Merge branch 'HKUDS:main' into main

README-zh.md

@@ -294,6 +294,16 @@ class QueryParam:
     top_k: int = int(os.getenv("TOP_K", "60"))
     """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
 
+    chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
+    """Number of text chunks to retrieve initially from vector search.
+    If None, defaults to top_k value.
+    """
+
+    chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
+    """Number of text chunks to keep after reranking.
+    If None, keeps all chunks returned from initial retrieval.
+    """
+
     max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
     """Maximum number of tokens allowed for each retrieved text chunk."""
 
@@ -849,6 +859,18 @@ rag = LightRAG(
 
 </details>
 
+### LightRAG实例间的数据隔离
+
+通过 workspace 参数可以不同实现不同LightRAG实例之间的存储数据隔离。LightRAG在初始化后workspace就已经确定，之后修改workspace是无效的。下面是不同类型的存储实现工作空间的方式：
+
+- **对于本地基于文件的数据库，数据隔离通过工作空间子目录实现：** JsonKVStorage, JsonDocStatusStorage, NetworkXStorage, NanoVectorDBStorage, FaissVectorDBStorage。
+- **对于将数据存储在集合（collection）中的数据库，通过在集合名称前添加工作空间前缀来实现：** RedisKVStorage, RedisDocStatusStorage, MilvusVectorDBStorage, QdrantVectorDBStorage, MongoKVStorage, MongoDocStatusStorage, MongoVectorDBStorage, MongoGraphStorage, PGGraphStorage。
+- **对于关系型数据库，数据隔离通过向表中添加 `workspace` 字段进行数据的逻辑隔离：** PGKVStorage, PGVectorStorage, PGDocStatusStorage。
+
+* **对于Neo4j图数据库，通过label来实现数据的逻辑隔离**：Neo4JStorage
+
+为了保持对遗留数据的兼容，在未配置工作空间时PostgreSQL的默认工作空间为`default`，Neo4j的默认工作空间为`base`。对于所有的外部存储，系统都提供了专用的工作空间环境变量，用于覆盖公共的 `WORKSPACE`环境变量配置。这些适用于指定存储类型的工作空间环境变量为：`REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`。
+
 ## 编辑实体和关系
 
 LightRAG现在支持全面的知识图谱管理功能，允许您在知识图谱中创建、编辑和删除实体和关系。

README.md

@@ -153,7 +153,7 @@ curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_d
 python examples/lightrag_openai_demo.py
 ```
 
-For a streaming response implementation example, please see `examples/lightrag_openai_compatible_demo.py`. Prior to execution, ensure you modify the sample code’s LLM and embedding configurations accordingly.
+For a streaming response implementation example, please see `examples/lightrag_openai_compatible_demo.py`. Prior to execution, ensure you modify the sample code's LLM and embedding configurations accordingly.
 
 **Note 1**: When running the demo program, please be aware that different test scripts may use different embedding models. If you switch to a different embedding model, you must clear the data directory (`./dickens`); otherwise, the program may encounter errors. If you wish to retain the LLM cache, you can preserve the `kv_store_llm_response_cache.json` file while clearing the data directory.
 
@@ -239,6 +239,7 @@ A full list of LightRAG init parameters:
 | **Parameter** | **Type** | **Explanation** | **Default** |
 |--------------|----------|-----------------|-------------|
 | **working_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
+| **workspace** | str | Workspace name for data isolation between different LightRAG Instances |  |
 | **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` |
 | **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` |
 | **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` |
@@ -300,6 +301,16 @@ class QueryParam:
     top_k: int = int(os.getenv("TOP_K", "60"))
     """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
 
+    chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
+    """Number of text chunks to retrieve initially from vector search.
+    If None, defaults to top_k value.
+    """
+
+    chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
+    """Number of text chunks to keep after reranking.
+    If None, keeps all chunks returned from initial retrieval.
+    """
+
     max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
     """Maximum number of tokens allowed for each retrieved text chunk."""
 
@@ -860,6 +871,52 @@ rag = LightRAG(
 
 </details>
 
+<details>
+<summary> <b>Using Memgraph for Storage</b> </summary>
+
+* Memgraph is a high-performance, in-memory graph database compatible with the Neo4j Bolt protocol.
+* You can run Memgraph locally using Docker for easy testing:
+* See: https://memgraph.com/download
+
+```python
+export MEMGRAPH_URI="bolt://localhost:7687"
+
+# Setup logger for LightRAG
+setup_logger("lightrag", level="INFO")
+
+# When you launch the project, override the default KG: NetworkX
+# by specifying kg="MemgraphStorage".
+
+# Note: Default settings use NetworkX
+# Initialize LightRAG with Memgraph implementation.
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
+        graph_storage="MemgraphStorage", #<-----------override KG default
+    )
+
+    # Initialize database connections
+    await rag.initialize_storages()
+    # Initialize pipeline status for document processing
+    await initialize_pipeline_status()
+
+    return rag
+```
+
+</details>
+
+### Data Isolation Between LightRAG Instances
+
+The `workspace` parameter ensures data isolation between different LightRAG instances. Once initialized, the `workspace` is immutable and cannot be changed.Here is how workspaces are implemented for different types of storage:
+
+- **For local file-based databases, data isolation is achieved through workspace subdirectories:** `JsonKVStorage`, `JsonDocStatusStorage`, `NetworkXStorage`, `NanoVectorDBStorage`, `FaissVectorDBStorage`.
+- **For databases that store data in collections, it's done by adding a workspace prefix to the collection name:** `RedisKVStorage`, `RedisDocStatusStorage`, `MilvusVectorDBStorage`, `QdrantVectorDBStorage`, `MongoKVStorage`, `MongoDocStatusStorage`, `MongoVectorDBStorage`, `MongoGraphStorage`, `PGGraphStorage`.
+- **For relational databases, data isolation is achieved by adding a `workspace` field to the tables for logical data separation:** `PGKVStorage`, `PGVectorStorage`, `PGDocStatusStorage`.
+- **For the Neo4j graph database, logical data isolation is achieved through labels:** `Neo4JStorage`
+
+To maintain compatibility with legacy data, the default workspace for PostgreSQL is `default` and for Neo4j is `base` when no workspace is configured. For all external storages, the system provides dedicated workspace environment variables to override the common `WORKSPACE` environment variable configuration. These storage-specific workspace environment variables are: `REDIS_WORKSPACE`, `MILVUS_WORKSPACE`, `QDRANT_WORKSPACE`, `MONGODB_WORKSPACE`, `POSTGRES_WORKSPACE`, `NEO4J_WORKSPACE`.
+
 ## Edit Entities and Relations
 
 LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.

config.ini.example

@@ -21,3 +21,6 @@ password = your_password
 database = your_database
 workspace = default  # 可选,默认为default
 max_connections = 12
+
+[memgraph]
+uri = bolt://localhost:7687

docs/rerank_integration.md

@@ -0,0 +1,275 @@
+# Rerank Integration in LightRAG
+
+This document explains how to configure and use the rerank functionality in LightRAG to improve retrieval quality.
+
+## Overview
+
+Reranking is an optional feature that improves the quality of retrieved documents by re-ordering them based on their relevance to the query. This is particularly useful when you want higher precision in document retrieval across all query modes (naive, local, global, hybrid, mix).
