diff --git a/README.md b/README.md
index d3716416..6c8861a2 100644
--- a/README.md
+++ b/README.md
@@ -441,11 +441,16 @@ if __name__ == "__main__":
- [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py)
- [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py)
+
+
### Conversation History Support
LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
+
+ Usage Example
+
```python
# Create conversation history
conversation_history = [
@@ -468,10 +473,15 @@ response = rag.query(
)
```
+
+
### Custom Prompt Support
LightRAG now supports custom prompts for fine-tuned control over the system's behavior. Here's how to use it:
+
+ Usage Example
+
```python
# Create query parameters
query_param = QueryParam(
@@ -506,6 +516,8 @@ response_custom = rag.query(
print(response_custom)
```
+
+
### Separate Keyword Extraction
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.
@@ -519,7 +531,8 @@ The function operates by dividing the input into two parts:
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**
+
+ Usage Example
This `example` shows how to tailor the function for educational content, focusing on detailed explanations for older students.
@@ -531,67 +544,6 @@ rag.query_with_separate_keyword_extraction(
)
```
-### Insert Custom KG
-
-```python
-custom_kg = {
- "chunks": [
- {
- "content": "Alice and Bob are collaborating on quantum computing research.",
- "source_id": "doc-1"
- }
- ],
- "entities": [
- {
- "entity_name": "Alice",
- "entity_type": "person",
- "description": "Alice is a researcher specializing in quantum physics.",
- "source_id": "doc-1"
- },
- {
- "entity_name": "Bob",
- "entity_type": "person",
- "description": "Bob is a mathematician.",
- "source_id": "doc-1"
- },
- {
- "entity_name": "Quantum Computing",
- "entity_type": "technology",
- "description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
- "source_id": "doc-1"
- }
- ],
- "relationships": [
- {
- "src_id": "Alice",
- "tgt_id": "Bob",
- "description": "Alice and Bob are research partners.",
- "keywords": "collaboration research",
- "weight": 1.0,
- "source_id": "doc-1"
- },
- {
- "src_id": "Alice",
- "tgt_id": "Quantum Computing",
- "description": "Alice conducts research on quantum computing.",
- "keywords": "research expertise",
- "weight": 1.0,
- "source_id": "doc-1"
- },
- {
- "src_id": "Bob",
- "tgt_id": "Quantum Computing",
- "description": "Bob researches quantum computing.",
- "keywords": "research application",
- "weight": 1.0,
- "source_id": "doc-1"
- }
- ]
-}
-
-rag.insert_custom_kg(custom_kg)
-```
-
## Insert
@@ -683,6 +635,70 @@ rag.insert(text_content.decode('utf-8'))
+
+ Insert Custom KG
+
+```python
+custom_kg = {
+ "chunks": [
+ {
+ "content": "Alice and Bob are collaborating on quantum computing research.",
+ "source_id": "doc-1"
+ }
+ ],
+ "entities": [
+ {
+ "entity_name": "Alice",
+ "entity_type": "person",
+ "description": "Alice is a researcher specializing in quantum physics.",
+ "source_id": "doc-1"
+ },
+ {
+ "entity_name": "Bob",
+ "entity_type": "person",
+ "description": "Bob is a mathematician.",
+ "source_id": "doc-1"
+ },
+ {
+ "entity_name": "Quantum Computing",
+ "entity_type": "technology",
+ "description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
+ "source_id": "doc-1"
+ }
+ ],
+ "relationships": [
+ {
+ "src_id": "Alice",
+ "tgt_id": "Bob",
+ "description": "Alice and Bob are research partners.",
+ "keywords": "collaboration research",
+ "weight": 1.0,
+ "source_id": "doc-1"
+ },
+ {
+ "src_id": "Alice",
+ "tgt_id": "Quantum Computing",
+ "description": "Alice conducts research on quantum computing.",
+ "keywords": "research expertise",
+ "weight": 1.0,
+ "source_id": "doc-1"
+ },
+ {
+ "src_id": "Bob",
+ "tgt_id": "Quantum Computing",
+ "description": "Bob researches quantum computing.",
+ "keywords": "research application",
+ "weight": 1.0,
+ "source_id": "doc-1"
+ }
+ ]
+}
+
+rag.insert_custom_kg(custom_kg)
+```
+
+
+
Citation Functionality
@@ -842,7 +858,8 @@ rag.delete_by_doc_id("doc_id")
LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.
-### Create Entities and Relations
+
+ Create Entities and Relations
```python
# Create new entity
@@ -865,7 +882,10 @@ relation = rag.create_relation("Google", "Gmail", {
})
```
-### Edit Entities and Relations
+
+
+
+ Edit Entities and Relations
```python
# Edit an existing entity
@@ -902,6 +922,8 @@ 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
@@ -910,7 +932,8 @@ LightRAG allows you to export your knowledge graph data in various formats for a
### Export Functions
-#### Basic Usage
+
+ Basic Usage
```python
# Basic CSV export (default format)
@@ -920,7 +943,10 @@ rag.export_data("knowledge_graph.csv")
rag.export_data("output.xlsx", file_format="excel")
```
-#### Different File Formats supported
+
+
+
+ Different File Formats supported
```python
#Export data in CSV format
@@ -935,13 +961,18 @@ rag.export_data("graph_data.md", file_format="md")
# Export data in Text
rag.export_data("graph_data.txt", file_format="txt")
```
-#### Additional Options
+
+
+
+ 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:
diff --git a/env.example b/env.example
index 4bf9a340..b81d82b9 100644
--- a/env.example
+++ b/env.example
@@ -3,9 +3,11 @@
### Server Configuration
# HOST=0.0.0.0
# PORT=9621
-# WORKERS=1
-# NAMESPACE_PREFIX=lightrag # separating data from difference Lightrag instances
-# MAX_GRAPH_NODES=1000 # Max nodes return from grap retrieval
+# WORKERS=2
+### separating data from difference Lightrag instances
+# NAMESPACE_PREFIX=lightrag
+### Max nodes return from grap retrieval
+# MAX_GRAPH_NODES=1000
# CORS_ORIGINS=http://localhost:3000,http://localhost:8080
### Optional SSL Configuration
@@ -13,7 +15,7 @@
# SSL_CERTFILE=/path/to/cert.pem
# SSL_KEYFILE=/path/to/key.pem
-### Directory Configuration
+### Directory Configuration (defaults to current working directory)
# WORKING_DIR=
# INPUT_DIR=
@@ -23,9 +25,10 @@
### Logging level
# LOG_LEVEL=INFO
# VERBOSE=False
-# LOG_DIR=/path/to/log/directory # Log file directory path, defaults to current working directory
-# LOG_MAX_BYTES=10485760 # Log file max size in bytes, defaults to 10MB
-# LOG_BACKUP_COUNT=5 # Number of backup files to keep, defaults to 5
+# LOG_MAX_BYTES=10485760
+# LOG_BACKUP_COUNT=5
+### Logfile location (defaults to current working directory)
+# LOG_DIR=/path/to/log/directory
### Settings for RAG query
# HISTORY_TURNS=3
@@ -36,28 +39,37 @@
# MAX_TOKEN_ENTITY_DESC=4000
### Settings for document indexing
-ENABLE_LLM_CACHE_FOR_EXTRACT=true # Enable LLM cache for entity extraction
