label>:
+      <label className="text-primary/60 tracking-wide whitespace-nowrap">{getPropertyNameTranslation(name)}</label>:
       <Text
-        className="hover:bg-primary/20 rounded p-1 text-ellipsis"
+        className="hover:bg-primary/20 rounded p-1 overflow-hidden text-ellipsis"
         tooltipClassName="max-w-80"
         text={value}
-        tooltip={tooltip || value}
+        tooltip={tooltip || (typeof value === 'string' ? value : JSON.stringify(value, null, 2))}
         side="left"
         onClick={onClick}
       />

lightrag_webui/src/components/graph/Settings.tsx

@@ -8,9 +8,10 @@ import Input from '@/components/ui/Input'
 import { controlButtonVariant } from '@/lib/constants'
 import { useSettingsStore } from '@/stores/settings'
 import { useBackendState } from '@/stores/state'
+import { useGraphStore } from '@/stores/graph'
 
-import { SettingsIcon } from 'lucide-react'
+import { SettingsIcon, RefreshCwIcon } from 'lucide-react'
-import { useTranslation } from "react-i18next";
+import { useTranslation } from 'react-i18next';
 
 /**
  * Component that displays a checkbox with a label.
@@ -114,6 +115,7 @@ const LabeledNumberInput = ({
 export default function Settings() {
   const [opened, setOpened] = useState<boolean>(false)
   const [tempApiKey, setTempApiKey] = useState<string>('')
+  const refreshLayout = useGraphStore.use.refreshLayout()
 
   const showPropertyPanel = useSettingsStore.use.showPropertyPanel()
   const showNodeSearchBar = useSettingsStore.use.showNodeSearchBar()
@@ -208,9 +210,18 @@ export default function Settings() {
   const { t } = useTranslation();
 
   return (
+    <>
+      <Button
+        variant={controlButtonVariant}
+        tooltip={t('graphPanel.sideBar.settings.refreshLayout')}
+        size="icon"
+        onClick={refreshLayout}
+      >
+        <RefreshCwIcon />
+      </Button>
       <Popover open={opened} onOpenChange={setOpened}>
         <PopoverTrigger asChild>
-        <Button variant={controlButtonVariant} tooltip={t("graphPanel.sideBar.settings.settings")} size="icon">
+          <Button variant={controlButtonVariant} tooltip={t('graphPanel.sideBar.settings.settings')} size="icon">
             <SettingsIcon />
           </Button>
         </PopoverTrigger>
@@ -224,7 +235,7 @@ export default function Settings() {
             <LabeledCheckBox
               checked={enableHealthCheck}
               onCheckedChange={setEnableHealthCheck}
-            label={t("graphPanel.sideBar.settings.healthCheck")}
+              label={t('graphPanel.sideBar.settings.healthCheck')}
             />
 
             <Separator />
@@ -232,12 +243,12 @@ export default function Settings() {
             <LabeledCheckBox
               checked={showPropertyPanel}
               onCheckedChange={setShowPropertyPanel}
-            label={t("graphPanel.sideBar.settings.showPropertyPanel")}
+              label={t('graphPanel.sideBar.settings.showPropertyPanel')}
             />
             <LabeledCheckBox
               checked={showNodeSearchBar}
               onCheckedChange={setShowNodeSearchBar}
-            label={t("graphPanel.sideBar.settings.showSearchBar")}
+              label={t('graphPanel.sideBar.settings.showSearchBar')}
             />
 
             <Separator />
@@ -245,12 +256,12 @@ export default function Settings() {
             <LabeledCheckBox
               checked={showNodeLabel}
               onCheckedChange={setShowNodeLabel}
-            label={t("graphPanel.sideBar.settings.showNodeLabel")}
+              label={t('graphPanel.sideBar.settings.showNodeLabel')}
             />
             <LabeledCheckBox
               checked={enableNodeDrag}
               onCheckedChange={setEnableNodeDrag}
-            label={t("graphPanel.sideBar.settings.nodeDraggable")}
+              label={t('graphPanel.sideBar.settings.nodeDraggable')}
             />
 
             <Separator />
@@ -258,50 +269,50 @@ export default function Settings() {
             <LabeledCheckBox
               checked={showEdgeLabel}
               onCheckedChange={setShowEdgeLabel}
-            label={t("graphPanel.sideBar.settings.showEdgeLabel")}
+              label={t('graphPanel.sideBar.settings.showEdgeLabel')}
             />
             <LabeledCheckBox
               checked={enableHideUnselectedEdges}
               onCheckedChange={setEnableHideUnselectedEdges}
-            label={t("graphPanel.sideBar.settings.hideUnselectedEdges")}
+              label={t('graphPanel.sideBar.settings.hideUnselectedEdges')}
             />
             <LabeledCheckBox
               checked={enableEdgeEvents}
               onCheckedChange={setEnableEdgeEvents}
-            label={t("graphPanel.sideBar.settings.edgeEvents")}
+              label={t('graphPanel.sideBar.settings.edgeEvents')}
             />
 
             <Separator />
             <LabeledNumberInput
-            label={t("graphPanel.sideBar.settings.maxQueryDepth")}
+              label={t('graphPanel.sideBar.settings.maxQueryDepth')}
               min={1}
               value={graphQueryMaxDepth}
               onEditFinished={setGraphQueryMaxDepth}
             />
             <LabeledNumberInput
-            label={t("graphPanel.sideBar.settings.minDegree")}
+              label={t('graphPanel.sideBar.settings.minDegree')}
               min={0}
               value={graphMinDegree}
               onEditFinished={setGraphMinDegree}
             />
             <LabeledNumberInput
-            label={t("graphPanel.sideBar.settings.maxLayoutIterations")}
+              label={t('graphPanel.sideBar.settings.maxLayoutIterations')}
               min={1}
-            max={20}
+              max={30}
               value={graphLayoutMaxIterations}
               onEditFinished={setGraphLayoutMaxIterations}
             />
             <Separator />
 
             <div className="flex flex-col gap-2">
-            <label className="text-sm font-medium">{t("graphPanel.sideBar.settings.apiKey")}</label>
+              <label className="text-sm font-medium">{t('graphPanel.sideBar.settings.apiKey')}</label>
               <form className="flex h-6 gap-2" onSubmit={(e) => e.preventDefault()}>
                 <div className="w-0 flex-1">
                   <Input
                     type="password"
                     value={tempApiKey}
                     onChange={handleTempApiKeyChange}
-                  placeholder={t("graphPanel.sideBar.settings.enterYourAPIkey")}
+                    placeholder={t('graphPanel.sideBar.settings.enterYourAPIkey')}
                     className="max-h-full w-full min-w-0"
                     autoComplete="off"
                   />
@@ -312,12 +323,13 @@ export default function Settings() {
                   size="sm"
                   className="max-h-full shrink-0"
                 >
-                {t("graphPanel.sideBar.settings.save")}
+                  {t('graphPanel.sideBar.settings.save')}
                 </Button>
               </form>
             </div>
           </div>
         </PopoverContent>
       </Popover>
+    </>
   )
 }

lightrag_webui/src/components/graph/SettingsDisplay.tsx

@@ -0,0 +1,21 @@
+import { useSettingsStore } from '@/stores/settings'
+import { useTranslation } from 'react-i18next'
+
+/**
+ * Component that displays current values of important graph settings
+ * Positioned to the right of the toolbar at the bottom-left corner
+ */
+const SettingsDisplay = () => {
+  const { t } = useTranslation()
+  const graphQueryMaxDepth = useSettingsStore.use.graphQueryMaxDepth()
+  const graphMinDegree = useSettingsStore.use.graphMinDegree()
+
+  return (
+    <div className="absolute bottom-2 left-[calc(2rem+2.5rem)] flex items-center gap-2 text-xs text-gray-400">
+      <div>{t('graphPanel.sideBar.settings.depth')}: {graphQueryMaxDepth}</div>
+      <div>{t('graphPanel.sideBar.settings.degree')}: {graphMinDegree}</div>
+    </div>
+  )
+}
+
+export default SettingsDisplay

lightrag_webui/src/components/retrieval/QuerySettings.tsx

@@ -25,7 +25,7 @@ export default function QuerySettings() {
   }, [])
 
   return (
-    <Card className="flex shrink-0 flex-col">
+    <Card className="flex shrink-0 flex-col min-w-[180px]">
       <CardHeader className="px-4 pt-4 pb-2">
         <CardTitle>{t('retrievePanel.querySettings.parametersTitle')}</CardTitle>
         <CardDescription>{t('retrievePanel.querySettings.parametersDescription')}</CardDescription>

lightrag_webui/src/components/ui/AsyncSearch.tsx

@@ -1,4 +1,4 @@
-import { useState, useEffect, useCallback } from 'react'
+import React, { useState, useEffect, useCallback } from 'react'
 import { Loader2 } from 'lucide-react'
 import { useDebounce } from '@/hooks/useDebounce'
 