+
+## Architecture
+
+The rerank integration follows a simplified design pattern:
+
+- **Single Function Configuration**: All rerank settings (model, API keys, top_k, etc.) are contained within the rerank function
+- **Async Processing**: Non-blocking rerank operations
+- **Error Handling**: Graceful fallback to original results
+- **Optional Feature**: Can be enabled/disabled via configuration
+- **Code Reuse**: Single generic implementation for Jina/Cohere compatible APIs
+
+## Configuration
+
+### Environment Variables
+
+Set this variable in your `.env` file or environment:
+
+```bash
+# Enable/disable reranking
+ENABLE_RERANK=True
+```
+
+### Programmatic Configuration
+
+```python
+from lightrag import LightRAG
+from lightrag.rerank import custom_rerank, RerankModel
+
+# Method 1: Using a custom rerank function with all settings included
+async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+    return await custom_rerank(
+        query=query,
+        documents=documents,
+        model="BAAI/bge-reranker-v2-m3",
+        base_url="https://api.your-provider.com/v1/rerank",
+        api_key="your_api_key_here",
+        top_k=top_k or 10,  # Handle top_k within the function
+        **kwargs
+    )
+
+rag = LightRAG(
+    working_dir="./rag_storage",
+    llm_model_func=your_llm_func,
+    embedding_func=your_embedding_func,
+    enable_rerank=True,
+    rerank_model_func=my_rerank_func,
+)
+
+# Method 2: Using RerankModel wrapper
+rerank_model = RerankModel(
+    rerank_func=custom_rerank,
+    kwargs={
+        "model": "BAAI/bge-reranker-v2-m3",
+        "base_url": "https://api.your-provider.com/v1/rerank",
+        "api_key": "your_api_key_here",
+    }
+)
+
+rag = LightRAG(
+    working_dir="./rag_storage",
+    llm_model_func=your_llm_func,
+    embedding_func=your_embedding_func,
+    enable_rerank=True,
+    rerank_model_func=rerank_model.rerank,
+)
+```
+
+## Supported Providers
+
+### 1. Custom/Generic API (Recommended)
+
+For Jina/Cohere compatible APIs:
+
+```python
+from lightrag.rerank import custom_rerank
+
+# Your custom API endpoint
+result = await custom_rerank(
+    query="your query",
+    documents=documents,
+    model="BAAI/bge-reranker-v2-m3",
+    base_url="https://api.your-provider.com/v1/rerank",
+    api_key="your_api_key_here",
+    top_k=10
+)
+```
+
+### 2. Jina AI
+
+```python
+from lightrag.rerank import jina_rerank
+
+result = await jina_rerank(
+    query="your query",
+    documents=documents,
+    model="BAAI/bge-reranker-v2-m3",
+    api_key="your_jina_api_key",
+    top_k=10
+)
+```
+
+### 3. Cohere
+
+```python
+from lightrag.rerank import cohere_rerank
+
+result = await cohere_rerank(
+    query="your query",
+    documents=documents,
+    model="rerank-english-v2.0",
+    api_key="your_cohere_api_key",
+    top_k=10
+)
+```
+
+## Integration Points
+
+Reranking is automatically applied at these key retrieval stages:
+
+1. **Naive Mode**: After vector similarity search in `_get_vector_context`
+2. **Local Mode**: After entity retrieval in `_get_node_data`
+3. **Global Mode**: After relationship retrieval in `_get_edge_data`
+4. **Hybrid/Mix Modes**: Applied to all relevant components
+
+## Configuration Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `enable_rerank` | bool | False | Enable/disable reranking |
+| `rerank_model_func` | callable | None | Custom rerank function containing all configurations (model, API keys, top_k, etc.) |
+
+## Example Usage
+
+### Basic Usage
+
+```python
+import asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm.openai import gpt_4o_mini_complete, openai_embedding
+from lightrag.kg.shared_storage import initialize_pipeline_status
+from lightrag.rerank import jina_rerank
+
+async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+    """Custom rerank function with all settings included"""
+    return await jina_rerank(
+        query=query,
+        documents=documents,
+        model="BAAI/bge-reranker-v2-m3",
+        api_key="your_jina_api_key_here",
+        top_k=top_k or 10,  # Default top_k if not provided
+        **kwargs
+    )
+
+async def main():
+    # Initialize with rerank enabled
+    rag = LightRAG(
+        working_dir="./rag_storage",
+        llm_model_func=gpt_4o_mini_complete,
+        embedding_func=openai_embedding,
+        enable_rerank=True,
+        rerank_model_func=my_rerank_func,
+    )
+
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+
+    # Insert documents
+    await rag.ainsert([
+        "Document 1 content...",
+        "Document 2 content...",
+    ])
+
+    # Query with rerank (automatically applied)
+    result = await rag.aquery(
+        "Your question here",
+        param=QueryParam(mode="hybrid", top_k=5)  # This top_k is passed to rerank function
+    )
+
+    print(result)
+
+asyncio.run(main())
+```
+
+### Direct Rerank Usage
+
+```python
+from lightrag.rerank import custom_rerank
+
+async def test_rerank():
+    documents = [
+        {"content": "Text about topic A"},
+        {"content": "Text about topic B"},
+        {"content": "Text about topic C"},
+    ]
+
+    reranked = await custom_rerank(
+        query="Tell me about topic A",
+        documents=documents,
+        model="BAAI/bge-reranker-v2-m3",
+        base_url="https://api.your-provider.com/v1/rerank",
+        api_key="your_api_key_here",
+        top_k=2
+    )
+
+    for doc in reranked:
+        print(f"Score: {doc.get('rerank_score')}, Content: {doc.get('content')}")
+```
+
+## Best Practices
+
+1. **Self-Contained Functions**: Include all necessary configurations (API keys, models, top_k handling) within your rerank function
+2. **Performance**: Use reranking selectively for better performance vs. quality tradeoff
+3. **API Limits**: Monitor API usage and implement rate limiting within your rerank function
+4. **Fallback**: Always handle rerank failures gracefully (returns original results)
+5. **Top-k Handling**: Handle top_k parameter appropriately within your rerank function
+6. **Cost Management**: Consider rerank API costs in your budget planning
+
+## Troubleshooting
+
+### Common Issues
+
+1. **API Key Missing**: Ensure API keys are properly configured within your rerank function
+2. **Network Issues**: Check API endpoints and network connectivity
+3. **Model Errors**: Verify the rerank model name is supported by your API
+4. **Document Format**: Ensure documents have `content` or `text` fields
+
+### Debug Mode
+
+Enable debug logging to see rerank operations:
+
+```python
+import logging
+logging.getLogger("lightrag.rerank").setLevel(logging.DEBUG)
+```
+
+### Error Handling
+
+The rerank integration includes automatic fallback:
+
+```python
+# If rerank fails, original documents are returned
+# No exceptions are raised to the user
+# Errors are logged for debugging
+```
+
+## API Compatibility
+
+The generic rerank API expects this response format:
+
+```json
+{
+  "results": [
+    {
+      "index": 0,
+      "relevance_score": 0.95
+    },
+    {
+      "index": 2,
+      "relevance_score": 0.87
+    }
+  ]
+}
+```
+
+This is compatible with:
+- Jina AI Rerank API
+- Cohere Rerank API
+- Custom APIs following the same format

env.example

@@ -42,13 +42,31 @@ OLLAMA_EMULATING_MODEL_TAG=latest
 ### Logfile location (defaults to current working directory)
 # LOG_DIR=/path/to/log/directory
 
-### Settings for RAG query
+### RAG Configuration
+### Chunk size for document splitting, 500~1500 is recommended
+# CHUNK_SIZE=1200
+# CHUNK_OVERLAP_SIZE=100
+# MAX_TOKEN_SUMMARY=500
+
+### RAG Query Configuration
 # HISTORY_TURNS=3
-# COSINE_THRESHOLD=0.2
-# TOP_K=60
-# MAX_TOKEN_TEXT_CHUNK=4000
+# MAX_TOKEN_TEXT_CHUNK=6000
 # MAX_TOKEN_RELATION_DESC=4000
 # MAX_TOKEN_ENTITY_DESC=4000
+# COSINE_THRESHOLD=0.2
+### Number of entities or relations to retrieve from KG
+# TOP_K=60
+### Number of text chunks to retrieve initially from vector search
+# CHUNK_TOP_K=5
+
+### Rerank Configuration
+# ENABLE_RERANK=False
+### Number of text chunks to keep after reranking (should be <= CHUNK_TOP_K)
+# CHUNK_RERANK_TOP_K=5
+### Rerank model configuration (required when ENABLE_RERANK=True)
+# RERANK_MODEL=BAAI/bge-reranker-v2-m3
+# RERANK_BINDING_HOST=https://api.your-rerank-provider.com/v1/rerank
+# RERANK_BINDING_API_KEY=your_rerank_api_key_here
 
 ### Entity and relation summarization configuration
 ### Language: English, Chinese, French, German ...
@@ -62,9 +80,6 @@ SUMMARY_LANGUAGE=English
 
 ### Number of parallel processing documents(Less than MAX_ASYNC/2 is recommended)
 # MAX_PARALLEL_INSERT=2
-### Chunk size for document splitting, 500~1500 is recommended
-# CHUNK_SIZE=1200
-# CHUNK_OVERLAP_SIZE=100
 
 ### LLM Configuration
 ENABLE_LLM_CACHE=true
@@ -134,13 +149,14 @@ EMBEDDING_BINDING_HOST=http://localhost:11434
 # LIGHTRAG_VECTOR_STORAGE=QdrantVectorDBStorage
 ### Graph Storage (Recommended for production deployment)
 # LIGHTRAG_GRAPH_STORAGE=Neo4JStorage
+# LIGHTRAG_GRAPH_STORAGE=MemgraphStorage
 
 ####################################################################
 ### Default workspace for all storage types
 ### For the purpose of isolation of data for each LightRAG instance
 ### Valid characters: a-z, A-Z, 0-9, and _
 ####################################################################
-# WORKSPACE=doc—
+# WORKSPACE=space1
 
 ### PostgreSQL Configuration
 POSTGRES_HOST=localhost
@@ -179,3 +195,10 @@ QDRANT_URL=http://localhost:6333
 ### Redis
 REDIS_URI=redis://localhost:6379
 # REDIS_WORKSPACE=forced_workspace_name
+
+### Memgraph Configuration
+MEMGRAPH_URI=bolt://localhost:7687
+MEMGRAPH_USERNAME=
+MEMGRAPH_PASSWORD=
+MEMGRAPH_DATABASE=memgraph
+# MEMGRAPH_WORKSPACE=forced_workspace_name

examples/rerank_example.py

@@ -0,0 +1,233 @@
+"""
+LightRAG Rerank Integration Example
+
+This example demonstrates how to use rerank functionality with LightRAG
+to improve retrieval quality across different query modes.