+ENABLE_LLM_CACHE_FOR_EXTRACT=true
SUMMARY_LANGUAGE=English
# CHUNK_SIZE=1200
# CHUNK_OVERLAP_SIZE=100
-# MAX_TOKEN_SUMMARY=500 # Max tokens for entity or relations summary
-# MAX_PARALLEL_INSERT=2 # Number of parallel processing documents in one patch
+### Max tokens for entity or relations summary
+# MAX_TOKEN_SUMMARY=500
+### Number of parallel processing documents in one patch
+# MAX_PARALLEL_INSERT=2
-# EMBEDDING_BATCH_NUM=32 # num of chunks send to Embedding in one request
-# EMBEDDING_FUNC_MAX_ASYNC=16 # Max concurrency requests for Embedding
+### Num of chunks send to Embedding in single request
+# EMBEDDING_BATCH_NUM=32
+### Max concurrency requests for Embedding
+# EMBEDDING_FUNC_MAX_ASYNC=16
# MAX_EMBED_TOKENS=8192
-### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
-TIMEOUT=150 # Time out in seconds for LLM, None for infinite timeout
+### LLM Configuration
+### Time out in seconds for LLM, None for infinite timeout
+TIMEOUT=150
+### Some models like o1-mini require temperature to be set to 1
TEMPERATURE=0.5
-MAX_ASYNC=4 # Max concurrency requests of LLM
-MAX_TOKENS=32768 # Max tokens send to LLM (less than context size of the model)
+### Max concurrency requests of LLM
+MAX_ASYNC=4
+### Max tokens send to LLM (less than context size of the model)
+MAX_TOKENS=32768
+### Ollama example (For local services installed with docker, you can use host.docker.internal as host)
LLM_BINDING=ollama
LLM_MODEL=mistral-nemo:latest
LLM_BINDING_API_KEY=your_api_key
-### Ollama example
LLM_BINDING_HOST=http://localhost:11434
+
### OpenAI alike example
# LLM_BINDING=openai
# LLM_MODEL=gpt-4o
@@ -103,9 +115,10 @@ ORACLE_DSN=localhost:1521/XEPDB1
ORACLE_USER=your_username
ORACLE_PASSWORD='your_password'
ORACLE_CONFIG_DIR=/path/to/oracle/config
-#ORACLE_WALLET_LOCATION=/path/to/wallet # optional
-#ORACLE_WALLET_PASSWORD='your_password' # optional
-#ORACLE_WORKSPACE=default # separating all data from difference Lightrag instances(deprecated, use NAMESPACE_PREFIX in future)
+#ORACLE_WALLET_LOCATION=/path/to/wallet
+#ORACLE_WALLET_PASSWORD='your_password'
+### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future)
+#ORACLE_WORKSPACE=default
### TiDB Configuration
TIDB_HOST=localhost
@@ -113,7 +126,8 @@ TIDB_PORT=4000
TIDB_USER=your_username
TIDB_PASSWORD='your_password'
TIDB_DATABASE=your_database
-#TIDB_WORKSPACE=default # separating all data from difference Lightrag instances(deprecated, use NAMESPACE_PREFIX in future)
+### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future)
+#TIDB_WORKSPACE=default
### PostgreSQL Configuration
POSTGRES_HOST=localhost
@@ -121,7 +135,8 @@ POSTGRES_PORT=5432
POSTGRES_USER=your_username
POSTGRES_PASSWORD='your_password'
POSTGRES_DATABASE=your_database
-#POSTGRES_WORKSPACE=default # separating all data from difference Lightrag instances(deprecated, use NAMESPACE_PREFIX in future)
+### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future)
+#POSTGRES_WORKSPACE=default
### Independent AGM Configuration(not for AMG embedded in PostreSQL)
AGE_POSTGRES_DB=
@@ -130,8 +145,9 @@ AGE_POSTGRES_PASSWORD=
AGE_POSTGRES_HOST=
# AGE_POSTGRES_PORT=8529
+### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future)
# AGE Graph Name(apply to PostgreSQL and independent AGM)
-# AGE_GRAPH_NAME=lightrag # deprecated, use NAME_SPACE_PREFIX instead
+# AGE_GRAPH_NAME=lightrag
### Neo4j Configuration
NEO4J_URI=neo4j+s://xxxxxxxx.databases.neo4j.io
@@ -141,7 +157,8 @@ NEO4J_PASSWORD='your_password'
### MongoDB Configuration
MONGO_URI=mongodb://root:root@localhost:27017/
MONGO_DATABASE=LightRAG
-MONGODB_GRAPH=false # deprecated (keep for backward compatibility)
+### separating all data from difference Lightrag instances(deprecating, use NAMESPACE_PREFIX in future)
+# MONGODB_GRAPH=false
### Milvus Configuration
MILVUS_URI=http://localhost:19530
@@ -158,11 +175,11 @@ QDRANT_URL=http://localhost:16333
REDIS_URI=redis://localhost:6379
### For JWT Auth
-AUTH_ACCOUNTS='admin:admin123,user1:pass456' # username:password,username:password
-TOKEN_SECRET=Your-Key-For-LightRAG-API-Server # JWT key
-#TOKEN_EXPIRE_HOURS=4 # Expire duration, default 4
-#GUEST_TOKEN_EXPIRE_HOURS=2 # Guest expire duration, default 2
-#JWT_ALGORITHM=HS256 # JWT encode algorithm, default HS256
+#AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+#TOKEN_SECRET=Your-Key-For-LightRAG-API-Server
+#TOKEN_EXPIRE_HOURS=4
+#GUEST_TOKEN_EXPIRE_HOURS=2
+#JWT_ALGORITHM=HS256
### API-Key to access LightRAG Server API
# LIGHTRAG_API_KEY=your-secure-api-key-here
diff --git a/examples/lightrag_gemini_track_token_demo.py b/examples/lightrag_gemini_track_token_demo.py
new file mode 100644
index 00000000..a72fc717
--- /dev/null
+++ b/examples/lightrag_gemini_track_token_demo.py
@@ -0,0 +1,151 @@
+# pip install -q -U google-genai to use gemini as a client
+
+import os
+import asyncio
+import numpy as np
+import nest_asyncio
+from google import genai
+from google.genai import types
+from dotenv import load_dotenv
+from lightrag.utils import EmbeddingFunc
+from lightrag import LightRAG, QueryParam
+from lightrag.kg.shared_storage import initialize_pipeline_status
+from lightrag.llm.siliconcloud import siliconcloud_embedding
+from lightrag.utils import setup_logger
+from lightrag.utils import TokenTracker
+
+setup_logger("lightrag", level="DEBUG")
+
+# Apply nest_asyncio to solve event loop issues
+nest_asyncio.apply()
+
+load_dotenv()
+gemini_api_key = os.getenv("GEMINI_API_KEY")
+siliconflow_api_key = os.getenv("SILICONFLOW_API_KEY")
+
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+ os.mkdir(WORKING_DIR)
+
+token_tracker = TokenTracker()
+
+
+async def llm_model_func(
+ prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+ # 1. Initialize the GenAI Client with your Gemini API Key
+ client = genai.Client(api_key=gemini_api_key)
+
+ # 2. Combine prompts: system prompt, history, and user prompt
+ if history_messages is None:
+ history_messages = []
+
+ combined_prompt = ""
+ if system_prompt:
+ combined_prompt += f"{system_prompt}\n"
+
+ for msg in history_messages:
+ # Each msg is expected to be a dict: {"role": "...", "content": "..."}
+ combined_prompt += f"{msg['role']}: {msg['content']}\n"
+
+ # Finally, add the new user prompt
+ combined_prompt += f"user: {prompt}"
+
+ # 3. Call the Gemini model
+ response = client.models.generate_content(
+ model="gemini-2.0-flash",
+ contents=[combined_prompt],
+ config=types.GenerateContentConfig(