@@ -193,7 +193,7 @@ export function AsyncSearch<T>({
             </div>
           )}
         </div>
-        <CommandList hidden={!open || debouncedSearchTerm.length === 0}>
+        <CommandList hidden={!open}>
           {error && <div className="text-destructive p-4 text-center">{error}</div>}
           {loading && options.length === 0 && (loadingSkeleton || <DefaultLoadingSkeleton />)}
           {!loading &&
@@ -204,7 +204,7 @@ export function AsyncSearch<T>({
             ))}
           <CommandGroup>
             {options.map((option, idx) => (
-              <>
+              <React.Fragment key={getOptionValue(option) + `-fragment-${idx}`}>
                 <CommandItem
                   key={getOptionValue(option) + `${idx}`}
                   value={getOptionValue(option)}
@@ -215,9 +215,9 @@ export function AsyncSearch<T>({
                   {renderOption(option)}
                 </CommandItem>
                 {idx !== options.length - 1 && (
-                  <div key={idx} className="bg-foreground/10 h-[1px]" />
+                  <div key={`divider-${idx}`} className="bg-foreground/10 h-[1px]" />
                 )}
-              </>
+              </React.Fragment>
             ))}
           </CommandGroup>
         </CommandList>

lightrag_webui/src/components/ui/TabContent.tsx

@@ -0,0 +1,37 @@
+import React, { useEffect } from 'react';
+import { useTabVisibility } from '@/contexts/useTabVisibility';
+
+interface TabContentProps {
+  tabId: string;
+  children: React.ReactNode;
+  className?: string;
+}
+
+/**
+ * TabContent component that manages visibility based on tab selection
+ * Works with the TabVisibilityContext to show/hide content based on active tab
+ */
+const TabContent: React.FC<TabContentProps> = ({ tabId, children, className = '' }) => {
+  const { isTabVisible, setTabVisibility } = useTabVisibility();
+  const isVisible = isTabVisible(tabId);
+
+  // Register this tab with the context when mounted
+  useEffect(() => {
+    setTabVisibility(tabId, true);
+
+    // Cleanup when unmounted
+    return () => {
+      setTabVisibility(tabId, false);
+    };
+  }, [tabId, setTabVisibility]);
+
+  // Use CSS to hide content instead of not rendering it
+  // This prevents components from unmounting when tabs are switched
+  return (
+    <div className={`${className} ${isVisible ? '' : 'hidden'}`}>
+      {children}
+    </div>
+  );
+};
+
+export default TabContent;

lightrag_webui/src/components/ui/Tabs.tsx

@@ -42,9 +42,13 @@ const TabsContent = React.forwardRef<
   <TabsPrimitive.Content
     ref={ref}
     className={cn(
-      'ring-offset-background focus-visible:ring-ring mt-2 focus-visible:ring-2 focus-visible:ring-offset-2 focus-visible:outline-none',
+      'ring-offset-background focus-visible:ring-ring focus-visible:ring-2 focus-visible:ring-offset-2 focus-visible:outline-none',
+      'data-[state=inactive]:invisible data-[state=active]:visible',
+      'h-full w-full',
       className
     )}
+    // Force mounting of inactive tabs to preserve WebGL contexts
+    forceMount
     {...props}
   />
 ))

lightrag_webui/src/components/ui/Tooltip.tsx

@@ -10,30 +10,43 @@ const TooltipTrigger = TooltipPrimitive.Trigger
 
 const processTooltipContent = (content: string) => {
   if (typeof content !== 'string') return content
-  return content.split('\\n').map((line, i) => (
+  return (
-    <React.Fragment key={i}>
+    <div className="relative top-0 pt-1 whitespace-pre-wrap break-words">
-      {line}
+      {content}
-      {i < content.split('\\n').length - 1 && <br />}
+    </div>
-    </React.Fragment>
+  )
-  ))
 }
 
 const TooltipContent = React.forwardRef<
   React.ComponentRef<typeof TooltipPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content>
+  React.ComponentPropsWithoutRef<typeof TooltipPrimitive.Content> & {
->(({ className, sideOffset = 4, children, ...props }, ref) => (
+    side?: 'top' | 'right' | 'bottom' | 'left'
+    align?: 'start' | 'center' | 'end'
+  }
+>(({ className, side = 'left', align = 'start', children, ...props }, ref) => {
+  const contentRef = React.useRef<HTMLDivElement>(null);
+
+  React.useEffect(() => {
+    if (contentRef.current) {
+      contentRef.current.scrollTop = 0;
+    }
+  }, [children]);
+
+  return (
     <TooltipPrimitive.Content
       ref={ref}
-    sideOffset={sideOffset}
+      side={side}
+      align={align}
       className={cn(
-      'bg-popover text-popover-foreground animate-in fade-in-0 zoom-in-95 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 z-50 mx-1 max-w-sm overflow-hidden rounded-md border px-3 py-2 text-sm shadow-md',
+        'bg-popover text-popover-foreground animate-in fade-in-0 zoom-in-95 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2 max-h-[60vh] overflow-y-auto whitespace-pre-wrap break-words rounded-md border px-3 py-2 text-sm shadow-md',
         className
       )}
       {...props}
     >
       {typeof children === 'string' ? processTooltipContent(children) : children}
     </TooltipPrimitive.Content>
-))
+  );
+})
 TooltipContent.displayName = TooltipPrimitive.Content.displayName
 
 export { Tooltip, TooltipTrigger, TooltipContent, TooltipProvider }

lightrag_webui/src/contexts/TabVisibilityProvider.tsx

@@ -0,0 +1,53 @@
+import React, { useState, useEffect, useMemo } from 'react';
+import { TabVisibilityContext } from './context';
+import { TabVisibilityContextType } from './types';
+import { useSettingsStore } from '@/stores/settings';
+
+interface TabVisibilityProviderProps {
+  children: React.ReactNode;
+}
+
+/**
+ * Provider component for the TabVisibility context
+ * Manages the visibility state of tabs throughout the application
+ */
+export const TabVisibilityProvider: React.FC<TabVisibilityProviderProps> = ({ children }) => {
+  // Get current tab from settings store
+  const currentTab = useSettingsStore.use.currentTab();
+
+  // Initialize visibility state with current tab as visible
+  const [visibleTabs, setVisibleTabs] = useState<Record<string, boolean>>(() => ({
+    [currentTab]: true
+  }));
+
+  // Update visibility when current tab changes
+  useEffect(() => {
+    setVisibleTabs((prev) => ({
+      ...prev,
+      [currentTab]: true
+    }));
+  }, [currentTab]);
+
+  // Create the context value with memoization to prevent unnecessary re-renders
+  const contextValue = useMemo<TabVisibilityContextType>(
+    () => ({
+      visibleTabs,
+      setTabVisibility: (tabId: string, isVisible: boolean) => {
+        setVisibleTabs((prev) => ({
+          ...prev,
+          [tabId]: isVisible,
+        }));
+      },
+      isTabVisible: (tabId: string) => !!visibleTabs[tabId],
+    }),
+    [visibleTabs]
+  );
+
+  return (
+    <TabVisibilityContext.Provider value={contextValue}>
+      {children}
+    </TabVisibilityContext.Provider>
+  );
+};
+
+export default TabVisibilityProvider;