+
+Configuration Required:
+1. Set your LLM API key and base URL in llm_model_func()
+2. Set your embedding API key and base URL in embedding_func()
+3. Set your rerank API key and base URL in the rerank configuration
+4. Or use environment variables (.env file):
+   - ENABLE_RERANK=True
+"""
+
+import asyncio
+import os
+import numpy as np
+
+from lightrag import LightRAG, QueryParam
+from lightrag.rerank import custom_rerank, RerankModel
+from lightrag.llm.openai import openai_complete_if_cache, openai_embed
+from lightrag.utils import EmbeddingFunc, setup_logger
+from lightrag.kg.shared_storage import initialize_pipeline_status
+
+# Set up your working directory
+WORKING_DIR = "./test_rerank"
+setup_logger("test_rerank")
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **kwargs
+) -> str:
+    return await openai_complete_if_cache(
+        "gpt-4o-mini",
+        prompt,
+        system_prompt=system_prompt,
+        history_messages=history_messages,
+        api_key="your_llm_api_key_here",
+        base_url="https://api.your-llm-provider.com/v1",
+        **kwargs,
+    )
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    return await openai_embed(
+        texts,
+        model="text-embedding-3-large",
+        api_key="your_embedding_api_key_here",
+        base_url="https://api.your-embedding-provider.com/v1",
+    )
+
+
+async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+    """Custom rerank function with all settings included"""
+    return await custom_rerank(
+        query=query,
+        documents=documents,
+        model="BAAI/bge-reranker-v2-m3",
+        base_url="https://api.your-rerank-provider.com/v1/rerank",
+        api_key="your_rerank_api_key_here",
+        top_k=top_k or 10,  # Default top_k if not provided
+        **kwargs,
+    )
+
+
+async def create_rag_with_rerank():
+    """Create LightRAG instance with rerank configuration"""
+
+    # Get embedding dimension
+    test_embedding = await embedding_func(["test"])
+    embedding_dim = test_embedding.shape[1]
+    print(f"Detected embedding dimension: {embedding_dim}")
+
+    # Method 1: Using custom rerank function
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dim,
+            max_token_size=8192,
+            func=embedding_func,
+        ),
+        # Simplified Rerank Configuration
+        enable_rerank=True,
+        rerank_model_func=my_rerank_func,
+    )
+
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+
+    return rag
+
+
+async def create_rag_with_rerank_model():
+    """Alternative: Create LightRAG instance using RerankModel wrapper"""
+
+    # Get embedding dimension
+    test_embedding = await embedding_func(["test"])
+    embedding_dim = test_embedding.shape[1]
+    print(f"Detected embedding dimension: {embedding_dim}")
+
+    # Method 2: Using RerankModel wrapper
+    rerank_model = RerankModel(
+        rerank_func=custom_rerank,
+        kwargs={
+            "model": "BAAI/bge-reranker-v2-m3",
+            "base_url": "https://api.your-rerank-provider.com/v1/rerank",
+            "api_key": "your_rerank_api_key_here",
+        },
+    )
+
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dim,
+            max_token_size=8192,
+            func=embedding_func,
+        ),
+        enable_rerank=True,
+        rerank_model_func=rerank_model.rerank,
+    )
+
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+
+    return rag
+
+
+async def test_rerank_with_different_topk():
+    """
+    Test rerank functionality with different top_k settings
+    """
+    print("🚀 Setting up LightRAG with Rerank functionality...")
+
+    rag = await create_rag_with_rerank()
+
+    # Insert sample documents
+    sample_docs = [
+        "Reranking improves retrieval quality by re-ordering documents based on relevance.",
+        "LightRAG is a powerful retrieval-augmented generation system with multiple query modes.",
+        "Vector databases enable efficient similarity search in high-dimensional embedding spaces.",
+        "Natural language processing has evolved with large language models and transformers.",
+        "Machine learning algorithms can learn patterns from data without explicit programming.",
+    ]
+
+    print("📄 Inserting sample documents...")
+    await rag.ainsert(sample_docs)
+
+    query = "How does reranking improve retrieval quality?"
+    print(f"\n🔍 Testing query: '{query}'")
+    print("=" * 80)
+
+    # Test different top_k values to show parameter priority
+    top_k_values = [2, 5, 10]
+
+    for top_k in top_k_values:
+        print(f"\n📊 Testing with QueryParam(top_k={top_k}):")
+
+        # Test naive mode with specific top_k
+        result = await rag.aquery(query, param=QueryParam(mode="naive", top_k=top_k))
+        print(f"   Result length: {len(result)} characters")
+        print(f"   Preview: {result[:100]}...")
+
+
+async def test_direct_rerank():
+    """Test rerank function directly"""
+    print("\n🔧 Direct Rerank API Test")
+    print("=" * 40)
+
+    documents = [
+        {"content": "Reranking significantly improves retrieval quality"},
+        {"content": "LightRAG supports advanced reranking capabilities"},
+        {"content": "Vector search finds semantically similar documents"},
+        {"content": "Natural language processing with modern transformers"},
+        {"content": "The quick brown fox jumps over the lazy dog"},
+    ]
+
+    query = "rerank improve quality"
+    print(f"Query: '{query}'")
+    print(f"Documents: {len(documents)}")
+
+    try:
+        reranked_docs = await custom_rerank(
+            query=query,
+            documents=documents,
+            model="BAAI/bge-reranker-v2-m3",
+            base_url="https://api.your-rerank-provider.com/v1/rerank",
+            api_key="your_rerank_api_key_here",
+            top_k=3,
+        )
+
+        print("\n✅ Rerank Results:")
+        for i, doc in enumerate(reranked_docs):
+            score = doc.get("rerank_score", "N/A")
+            content = doc.get("content", "")[:60]
+            print(f"  {i+1}. Score: {score:.4f} | {content}...")
+
+    except Exception as e:
+        print(f"❌ Rerank failed: {e}")
+
+
+async def main():
+    """Main example function"""
+    print("🎯 LightRAG Rerank Integration Example")
+    print("=" * 60)
+
+    try:
+        # Test rerank with different top_k values
+        await test_rerank_with_different_topk()
+
+        # Test direct rerank
+        await test_direct_rerank()
+
+        print("\n✅ Example completed successfully!")