+ max_output_tokens=5000, temperature=0, top_k=10
+ ),
+ )
+
+ # 4. Get token counts with null safety
+ usage = getattr(response, "usage_metadata", None)
+ prompt_tokens = getattr(usage, "prompt_token_count", 0) or 0
+ completion_tokens = getattr(usage, "candidates_token_count", 0) or 0
+ total_tokens = getattr(usage, "total_token_count", 0) or (
+ prompt_tokens + completion_tokens
+ )
+
+ token_counts = {
+ "prompt_tokens": prompt_tokens,
+ "completion_tokens": completion_tokens,
+ "total_tokens": total_tokens,
+ }
+
+ token_tracker.add_usage(token_counts)
+
+ # 5. Return the response text
+ return response.text
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+ return await siliconcloud_embedding(
+ texts,
+ model="BAAI/bge-m3",
+ api_key=siliconflow_api_key,
+ max_token_size=512,
+ )
+
+
+async def initialize_rag():
+ rag = LightRAG(
+ working_dir=WORKING_DIR,
+ entity_extract_max_gleaning=1,
+ enable_llm_cache=True,
+ enable_llm_cache_for_entity_extract=True,
+ embedding_cache_config={"enabled": True, "similarity_threshold": 0.90},
+ llm_model_func=llm_model_func,
+ embedding_func=EmbeddingFunc(
+ embedding_dim=1024,
+ max_token_size=8192,
+ func=embedding_func,
+ ),
+ )
+
+ await rag.initialize_storages()
+ await initialize_pipeline_status()
+
+ return rag
+
+
+def main():
+ # Initialize RAG instance
+ rag = asyncio.run(initialize_rag())
+
+ with open("./book.txt", "r", encoding="utf-8") as f:
+ rag.insert(f.read())
+
+ # Context Manager Method
+ with token_tracker:
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="naive")
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="local")
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?",
+ param=QueryParam(mode="global"),
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?",
+ param=QueryParam(mode="hybrid"),
+ )
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/examples/lightrag_siliconcloud_track_token_demo.py b/examples/lightrag_siliconcloud_track_token_demo.py
new file mode 100644
index 00000000..d82a30bc
--- /dev/null
+++ b/examples/lightrag_siliconcloud_track_token_demo.py
@@ -0,0 +1,110 @@
+import os
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.openai import openai_complete_if_cache
+from lightrag.llm.siliconcloud import siliconcloud_embedding
+from lightrag.utils import EmbeddingFunc
+from lightrag.utils import TokenTracker
+import numpy as np
+from lightrag.kg.shared_storage import initialize_pipeline_status
+from dotenv import load_dotenv
+
+load_dotenv()
+
+token_tracker = TokenTracker()
+WORKING_DIR = "./dickens"
+
+if not os.path.exists(WORKING_DIR):
+ os.mkdir(WORKING_DIR)
+
+
+async def llm_model_func(
+ prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
+) -> str:
+ return await openai_complete_if_cache(
+ "Qwen/Qwen2.5-7B-Instruct",
+ prompt,
+ system_prompt=system_prompt,
+ history_messages=history_messages,
+ api_key=os.getenv("SILICONFLOW_API_KEY"),
+ base_url="https://api.siliconflow.cn/v1/",
+ token_tracker=token_tracker,
+ **kwargs,
+ )
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+ return await siliconcloud_embedding(
+ texts,
+ model="BAAI/bge-m3",
+ api_key=os.getenv("SILICONFLOW_API_KEY"),
+ max_token_size=512,
+ )
+
+
+# function test
+async def test_funcs():
+ # Context Manager Method
+ with token_tracker:
+ result = await llm_model_func("How are you?")
+ print("llm_model_func: ", result)
+
+
+asyncio.run(test_funcs())
+
+
+async def initialize_rag():
+ rag = LightRAG(
+ working_dir=WORKING_DIR,
+ llm_model_func=llm_model_func,
+ embedding_func=EmbeddingFunc(
+ embedding_dim=1024, max_token_size=512, func=embedding_func
+ ),
+ )
+
+ await rag.initialize_storages()
+ await initialize_pipeline_status()
+
+ return rag
+
+
+def main():
+ # Initialize RAG instance
+ rag = asyncio.run(initialize_rag())
+
+ # Reset tracker before processing queries
+ token_tracker.reset()
+
+ with open("./book.txt", "r", encoding="utf-8") as f:
+ rag.insert(f.read())
+
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="naive")
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="local")
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="global")
+ )
+ )
+
+ print(
+ rag.query(
+ "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+ )
+ )
+
+ # Display final token usage after main query
+ print("Token usage:", token_tracker.get_usage())
+
+
+if __name__ == "__main__":
+ main()
diff --git a/lightrag/__init__.py b/lightrag/__init__.py
index 62b427bf..1c698ff6 100644
--- a/lightrag/__init__.py
+++ b/lightrag/__init__.py
@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
-__version__ = "1.3.0"
+__version__ = "1.3.1"
__author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG"
diff --git a/lightrag/api/README-zh.md b/lightrag/api/README-zh.md
index 839aa2ef..4bf31a61 100644
--- a/lightrag/api/README-zh.md
+++ b/lightrag/api/README-zh.md
@@ -52,7 +52,8 @@ LLM_BINDING=openai
LLM_MODEL=gpt-4o
LLM_BINDING_HOST=https://api.openai.com/v1
LLM_BINDING_API_KEY=your_api_key
-MAX_TOKENS=32768 # 发送给 LLM 的最大 token 数（小于模型上下文大小）
+### 发送给 LLM 的最大 token 数（小于模型上下文大小）
+MAX_TOKENS=32768
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
@@ -68,7 +69,8 @@ LLM_BINDING=ollama
LLM_MODEL=mistral-nemo:latest
LLM_BINDING_HOST=http://localhost:11434
# LLM_BINDING_API_KEY=your_api_key
-MAX_TOKENS=8192 # 发送给 LLM 的最大 token 数（基于您的 Ollama 服务器容量）
+### 发送给 LLM 的最大 token 数（基于您的 Ollama 服务器容量）
+MAX_TOKENS=8192
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
@@ -117,9 +119,12 @@ LightRAG 服务器可以在 `Gunicorn + Uvicorn` 预加载模式下运行。Guni
虽然 LightRAG 服务器使用一个工作进程来处理文档索引流程，但通过 Uvicorn 的异步任务支持，可以并行处理多个文件。文档索引速度的瓶颈主要在于 LLM。如果您的 LLM 支持高并发，您可以通过增加 LLM 的并发级别来加速文档索引。以下是几个与并发处理相关的环境变量及其默认值：
```
-WORKERS=2 # 工作进程数，不大于 (2 x 核心数) + 1
-MAX_PARALLEL_INSERT=2 # 一批中并行处理的文件数
-MAX_ASYNC=4 # LLM 的最大并发请求数
+### 工作进程数，数字不大于 (2 x 核心数) + 1
+WORKERS=2
+### 一批中并行处理的文件数
+MAX_PARALLEL_INSERT=2
+# LLM 的最大并发请求数
+MAX_ASYNC=4
```
### 将 Lightrag 安装为 Linux 服务
@@ -201,10 +206,9 @@ LightRAG API 服务器使用基于 HS256 算法的 JWT 认证。要启用安全
```bash
# JWT 认证
-AUTH_USERNAME=admin # 登录名
-AUTH_PASSWORD=admin123 # 密码
-TOKEN_SECRET=your-key # JWT 密钥
-TOKEN_EXPIRE_HOURS=4 # 过期时间
+AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+TOKEN_SECRET='your-key'
+TOKEN_EXPIRE_HOURS=4
```
> 目前仅支持配置一个管理员账户和密码。尚未开发和实现完整的账户系统。
@@ -238,8 +242,11 @@ LLM_BINDING=azure_openai
LLM_BINDING_HOST=your-azure-endpoint
LLM_MODEL=your-model-deployment-name
LLM_BINDING_API_KEY=your-azure-api-key
-AZURE_OPENAI_API_VERSION=2024-08-01-preview # 可选，默认为最新版本