lightrag_webui/src/contexts/context.ts

@@ -0,0 +1,12 @@
+import { createContext } from 'react';
+import { TabVisibilityContextType } from './types';
+
+// Default context value
+const defaultContext: TabVisibilityContextType = {
+  visibleTabs: {},
+  setTabVisibility: () => {},
+  isTabVisible: () => false,
+};
+
+// Create the context
+export const TabVisibilityContext = createContext<TabVisibilityContextType>(defaultContext);

lightrag_webui/src/contexts/types.ts

@@ -0,0 +1,5 @@
+export interface TabVisibilityContextType {
+  visibleTabs: Record<string, boolean>;
+  setTabVisibility: (tabId: string, isVisible: boolean) => void;
+  isTabVisible: (tabId: string) => boolean;
+}

lightrag_webui/src/contexts/useTabVisibility.ts

@@ -0,0 +1,17 @@
+import { useContext } from 'react';
+import { TabVisibilityContext } from './context';
+import { TabVisibilityContextType } from './types';
+
+/**
+ * Custom hook to access the tab visibility context
+ * @returns The tab visibility context
+ */
+export const useTabVisibility = (): TabVisibilityContextType => {
+  const context = useContext(TabVisibilityContext);
+
+  if (!context) {
+    throw new Error('useTabVisibility must be used within a TabVisibilityProvider');
+  }
+
+  return context;
+};

lightrag_webui/src/features/ApiSite.tsx

@@ -1,5 +1,40 @@
+import { useState, useEffect } from 'react'
+import { useTabVisibility } from '@/contexts/useTabVisibility'
 import { backendBaseUrl } from '@/lib/constants'
+import { useTranslation } from 'react-i18next'
 
 export default function ApiSite() {
-  return <iframe src={backendBaseUrl + '/docs'} className="size-full" />
+  const { t } = useTranslation()
+  const { isTabVisible } = useTabVisibility()
+  const isApiTabVisible = isTabVisible('api')
+  const [iframeLoaded, setIframeLoaded] = useState(false)
+
+  // Load the iframe once on component mount
+  useEffect(() => {
+    if (!iframeLoaded) {
+      setIframeLoaded(true)
+    }
+  }, [iframeLoaded])
+
+  // Use CSS to hide content when tab is not visible
+  return (
+    <div className={`size-full ${isApiTabVisible ? '' : 'hidden'}`}>
+      {iframeLoaded ? (
+        <iframe
+          src={backendBaseUrl + '/docs'}
+          className="size-full w-full h-full"
+          style={{ width: '100%', height: '100%', border: 'none' }}
+          // Use key to ensure iframe doesn't reload
+          key="api-docs-iframe"
+        />
+      ) : (
+        <div className="flex h-full w-full items-center justify-center bg-background">
+          <div className="text-center">
+            <div className="mb-2 h-8 w-8 animate-spin rounded-full border-4 border-primary border-t-transparent"></div>
+            <p>{t('apiSite.loading')}</p>
+          </div>
+        </div>
+      )}
+    </div>
+  )
 }

lightrag_webui/src/features/DocumentManager.tsx

@@ -1,5 +1,6 @@
-import { useState, useEffect, useCallback } from 'react'
+import { useState, useEffect, useCallback, useRef } from 'react'
 import { useTranslation } from 'react-i18next'
+import { useTabVisibility } from '@/contexts/useTabVisibility'
 import Button from '@/components/ui/Button'
 import {
   Table,
@@ -26,6 +27,9 @@ export default function DocumentManager() {
   const { t } = useTranslation()
   const health = useBackendState.use.health()
   const [docs, setDocs] = useState<DocsStatusesResponse | null>(null)
+  const { isTabVisible } = useTabVisibility()
+  const isDocumentsTabVisible = isTabVisible('documents')
+  const initialLoadRef = useRef(false)
 
   const fetchDocuments = useCallback(async () => {
     try {
@@ -48,11 +52,15 @@ export default function DocumentManager() {
     } catch (err) {
       toast.error(t('documentPanel.documentManager.errors.loadFailed', { error: errorMessage(err) }))
     }
-  }, [setDocs])
+  }, [setDocs, t])
 
+  // Only fetch documents when the tab becomes visible for the first time
   useEffect(() => {
+    if (isDocumentsTabVisible && !initialLoadRef.current) {
       fetchDocuments()
-  }, []) // eslint-disable-line react-hooks/exhaustive-deps
+      initialLoadRef.current = true
+    }
+  }, [isDocumentsTabVisible, fetchDocuments])
 
   const scanDocuments = useCallback(async () => {
     try {
@@ -61,21 +69,24 @@ export default function DocumentManager() {
     } catch (err) {
       toast.error(t('documentPanel.documentManager.errors.scanFailed', { error: errorMessage(err) }))
     }
-  }, [])
+  }, [t])
 
+  // Only set up polling when the tab is visible and health is good
   useEffect(() => {
-    const interval = setInterval(async () => {
+    if (!isDocumentsTabVisible || !health) {
-      if (!health) {
       return
     }
+
+    const interval = setInterval(async () => {
       try {
         await fetchDocuments()
       } catch (err) {
         toast.error(t('documentPanel.documentManager.errors.scanProgressFailed', { error: errorMessage(err) }))
       }
     }, 5000)
+
     return () => clearInterval(interval)
-  }, [health, fetchDocuments])
+  }, [health, fetchDocuments, t, isDocumentsTabVisible])
 
   return (
     <Card className="!size-full !rounded-none !border-none">

lightrag_webui/src/features/GraphViewer.tsx

@@ -1,4 +1,5 @@
-import { useEffect, useState, useCallback, useMemo } from 'react'
+import { useEffect, useState, useCallback, useMemo, useRef } from 'react'
+import { useTabVisibility } from '@/contexts/useTabVisibility'
 // import { MiniMap } from '@react-sigma/minimap'
 import { SigmaContainer, useRegisterEvents, useSigma } from '@react-sigma/core'
 import { Settings as SigmaSettings } from 'sigma/settings'
@@ -17,6 +18,7 @@ import Settings from '@/components/graph/Settings'
 import GraphSearch from '@/components/graph/GraphSearch'
 import GraphLabels from '@/components/graph/GraphLabels'
 import PropertiesView from '@/components/graph/PropertiesView'
+import SettingsDisplay from '@/components/graph/SettingsDisplay'
 
 import { useSettingsStore } from '@/stores/settings'
 import { useGraphStore } from '@/stores/graph'
@@ -90,8 +92,12 @@ const GraphEvents = () => {
         }
       },
       // Disable the autoscale at the first down interaction
-      mousedown: () => {
+      mousedown: (e) => {
-        if (!sigma.getCustomBBox()) sigma.setCustomBBox(sigma.getBBox())
+        // Only set custom BBox if it's a drag operation (mouse button is pressed)
+        const mouseEvent = e.original as MouseEvent;
+        if (mouseEvent.buttons !== 0 && !sigma.getCustomBBox()) {
+          sigma.setCustomBBox(sigma.getBBox())
+        }
       }
     })
   }, [registerEvents, sigma, draggedNode])
@@ -101,27 +107,46 @@ const GraphEvents = () => {
 
 const GraphViewer = () => {
   const [sigmaSettings, setSigmaSettings] = useState(defaultSigmaSettings)
+  const sigmaRef = useRef<any>(null)
+  const initAttemptedRef = useRef(false)
 
   const selectedNode = useGraphStore.use.selectedNode()
   const focusedNode = useGraphStore.use.focusedNode()
   const moveToSelectedNode = useGraphStore.use.moveToSelectedNode()
+  const isFetching = useGraphStore.use.isFetching()
+  const shouldRender = useGraphStore.use.shouldRender() // Rendering control state
+
+  // Get tab visibility
+  const { isTabVisible } = useTabVisibility()
+  const isGraphTabVisible = isTabVisible('knowledge-graph')
 
   const showPropertyPanel = useSettingsStore.use.showPropertyPanel()
   const showNodeSearchBar = useSettingsStore.use.showNodeSearchBar()
-  const renderLabels = useSettingsStore.use.showNodeLabel()
-
-  const enableEdgeEvents = useSettingsStore.use.enableEdgeEvents()
   const enableNodeDrag = useSettingsStore.use.enableNodeDrag()
-  const renderEdgeLabels = useSettingsStore.use.showEdgeLabel()
 