+        print("\n💡 Key Points:")
+        print("   ✓ All rerank configurations are contained within rerank_model_func")
+        print("   ✓ Rerank improves document relevance ordering")
+        print("   ✓ Configure API keys within your rerank function")
+        print("   ✓ Monitor API usage and costs when using rerank services")
+
+    except Exception as e:
+        print(f"\n❌ Example failed: {e}")
+        import traceback
+
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

lightrag/__init__.py

@@ -1,5 +1,5 @@
 from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
 
-__version__ = "1.3.10"
+__version__ = "1.4.0"
 __author__ = "Zirui Guo"
 __url__ = "https://github.com/HKUDS/LightRAG"

lightrag/api/config.py

@@ -165,6 +165,24 @@ def parse_args() -> argparse.Namespace:
         default=get_env_value("TOP_K", 60, int),
         help="Number of most similar results to return (default: from env or 60)",
     )
+    parser.add_argument(
+        "--chunk-top-k",
+        type=int,
+        default=get_env_value("CHUNK_TOP_K", 15, int),
+        help="Number of text chunks to retrieve initially from vector search (default: from env or 15)",
+    )
+    parser.add_argument(
+        "--chunk-rerank-top-k",
+        type=int,
+        default=get_env_value("CHUNK_RERANK_TOP_K", 5, int),
+        help="Number of text chunks to keep after reranking (default: from env or 5)",
+    )
+    parser.add_argument(
+        "--enable-rerank",
+        action="store_true",
+        default=get_env_value("ENABLE_RERANK", False, bool),
+        help="Enable rerank functionality (default: from env or False)",
+    )
     parser.add_argument(
         "--cosine-threshold",
         type=float,
@@ -295,6 +313,11 @@ def parse_args() -> argparse.Namespace:
     args.guest_token_expire_hours = get_env_value("GUEST_TOKEN_EXPIRE_HOURS", 24, int)
     args.jwt_algorithm = get_env_value("JWT_ALGORITHM", "HS256")
 
+    # Rerank model configuration
+    args.rerank_model = get_env_value("RERANK_MODEL", "BAAI/bge-reranker-v2-m3")
+    args.rerank_binding_host = get_env_value("RERANK_BINDING_HOST", None)
+    args.rerank_binding_api_key = get_env_value("RERANK_BINDING_API_KEY", None)
+
     ollama_server_infos.LIGHTRAG_MODEL = args.simulated_model_name
 
     return args

lightrag/api/lightrag_server.py

@@ -291,6 +291,32 @@ def create_app(args):
         ),
     )
 
+    # Configure rerank function if enabled
+    rerank_model_func = None
+    if args.enable_rerank and args.rerank_binding_api_key and args.rerank_binding_host:
+        from lightrag.rerank import custom_rerank
+
+        async def server_rerank_func(
+            query: str, documents: list, top_k: int = None, **kwargs
+        ):
+            """Server rerank function with configuration from environment variables"""
+            return await custom_rerank(
+                query=query,
+                documents=documents,
+                model=args.rerank_model,
+                base_url=args.rerank_binding_host,
+                api_key=args.rerank_binding_api_key,
+                top_k=top_k,
+                **kwargs,
+            )
+
+        rerank_model_func = server_rerank_func
+        logger.info(f"Rerank enabled with model: {args.rerank_model}")
+    elif args.enable_rerank:
+        logger.warning(
+            "Rerank enabled but RERANK_BINDING_API_KEY or RERANK_BINDING_HOST not configured. Rerank will be disabled."
+        )
+
     # Initialize RAG
     if args.llm_binding in ["lollms", "ollama", "openai"]:
         rag = LightRAG(
@@ -324,6 +350,8 @@ def create_app(args):
             },
             enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
             enable_llm_cache=args.enable_llm_cache,
+            enable_rerank=args.enable_rerank,
+            rerank_model_func=rerank_model_func,
             auto_manage_storages_states=False,
             max_parallel_insert=args.max_parallel_insert,
             max_graph_nodes=args.max_graph_nodes,
@@ -352,6 +380,8 @@ def create_app(args):
             },
             enable_llm_cache_for_entity_extract=args.enable_llm_cache_for_extract,
             enable_llm_cache=args.enable_llm_cache,
+            enable_rerank=args.enable_rerank,
+            rerank_model_func=rerank_model_func,
             auto_manage_storages_states=False,
             max_parallel_insert=args.max_parallel_insert,
             max_graph_nodes=args.max_graph_nodes,
@@ -478,6 +508,12 @@ def create_app(args):
                     "enable_llm_cache": args.enable_llm_cache,
                     "workspace": args.workspace,
                     "max_graph_nodes": args.max_graph_nodes,
+                    # Rerank configuration
+                    "enable_rerank": args.enable_rerank,
+                    "rerank_model": args.rerank_model if args.enable_rerank else None,
+                    "rerank_binding_host": args.rerank_binding_host
+                    if args.enable_rerank
+                    else None,
                 },
                 "auth_mode": auth_mode,
                 "pipeline_busy": pipeline_status.get("busy", False),

lightrag/api/routers/query_routes.py

@@ -49,6 +49,18 @@ class QueryRequest(BaseModel):
         description="Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode.",
     )
 
+    chunk_top_k: Optional[int] = Field(
+        ge=1,
+        default=None,
+        description="Number of text chunks to retrieve initially from vector search.",
+    )
+
+    chunk_rerank_top_k: Optional[int] = Field(
+        ge=1,
+        default=None,
+        description="Number of text chunks to keep after reranking.",
+    )
+
     max_token_for_text_unit: Optional[int] = Field(
         gt=1,
         default=None,

lightrag/base.py

@@ -60,7 +60,17 @@ class QueryParam:
     top_k: int = int(os.getenv("TOP_K", "60"))
     """Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
 
-    max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
+    chunk_top_k: int = int(os.getenv("CHUNK_TOP_K", "5"))
+    """Number of text chunks to retrieve initially from vector search.
+    If None, defaults to top_k value.
+    """
+
+    chunk_rerank_top_k: int = int(os.getenv("CHUNK_RERANK_TOP_K", "5"))
+    """Number of text chunks to keep after reranking.
+    If None, keeps all chunks returned from initial retrieval.
+    """
+
+    max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "6000"))
     """Maximum number of tokens allowed for each retrieved text chunk."""
 
     max_token_for_global_context: int = int(
@@ -280,21 +290,6 @@ class BaseKVStorage(StorageNameSpace, ABC):
              False: if the cache drop failed, or the cache mode is not supported
         """
 
-    # async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool:
-    #     """Delete specific cache records from storage by chunk IDs
-
-    #     Importance notes for in-memory storage:
-    #     1. Changes will be persisted to disk during the next index_done_callback
-    #     2. update flags to notify other processes that data persistence is needed
-
-    #     Args:
-    #         chunk_ids (list[str]): List of chunk IDs to be dropped from storage
-
-    #     Returns:
-    #          True: if the cache drop successfully
-    #          False: if the cache drop failed, or the operation is not supported
-    #     """
-
 
 @dataclass
 class BaseGraphStorage(StorageNameSpace, ABC):

lightrag/kg/__init__.py

@@ -15,6 +15,7 @@ STORAGE_IMPLEMENTATIONS = {
             "Neo4JStorage",
             "PGGraphStorage",
             "MongoGraphStorage",
+            "MemgraphStorage",
             # "AGEStorage",
             # "TiDBGraphStorage",
             # "GremlinStorage",
@@ -57,6 +58,7 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
     "NetworkXStorage": [],
     "Neo4JStorage": ["NEO4J_URI", "NEO4J_USERNAME", "NEO4J_PASSWORD"],
     "MongoGraphStorage": [],
+    "MemgraphStorage": ["MEMGRAPH_URI"],
     # "TiDBGraphStorage": ["TIDB_USER", "TIDB_PASSWORD", "TIDB_DATABASE"],
     "AGEStorage": [
         "AGE_POSTGRES_DB",
@@ -111,6 +113,7 @@ STORAGES = {
     "PGDocStatusStorage": ".kg.postgres_impl",
     "FaissVectorDBStorage": ".kg.faiss_impl",
     "QdrantVectorDBStorage": ".kg.qdrant_impl",
+    "MemgraphStorage": ".kg.memgraph_impl",
 }
 
 

lightrag/kg/memgraph_impl.py

@@ -0,0 +1,906 @@
+import os
+from dataclasses import dataclass
+from typing import final
+import configparser
+
+from ..utils import logger
+from ..base import BaseGraphStorage
+from ..types import KnowledgeGraph, KnowledgeGraphNode, KnowledgeGraphEdge
+from ..constants import GRAPH_FIELD_SEP
+import pipmaster as pm
+
+if not pm.is_installed("neo4j"):
+    pm.install("neo4j")
+
+from neo4j import (
+    AsyncGraphDatabase,
+    AsyncManagedTransaction,
+)
+
+from dotenv import load_dotenv
+
+# use the .env that is inside the current folder
+load_dotenv(dotenv_path=".env", override=False)
+
+MAX_GRAPH_NODES = int(os.getenv("MAX_GRAPH_NODES", 1000))
+
+config = configparser.ConfigParser()
+config.read("config.ini", "utf-8")
+
+
+@final
+@dataclass
+class MemgraphStorage(BaseGraphStorage):
+    def __init__(self, namespace, global_config, embedding_func, workspace=None):
+        memgraph_workspace = os.environ.get("MEMGRAPH_WORKSPACE")
+        if memgraph_workspace and memgraph_workspace.strip():
+            workspace = memgraph_workspace
+        super().__init__(
+            namespace=namespace,
+            workspace=workspace or "",
+            global_config=global_config,
+            embedding_func=embedding_func,
+        )
+        self._driver = None
+
+    def _get_workspace_label(self) -> str:
+        """Get workspace label, return 'base' for compatibility when workspace is empty"""
+        workspace = getattr(self, "workspace", None)
+        return workspace if workspace else "base"
+
+    async def initialize(self):
+        URI = os.environ.get(
+            "MEMGRAPH_URI",
+            config.get("memgraph", "uri", fallback="bolt://localhost:7687"),
+        )
+        USERNAME = os.environ.get(
+            "MEMGRAPH_USERNAME", config.get("memgraph", "username", fallback="")
+        )
+        PASSWORD = os.environ.get(
+            "MEMGRAPH_PASSWORD", config.get("memgraph", "password", fallback="")
+        )
+        DATABASE = os.environ.get(
+            "MEMGRAPH_DATABASE", config.get("memgraph", "database", fallback="memgraph")
+        )
+
+        self._driver = AsyncGraphDatabase.driver(
+            URI,
+            auth=(USERNAME, PASSWORD),
+        )
+        self._DATABASE = DATABASE
+        try:
+            async with self._driver.session(database=DATABASE) as session:
+                # Create index for base nodes on entity_id if it doesn't exist
+                try:
+                    workspace_label = self._get_workspace_label()
+                    await session.run(
+                        f"""CREATE INDEX ON :{workspace_label}(entity_id)"""
+                    )
+                    logger.info(
+                        f"Created index on :{workspace_label}(entity_id) in Memgraph."