-EMBEDDING_BINDING=azure_openai # 如果使用 Azure OpenAI 进行嵌入
+### API Version可选，默认为最新版本
+AZURE_OPENAI_API_VERSION=2024-08-01-preview
+
+### 如果使用 Azure OpenAI 进行嵌入
+EMBEDDING_BINDING=azure_openai
EMBEDDING_MODEL=your-embedding-deployment-name
```
@@ -362,7 +369,47 @@ LIGHTRAG_DOC_STATUS_STORAGE=PGDocStatusStorage
| --embedding-binding | ollama | 嵌入绑定类型（lollms、ollama、openai、azure_openai） |
| auto-scan-at-startup | - | 扫描输入目录中的新文件并开始索引 |
-### 使用示例
+### .env 文件示例
+
+```bash
+### Server Configuration
+# HOST=0.0.0.0
+PORT=9621
+WORKERS=2
+
+### Settings for document indexing
+ENABLE_LLM_CACHE_FOR_EXTRACT=true
+SUMMARY_LANGUAGE=Chinese
+MAX_PARALLEL_INSERT=2
+
+### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
+TIMEOUT=200
+TEMPERATURE=0.0
+MAX_ASYNC=4
+MAX_TOKENS=32768
+
+LLM_BINDING=openai
+LLM_MODEL=gpt-4o-mini
+LLM_BINDING_HOST=https://api.openai.com/v1
+LLM_BINDING_API_KEY=your-api-key
+
+### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
+EMBEDDING_MODEL=bge-m3:latest
+EMBEDDING_DIM=1024
+EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING_HOST=http://localhost:11434
+
+### For JWT Auth
+# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+# TOKEN_SECRET=your-key-for-LightRAG-API-Server-xxx
+# TOKEN_EXPIRE_HOURS=48
+
+# LIGHTRAG_API_KEY=your-secure-api-key-here-123
+# WHITELIST_PATHS=/api/*
+# WHITELIST_PATHS=/health,/api/*
+```
+
+
#### 使用 ollama 默认本地服务器作为 llm 和嵌入后端运行 Lightrag 服务器
diff --git a/lightrag/api/README.md b/lightrag/api/README.md
index e042fd46..8b2e8177 100644
--- a/lightrag/api/README.md
+++ b/lightrag/api/README.md
@@ -52,7 +52,8 @@ LLM_BINDING=openai
LLM_MODEL=gpt-4o
LLM_BINDING_HOST=https://api.openai.com/v1
LLM_BINDING_API_KEY=your_api_key
-MAX_TOKENS=32768 # Max tokens send to LLM (less than model context size)
+### Max tokens send to LLM (less than model context size)
+MAX_TOKENS=32768
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
@@ -68,7 +69,8 @@ LLM_BINDING=ollama
LLM_MODEL=mistral-nemo:latest
LLM_BINDING_HOST=http://localhost:11434
# LLM_BINDING_API_KEY=your_api_key
-MAX_TOKENS=8192 # Max tokens send to LLM (base on your Ollama Server capacity)
+### Max tokens send to LLM (base on your Ollama Server capacity)
+MAX_TOKENS=8192
EMBEDDING_BINDING=ollama
EMBEDDING_BINDING_HOST=http://localhost:11434
@@ -90,7 +92,9 @@ lightrag-server
```
lightrag-gunicorn --workers 4
```
-The `.env` file must be placed in the startup directory. Upon launching, the LightRAG Server will create a documents directory (default is `./inputs`) and a data directory (default is `./rag_storage`). This allows you to initiate multiple instances of LightRAG Server from different directories, with each instance configured to listen on a distinct network port.
+The `.env` file **must be placed in the startup directory**.
+
+Upon launching, the LightRAG Server will create a documents directory (default is `./inputs`) and a data directory (default is `./rag_storage`). This allows you to initiate multiple instances of LightRAG Server from different directories, with each instance configured to listen on a distinct network port.
Here are some common used startup parameters:
@@ -100,6 +104,8 @@ Here are some common used startup parameters:
- `--log-level`: Logging level (default: INFO)
- --input-dir: specifying the directory to scan for documents (default: ./input)
+> The requirement for the .env file to be in the startup directory is intentionally designed this way. The purpose is to support users in launching multiple LightRAG instances simultaneously. Allow different .env files for different instances.
+
### Auto scan on startup
When starting any of the servers with the `--auto-scan-at-startup` parameter, the system will automatically:
@@ -117,9 +123,12 @@ The LightRAG Server can operate in the `Gunicorn + Uvicorn` preload mode. Gunico
Though LightRAG Server uses one workers to process the document indexing pipeline, with aysnc task supporting of Uvicorn, multiple files can be processed in parallell. The bottleneck of document indexing speed mainly lies with the LLM. If your LLM supports high concurrency, you can accelerate document indexing by increasing the concurrency level of the LLM. Below are several environment variables related to concurrent processing, along with their default values:
```
-WORKERS=2 # Num of worker processes, not greater then (2 x number_of_cores) + 1
-MAX_PARALLEL_INSERT=2 # Num of parallel files to process in one batch
-MAX_ASYNC=4 # Max concurrency requests of LLM
+### Num of worker processes, not greater then (2 x number_of_cores) + 1
+WORKERS=2
+### Num of parallel files to process in one batch
+MAX_PARALLEL_INSERT=2
+### Max concurrency requests of LLM
+MAX_ASYNC=4
```
### Install Lightrag as a Linux Service
@@ -203,10 +212,9 @@ LightRAG API Server implements JWT-based authentication using HS256 algorithm. T
```bash
# For jwt auth
-AUTH_USERNAME=admin # login name
-AUTH_PASSWORD=admin123 # password
-TOKEN_SECRET=your-key # JWT key
-TOKEN_EXPIRE_HOURS=4 # expire duration
+AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+TOKEN_SECRET='your-key'
+TOKEN_EXPIRE_HOURS=4
```
> Currently, only the configuration of an administrator account and password is supported. A comprehensive account system is yet to be developed and implemented.
@@ -243,10 +251,12 @@ LLM_BINDING=azure_openai
LLM_BINDING_HOST=your-azure-endpoint
LLM_MODEL=your-model-deployment-name
LLM_BINDING_API_KEY=your-azure-api-key
-AZURE_OPENAI_API_VERSION=2024-08-01-preview # optional, defaults to latest version
-EMBEDDING_BINDING=azure_openai # if using Azure OpenAI for embeddings
-EMBEDDING_MODEL=your-embedding-deployment-name
+### API version is optional, defaults to latest version
+AZURE_OPENAI_API_VERSION=2024-08-01-preview
+### if using Azure OpenAI for embeddings
+EMBEDDING_BINDING=azure_openai
+EMBEDDING_MODEL=your-embedding-deployment-name
```
@@ -370,76 +380,47 @@ You can not change storage implementation selection after you add documents to L
| --embedding-binding | ollama | Embedding binding type (lollms, ollama, openai, azure_openai) |
| auto-scan-at-startup | - | Scan input directory for new files and start indexing |
-### Example Usage
-
-#### Running a Lightrag server with ollama default local server as llm and embedding backends
-
-Ollama is the default backend for both llm and embedding, so by default you can run lightrag-server with no parameters and the default ones will be used. Make sure ollama is installed and is running and default models are already installed on ollama.