+  // Handle component mount/unmount and tab visibility
   useEffect(() => {
-    setSigmaSettings({
+    // When component mounts or tab becomes visible
-      ...defaultSigmaSettings,
+    if (isGraphTabVisible && !shouldRender && !isFetching && !initAttemptedRef.current) {
-      enableEdgeEvents,
+      // If tab is visible but graph is not rendering, try to enable rendering
-      renderEdgeLabels,
+      useGraphStore.getState().setShouldRender(true)
-      renderLabels
+      initAttemptedRef.current = true
-    })
+      console.log('Graph viewer initialized')
-  }, [renderLabels, enableEdgeEvents, renderEdgeLabels])
+    }
+
+    // Cleanup function when component unmounts
+    return () => {
+      // Only log cleanup, don't actually clean up the WebGL context
+      // This allows the WebGL context to persist across tab switches
+      console.log('Graph viewer cleanup')
+    }
+  }, [isGraphTabVisible, shouldRender, isFetching])
+
+  // Initialize sigma settings once on component mount
+  // All dynamic settings will be updated in GraphControl using useSetSettings
+  useEffect(() => {
+    setSigmaSettings(defaultSigmaSettings)
+  }, [])
 
   const onSearchFocus = useCallback((value: GraphSearchOption | null) => {
     if (value === null) useGraphStore.getState().setFocusedNode(null)
@@ -142,8 +167,17 @@ const GraphViewer = () => {
     [selectedNode]
   )
 
+  // Since TabsContent now forces mounting of all tabs, we need to conditionally render
+  // the SigmaContainer based on visibility to avoid unnecessary rendering
   return (
-    <SigmaContainer settings={sigmaSettings} className="!bg-background !size-full overflow-hidden">
+    <div className="relative h-full w-full">
+      {/* Only render the SigmaContainer when the tab is visible */}
+      {isGraphTabVisible ? (
+        <SigmaContainer
+          settings={sigmaSettings}
+          className="!bg-background !size-full overflow-hidden"
+          ref={sigmaRef}
+        >
           <GraphControl />
 
           {enableNodeDrag && <GraphEvents />}
@@ -178,7 +212,28 @@ const GraphViewer = () => {
           {/* <div className="absolute bottom-2 right-2 flex flex-col rounded-xl border-2">
             <MiniMap width="100px" height="100px" />
           </div> */}
+
+          <SettingsDisplay />
         </SigmaContainer>
+      ) : (
+        // Placeholder when tab is not visible
+        <div className="flex h-full w-full items-center justify-center">
+          <div className="text-center text-muted-foreground">
+            {/* Placeholder content */}
+          </div>
+        </div>
+      )}
+
+      {/* Loading overlay - shown when data is loading */}
+      {isFetching && (
+        <div className="absolute inset-0 flex items-center justify-center bg-background/80 z-10">
+          <div className="text-center">
+            <div className="mb-2 h-8 w-8 animate-spin rounded-full border-4 border-primary border-t-transparent"></div>
+            <p>Loading Graph Data...</p>
+          </div>
+        </div>
+      )}
+    </div>
   )
 }
 

lightrag_webui/src/features/SiteHeader.tsx

@@ -1,6 +1,6 @@
 import Button from '@/components/ui/Button'
 import { SiteInfo } from '@/lib/constants'
-import ThemeToggle from '@/components/ThemeToggle'
+import AppSettings from '@/components/AppSettings'
 import { TabsList, TabsTrigger } from '@/components/ui/Tabs'
 import { useSettingsStore } from '@/stores/settings'
 import { cn } from '@/lib/utils'
@@ -67,12 +67,14 @@ export default function SiteHeader() {
       </div>
 
       <nav className="flex items-center">
+        <div className="flex items-center gap-2">
           <Button variant="ghost" size="icon" side="bottom" tooltip={t('header.projectRepository')}>
             <a href={SiteInfo.github} target="_blank" rel="noopener noreferrer">
               <GithubIcon className="size-4" aria-hidden="true" />
             </a>
           </Button>
-        <ThemeToggle />
+          <AppSettings />
+        </div>
       </nav>
     </header>
   )

lightrag_webui/src/hooks/useLightragGraph.tsx

@@ -1,11 +1,12 @@
 import Graph, { DirectedGraph } from 'graphology'
-import { useCallback, useEffect } from 'react'
+import { useCallback, useEffect, useRef } from 'react'
 import { randomColor, errorMessage } from '@/lib/utils'
 import * as Constants from '@/lib/constants'
 import { useGraphStore, RawGraph } from '@/stores/graph'
 import { queryGraphs } from '@/api/lightrag'
 import { useBackendState } from '@/stores/state'
 import { useSettingsStore } from '@/stores/settings'
+import { useTabVisibility } from '@/contexts/useTabVisibility'
 
 import seedrandom from 'seedrandom'
 
@@ -136,15 +137,23 @@ const fetchGraph = async (label: string, maxDepth: number, minDegree: number) =>
   return rawGraph
 }
 
+// Create a new graph instance with the raw graph data
 const createSigmaGraph = (rawGraph: RawGraph | null) => {
+  // Always create a new graph instance
   const graph = new DirectedGraph()
 
+  // Add nodes from raw graph data
   for (const rawNode of rawGraph?.nodes ?? []) {
+    // Ensure we have fresh random positions for nodes
+    seedrandom(rawNode.id + Date.now().toString(), { global: true })
+    const x = Math.random()
+    const y = Math.random()
+
     graph.addNode(rawNode.id, {
       label: rawNode.labels.join(', '),
       color: rawNode.color,
-      x: rawNode.x,
+      x: x,
-      y: rawNode.y,
+      y: y,
       size: rawNode.size,
       // for node-border
       borderColor: Constants.nodeBorderColor,
@@ -152,6 +161,7 @@ const createSigmaGraph = (rawGraph: RawGraph | null) => {
     })
   }
 
+  // Add edges from raw graph data
   for (const rawEdge of rawGraph?.edges ?? []) {
     rawEdge.dynamicId = graph.addDirectedEdge(rawEdge.source, rawEdge.target, {
       label: rawEdge.type || undefined
@@ -161,14 +171,30 @@ const createSigmaGraph = (rawGraph: RawGraph | null) => {
   return graph
 }
 