+                    )
+                except Exception as e:
+                    # Index may already exist, which is not an error
+                    logger.warning(
+                        f"Index creation on :{workspace_label}(entity_id) may have failed or already exists: {e}"
+                    )
+                await session.run("RETURN 1")
+                logger.info(f"Connected to Memgraph at {URI}")
+        except Exception as e:
+            logger.error(f"Failed to connect to Memgraph at {URI}: {e}")
+            raise
+
+    async def finalize(self):
+        if self._driver is not None:
+            await self._driver.close()
+            self._driver = None
+
+    async def __aexit__(self, exc_type, exc, tb):
+        await self.finalize()
+
+    async def index_done_callback(self):
+        # Memgraph handles persistence automatically
+        pass
+
+    async def has_node(self, node_id: str) -> bool:
+        """
+        Check if a node exists in the graph.
+
+        Args:
+            node_id: The ID of the node to check.
+
+        Returns:
+            bool: True if the node exists, False otherwise.
+
+        Raises:
+            Exception: If there is an error checking the node existence.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN count(n) > 0 AS node_exists"
+                result = await session.run(query, entity_id=node_id)
+                single_result = await result.single()
+                await result.consume()  # Ensure result is fully consumed
+                return (
+                    single_result["node_exists"] if single_result is not None else False
+                )
+            except Exception as e:
+                logger.error(f"Error checking node existence for {node_id}: {str(e)}")
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def has_edge(self, source_node_id: str, target_node_id: str) -> bool:
+        """
+        Check if an edge exists between two nodes in the graph.
+
+        Args:
+            source_node_id: The ID of the source node.
+            target_node_id: The ID of the target node.
+
+        Returns:
+            bool: True if the edge exists, False otherwise.
+
+        Raises:
+            Exception: If there is an error checking the edge existence.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = (
+                    f"MATCH (a:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(b:`{workspace_label}` {{entity_id: $target_entity_id}}) "
+                    "RETURN COUNT(r) > 0 AS edgeExists"
+                )
+                result = await session.run(
+                    query,
+                    source_entity_id=source_node_id,
+                    target_entity_id=target_node_id,
+                )  # type: ignore
+                single_result = await result.single()
+                await result.consume()  # Ensure result is fully consumed
+                return (
+                    single_result["edgeExists"] if single_result is not None else False
+                )
+            except Exception as e:
+                logger.error(
+                    f"Error checking edge existence between {source_node_id} and {target_node_id}: {str(e)}"
+                )
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def get_node(self, node_id: str) -> dict[str, str] | None:
+        """Get node by its label identifier, return only node properties
+
+        Args:
+            node_id: The node label to look up
+
+        Returns:
+            dict: Node properties if found
+            None: If node not found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = (
+                    f"MATCH (n:`{workspace_label}` {{entity_id: $entity_id}}) RETURN n"
+                )
+                result = await session.run(query, entity_id=node_id)
+                try:
+                    records = await result.fetch(
+                        2
+                    )  # Get 2 records for duplication check
+
+                    if len(records) > 1:
+                        logger.warning(
+                            f"Multiple nodes found with label '{node_id}'. Using first node."
+                        )
+                    if records:
+                        node = records[0]["n"]
+                        node_dict = dict(node)
+                        # Remove workspace label from labels list if it exists
+                        if "labels" in node_dict:
+                            node_dict["labels"] = [
+                                label
+                                for label in node_dict["labels"]
+                                if label != workspace_label
+                            ]
+                        return node_dict
+                    return None
+                finally:
+                    await result.consume()  # Ensure result is fully consumed
+            except Exception as e:
+                logger.error(f"Error getting node for {node_id}: {str(e)}")
+                raise
+
+    async def node_degree(self, node_id: str) -> int:
+        """Get the degree (number of relationships) of a node with the given label.
+        If multiple nodes have the same label, returns the degree of the first node.
+        If no node is found, returns 0.
+
+        Args:
+            node_id: The label of the node
+
+        Returns:
+            int: The number of relationships the node has, or 0 if no node found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                    MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+                    OPTIONAL MATCH (n)-[r]-()
+                    RETURN COUNT(r) AS degree
+                """
+                result = await session.run(query, entity_id=node_id)
+                try:
+                    record = await result.single()
+
+                    if not record:
+                        logger.warning(f"No node found with label '{node_id}'")
+                        return 0
+
+                    degree = record["degree"]
+                    return degree
+                finally:
+                    await result.consume()  # Ensure result is fully consumed
+            except Exception as e:
+                logger.error(f"Error getting node degree for {node_id}: {str(e)}")
+                raise
+
+    async def get_all_labels(self) -> list[str]:
+        """
+        Get all existing node labels in the database
+        Returns:
+            ["Person", "Company", ...]  # Alphabetically sorted label list
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (n:`{workspace_label}`)
+                WHERE n.entity_id IS NOT NULL
+                RETURN DISTINCT n.entity_id AS label
+                ORDER BY label
+                """
+                result = await session.run(query)
+                labels = []
+                async for record in result:
+                    labels.append(record["label"])
+                await result.consume()
+                return labels
+            except Exception as e:
+                logger.error(f"Error getting all labels: {str(e)}")
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def get_node_edges(self, source_node_id: str) -> list[tuple[str, str]] | None:
+        """Retrieves all edges (relationships) for a particular node identified by its label.
+
+        Args:
+            source_node_id: Label of the node to get edges for
+
+        Returns:
+            list[tuple[str, str]]: List of (source_label, target_label) tuples representing edges
+            None: If no edges found
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            async with self._driver.session(
+                database=self._DATABASE, default_access_mode="READ"
+            ) as session:
+                try:
+                    workspace_label = self._get_workspace_label()
+                    query = f"""MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+                            OPTIONAL MATCH (n)-[r]-(connected:`{workspace_label}`)
+                            WHERE connected.entity_id IS NOT NULL
+                            RETURN n, r, connected"""
+                    results = await session.run(query, entity_id=source_node_id)
+
+                    edges = []
+                    async for record in results:
+                        source_node = record["n"]
+                        connected_node = record["connected"]
+
+                        # Skip if either node is None
+                        if not source_node or not connected_node:
+                            continue
+
+                        source_label = (
+                            source_node.get("entity_id")
+                            if source_node.get("entity_id")
+                            else None
+                        )
+                        target_label = (
+                            connected_node.get("entity_id")
+                            if connected_node.get("entity_id")
+                            else None
+                        )
+
+                        if source_label and target_label:
+                            edges.append((source_label, target_label))
+
+                    await results.consume()  # Ensure results are consumed
+                    return edges
+                except Exception as e:
+                    logger.error(
+                        f"Error getting edges for node {source_node_id}: {str(e)}"
+                    )
+                    await results.consume()  # Ensure results are consumed even on error
+                    raise
+        except Exception as e:
+            logger.error(f"Error in get_node_edges for {source_node_id}: {str(e)}")
+            raise
+
+    async def get_edge(
+        self, source_node_id: str, target_node_id: str
+    ) -> dict[str, str] | None:
+        """Get edge properties between two nodes.