+### .env Examples
```bash
-# Run lightrag with ollama, mistral-nemo:latest for llm, and bge-m3:latest for embedding
-lightrag-server
+### Server Configuration
+# HOST=0.0.0.0
+PORT=9621
+WORKERS=2
+
+### Settings for document indexing
+ENABLE_LLM_CACHE_FOR_EXTRACT=true
+SUMMARY_LANGUAGE=Chinese
+MAX_PARALLEL_INSERT=2
+
+### LLM Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
+TIMEOUT=200
+TEMPERATURE=0.0
+MAX_ASYNC=4
+MAX_TOKENS=32768
+
+LLM_BINDING=openai
+LLM_MODEL=gpt-4o-mini
+LLM_BINDING_HOST=https://api.openai.com/v1
+LLM_BINDING_API_KEY=your-api-key
+
+### Embedding Configuration (Use valid host. For local services installed with docker, you can use host.docker.internal)
+EMBEDDING_MODEL=bge-m3:latest
+EMBEDDING_DIM=1024
+EMBEDDING_BINDING=ollama
+EMBEDDING_BINDING_HOST=http://localhost:11434
+
+### For JWT Auth
+# AUTH_ACCOUNTS='admin:admin123,user1:pass456'
+# TOKEN_SECRET=your-key-for-LightRAG-API-Server-xxx
+# TOKEN_EXPIRE_HOURS=48
+
+# LIGHTRAG_API_KEY=your-secure-api-key-here-123
+# WHITELIST_PATHS=/api/*
+# WHITELIST_PATHS=/health,/api/*
-# Using an authentication key
-lightrag-server --key my-key
```
-#### Running a Lightrag server with lollms default local server as llm and embedding backends
-
-```bash
-# Run lightrag with lollms, mistral-nemo:latest for llm, and bge-m3:latest for embedding
-# Configure LLM_BINDING=lollms and EMBEDDING_BINDING=lollms in .env or config.ini
-lightrag-server
-
-# Using an authentication key
-lightrag-server --key my-key
-```
-
-#### Running a Lightrag server with openai server as llm and embedding backends
-
-```bash
-# Run lightrag with openai, GPT-4o-mini for llm, and text-embedding-3-small for embedding
-# Configure in .env or config.ini:
-# LLM_BINDING=openai
-# LLM_MODEL=GPT-4o-mini
-# EMBEDDING_BINDING=openai
-# EMBEDDING_MODEL=text-embedding-3-small
-lightrag-server
-
-# Using an authentication key
-lightrag-server --key my-key
-```
-
-#### Running a Lightrag server with azure openai server as llm and embedding backends
-
-```bash
-# Run lightrag with azure_openai
-# Configure in .env or config.ini:
-# LLM_BINDING=azure_openai
-# LLM_MODEL=your-model
-# EMBEDDING_BINDING=azure_openai
-# EMBEDDING_MODEL=your-embedding-model
-lightrag-server
-
-# Using an authentication key
-lightrag-server --key my-key
-```
-
-**Important Notes:**
-- For LoLLMs: Make sure the specified models are installed in your LoLLMs instance
-- For Ollama: Make sure the specified models are installed in your Ollama instance
-- For OpenAI: Ensure you have set up your OPENAI_API_KEY environment variable
-- For Azure OpenAI: Build and configure your server as stated in the Prequisites section
-
-For help on any server, use the --help flag:
-```bash
-lightrag-server --help
-```
-
-Note: If you don't need the API functionality, you can install the base package without API support using:
-```bash
-pip install lightrag-hku
-```
## API Endpoints
diff --git a/lightrag/api/__init__.py b/lightrag/api/__init__.py
index 5dd9d398..ec1959de 100644
--- a/lightrag/api/__init__.py
+++ b/lightrag/api/__init__.py
@@ -1 +1 @@
-__api_version__ = "1.2.7"
+__api_version__ = "1.2.8"
diff --git a/lightrag/api/auth.py b/lightrag/api/auth.py
index de02721b..0b61095d 100644
--- a/lightrag/api/auth.py
+++ b/lightrag/api/auth.py
@@ -7,7 +7,10 @@ from pydantic import BaseModel
from .config import global_args
-load_dotenv()
+# use the .env that is inside the current folder
+# allows to use different .env file for each lightrag instance
+# the OS environment variables take precedence over the .env file
+load_dotenv(dotenv_path=".env", override=False)
class TokenPayload(BaseModel):
diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index a00eab6c..f23391a9 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -50,15 +50,18 @@ from lightrag.kg.shared_storage import (
from fastapi.security import OAuth2PasswordRequestForm
from lightrag.api.auth import auth_handler
-# Load environment variables
-# Updated to use the .env that is inside the current folder
-# This update allows the user to put a different.env file for each lightrag folder
-load_dotenv(".env")
+# use the .env that is inside the current folder
+# allows to use different .env file for each lightrag instance
+# the OS environment variables take precedence over the .env file
+load_dotenv(dotenv_path=".env", override=False)
# Initialize config parser
config = configparser.ConfigParser()
config.read("config.ini")
+# Global authentication configuration
+auth_configured = bool(auth_handler.accounts)
+
def create_app(args):
# Setup logging
@@ -429,9 +432,7 @@ def create_app(args):
try:
pipeline_status = await get_namespace_data("pipeline_status")
- username = os.getenv("AUTH_USERNAME")
- password = os.getenv("AUTH_PASSWORD")
- if not (username and password):
+ if not auth_configured:
auth_mode = "disabled"
else:
auth_mode = "enabled"
diff --git a/lightrag/api/routers/document_routes.py b/lightrag/api/routers/document_routes.py
index 2a7873b8..e30b538f 100644
--- a/lightrag/api/routers/document_routes.py
+++ b/lightrag/api/routers/document_routes.py
@@ -540,6 +540,7 @@ def create_document_routes(
Returns:
InsertResponse: A response object containing the upload status and a message.
+ status can be "success", "duplicated", or error is thrown.
Raises:
HTTPException: If the file type is not supported (400) or other errors occur (500).
@@ -552,6 +553,13 @@ def create_document_routes(
)
file_path = doc_manager.input_dir / file.filename
+ # Check if file already exists
+ if file_path.exists():
+ return InsertResponse(
+ status="duplicated",
+ message=f"File '{file.filename}' already exists in the input directory.",
+ )
+
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
diff --git a/lightrag/api/run_with_gunicorn.py b/lightrag/api/run_with_gunicorn.py
index 8d71ba36..065a12a1 100644
--- a/lightrag/api/run_with_gunicorn.py
+++ b/lightrag/api/run_with_gunicorn.py
@@ -11,9 +11,10 @@ from lightrag.api.utils_api import parse_args, display_splash_screen, check_env_
from lightrag.kg.shared_storage import initialize_share_data, finalize_share_data
from dotenv import load_dotenv
-# Updated to use the .env that is inside the current folder
-# This update allows the user to put a different.env file for each lightrag folder
-load_dotenv(".env")
+# use the .env that is inside the current folder
+# allows to use different .env file for each lightrag instance
+# the OS environment variables take precedence over the .env file
+load_dotenv(dotenv_path=".env", override=False)
def check_and_install_dependencies():
diff --git a/lightrag/api/utils_api.py b/lightrag/api/utils_api.py
index 62ceecce..cbc28979 100644
--- a/lightrag/api/utils_api.py
+++ b/lightrag/api/utils_api.py
@@ -2,18 +2,17 @@
Utility functions for the LightRAG API.
"""
-import argparse
import os
-import sys
+import argparse
from typing import Optional, List, Tuple
-
+import sys
from ascii_colors import ASCIIColors
-from dotenv import load_dotenv
+from lightrag.api import __api_version__ as api_version
+from lightrag import __version__ as core_version
from fastapi import HTTPException, Security, Request, status
+from dotenv import load_dotenv
from fastapi.security import APIKeyHeader, OAuth2PasswordBearer
from starlette.status import HTTP_403_FORBIDDEN
-
-from lightrag.api import __api_version__
from .auth import auth_handler
from .config import ollama_server_infos
from ..prompt import PROMPTS
@@ -25,9 +24,7 @@ def check_env_file():
Returns True if should continue, False if should exit.
