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Add chinese version of README

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- Add introduction of  graph visualization in LightRAG Server
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yangdx
2025-03-25 12:51:05 +08:00
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commit 6441d90682
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README.md
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@@ -28,22 +28,10 @@
</tr>
</table>
<div align="center">
This repository hosts the code of LightRAG. The structure of this code is based on <a href="https://github.com/gusye1234/nano-graphrag">nano-graphrag</a>.
<img src="https://i-blog.csdnimg.cn/direct/b2aaf634151b4706892693ffb43d9093.png" width="800" alt="LightRAG Diagram">
</div>
</div>
</br>
<img src="./README.assets/b2aaf634151b4706892693ffb43d9093.png" width="800" alt="LightRAG Diagram">
<details>
<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
🎉 News
</summary>
## 🎉 News
- [X] [2025.03.18]🎯📢LightRAG now supports citation functionality.
- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
@@ -63,8 +51,6 @@ This repository hosts the code of LightRAG. The structure of this code is based
- [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)!
</details>
<details>
<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
Algorithm Flowchart
@@ -779,7 +765,7 @@ For production level scenarios you will most likely want to leverage an enterpri
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- drop if necessary
drop INDEX entity_p_idx;
drop INDEX vertex_p_idx;
@@ -1081,33 +1067,33 @@ Valid modes are:
<details>
<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` |
| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types:`NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
| **entity\_extract\_max\_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
| **entity\_summary\_to\_max\_tokens** | `int` | Maximum token size for each entity summary | `500` |
| **node\_embedding\_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
| **node2vec\_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
| **embedding\_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
| **embedding\_batch\_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
| **embedding\_func\_max\_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
| **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 | `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}` |
| **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` |
| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
| **chunk_token_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
| **chunk_overlap_token_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
| **tiktoken_model_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
| **entity_extract_max_gleaning** | `int` | Number of loops in the entity extraction process, appending history messages | `1` |
| **entity_summary_to_max_tokens** | `int` | Maximum token size for each entity summary | `500` |
| **node_embedding_algorithm** | `str` | Algorithm for node embedding (currently not used) | `node2vec` |
| **node2vec_params** | `dict` | Parameters for node embedding | `{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}` |
| **embedding_func** | `EmbeddingFunc` | Function to generate embedding vectors from text | `openai_embed` |
| **embedding_batch_num** | `int` | Maximum batch size for embedding processes (multiple texts sent per batch) | `32` |
| **embedding_func_max_async** | `int` | Maximum number of concurrent asynchronous embedding processes | `16` |
| **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 | `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: `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. `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. `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>
@@ -1132,166 +1118,9 @@ LightRag can be installed with API support to serve a Fast api interface to perf
## Graph Visualization
<details>
<summary> <b>Graph visualization with html</b> </summary>
The LightRAG Server offers a comprehensive knowledge graph visualization feature. It supports various gravity layouts, node queries, subgraph filtering, and more. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).**
* The following code can be found in `examples/graph_visual_with_html.py`
```python
import networkx as nx
from pyvis.network import Network
# Load the GraphML file
G = nx.read_graphml('./dickens/graph_chunk_entity_relation.graphml')
# Create a Pyvis network
net = Network(notebook=True)
# Convert NetworkX graph to Pyvis network
net.from_nx(G)
# Save and display the network
net.show('knowledge_graph.html')
```
</details>
<details>
<summary> <b>Graph visualization with Neo4</b> </summary>
* The following code can be found in `examples/graph_visual_with_neo4j.py`
```python
import os
import json
from lightrag.utils import xml_to_json
from neo4j import GraphDatabase
# Constants
WORKING_DIR = "./dickens"
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100
# Neo4j connection credentials
NEO4J_URI = "bolt://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "your_password"
def convert_xml_to_json(xml_path, output_path):
"""Converts XML file to JSON and saves the output."""
if not os.path.exists(xml_path):
print(f"Error: File not found - {xml_path}")
return None
json_data = xml_to_json(xml_path)
if json_data:
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(json_data, f, ensure_ascii=False, indent=2)
print(f"JSON file created: {output_path}")
return json_data
else:
print("Failed to create JSON data")
return None
def process_in_batches(tx, query, data, batch_size):
"""Process data in batches and execute the given query."""
for i in range(0, len(data), batch_size):
batch = data[i:i + batch_size]
tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch})
def main():
# Paths
xml_file = os.path.join(WORKING_DIR, 'graph_chunk_entity_relation.graphml')
json_file = os.path.join(WORKING_DIR, 'graph_data.json')
# Convert XML to JSON
json_data = convert_xml_to_json(xml_file, json_file)
if json_data is None:
return
# Load nodes and edges
nodes = json_data.get('nodes', [])
edges = json_data.get('edges', [])
# Neo4j queries
create_nodes_query = """
UNWIND $nodes AS node
MERGE (e:Entity {id: node.id})
SET e.entity_type = node.entity_type,
e.description = node.description,
e.source_id = node.source_id,
e.displayName = node.id
REMOVE e:Entity
WITH e, node
CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
RETURN count(*)
"""
create_edges_query = """
UNWIND $edges AS edge
MATCH (source {id: edge.source})
MATCH (target {id: edge.target})
WITH source, target, edge,
CASE
WHEN edge.keywords CONTAINS 'lead' THEN 'lead'
WHEN edge.keywords CONTAINS 'participate' THEN 'participate'
WHEN edge.keywords CONTAINS 'uses' THEN 'uses'
WHEN edge.keywords CONTAINS 'located' THEN 'located'
WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs'
ELSE REPLACE(SPLIT(edge.keywords, ',')[0], '\"', '')
END AS relType
CALL apoc.create.relationship(source, relType, {
weight: edge.weight,
description: edge.description,
keywords: edge.keywords,
source_id: edge.source_id
}, target) YIELD rel
RETURN count(*)
"""
set_displayname_and_labels_query = """
MATCH (n)
SET n.displayName = n.id
WITH n
CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node
RETURN count(*)
"""
# Create a Neo4j driver
driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))
try:
# Execute queries in batches
with driver.session() as session:
# Insert nodes in batches
session.execute_write(process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES)
# Insert edges in batches
session.execute_write(process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES)
# Set displayName and labels
session.run(set_displayname_and_labels_query)
except Exception as e:
print(f"Error occurred: {e}")
finally:
driver.close()
if __name__ == "__main__":
main()
```
</details>
<details>
<summary> <b>Graphml 3d visualizer</b> </summary>
LightRag can be installed with Tools support to add extra tools like the graphml 3d visualizer.