+
+        Args:
+            source_node_id: Label of the source node
+            target_node_id: Label of the target node
+
+        Returns:
+            dict: Edge properties if found, default properties if not found or on error
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (start:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(end:`{workspace_label}` {{entity_id: $target_entity_id}})
+                RETURN properties(r) as edge_properties
+                """
+                result = await session.run(
+                    query,
+                    source_entity_id=source_node_id,
+                    target_entity_id=target_node_id,
+                )
+                records = await result.fetch(2)
+                await result.consume()
+                if records:
+                    edge_result = dict(records[0]["edge_properties"])
+                    for key, default_value in {
+                        "weight": 0.0,
+                        "source_id": None,
+                        "description": None,
+                        "keywords": None,
+                    }.items():
+                        if key not in edge_result:
+                            edge_result[key] = default_value
+                            logger.warning(
+                                f"Edge between {source_node_id} and {target_node_id} is missing property: {key}. Using default value: {default_value}"
+                            )
+                    return edge_result
+                return None
+            except Exception as e:
+                logger.error(
+                    f"Error getting edge between {source_node_id} and {target_node_id}: {str(e)}"
+                )
+                await result.consume()  # Ensure the result is consumed even on error
+                raise
+
+    async def upsert_node(self, node_id: str, node_data: dict[str, str]) -> None:
+        """
+        Upsert a node in the Neo4j database.
+
+        Args:
+            node_id: The unique identifier for the node (used as label)
+            node_data: Dictionary of node properties
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        properties = node_data
+        entity_type = properties["entity_type"]
+        if "entity_id" not in properties:
+            raise ValueError("Neo4j: node properties must contain an 'entity_id' field")
+
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                workspace_label = self._get_workspace_label()
+
+                async def execute_upsert(tx: AsyncManagedTransaction):
+                    query = f"""
+                    MERGE (n:`{workspace_label}` {{entity_id: $entity_id}})
+                    SET n += $properties
+                    SET n:`{entity_type}`
+                    """
+                    result = await tx.run(
+                        query, entity_id=node_id, properties=properties
+                    )
+                    await result.consume()  # Ensure result is fully consumed
+
+                await session.execute_write(execute_upsert)
+        except Exception as e:
+            logger.error(f"Error during upsert: {str(e)}")
+            raise
+
+    async def upsert_edge(
+        self, source_node_id: str, target_node_id: str, edge_data: dict[str, str]
+    ) -> None:
+        """
+        Upsert an edge and its properties between two nodes identified by their labels.
+        Ensures both source and target nodes exist and are unique before creating the edge.
+        Uses entity_id property to uniquely identify nodes.
+
+        Args:
+            source_node_id (str): Label of the source node (used as identifier)
+            target_node_id (str): Label of the target node (used as identifier)
+            edge_data (dict): Dictionary of properties to set on the edge
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            edge_properties = edge_data
+            async with self._driver.session(database=self._DATABASE) as session:
+
+                async def execute_upsert(tx: AsyncManagedTransaction):
+                    workspace_label = self._get_workspace_label()
+                    query = f"""
+                    MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})
+                    WITH source
+                    MATCH (target:`{workspace_label}` {{entity_id: $target_entity_id}})
+                    MERGE (source)-[r:DIRECTED]-(target)
+                    SET r += $properties
+                    RETURN r, source, target
+                    """
+                    result = await tx.run(
+                        query,
+                        source_entity_id=source_node_id,
+                        target_entity_id=target_node_id,
+                        properties=edge_properties,
+                    )
+                    try:
+                        await result.fetch(2)
+                    finally:
+                        await result.consume()  # Ensure result is consumed
+
+                await session.execute_write(execute_upsert)
+        except Exception as e:
+            logger.error(f"Error during edge upsert: {str(e)}")
+            raise
+
+    async def delete_node(self, node_id: str) -> None:
+        """Delete a node with the specified label
+
+        Args:
+            node_id: The label of the node to delete
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        async def _do_delete(tx: AsyncManagedTransaction):
+            workspace_label = self._get_workspace_label()
+            query = f"""
+            MATCH (n:`{workspace_label}` {{entity_id: $entity_id}})
+            DETACH DELETE n
+            """
+            result = await tx.run(query, entity_id=node_id)
+            logger.debug(f"Deleted node with label {node_id}")
+            await result.consume()
+
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                await session.execute_write(_do_delete)
+        except Exception as e:
+            logger.error(f"Error during node deletion: {str(e)}")
+            raise
+
+    async def remove_nodes(self, nodes: list[str]):
+        """Delete multiple nodes
+
+        Args:
+            nodes: List of node labels to be deleted
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        for node in nodes:
+            await self.delete_node(node)
+
+    async def remove_edges(self, edges: list[tuple[str, str]]):
+        """Delete multiple edges
+
+        Args:
+            edges: List of edges to be deleted, each edge is a (source, target) tuple
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        for source, target in edges:
+
+            async def _do_delete_edge(tx: AsyncManagedTransaction):
+                workspace_label = self._get_workspace_label()
+                query = f"""
+                MATCH (source:`{workspace_label}` {{entity_id: $source_entity_id}})-[r]-(target:`{workspace_label}` {{entity_id: $target_entity_id}})
+                DELETE r
+                """
+                result = await tx.run(
+                    query, source_entity_id=source, target_entity_id=target
+                )
+                logger.debug(f"Deleted edge from '{source}' to '{target}'")
+                await result.consume()  # Ensure result is fully consumed
+
+            try:
+                async with self._driver.session(database=self._DATABASE) as session:
+                    await session.execute_write(_do_delete_edge)
+            except Exception as e:
+                logger.error(f"Error during edge deletion: {str(e)}")
+                raise
+
+    async def drop(self) -> dict[str, str]:
+        """Drop all data from the current workspace and clean up resources
+
+        This method will delete all nodes and relationships in the Memgraph database.
+
+        Returns:
+            dict[str, str]: Operation status and message
+            - On success: {"status": "success", "message": "data dropped"}
+            - On failure: {"status": "error", "message": "<error details>"}
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        try:
+            async with self._driver.session(database=self._DATABASE) as session:
+                workspace_label = self._get_workspace_label()
+                query = f"MATCH (n:`{workspace_label}`) DETACH DELETE n"
+                result = await session.run(query)
+                await result.consume()
+                logger.info(
+                    f"Dropped workspace {workspace_label} from Memgraph database {self._DATABASE}"
+                )
+                return {"status": "success", "message": "workspace data dropped"}
+        except Exception as e:
+            logger.error(
+                f"Error dropping workspace {workspace_label} from Memgraph database {self._DATABASE}: {e}"
+            )
+            return {"status": "error", "message": str(e)}
+
+    async def edge_degree(self, src_id: str, tgt_id: str) -> int:
+        """Get the total degree (sum of relationships) of two nodes.
+
+        Args:
+            src_id: Label of the source node
+            tgt_id: Label of the target node
+
+        Returns:
+            int: Sum of the degrees of both nodes
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        src_degree = await self.node_degree(src_id)
+        trg_degree = await self.node_degree(tgt_id)
+
+        # Convert None to 0 for addition
+        src_degree = 0 if src_degree is None else src_degree
+        trg_degree = 0 if trg_degree is None else trg_degree
+
+        degrees = int(src_degree) + int(trg_degree)
+        return degrees
+
+    async def get_nodes_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Get all nodes that are associated with the given chunk_ids.
+
+        Args:
+            chunk_ids: List of chunk IDs to find associated nodes for
+
+        Returns:
+            list[dict]: A list of nodes, where each node is a dictionary of its properties.
+                        An empty list if no matching nodes are found.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            query = f"""
+            UNWIND $chunk_ids AS chunk_id
+            MATCH (n:`{workspace_label}`)
+            WHERE n.source_id IS NOT NULL AND chunk_id IN split(n.source_id, $sep)
+            RETURN DISTINCT n
+            """
+            result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP)
+            nodes = []
+            async for record in result:
+                node = record["n"]
+                node_dict = dict(node)
+                node_dict["id"] = node_dict.get("entity_id")
+                nodes.append(node_dict)
+            await result.consume()
+            return nodes
+
+    async def get_edges_by_chunk_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Get all edges that are associated with the given chunk_ids.
+
+        Args:
+            chunk_ids: List of chunk IDs to find associated edges for
+
+        Returns:
+            list[dict]: A list of edges, where each edge is a dictionary of its properties.
+                        An empty list if no matching edges are found.