-const lastQueryLabel = { label: '', maxQueryDepth: 0, minDegree: 0 }
-
 const useLightrangeGraph = () => {
   const queryLabel = useSettingsStore.use.queryLabel()
   const rawGraph = useGraphStore.use.rawGraph()
   const sigmaGraph = useGraphStore.use.sigmaGraph()
   const maxQueryDepth = useSettingsStore.use.graphQueryMaxDepth()
   const minDegree = useSettingsStore.use.graphMinDegree()
+  const isFetching = useGraphStore.use.isFetching()
+
+  // Get tab visibility
+  const { isTabVisible } = useTabVisibility()
+  const isGraphTabVisible = isTabVisible('knowledge-graph')
+
+  // Track previous parameters to detect actual changes
+  const prevParamsRef = useRef({ queryLabel, maxQueryDepth, minDegree })
+
+  // Use ref to track if data has been loaded and initial load
+  const dataLoadedRef = useRef(false)
+  const initialLoadRef = useRef(false)
+
+  // Check if parameters have changed
+  const paramsChanged =
+    prevParamsRef.current.queryLabel !== queryLabel ||
+    prevParamsRef.current.maxQueryDepth !== maxQueryDepth ||
+    prevParamsRef.current.minDegree !== minDegree
 
   const getNode = useCallback(
     (nodeId: string) => {
@@ -184,35 +210,131 @@ const useLightrangeGraph = () => {
     [rawGraph]
   )
 
-  useEffect(() => {
+  // Track if a fetch is in progress to prevent multiple simultaneous fetches
-    if (queryLabel) {
+  const fetchInProgressRef = useRef(false)
-      if (lastQueryLabel.label !== queryLabel ||
-          lastQueryLabel.maxQueryDepth !== maxQueryDepth ||
-          lastQueryLabel.minDegree !== minDegree) {
-        lastQueryLabel.label = queryLabel
-        lastQueryLabel.maxQueryDepth = maxQueryDepth
-        lastQueryLabel.minDegree = minDegree
 
+  // Data fetching logic - simplified but preserving TAB visibility check
+  useEffect(() => {
+    // Skip if fetch is already in progress
+    if (fetchInProgressRef.current) {
+      return
+    }
+
+    // If there's no query label, reset the graph
+    if (!queryLabel) {
+      if (rawGraph !== null || sigmaGraph !== null) {
         const state = useGraphStore.getState()
         state.reset()
-        fetchGraph(queryLabel, maxQueryDepth, minDegree).then((data) => {
+        state.setGraphDataFetchAttempted(false)
-          // console.debug('Query label: ' + queryLabel)
+        state.setLabelsFetchAttempted(false)
-          state.setSigmaGraph(createSigmaGraph(data))
+      }
-          data?.buildDynamicMap()
+      dataLoadedRef.current = false
-          state.setRawGraph(data)
+      initialLoadRef.current = false
+      return
+    }
+
+    // Check if parameters have changed
+    if (!isFetching && !fetchInProgressRef.current &&
+        (paramsChanged || !useGraphStore.getState().graphDataFetchAttempted)) {
+
+      // Only fetch data if the Graph tab is visible
+      if (!isGraphTabVisible) {
+        console.log('Graph tab not visible, skipping data fetch');
+        return;
+      }
+
+      // Set flags
+      fetchInProgressRef.current = true
+      useGraphStore.getState().setGraphDataFetchAttempted(true)
+
+      const state = useGraphStore.getState()
+      state.setIsFetching(true)
+      state.setShouldRender(false) // Disable rendering during data loading
+
+      // Clear selection and highlighted nodes before fetching new graph
+      state.clearSelection()
+      if (state.sigmaGraph) {
+        state.sigmaGraph.forEachNode((node) => {
+          state.sigmaGraph?.setNodeAttribute(node, 'highlighted', false)
         })
       }
-    } else {
+
+      // Update parameter reference
+      prevParamsRef.current = { queryLabel, maxQueryDepth, minDegree }
+
+      console.log('Fetching graph data...')
+
+      // Use a local copy of the parameters
+      const currentQueryLabel = queryLabel
+      const currentMaxQueryDepth = maxQueryDepth
+      const currentMinDegree = minDegree
+
+      // Fetch graph data
+      fetchGraph(currentQueryLabel, currentMaxQueryDepth, currentMinDegree).then((data) => {
         const state = useGraphStore.getState()
+
+        // Reset state
         state.reset()
-      state.setSigmaGraph(new DirectedGraph())
+
+        // Create and set new graph directly
+        const newSigmaGraph = createSigmaGraph(data)
+        data?.buildDynamicMap()
+
+        // Set new graph data
+        state.setSigmaGraph(newSigmaGraph)
+        state.setRawGraph(data)
+
+        // No longer need to extract labels from graph data
+
+        // Update flags
+        dataLoadedRef.current = true
+        initialLoadRef.current = true
+        fetchInProgressRef.current = false
+
+        // Reset camera view
+        state.setMoveToSelectedNode(true)
+
+        // Enable rendering if the tab is visible
+        state.setShouldRender(isGraphTabVisible)
+        state.setIsFetching(false)
+      }).catch((error) => {
+        console.error('Error fetching graph data:', error)
+
+        // Reset state on error
+        const state = useGraphStore.getState()
+        state.setIsFetching(false)
+        state.setShouldRender(isGraphTabVisible)
+        dataLoadedRef.current = false
+        fetchInProgressRef.current = false
+        state.setGraphDataFetchAttempted(false)
+      })
     }
-  }, [queryLabel, maxQueryDepth, minDegree])
+  }, [queryLabel, maxQueryDepth, minDegree, isFetching, paramsChanged, isGraphTabVisible, rawGraph, sigmaGraph])
+
+  // Update rendering state and handle tab visibility changes
+  useEffect(() => {
+    // When tab becomes visible
+    if (isGraphTabVisible) {
+      // If we have data, enable rendering
+      if (rawGraph) {
+        useGraphStore.getState().setShouldRender(true)
+      }
+
+      // We no longer reset the fetch attempted flag here to prevent continuous API calls
+    } else {
+      // When tab becomes invisible, disable rendering
+      useGraphStore.getState().setShouldRender(false)
+    }
+  }, [isGraphTabVisible, rawGraph])
 
   const lightrageGraph = useCallback(() => {
+    // If we already have a graph instance, return it
     if (sigmaGraph) {
       return sigmaGraph as Graph<NodeType, EdgeType>
     }
+
+    // If no graph exists yet, create a new one and store it
+    console.log('Creating new Sigma graph instance')
     const graph = new DirectedGraph()
     useGraphStore.getState().setSigmaGraph(graph)
     return graph as Graph<NodeType, EdgeType>

lightrag_webui/src/i18n.js

@@ -1,21 +0,0 @@
-import i18n from "i18next";
-import { initReactI18next } from "react-i18next";
-
-import en from "./locales/en.json";
-import zh from "./locales/zh.json";
-
-i18n
-  .use(initReactI18next)
-  .init({
-    resources: {
-      en: { translation: en },
-      zh: { translation: zh }
-    },
-    lng: "en", // default
-    fallbackLng: "en",
-    interpolation: {
-      escapeValue: false
-    }
-  });
-
-export default i18n;

lightrag_webui/src/i18n.ts

@@ -0,0 +1,37 @@
+import i18n from 'i18next'
+import { initReactI18next } from 'react-i18next'
+import { useSettingsStore } from '@/stores/settings'
+
+import en from './locales/en.json'
+import zh from './locales/zh.json'
+
+// Function to sync i18n with store state
+export const initializeI18n = async (): Promise<typeof i18n> => {
+  // Get initial language from store
+  const initialLanguage = useSettingsStore.getState().language
+
+  // Initialize with store language
+  await i18n.use(initReactI18next).init({
+    resources: {
+      en: { translation: en },
+      zh: { translation: zh }
+    },
+    lng: initialLanguage,
+    fallbackLng: 'en',
+    interpolation: {
+      escapeValue: false
+    }
+  })
+
+  // Subscribe to language changes
+  useSettingsStore.subscribe((state) => {
+    const currentLanguage = state.language
+    if (i18n.language !== currentLanguage) {
+      i18n.changeLanguage(currentLanguage)
+    }
+  })
+
+  return i18n
+}
+
+export default i18n

lightrag_webui/src/lib/constants.ts

@@ -15,8 +15,8 @@ export const edgeColorDarkTheme = '#969696'
 export const edgeColorSelected = '#F57F17'
 export const edgeColorHighlighted = '#B2EBF2'
 