"""
if not os.path.exists(".env"):
- warning_msg = (
- "Warning: .env file not found. Some features may not work properly."
- )
+ warning_msg = "Warning: Startup directory must contain .env file for multi-instance support."
ASCIIColors.yellow(warning_msg)
# Check if running in interactive terminal
@@ -39,8 +36,10 @@ def check_env_file():
return True
-# Load environment variables
-load_dotenv()
+# use the .env that is inside the current folder
+# allows to use different .env file for each lightrag instance
+# the OS environment variables take precedence over the .env file
+load_dotenv(dotenv_path=".env", override=False)
# Get whitelist paths from environment variable, only once during initialization
default_whitelist = "/health,/api/*"
@@ -182,7 +181,7 @@ def display_splash_screen(args: argparse.Namespace) -> None:
# Banner
ASCIIColors.cyan(f"""
╔══════════════════════════════════════════════════════════════╗
- ║ 🚀 LightRAG Server v{__api_version__} ║
+ ║ 🚀 LightRAG Server v{core_version}/{api_version} ║
║ Fast, Lightweight RAG Server Implementation ║
╚══════════════════════════════════════════════════════════════╝
""")
diff --git a/lightrag/api/webui/assets/index-CD5HxTy1.css b/lightrag/api/webui/assets/index-CD5HxTy1.css
new file mode 100644
index 00000000..a0ab321b
--- /dev/null
+++ b/lightrag/api/webui/assets/index-CD5HxTy1.css
@@ -0,0 +1 @@
+/*! tailwindcss v4.0.8 | MIT License | https://tailwindcss.com */@layer theme{:root,:host{--font-sans:ui-sans-serif,system-ui,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-mono:ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;--color-red-100:oklch(.936 .032 17.717);--color-red-400:oklch(.704 .191 22.216);--color-red-500:oklch(.637 .237 25.331);--color-red-600:oklch(.577 .245 27.325);--color-red-950:oklch(.258 .092 26.042);--color-amber-100:oklch(.962 .059 95.617);--color-amber-200:oklch(.924 .12 95.746);--color-amber-700:oklch(.555 .163 48.998);--color-amber-800:oklch(.473 .137 46.201);--color-amber-900:oklch(.414 .112 45.904);--color-yellow-600:oklch(.681 .162 75.834);--color-green-500:oklch(.723 .219 149.579);--color-green-600:oklch(.627 .194 149.214);--color-emerald-50:oklch(.979 .021 166.113);--color-emerald-400:oklch(.765 .177 163.223);--color-emerald-700:oklch(.508 .118 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286.033);--color-black:#000;--color-white:#fff;--spacing:.25rem;--container-xs:20rem;--container-lg:32rem;--container-xl:36rem;--text-xs:.75rem;--text-xs--line-height:calc(1/.75);--text-sm:.875rem;--text-sm--line-height:calc(1.25/.875);--text-base:1rem;--text-base--line-height: 1.5 ;--text-lg:1.125rem;--text-lg--line-height:calc(1.75/1.125);--text-3xl:1.875rem;--text-3xl--line-height: 1.2 ;--font-weight-medium:500;--font-weight-semibold:600;--font-weight-bold:700;--tracking-tight:-.025em;--tracking-wide:.025em;--tracking-widest:.1em;--leading-relaxed:1.625;--radius-xs:.125rem;--animate-spin:spin 1s linear infinite;--animate-pulse:pulse 2s 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diff --git a/lightrag/api/webui/assets/index-CbzkrOyx.css b/lightrag/api/webui/assets/index-CbzkrOyx.css
deleted file mode 100644
index d29a2fd7..00000000
--- a/lightrag/api/webui/assets/index-CbzkrOyx.css
+++ /dev/null
@@ -1 +0,0 @@
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tr:last-child{border-style:var(--tw-border-style);border-width:0}.\[\&\:\:-webkit-inner-spin-button\]\:appearance-none::-webkit-inner-spin-button{-webkit-appearance:none;-moz-appearance:none;appearance:none}.\[\&\:\:-webkit-inner-spin-button\]\:opacity-100::-webkit-inner-spin-button{opacity:1}.\[\&\:\:-webkit-outer-spin-button\]\:appearance-none::-webkit-outer-spin-button{-webkit-appearance:none;-moz-appearance:none;appearance:none}.\[\&\:\:-webkit-outer-spin-button\]\:opacity-100::-webkit-outer-spin-button{opacity:1}.\[\&\:has\(\[role\=checkbox\]\)\]\:pr-0:has([role=checkbox]){padding-right:calc(var(--spacing)*0)}.\[\&\>\[role\=checkbox\]\]\:translate-y-\[2px\]>[role=checkbox]{--tw-translate-y:2px;translate:var(--tw-translate-x)var(--tw-translate-y)}.\[\&\>span\]\:line-clamp-1>span{-webkit-line-clamp:1;-webkit-box-orient:vertical;display:-webkit-box;overflow:hidden}.\[\&\>svg\]\:absolute>svg{position:absolute}.\[\&\>svg\]\:top-4>svg{top:calc(var(--spacing)*4)}.\[\&\>svg\]\:left-4>svg{left:calc(var(--spacing)*4)}.\[\&\>svg\]\:text-destructive>svg{color:var(--destructive)}.\[\&\>svg\]\:text-foreground>svg{color:var(--foreground)}.\[\&\>svg\+div\]\:translate-y-\[-3px\]>svg+div{--tw-translate-y:-3px;translate:var(--tw-translate-x)var(--tw-translate-y)}.\[\&\>svg\~\*\]\:pl-7>svg~*{padding-left:calc(var(--spacing)*7)}.\[\&\>tr\]\:last\:border-b-0>tr:last-child{border-bottom-style:var(--tw-border-style);border-bottom-width:0}}:root{--background:#fff;--foreground:#09090b;--card:#fff;--card-foreground:#09090b;--popover:#fff;--popover-foreground:#09090b;--primary:#18181b;--primary-foreground:#fafafa;--secondary:#f4f4f5;--secondary-foreground:#18181b;--muted:#f4f4f5;--muted-foreground:#71717a;--accent:#f4f4f5;--accent-foreground:#18181b;--destructive:#ef4444;--destructive-foreground:#fafafa;--border:#e4e4e7;--input:#e4e4e7;--ring:#09090b;--chart-1:#e76e50;--chart-2:#2a9d90;--chart-3:#274754;--chart-4:#e8c468;--chart-5:#f4a462;--radius:.6rem;--sidebar-background:#fafafa;--sidebar-foreground:#3f3f46;--sidebar-primary:#18181b;--sidebar-primary-foreground:#fafafa;--sidebar-accent:#f4f4f5;--sidebar-accent-foreground:#18181b;--sidebar-border:#e5e7eb;--sidebar-ring:#3b82f6}.dark{--background:#09090b;--foreground:#fafafa;--card:#09090b;--card-foreground:#fafafa;--popover:#09090b;--popover-foreground:#fafafa;--primary:#fafafa;--primary-foreground:#18181b;--secondary:#27272a;--secondary-foreground:#fafafa;--muted:#27272a;--muted-foreground:#a1a1aa;--accent:#27272a;--accent-foreground:#fafafa;--destructive:#7f1d1d;--destructive-foreground:#fafafa;--border:#27272a;--input:#27272a;--ring:#d4d4d8;--chart-1:#2662d9;--chart-2:#2eb88a;--chart-3:#e88c30;--chart-4:#af57db;--chart-5:#e23670;--sidebar-background:#18181b;--sidebar-foreground:#f4f4f5;--sidebar-primary:#1d4ed8;--sidebar-primary-foreground:#fff;--sidebar-accent:#27272a;--sidebar-accent-foreground:#f4f4f5;--sidebar-border:#27272a;--sidebar-ring:#3b82f6}::-webkit-scrollbar{width:10px;height:10px}::-webkit-scrollbar-thumb{background-color:#ccc;border-radius:5px}::-webkit-scrollbar-track{background-color:#f2f2f2}.dark ::-webkit-scrollbar-thumb{background-color:#e6e6e6}.dark ::-webkit-scrollbar-track{background-color:#000}@keyframes