[LightRag Visualizer](lightrag/tools/lightrag_visualizer/README.md)
</details>
![iShot_2025-03-23_12.40.08](./README.assets/iShot_2025-03-23_12.40.08.png)
## Evaluation
@@ -1386,28 +1215,28 @@ Output your evaluation in the following JSON format:
### 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%** |
| **Empowerment** | 32.4% | **67.6%** | 38.8% | **61.2%** | 16.4% | **83.6%** | 42.8% | **57.2%** |
| **Overall** | 32.4% | **67.6%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 40.0% | **60.0%** |
| | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** | RQ-RAG | **LightRAG** |
| **Comprehensiveness** | 31.6% | **68.4%** | 38.8% | **61.2%** | 15.2% | **84.8%** | 39.2% | **60.8%** |
| **Diversity** | 29.2% | **70.8%** | 39.2% | **60.8%** | 11.6% | **88.4%** | 30.8% | **69.2%** |
| **Empowerment** | 31.6% | **68.4%** | 36.4% | **63.6%** | 15.2% | **84.8%** | 42.4% | **57.6%** |
| **Overall** | 32.4% | **67.6%** | 38.0% | **62.0%** | 14.4% | **85.6%** | 40.0% | **60.0%** |
| | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** | HyDE | **LightRAG** |
| **Comprehensiveness** | 26.0% | **74.0%** | 41.6% | **58.4%** | 26.8% | **73.2%** | 40.4% | **59.6%** |
| **Diversity** | 24.0% | **76.0%** | 38.8% | **61.2%** | 20.0% | **80.0%** | 32.4% | **67.6%** |
| **Empowerment** | 25.2% | **74.8%** | 40.8% | **59.2%** | 26.0% | **74.0%** | 46.0% | **54.0%** |
| **Overall** | 24.8% | **75.2%** | 41.6% | **58.4%** | 26.4% | **73.6%** | 42.4% | **57.6%** |
| | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** | GraphRAG | **LightRAG** |
| **Comprehensiveness** | 45.6% | **54.4%** | 48.4% | **51.6%** | 48.4% | **51.6%** | **50.4%** | 49.6% |
| **Diversity** | 22.8% | **77.2%** | 40.8% | **59.2%** | 26.4% | **73.6%** | 36.0% | **64.0%** |
| **Empowerment** | 41.2% | **58.8%** | 45.2% | **54.8%** | 43.6% | **56.4%** | **50.8%** | 49.2% |
| **Overall** | 45.2% | **54.8%** | 48.0% | **52.0%** | 47.2% | **52.8%** | **50.4%** | 49.6% |
| |**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%**|
|**Empowerment**|32.4%|**67.6%**|38.8%|**61.2%**|16.4%|**83.6%**|42.8%|**57.2%**|
|**Overall**|32.4%|**67.6%**|38.8%|**61.2%**|15.2%|**84.8%**|40.0%|**60.0%**|
| |RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|RQ-RAG|**LightRAG**|
|**Comprehensiveness**|31.6%|**68.4%**|38.8%|**61.2%**|15.2%|**84.8%**|39.2%|**60.8%**|
|**Diversity**|29.2%|**70.8%**|39.2%|**60.8%**|11.6%|**88.4%**|30.8%|**69.2%**|
|**Empowerment**|31.6%|**68.4%**|36.4%|**63.6%**|15.2%|**84.8%**|42.4%|**57.6%**|
|**Overall**|32.4%|**67.6%**|38.0%|**62.0%**|14.4%|**85.6%**|40.0%|**60.0%**|
| |HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|HyDE|**LightRAG**|
|**Comprehensiveness**|26.0%|**74.0%**|41.6%|**58.4%**|26.8%|**73.2%**|40.4%|**59.6%**|
|**Diversity**|24.0%|**76.0%**|38.8%|**61.2%**|20.0%|**80.0%**|32.4%|**67.6%**|
|**Empowerment**|25.2%|**74.8%**|40.8%|**59.2%**|26.0%|**74.0%**|46.0%|**54.0%**|
|**Overall**|24.8%|**75.2%**|41.6%|**58.4%**|26.4%|**73.6%**|42.4%|**57.6%**|
| |GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|GraphRAG|**LightRAG**|
|**Comprehensiveness**|45.6%|**54.4%**|48.4%|**51.6%**|48.4%|**51.6%**|**50.4%**|49.6%|
|**Diversity**|22.8%|**77.2%**|40.8%|**59.2%**|26.4%|**73.6%**|36.0%|**64.0%**|
|**Empowerment**|41.2%|**58.8%**|45.2%|**54.8%**|43.6%|**56.4%**|**50.8%**|49.2%|
|**Overall**|45.2%|**54.8%**|48.0%|**52.0%**|47.2%|**52.8%**|**50.4%**|49.6%|
## Reproduce