-export const searchResultLimit = 20
+export const searchResultLimit = 50
-export const labelListLimit = 40
+export const labelListLimit = 100
 
 export const minNodeSize = 4
 export const maxNodeSize = 20

lightrag_webui/src/locales/en.json

@@ -1,4 +1,11 @@
 {
+  "settings": {
+    "language": "Language",
+    "theme": "Theme",
+    "light": "Light",
+    "dark": "Dark",
+    "system": "System"
+  },
   "header": {
     "documents": "Documents",
     "knowledgeGraph": "Knowledge Graph",
@@ -79,9 +86,12 @@
         "maxQueryDepth": "Max Query Depth",
         "minDegree": "Minimum Degree",
         "maxLayoutIterations": "Max Layout Iterations",
+        "depth": "Depth",
+        "degree": "Degree",
         "apiKey": "API Key",
         "enterYourAPIkey": "Enter your API key",
-        "save": "Save"
+        "save": "Save",
+        "refreshLayout": "Refresh Layout"
       },
 
       "zoomControl": {
@@ -140,7 +150,14 @@
         "labels": "Labels",
         "degree": "Degree",
         "properties": "Properties",
-        "relationships": "Relationships"
+        "relationships": "Relationships",
+        "propertyNames": {
+          "description": "Description",
+          "entity_id": "Name",
+          "entity_type": "Type",
+          "source_id": "SrcID",
+          "Neighbour": "Neigh"
+        }
       },
       "edge": {
         "title": "Relationship",
@@ -230,5 +247,8 @@
       "streamResponse": "Stream Response",
       "streamResponseTooltip": "If True, enables streaming output for real-time responses"
     }
+  },
+  "apiSite": {
+    "loading": "Loading API Documentation..."
   }
 }