enter{0%{opacity:var(--tw-enter-opacity,1);transform:translate3d(var(--tw-enter-translate-x,0),var(--tw-enter-translate-y,0),0)scale3d(var(--tw-enter-scale,1),var(--tw-enter-scale,1),var(--tw-enter-scale,1))rotate(var(--tw-enter-rotate,0))}}@keyframes exit{to{opacity:var(--tw-exit-opacity,1);transform:translate3d(var(--tw-exit-translate-x,0),var(--tw-exit-translate-y,0),0)scale3d(var(--tw-exit-scale,1),var(--tw-exit-scale,1),var(--tw-exit-scale,1))rotate(var(--tw-exit-rotate,0))}}@keyframes spin{to{transform:rotate(360deg)}}@keyframes pulse{50%{opacity:.5}}@property --tw-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-z{syntax:"*";inherits:false;initial-value:0}@property --tw-scale-x{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-y{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-z{syntax:"*";inherits:false;initial-value:1}@property --tw-rotate-x{syntax:"*";inherits:false;initial-value:rotateX(0)}@property --tw-rotate-y{syntax:"*";inherits:false;initial-value:rotateY(0)}@property --tw-rotate-z{syntax:"*";inherits:false;initial-value:rotateZ(0)}@property --tw-skew-x{syntax:"*";inherits:false;initial-value:skewX(0)}@property --tw-skew-y{syntax:"*";inherits:false;initial-value:skewY(0)}@property --tw-space-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-border-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-gradient-position{syntax:"*";inherits:false}@property --tw-gradient-from{syntax:"";inherits:false;initial-value:#0000}@property --tw-gradient-via{syntax:"";inherits:false;initial-value:#0000}@property --tw-gradient-to{syntax:"";inherits:false;initial-value:#0000}@property --tw-gradient-stops{syntax:"*";inherits:false}@property --tw-gradient-via-stops{syntax:"*";inherits:false}@property --tw-gradient-from-position{syntax:"";inherits:false;initial-value:0%}@property --tw-gradient-via-position{syntax:"";inherits:false;initial-value:50%}@property --tw-gradient-to-position{syntax:"";inherits:false;initial-value:100%}@property --tw-leading{syntax:"*";inherits:false}@property --tw-font-weight{syntax:"*";inherits:false}@property --tw-tracking{syntax:"*";inherits:false}@property --tw-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-shadow-color{syntax:"*";inherits:false}@property --tw-inset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-shadow-color{syntax:"*";inherits:false}@property --tw-ring-color{syntax:"*";inherits:false}@property --tw-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-ring-color{syntax:"*";inherits:false}@property --tw-inset-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-ring-inset{syntax:"*";inherits:false}@property --tw-ring-offset-width{syntax:"";inherits:false;initial-value:0}@property --tw-ring-offset-color{syntax:"*";inherits:false;initial-value:#fff}@property --tw-ring-offset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-outline-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-blur{syntax:"*";inherits:false}@property --tw-brightness{syntax:"*";inherits:false}@property --tw-contrast{syntax:"*";inherits:false}@property --tw-grayscale{syntax:"*";inherits:false}@property --tw-hue-rotate{syntax:"*";inherits:false}@property --tw-invert{syntax:"*";inherits:false}@property --tw-opacity{syntax:"*";inherits:false}@property --tw-saturate{syntax:"*";inherits:false}@property --tw-sepia{syntax:"*";inherits:false}@property --tw-drop-shadow{syntax:"*";inherits:false}@property --tw-backdrop-blur{syntax:"*";inherits:false}@property --tw-backdrop-brightness{syntax:"*";inherits:false}@property --tw-backdrop-contrast{syntax:"*";inherits:false}@property --tw-backdrop-grayscale{syntax:"*";inherits:false}@property --tw-backdrop-hue-rotate{syntax:"*";inherits:false}@property --tw-backdrop-invert{syntax:"*";inherits:false}@property --tw-backdrop-opacity{syntax:"*";inherits:false}@property --tw-backdrop-saturate{syntax:"*";inherits:false}@property --tw-backdrop-sepia{syntax:"*";inherits:false}@property --tw-duration{syntax:"*";inherits:false}@property --tw-space-x-reverse{syntax:"*";inherits:false;initial-value:0}:root{--sigma-background-color:#fff;--sigma-controls-background-color:#fff;--sigma-controls-background-color-hover:rgba(0,0,0,.2);--sigma-controls-border-color:rgba(0,0,0,.2);--sigma-controls-color:#000;--sigma-controls-zindex:100;--sigma-controls-margin:5px;--sigma-controls-size:30px}div.react-sigma{height:100%;width:100%;position:relative;background:var(--sigma-background-color)}div.sigma-container{height:100%;width:100%}.react-sigma-controls{position:absolute;z-index:var(--sigma-controls-zindex);border:2px solid var(--sigma-controls-border-color);border-radius:4px;color:var(--sigma-controls-color);background-color:var(--sigma-controls-background-color)}.react-sigma-controls.bottom-right{bottom:var(--sigma-controls-margin);right:var(--sigma-controls-margin)}.react-sigma-controls.bottom-left{bottom:var(--sigma-controls-margin);left:var(--sigma-controls-margin)}.react-sigma-controls.top-right{top:var(--sigma-controls-margin);right:var(--sigma-controls-margin)}.react-sigma-controls.top-left{top:var(--sigma-controls-margin);left:var(--sigma-controls-margin)}.react-sigma-controls:first-child{border-top-left-radius:2px;border-top-right-radius:2px}.react-sigma-controls:last-child{border-bottom-left-radius:2px;border-bottom-right-radius:2px}.react-sigma-control{width:var(--sigma-controls-size);height:var(--sigma-controls-size);line-height:var(--sigma-controls-size);background-color:var(--sigma-controls-background-color);border-bottom:1px solid var(--sigma-controls-border-color)}.react-sigma-control:last-child{border-bottom:none}.react-sigma-control>*{box-sizing:border-box}.react-sigma-control>button{display:block;border:none;margin:0;padding:0;width:var(--sigma-controls-size);height:var(--sigma-controls-size);line-height:var(--sigma-controls-size);background-position:center;background-size:50%;background-repeat:no-repeat;background-color:var(--sigma-controls-background-color);clip:rect(0,0,0,0)}.react-sigma-control>button:hover{background-color:var(--sigma-controls-background-color-hover)}.react-sigma-search{background-color:var(--sigma-controls-background-color)}.react-sigma-search label{visibility:hidden}.react-sigma-search input{color:var(--sigma-controls-color);background-color:var(--sigma-controls-background-color);font-size:1em;width:100%;margin:0;border:none;padding:var(--sigma-controls-margin);box-sizing:border-box}:root{--sigma-grey-color:#ccc}.react-sigma .option.hoverable{cursor:pointer!important}.react-sigma .text-ellipsis{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.react-sigma .react-select__clear-indicator{cursor:pointer!important}.react-sigma .text-muted{color:var(--sigma-grey-color)}.react-sigma .text-italic{font-style:italic}.react-sigma .text-center{text-align:center}.react-sigma .graph-search{min-width:250px}.react-sigma .graph-search .option{padding:2px 8px}.react-sigma .graph-search .dropdown-indicator{font-size:1.25em;padding:4px}.react-sigma .graph-search .option.selected{background-color:var(--sigma-grey-color)}.react-sigma .node .render{position:relative;display:inline-block;width:1em;height:1em;border-radius:1em;background-color:var(--sigma-grey-color);margin-right:8px}.react-sigma .node{display:flex;flex-direction:row;align-items:center}.react-sigma .node .render{flex-grow:0;flex-shrink:0;margin-right:0 .25em}.react-sigma .node .label{flex-grow:1;flex-shrink:1}.react-sigma .edge{display:flex;flex-direction:column;align-items:flex-start;flex-grow:0;flex-shrink:0;flex-wrap:nowrap}.react-sigma .edge .node{font-size:.7em}.react-sigma .edge .body{display:flex;flex-direction:row;flex-grow:1;flex-shrink:1;min-height:.6em}.react-sigma .edge .body .render{display:flex;flex-direction:column;margin:0 2px}.react-sigma .edge .body .render .dash,.react-sigma .edge .body .render .dotted{display:inline-block;width:0;margin:0 2px;border:2px solid #ccc;flex-grow:1;flex-shrink:1}.react-sigma .edge .body .render .dotted{border-style:dotted}.react-sigma .edge .body .render .arrow{width:0;height:0;border-left:.3em solid transparent;border-right:.3em solid transparent;border-top:.6em solid red;flex-shrink:0;flex-grow:0;border-left-width:.3em;border-right-width:.3em}.react-sigma .edge .body .label{flex-grow:1;flex-shrink:1;text-align:center}
diff --git a/lightrag/api/webui/assets/index-DTDDxtXc.js b/lightrag/api/webui/assets/index-raheqJeu.js
similarity index 73%
rename from lightrag/api/webui/assets/index-DTDDxtXc.js
rename to lightrag/api/webui/assets/index-raheqJeu.js
index aabb0584..fb574679 100644
--- a/lightrag/api/webui/assets/index-DTDDxtXc.js
+++ b/lightrag/api/webui/assets/index-raheqJeu.js
@@ -1,4 +1,4 @@
-var X6=Object.defineProperty;var Z6=(e,t,n)=>t in e?X6(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Xr=(e,t,n)=>Z6(e,typeof t!="symbol"?t+"":t,n);function Q6(e,t){for(var n=0;nr[a]})}}}return Object.freeze(Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}))}(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const a of document.querySelectorAll('link[rel="modulepreload"]'))r(a);new MutationObserver(a=>{for(const o of a)if(o.type==="childList")for(const s of o.addedNodes)s.tagName==="LINK"&&s.rel==="modulepreload"&&r(s)}).observe(document,{childList:!0,subtree:!0});function n(a){const o={};return a.integrity&&(o.integrity=a.integrity),a.referrerPolicy&&(o.referrerPolicy=a.referrerPolicy),a.crossOrigin==="use-credentials"?o.credentials="include":a.crossOrigin==="anonymous"?o.credentials="omit":o.credentials="same-origin",o}function r(a){if(a.ep)return;a.ep=!0;const o=n(a);fetch(a.href,o)}})();var df=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function cn(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}function J6(e){if(e.__esModule)return e;var t=e.default;if(typeof t=="function"){var n=function r(){return this instanceof r?Reflect.construct(t,arguments,this.constructor):t.apply(this,arguments)};n.prototype=t.prototype}else n={};return Object.defineProperty(n,"__esModule",{value:!0}),Object.keys(e).forEach(function(r){var a=Object.getOwnPropertyDescriptor(e,r);Object.defineProperty(n,r,a.get?a:{enumerable:!0,get:function(){return e[r]}})}),n}var qh={exports:{}},Zl={};/**
+var eq=Object.defineProperty;var tq=(e,t,n)=>t in e?eq(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Xr=(e,t,n)=>tq(e,typeof t!="symbol"?t+"":t,n);function nq(e,t){for(var n=0;nr[a]})}}}return Object.freeze(Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}))}(function(){const t=document.createElement("link").relList;if(t&&t.supports&&t.supports("modulepreload"))return;for(const a of document.querySelectorAll('link[rel="modulepreload"]'))r(a);new MutationObserver(a=>{for(const o of a)if(o.type==="childList")for(const s of o.addedNodes)s.tagName==="LINK"&&s.rel==="modulepreload"&&r(s)}).observe(document,{childList:!0,subtree:!0});function n(a){const o={};return a.integrity&&(o.integrity=a.integrity),a.referrerPolicy&&(o.referrerPolicy=a.referrerPolicy),a.crossOrigin==="use-credentials"?o.credentials="include":a.crossOrigin==="anonymous"?o.credentials="omit":o.credentials="same-origin",o}function r(a){if(a.ep)return;a.ep=!0;const o=n(a);fetch(a.href,o)}})();var df=typeof globalThis<"u"?globalThis:typeof window<"u"?window:typeof global<"u"?global:typeof self<"u"?self:{};function cn(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}function rq(e){if(e.__esModule)return e;var t=e.default;if(typeof t=="function"){var n=function r(){return this instanceof r?Reflect.construct(t,arguments,this.constructor):t.apply(this,arguments)};n.prototype=t.prototype}else n={};return Object.defineProperty(n,"__esModule",{value:!0}),Object.keys(e).forEach(function(r){var a=Object.getOwnPropertyDescriptor(e,r);Object.defineProperty(n,r,a.get?a:{enumerable:!0,get:function(){return e[r]}})}),n}var qh={exports:{}},Zl={};/**
* @license React
* react-jsx-runtime.production.js
*
@@ -6,7 +6,7 @@ var X6=Object.defineProperty;var Z6=(e,t,n)=>t in e?X6(e,t,{enumerable:!0,config
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
- */var H_;function e9(){if(H_)return Zl;H_=1;var e=Symbol.for("react.transitional.element"),t=Symbol.for("react.fragment");function n(r,a,o){var s=null;if(o!==void 0&&(s=""+o),a.key!==void 0&&(s=""+a.key),"key"in a){o={};for(var u in a)u!=="key"&&(o[u]=a[u])}else o=a;return a=o.ref,{$$typeof:e,type:r,key:s,ref:a!==void 0?a:null,props:o}}return Zl.Fragment=t,Zl.jsx=n,Zl.jsxs=n,Zl}var $_;function t9(){return $_||($_=1,qh.exports=e9()),qh.exports}var E=t9(),Vh={exports:{}},it={};/**
+ */var W_;function aq(){if(W_)return Zl;W_=1;var e=Symbol.for("react.transitional.element"),t=Symbol.for("react.fragment");function n(r,a,o){var s=null;if(o!==void 0&&(s=""+o),a.key!==void 0&&(s=""+a.key),"key"in a){o={};for(var u in a)u!=="key"&&(o[u]=a[u])}else o=a;return a=o.ref,{$$typeof:e,type:r,key:s,ref:a!==void 0?a:null,props:o}}return Zl.Fragment=t,Zl.jsx=n,Zl.jsxs=n,Zl}var Y_;function oq(){return Y_||(Y_=1,qh.exports=aq()),qh.exports}var E=oq(),Vh={exports:{}},it={};/**
* @license React
* react.production.js
*
@@ -14,7 +14,7 @@ var X6=Object.defineProperty;var Z6=(e,t,n)=>t in e?X6(e,t,{enumerable:!0,config
*
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