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            query = f"""
+            UNWIND $chunk_ids AS chunk_id
+            MATCH (a:`{workspace_label}`)-[r]-(b:`{workspace_label}`)
+            WHERE r.source_id IS NOT NULL AND chunk_id IN split(r.source_id, $sep)
+            WITH a, b, r, a.entity_id AS source_id, b.entity_id AS target_id
+            // Ensure we only return each unique edge once by ordering the source and target
+            WITH a, b, r,
+                 CASE WHEN source_id <= target_id THEN source_id ELSE target_id END AS ordered_source,
+                 CASE WHEN source_id <= target_id THEN target_id ELSE source_id END AS ordered_target
+            RETURN DISTINCT ordered_source AS source, ordered_target AS target, properties(r) AS properties
+            """
+            result = await session.run(query, chunk_ids=chunk_ids, sep=GRAPH_FIELD_SEP)
+            edges = []
+            async for record in result:
+                edge_properties = record["properties"]
+                edge_properties["source"] = record["source"]
+                edge_properties["target"] = record["target"]
+                edges.append(edge_properties)
+            await result.consume()
+            return edges
+
+    async def get_knowledge_graph(
+        self,
+        node_label: str,
+        max_depth: int = 3,
+        max_nodes: int = MAX_GRAPH_NODES,
+    ) -> KnowledgeGraph:
+        """
+        Retrieve a connected subgraph of nodes where the label includes the specified `node_label`.
+
+        Args:
+            node_label: Label of the starting node, * means all nodes
+            max_depth: Maximum depth of the subgraph, Defaults to 3
+            max_nodes: Maxiumu nodes to return by BFS, Defaults to 1000
+
+        Returns:
+            KnowledgeGraph object containing nodes and edges, with an is_truncated flag
+            indicating whether the graph was truncated due to max_nodes limit
+
+        Raises:
+            Exception: If there is an error executing the query
+        """
+        if self._driver is None:
+            raise RuntimeError(
+                "Memgraph driver is not initialized. Call 'await initialize()' first."
+            )
+
+        result = KnowledgeGraph()
+        seen_nodes = set()
+        seen_edges = set()
+        workspace_label = self._get_workspace_label()
+        async with self._driver.session(
+            database=self._DATABASE, default_access_mode="READ"
+        ) as session:
+            try:
+                if node_label == "*":
+                    # First check if database has any nodes
+                    count_query = "MATCH (n) RETURN count(n) as total"
+                    count_result = None
+                    total_count = 0
+                    try:
+                        count_result = await session.run(count_query)
+                        count_record = await count_result.single()
+                        if count_record:
+                            total_count = count_record["total"]
+                            if total_count == 0:
+                                logger.debug("No nodes found in database")
+                                return result
+                            if total_count > max_nodes:
+                                result.is_truncated = True
+                                logger.info(
+                                    f"Graph truncated: {total_count} nodes found, limited to {max_nodes}"
+                                )
+                    finally:
+                        if count_result:
+                            await count_result.consume()
+
+                    # Run the main query to get nodes with highest degree
+                    main_query = f"""
+                    MATCH (n:`{workspace_label}`)
+                    OPTIONAL MATCH (n)-[r]-()
+                    WITH n, COALESCE(count(r), 0) AS degree
+                    ORDER BY degree DESC
+                    LIMIT $max_nodes
+                    WITH collect(n) AS kept_nodes
+                    MATCH (a)-[r]-(b)
+                    WHERE a IN kept_nodes AND b IN kept_nodes
+                    RETURN [node IN kept_nodes | {{node: node}}] AS node_info,
+                        collect(DISTINCT r) AS relationships
+                    """
+                    result_set = None
+                    try:
+                        result_set = await session.run(
+                            main_query, {"max_nodes": max_nodes}
+                        )
+                        record = await result_set.single()
+                        if not record:
+                            logger.debug("No record returned from main query")
+                            return result
+                    finally:
+                        if result_set:
+                            await result_set.consume()
+
+                else:
+                    bfs_query = f"""
+                    MATCH (start:`{workspace_label}`)
+                    WHERE start.entity_id = $entity_id
+                    WITH start
+                    CALL {{
+                        WITH start
+                        MATCH path = (start)-[*0..{max_depth}]-(node)
+                        WITH nodes(path) AS path_nodes, relationships(path) AS path_rels
+                        UNWIND path_nodes AS n
+                        WITH collect(DISTINCT n) AS all_nodes, collect(DISTINCT path_rels) AS all_rel_lists
+                        WITH all_nodes, reduce(r = [], x IN all_rel_lists | r + x) AS all_rels
+                        RETURN all_nodes, all_rels
+                    }}
+                    WITH all_nodes AS nodes, all_rels AS relationships, size(all_nodes) AS total_nodes
+                    WITH
+                    CASE
+                        WHEN total_nodes <= {max_nodes} THEN nodes
+                        ELSE nodes[0..{max_nodes}]
+                    END AS limited_nodes,
+                    relationships,
+                    total_nodes,
+                    total_nodes > {max_nodes} AS is_truncated
+                    RETURN
+                    [node IN limited_nodes | {{node: node}}] AS node_info,
+                    relationships,
+                    total_nodes,
+                    is_truncated
+                    """
+                    result_set = None
+                    try:
+                        result_set = await session.run(
+                            bfs_query,
+                            {
+                                "entity_id": node_label,
+                            },
+                        )
+                        record = await result_set.single()
+                        if not record:
+                            logger.debug(f"No nodes found for entity_id: {node_label}")
+                            return result
+
+                        # Check if the query indicates truncation
+                        if "is_truncated" in record and record["is_truncated"]:
+                            result.is_truncated = True
+                            logger.info(
+                                f"Graph truncated: breadth-first search limited to {max_nodes} nodes"
+                            )
+
+                    finally:
+                        if result_set:
+                            await result_set.consume()
+
+                # Process the record if it exists
+                if record and record["node_info"]:
+                    for node_info in record["node_info"]:
+                        node = node_info["node"]
+                        node_id = node.id
+                        if node_id not in seen_nodes:
+                            seen_nodes.add(node_id)
+                            result.nodes.append(
+                                KnowledgeGraphNode(
+                                    id=f"{node_id}",
+                                    labels=[node.get("entity_id")],
+                                    properties=dict(node),
+                                )
+                            )
+
+                    for rel in record["relationships"]:
+                        edge_id = rel.id
+                        if edge_id not in seen_edges:
+                            seen_edges.add(edge_id)
+                            start = rel.start_node
+                            end = rel.end_node
+                            result.edges.append(
+                                KnowledgeGraphEdge(
+                                    id=f"{edge_id}",
+                                    type=rel.type,
+                                    source=f"{start.id}",
+                                    target=f"{end.id}",
+                                    properties=dict(rel),
+                                )
+                            )
+
+                logger.info(
+                    f"Subgraph query successful | Node count: {len(result.nodes)} | Edge count: {len(result.edges)}"
+                )
+
+            except Exception as e:
+                logger.error(f"Error getting knowledge graph: {str(e)}")
+                # Return empty but properly initialized KnowledgeGraph on error
+                return KnowledgeGraph()
+
+        return result

lightrag/lightrag.py

@@ -240,6 +240,17 @@ class LightRAG:
     llm_model_kwargs: dict[str, Any] = field(default_factory=dict)
     """Additional keyword arguments passed to the LLM model function."""
 
+    # Rerank Configuration
+    # ---
+
+    enable_rerank: bool = field(
+        default=bool(os.getenv("ENABLE_RERANK", "False").lower() == "true")
+    )
+    """Enable reranking for improved retrieval quality. Defaults to False."""
+
+    rerank_model_func: Callable[..., object] | None = field(default=None)
+    """Function for reranking retrieved documents. All rerank configurations (model name, API keys, top_k, etc.) should be included in this function. Optional."""
+
     # Storage
     # ---
 
@@ -447,6 +458,14 @@ class LightRAG:
             )
         )
 
+        # Init Rerank
+        if self.enable_rerank and self.rerank_model_func:
+            logger.info("Rerank model initialized for improved retrieval quality")
+        elif self.enable_rerank and not self.rerank_model_func:
+            logger.warning(
+                "Rerank is enabled but no rerank_model_func provided. Reranking will be skipped."
+            )
+
         self._storages_status = StoragesStatus.CREATED
 
         if self.auto_manage_storages_states:
@@ -900,9 +919,15 @@ class LightRAG:
                 # Get first document's file path and total count for job name
                 first_doc_id, first_doc = next(iter(to_process_docs.items()))
                 first_doc_path = first_doc.file_path
-                path_prefix = first_doc_path[:20] + (
-                    "..." if len(first_doc_path) > 20 else ""
-                )
+
+                # Handle cases where first_doc_path is None
+                if first_doc_path:
+                    path_prefix = first_doc_path[:20] + (
+                        "..." if len(first_doc_path) > 20 else ""
+                    )
+                else:
+                    path_prefix = "unknown_source"
+
                 total_files = len(to_process_docs)
                 job_name = f"{path_prefix}[{total_files} files]"
                 pipeline_status["job_name"] = job_name

lightrag/operate.py

@@ -1527,6 +1527,7 @@ async def kg_query(
 
     # Build context
     context = await _build_query_context(
+        query,
         ll_keywords_str,
         hl_keywords_str,
         knowledge_graph_inst,
@@ -1746,84 +1747,52 @@ async def _get_vector_context(
     query: str,
     chunks_vdb: BaseVectorStorage,
     query_param: QueryParam,
-    tokenizer: Tokenizer,
-) -> tuple[list, list, list] | None:
+) -> list[dict]:
     """
-    Retrieve vector context from the vector database.