lightrag_webui/src/locales/zh.json

@@ -1,4 +1,11 @@
 {
+  "settings": {
+    "language": "语言",
+    "theme": "主题",
+    "light": "浅色",
+    "dark": "深色",
+    "system": "系统"
+  },
   "header": {
     "documents": "文档",
     "knowledgeGraph": "知识图谱",
@@ -6,41 +13,41 @@
     "api": "API",
     "projectRepository": "项目仓库",
     "themeToggle": {
-      "switchToLight": "切换到亮色主题",
+      "switchToLight": "切换到浅色主题",
-      "switchToDark": "切换到暗色主题"
+      "switchToDark": "切换到深色主题"
     }
   },
   "documentPanel": {
     "clearDocuments": {
-      "button": "清除",
+      "button": "清空",
-      "tooltip": "清除文档",
+      "tooltip": "清空文档",
-      "title": "清除文档",
+      "title": "清空文档",
-      "confirm": "您确定要清除所有文档吗？",
+      "confirm": "确定要清空所有文档吗？",
       "confirmButton": "确定",
-      "success": "文档已成功清除",
+      "success": "文档清空成功",
-      "failed": "清除文档失败:\n{{message}}",
+      "failed": "清空文档失败：\n{{message}}",
-      "error": "清除文档失败:\n{{error}}"
+      "error": "清空文档失败：\n{{error}}"
     },
     "uploadDocuments": {
       "button": "上传",
       "tooltip": "上传文档",
       "title": "上传文档",
-      "description": "拖放文档到此处或点击浏览。",
+      "description": "拖拽文件到此处或点击浏览",
-      "uploading": "正在上传 {{name}}: {{percent}}%",
+      "uploading": "正在上传 {{name}}：{{percent}}%",
-      "success": "上传成功:\n{{name}} 上传成功",
+      "success": "上传成功：\n{{name}} 上传完成",
-      "failed": "上传失败:\n{{name}}\n{{message}}",
+      "failed": "上传失败：\n{{name}}\n{{message}}",
-      "error": "上传失败:\n{{name}}\n{{error}}",
+      "error": "上传失败：\n{{name}}\n{{error}}",
       "generalError": "上传失败\n{{error}}",
-      "fileTypes": "支持的文件类型: TXT, MD, DOCX, PDF, PPTX, RTF, ODT, EPUB, HTML, HTM, TEX, JSON, XML, YAML, YML, CSV, LOG, CONF, INI, PROPERTIES, SQL, BAT, SH, C, CPP, PY, JAVA, JS, TS, SWIFT, GO, RB, PHP, CSS, SCSS, LESS"
+      "fileTypes": "支持的文件类型：TXT, MD, DOCX, PDF, PPTX, RTF, ODT, EPUB, HTML, HTM, TEX, JSON, XML, YAML, YML, CSV, LOG, CONF, INI, PROPERTIES, SQL, BAT, SH, C, CPP, PY, JAVA, JS, TS, SWIFT, GO, RB, PHP, CSS, SCSS, LESS"
     },
     "documentManager": {
       "title": "文档管理",
       "scanButton": "扫描",
       "scanTooltip": "扫描文档",
       "uploadedTitle": "已上传文档",
-      "uploadedDescription": "已上传文档及其状态列表。",
+      "uploadedDescription": "已上传文档列表及其状态",
-      "emptyTitle": "暂无文档",
+      "emptyTitle": "无文档",
-      "emptyDescription": "尚未上传任何文档。",
+      "emptyDescription": "还没有上传任何文档",
       "columns": {
         "id": "ID",
         "summary": "摘要",
@@ -54,7 +61,7 @@
       "status": {
         "completed": "已完成",
         "processing": "处理中",
-        "pending": "待处理",
+        "pending": "等待中",
         "failed": "失败"
       },
       "errors": {
@@ -74,39 +81,39 @@
         "showNodeLabel": "显示节点标签",
         "nodeDraggable": "节点可拖动",
         "showEdgeLabel": "显示边标签",
-        "hideUnselectedEdges": "隐藏未选中边",
+        "hideUnselectedEdges": "隐藏未选中的边",
         "edgeEvents": "边事件",
         "maxQueryDepth": "最大查询深度",
         "minDegree": "最小度数",
         "maxLayoutIterations": "最大布局迭代次数",
+        "depth": "深度",
+        "degree": "邻边",
         "apiKey": "API密钥",
         "enterYourAPIkey": "输入您的API密钥",
-        "save": "保存"
+        "save": "保存",
+        "refreshLayout": "刷新布局"
       },
-
       "zoomControl": {
         "zoomIn": "放大",
         "zoomOut": "缩小",
         "resetZoom": "重置缩放"
       },
-
       "layoutsControl": {
         "startAnimation": "开始布局动画",
         "stopAnimation": "停止布局动画",
-        "layoutGraph": "布局图",
+        "layoutGraph": "图布局",
         "layouts": {
-          "Circular": "环形布局",
+          "Circular": "环形",
-          "Circlepack": "圆形打包布局",
+          "Circlepack": "圆形打包",
-          "Random": "随机布局",
+          "Random": "随机",
-          "Noverlaps": "无重叠布局",
+          "Noverlaps": "无重叠",
-          "Force Directed": "力导向布局",
+          "Force Directed": "力导向",
-          "Force Atlas": "力导向图谱布局"
+          "Force Atlas": "力图"
         }
       },
-
       "fullScreenControl": {
         "fullScreen": "全屏",
-        "windowed": "窗口模式"
+        "windowed": "窗口"
       }
     },
     "statusIndicator": {
@@ -122,7 +129,7 @@
       "llmBinding": "LLM绑定",
       "llmBindingHost": "LLM绑定主机",
       "llmModel": "LLM模型",
-      "maxTokens": "最大 Token 数",
+      "maxTokens": "最大令牌数",
       "embeddingConfig": "嵌入配置",
       "embeddingBinding": "嵌入绑定",
       "embeddingBindingHost": "嵌入绑定主机",
@@ -140,96 +147,93 @@
         "labels": "标签",
         "degree": "度数",
         "properties": "属性",
-        "relationships": "关系"
+        "relationships": "关系",
+        "propertyNames": {
+          "description": "描述",
+          "entity_id": "名称",
+          "entity_type": "类型",
+          "source_id": "信源ID",
+          "Neighbour": "邻接"
+        }
       },
       "edge": {
         "title": "关系",
         "id": "ID",
         "type": "类型",
-        "source": "源",
+        "source": "源节点",
-        "target": "目标",
+        "target": "目标节点",
         "properties": "属性"
       }
     },
     "search": {
       "placeholder": "搜索节点...",
-      "message": "以及其它 {count} 项"
+      "message": "还有 {count} 个"
     },
     "graphLabels": {
       "selectTooltip": "选择查询标签",
       "noLabels": "未找到标签",
       "label": "标签",
       "placeholder": "搜索标签...",
-      "andOthers": "以及其它 {count} 个"
+      "andOthers": "还有 {count} 个"
     }
   },
   "retrievePanel": {
     "chatMessage": {
       "copyTooltip": "复制到剪贴板",
-      "copyError": "无法复制文本到剪贴板"
+      "copyError": "复制文本到剪贴板失败"
     },
-
     "retrieval": {
-      "startPrompt": "在下面输入您的查询以开始检索",
+      "startPrompt": "输入查询开始检索",
-      "clear": "清除",
+      "clear": "清空",
       "send": "发送",
-      "placeholder": "输入您的查询...",
+      "placeholder": "输入查询...",
-      "error": "错误：无法获取响应"
+      "error": "错误：获取响应失败"
     },
     "querySettings": {
-      "parametersTitle": "参数设置",
+      "parametersTitle": "参数",
       "parametersDescription": "配置查询参数",
-
       "queryMode": "查询模式",
-      "queryModeTooltip": "选择检索策略：\n• 朴素：不使用高级技术的基本搜索\n• 本地：基于上下文的信息检索\n• 全局：利用全局知识库\n• 混合：结合本地和全局检索\n• 综合：集成知识图谱与向量检索",
+      "queryModeTooltip": "选择检索策略：\n• Naive：基础搜索，无高级技术\n• Local：上下文相关信息检索\n• Global：利用全局知识库\n• Hybrid：结合本地和全局检索\n• Mix：整合知识图谱和向量检索",
       "queryModeOptions": {
         "naive": "朴素",
         "local": "本地",
         "global": "全局",
         "hybrid": "混合",
-        "mix": "综合"
+        "mix": "混合"
       },
-
       "responseFormat": "响应格式",
-      "responseFormatTooltip": "定义响应格式。例如：\n• 多个段落\n• 单个段落\n• 项目符号",
+      "responseFormatTooltip": "定义响应格式。例如：\n• 多段落\n• 单段落\n• 要点",
       "responseFormatOptions": {
-        "multipleParagraphs": "多个段落",
+        "multipleParagraphs": "多段落",
-        "singleParagraph": "单个段落",
+        "singleParagraph": "单段落",
-        "bulletPoints": "项目符号"
+        "bulletPoints": "要点"
       },
-
+      "topK": "Top K结果",
-      "topK": "Top K 结果数",
+      "topKTooltip": "检索的顶部项目数。在'local'模式下表示实体，在'global'模式下表示关系",
-      "topKTooltip": "要检索的前 K 个项目数量。在“本地”模式下表示实体，在“全局”模式下表示关系",
+      "topKPlaceholder": "结果数量",
-      "topKPlaceholder": "结果数",
+      "maxTokensTextUnit": "文本单元最大令牌数",
-
+      "maxTokensTextUnitTooltip": "每个检索文本块允许的最大令牌数",
-      "maxTokensTextUnit": "文本单元最大 Token 数",
+      "maxTokensGlobalContext": "全局上下文最大令牌数",
-      "maxTokensTextUnitTooltip": "每个检索到的文本块允许的最大 Token 数",
+      "maxTokensGlobalContextTooltip": "全局检索中关系描述的最大令牌数",
-
+      "maxTokensLocalContext": "本地上下文最大令牌数",
-      "maxTokensGlobalContext": "全局上下文最大 Token 数",
+      "maxTokensLocalContextTooltip": "本地检索中实体描述的最大令牌数",
-      "maxTokensGlobalContextTooltip": "在全局检索中为关系描述分配的最大 Token 数",
-
-      "maxTokensLocalContext": "本地上下文最大 Token 数",
-      "maxTokensLocalContextTooltip": "在本地检索中为实体描述分配的最大 Token 数",
-
       "historyTurns": "历史轮次",
-      "historyTurnsTooltip": "在响应上下文中考虑的完整对话轮次（用户-助手对）",
+      "historyTurnsTooltip": "响应上下文中考虑的完整对话轮次（用户-助手对）数量",
-      "historyTurnsPlaceholder": "历史轮次的数量",
+      "historyTurnsPlaceholder": "历史轮次数",
-
       "hlKeywords": "高级关键词",
-      "hlKeywordsTooltip": "检索时优先考虑的高级关键词。请用逗号分隔",
+      "hlKeywordsTooltip": "检索中优先考虑的高级关键词列表。用逗号分隔",
       "hlkeywordsPlaceHolder": "输入关键词",
-
       "llKeywords": "低级关键词",
-      "llKeywordsTooltip": "用于优化检索焦点的低级关键词。请用逗号分隔",
+      "llKeywordsTooltip": "用于细化检索重点的低级关键词列表。用逗号分隔",
-
+      "onlyNeedContext": "仅需上下文",
-      "onlyNeedContext": "仅需要上下文",
+      "onlyNeedContextTooltip": "如果为True，仅返回检索到的上下文而不生成响应",
-      "onlyNeedContextTooltip": "如果为 True，则仅返回检索到的上下文，而不会生成回复",
+      "onlyNeedPrompt": "仅需提示",
-
+      "onlyNeedPromptTooltip": "如果为True，仅返回生成的提示而不产生响应",
-      "onlyNeedPrompt": "仅需要提示",
-      "onlyNeedPromptTooltip": "如果为 True，则仅返回生成的提示，而不会生成回复",
-
       "streamResponse": "流式响应",
-      "streamResponseTooltip": "如果为 True，则启用流式输出以获得实时响应"
+      "streamResponseTooltip": "如果为True，启用实时流式输出响应"
-    }
+    }
+  },
+  "apiSite": {
+    "loading": "正在加载 API 文档..."
   }
 }

lightrag_webui/src/main.tsx

@@ -1,12 +1,5 @@
-import { StrictMode } from 'react'
 import { createRoot } from 'react-dom/client'
 import './index.css'
-import App from './App.tsx'
+import { Root } from '@/components/Root'
-import "./i18n";
 
-
+createRoot(document.getElementById('root')!).render(<Root />)
-createRoot(document.getElementById('root')!).render(
-  <StrictMode>
-    <App />
-  </StrictMode>
-)

lightrag_webui/src/stores/graph.ts

@@ -1,6 +1,7 @@
 import { create } from 'zustand'
 import { createSelectors } from '@/lib/utils'
 import { DirectedGraph } from 'graphology'
+import { getGraphLabels } from '@/api/lightrag'
 
 export type RawNodeType = {
   id: string
@@ -65,9 +66,17 @@ interface GraphState {
 
   rawGraph: RawGraph | null
   sigmaGraph: DirectedGraph | null
+  allDatabaseLabels: string[]
 
   moveToSelectedNode: boolean
+  isFetching: boolean
+  shouldRender: boolean
 
+  // Global flags to track data fetching attempts
+  graphDataFetchAttempted: boolean
+  labelsFetchAttempted: boolean
+
+  refreshLayout: () => void
   setSelectedNode: (nodeId: string | null, moveToSelectedNode?: boolean) => void
   setFocusedNode: (nodeId: string | null) => void
   setSelectedEdge: (edgeId: string | null) => void
@@ -79,19 +88,47 @@ interface GraphState {
 
   setRawGraph: (rawGraph: RawGraph | null) => void
   setSigmaGraph: (sigmaGraph: DirectedGraph | null) => void
+  setAllDatabaseLabels: (labels: string[]) => void
+  fetchAllDatabaseLabels: () => Promise<void>
+  setIsFetching: (isFetching: boolean) => void
+  setShouldRender: (shouldRender: boolean) => void
+
+  // Methods to set global flags
+  setGraphDataFetchAttempted: (attempted: boolean) => void
+  setLabelsFetchAttempted: (attempted: boolean) => void
 }
 
-const useGraphStoreBase = create<GraphState>()((set) => ({
+const useGraphStoreBase = create<GraphState>()((set, get) => ({
   selectedNode: null,
   focusedNode: null,
   selectedEdge: null,
   focusedEdge: null,
 
   moveToSelectedNode: false,
+  isFetching: false,
+  shouldRender: false,
+
+  // Initialize global flags
+  graphDataFetchAttempted: false,
+  labelsFetchAttempted: false,
 
   rawGraph: null,
   sigmaGraph: null,
+  allDatabaseLabels: ['*'],
 
+  refreshLayout: () => {
+    const currentGraph = get().sigmaGraph;
+    if (currentGraph) {
+      get().clearSelection();
+      get().setSigmaGraph(null);
+      setTimeout(() => {
+        get().setSigmaGraph(currentGraph);
+      }, 10);
+    }
+  },
+
+  setIsFetching: (isFetching: boolean) => set({ isFetching }),
+  setShouldRender: (shouldRender: boolean) => set({ shouldRender }),
   setSelectedNode: (nodeId: string | null, moveToSelectedNode?: boolean) =>
     set({ selectedNode: nodeId, moveToSelectedNode }),
   setFocusedNode: (nodeId: string | null) => set({ focusedNode: nodeId }),
@@ -104,25 +141,58 @@ const useGraphStoreBase = create<GraphState>()((set) => ({
       selectedEdge: null,
       focusedEdge: null
     }),
-  reset: () =>
+  reset: () => {
+    // Get the existing graph
+    const existingGraph = get().sigmaGraph;
+
+    // If we have an existing graph, clear it by removing all nodes
+    if (existingGraph) {
+      const nodes = Array.from(existingGraph.nodes());
+      nodes.forEach(node => existingGraph.dropNode(node));
+    }
+
     set({
       selectedNode: null,
       focusedNode: null,
       selectedEdge: null,
       focusedEdge: null,
       rawGraph: null,
-      sigmaGraph: null,
+      // Keep the existing graph instance but with cleared data
-      moveToSelectedNode: false
+      moveToSelectedNode: false,
-    }),
+      shouldRender: false
+    });
+  },
 
   setRawGraph: (rawGraph: RawGraph | null) =>
     set({
       rawGraph
     }),
 
-  setSigmaGraph: (sigmaGraph: DirectedGraph | null) => set({ sigmaGraph }),
+  setSigmaGraph: (sigmaGraph: DirectedGraph | null) => {
+    // Replace graph instance, no need to keep WebGL context
+    set({ sigmaGraph });
+  },
 
-  setMoveToSelectedNode: (moveToSelectedNode?: boolean) => set({ moveToSelectedNode })
+  setAllDatabaseLabels: (labels: string[]) => set({ allDatabaseLabels: labels }),
+
+  fetchAllDatabaseLabels: async () => {
+    try {
+      console.log('Fetching all database labels...');
+      const labels = await getGraphLabels();
+      set({ allDatabaseLabels: ['*', ...labels] });
+      return;
+    } catch (error) {
+      console.error('Failed to fetch all database labels:', error);
+      set({ allDatabaseLabels: ['*'] });
+      throw error;
+    }
+  },
+
+  setMoveToSelectedNode: (moveToSelectedNode?: boolean) => set({ moveToSelectedNode }),
+
+  // Methods to set global flags
+  setGraphDataFetchAttempted: (attempted: boolean) => set({ graphDataFetchAttempted: attempted }),
+  setLabelsFetchAttempted: (attempted: boolean) => set({ labelsFetchAttempted: attempted })
 }))
 
 const useGraphStore = createSelectors(useGraphStoreBase)

lightrag_webui/src/stores/settings.ts

@@ -5,6 +5,7 @@ import { defaultQueryLabel } from '@/lib/constants'
 import { Message, QueryRequest } from '@/api/lightrag'
 
 type Theme = 'dark' | 'light' | 'system'
+type Language = 'en' | 'zh'
 type Tab = 'documents' | 'knowledge-graph' | 'retrieval' | 'api'
 
 interface SettingsState {
@@ -46,6 +47,9 @@ interface SettingsState {
   theme: Theme
   setTheme: (theme: Theme) => void
 
+  language: Language
+  setLanguage: (lang: Language) => void
+
   enableHealthCheck: boolean
   setEnableHealthCheck: (enable: boolean) => void
 
@@ -57,7 +61,7 @@ const useSettingsStoreBase = create<SettingsState>()(
   persist(
     (set) => ({
       theme: 'system',
-
+      language: 'en',
       showPropertyPanel: true,
       showNodeSearchBar: true,
 
@@ -70,7 +74,7 @@ const useSettingsStoreBase = create<SettingsState>()(
 
       graphQueryMaxDepth: 3,
       graphMinDegree: 0,
-      graphLayoutMaxIterations: 10,
+      graphLayoutMaxIterations: 15,
 
       queryLabel: defaultQueryLabel,
 
@@ -99,6 +103,16 @@ const useSettingsStoreBase = create<SettingsState>()(
 
       setTheme: (theme: Theme) => set({ theme }),
 
+      setLanguage: (language: Language) => {
+        set({ language })
+        // Update i18n after state is updated
+        import('i18next').then(({ default: i18n }) => {
+          if (i18n.language !== language) {
+            i18n.changeLanguage(language)
+          }
+        })
+      },
+
       setGraphLayoutMaxIterations: (iterations: number) =>
         set({
           graphLayoutMaxIterations: iterations
@@ -129,7 +143,7 @@ const useSettingsStoreBase = create<SettingsState>()(
     {
       name: 'settings-storage',
       storage: createJSONStorage(() => localStorage),
-      version: 7,
+      version: 8,
       migrate: (state: any, version: number) => {
         if (version < 2) {
           state.showEdgeLabel = false
@@ -166,7 +180,11 @@ const useSettingsStoreBase = create<SettingsState>()(
         }
         if (version < 7) {
           state.graphQueryMaxDepth = 3
-          state.graphLayoutMaxIterations = 10
+          state.graphLayoutMaxIterations = 15
+        }
+        if (version < 8) {
+          state.graphMinDegree = 0
+          state.language = 'en'
         }
         return state
       }

lightrag_webui/vite.config.ts

@@ -26,7 +26,9 @@ export default defineConfig({
             target: import.meta.env.VITE_BACKEND_URL || 'http://localhost:9621',
             changeOrigin: true,
             rewrite: endpoint === '/api' ?
-              (path) => path.replace(/^\/api/, '') : undefined
+              (path) => path.replace(/^\/api/, '') :
+              endpoint === '/docs' || endpoint === '/openapi.json' ?
+                (path) => path : undefined
           }
         ])
       ) : {}

requirements.txt

@@ -4,6 +4,12 @@ future
 
 # Basic modules
 gensim
+
+# Additional Packages for export Functionality
+pandas>=2.0.0
+
+# Extra libraries are installed when needed using pipmaster
+
 pipmaster
 pydantic
 python-dotenv
@@ -13,5 +19,4 @@ tenacity
 
 # LLM packages
 tiktoken
-
+xlsxwriter>=3.1.0
-# Extra libraries are installed when needed using pipmaster