+    Retrieve text chunks from the vector database without reranking or truncation.
 
-    This function performs vector search to find relevant text chunks for a query,
-    formats them with file path and creation time information.
+    This function performs vector search to find relevant text chunks for a query.
+    Reranking and truncation will be handled later in the unified processing.
 
     Args:
         query: The query string to search for
         chunks_vdb: Vector database containing document chunks
-        query_param: Query parameters including top_k and ids
-        tokenizer: Tokenizer for counting tokens
+        query_param: Query parameters including chunk_top_k and ids
 
     Returns:
-        Tuple (empty_entities, empty_relations, text_units) for combine_contexts,
-        compatible with _get_edge_data and _get_node_data format
+        List of text chunks with metadata
     """
     try:
-        results = await chunks_vdb.query(
-            query, top_k=query_param.top_k, ids=query_param.ids
-        )
+        # Use chunk_top_k if specified, otherwise fall back to top_k
+        search_top_k = query_param.chunk_top_k or query_param.top_k
+
+        results = await chunks_vdb.query(query, top_k=search_top_k, ids=query_param.ids)
         if not results:
-            return [], [], []
+            return []
 
         valid_chunks = []
         for result in results:
             if "content" in result:
-                # Directly use content from chunks_vdb.query result
-                chunk_with_time = {
+                chunk_with_metadata = {
                     "content": result["content"],
                     "created_at": result.get("created_at", None),
                     "file_path": result.get("file_path", "unknown_source"),
+                    "source_type": "vector",  # Mark the source type
                 }
-                valid_chunks.append(chunk_with_time)
-
-        if not valid_chunks:
-            return [], [], []
-
-        maybe_trun_chunks = truncate_list_by_token_size(
-            valid_chunks,
-            key=lambda x: x["content"],
-            max_token_size=query_param.max_token_for_text_unit,
-            tokenizer=tokenizer,
-        )
+                valid_chunks.append(chunk_with_metadata)
 
-        logger.debug(
-            f"Truncate chunks from {len(valid_chunks)} to {len(maybe_trun_chunks)} (max tokens:{query_param.max_token_for_text_unit})"
-        )
         logger.info(
-            f"Query chunks: {len(maybe_trun_chunks)} chunks, top_k: {query_param.top_k}"
+            f"Naive query: {len(valid_chunks)} chunks (chunk_top_k: {search_top_k})"
         )
+        return valid_chunks
 
-        if not maybe_trun_chunks:
-            return [], [], []
-
-        # Create empty entities and relations contexts
-        entities_context = []
-        relations_context = []
-
-        # Create text_units_context directly as a list of dictionaries
-        text_units_context = []
-        for i, chunk in enumerate(maybe_trun_chunks):
-            text_units_context.append(
-                {
-                    "id": i + 1,
-                    "content": chunk["content"],
-                    "file_path": chunk["file_path"],
-                }
-            )
-
-        return entities_context, relations_context, text_units_context
     except Exception as e:
         logger.error(f"Error in _get_vector_context: {e}")
-        return [], [], []
+        return []
 
 
 async def _build_query_context(
+    query: str,
     ll_keywords: str,
     hl_keywords: str,
     knowledge_graph_inst: BaseGraphStorage,
@@ -1831,27 +1800,36 @@ async def _build_query_context(
     relationships_vdb: BaseVectorStorage,
     text_chunks_db: BaseKVStorage,
     query_param: QueryParam,
-    chunks_vdb: BaseVectorStorage = None,  # Add chunks_vdb parameter for mix mode
+    chunks_vdb: BaseVectorStorage = None,
 ):
     logger.info(f"Process {os.getpid()} building query context...")
 
-    # Handle local and global modes as before
+    # Collect all chunks from different sources
+    all_chunks = []
+    entities_context = []
+    relations_context = []
+
+    # Handle local and global modes
     if query_param.mode == "local":
-        entities_context, relations_context, text_units_context = await _get_node_data(
+        entities_context, relations_context, entity_chunks = await _get_node_data(
             ll_keywords,
             knowledge_graph_inst,
             entities_vdb,
             text_chunks_db,
             query_param,
         )
+        all_chunks.extend(entity_chunks)
+
     elif query_param.mode == "global":
-        entities_context, relations_context, text_units_context = await _get_edge_data(
+        entities_context, relations_context, relationship_chunks = await _get_edge_data(
             hl_keywords,
             knowledge_graph_inst,
             relationships_vdb,
             text_chunks_db,
             query_param,
         )
+        all_chunks.extend(relationship_chunks)
+
     else:  # hybrid or mix mode
         ll_data = await _get_node_data(
             ll_keywords,
@@ -1868,61 +1846,58 @@ async def _build_query_context(
             query_param,
         )
 
-        (
-            ll_entities_context,
-            ll_relations_context,
-            ll_text_units_context,
-        ) = ll_data
-
-        (
-            hl_entities_context,
-            hl_relations_context,
-            hl_text_units_context,
-        ) = hl_data
-
-        # Initialize vector data with empty lists
-        vector_entities_context, vector_relations_context, vector_text_units_context = (
-            [],
-            [],
-            [],
-        )
+        (ll_entities_context, ll_relations_context, ll_chunks) = ll_data
+        (hl_entities_context, hl_relations_context, hl_chunks) = hl_data
 
-        # Only get vector data if in mix mode
-        if query_param.mode == "mix" and hasattr(query_param, "original_query"):
-            # Get tokenizer from text_chunks_db
-            tokenizer = text_chunks_db.global_config.get("tokenizer")
+        # Collect chunks from entity and relationship sources
+        all_chunks.extend(ll_chunks)
+        all_chunks.extend(hl_chunks)
 
-            # Get vector context in triple format
-            vector_data = await _get_vector_context(
-                query_param.original_query,  # We need to pass the original query
+        # Get vector chunks if in mix mode
+        if query_param.mode == "mix" and chunks_vdb:
+            vector_chunks = await _get_vector_context(
+                query,
                 chunks_vdb,
                 query_param,
-                tokenizer,
             )
+            all_chunks.extend(vector_chunks)
 
-            # If vector_data is not None, unpack it
-            if vector_data is not None:
-                (
-                    vector_entities_context,
-                    vector_relations_context,
-                    vector_text_units_context,
-                ) = vector_data
-
-        # Combine and deduplicate the entities, relationships, and sources
+        # Combine entities and relations contexts
         entities_context = process_combine_contexts(
-            hl_entities_context, ll_entities_context, vector_entities_context
+            hl_entities_context, ll_entities_context
         )
         relations_context = process_combine_contexts(
-            hl_relations_context, ll_relations_context, vector_relations_context
+            hl_relations_context, ll_relations_context
         )
-        text_units_context = process_combine_contexts(
-            hl_text_units_context, ll_text_units_context, vector_text_units_context
+
+    # Process all chunks uniformly: deduplication, reranking, and token truncation
+    processed_chunks = await process_chunks_unified(
+        query=query,
+        chunks=all_chunks,
+        query_param=query_param,
+        global_config=text_chunks_db.global_config,
+        source_type="mixed",
+    )
+
+    # Build final text_units_context from processed chunks
+    text_units_context = []
+    for i, chunk in enumerate(processed_chunks):
+        text_units_context.append(
+            {
+                "id": i + 1,
+                "content": chunk["content"],
+                "file_path": chunk.get("file_path", "unknown_source"),
+            }
         )
+
+    logger.info(
+        f"Final context: {len(entities_context)} entities, {len(relations_context)} relations, {len(text_units_context)} chunks"
+    )
+
     # not necessary to use LLM to generate a response
     if not entities_context and not relations_context:
         return None
 
-    # 转换为 JSON 字符串
     entities_str = json.dumps(entities_context, ensure_ascii=False)
     relations_str = json.dumps(relations_context, ensure_ascii=False)
     text_units_str = json.dumps(text_units_context, ensure_ascii=False)
@@ -2069,16 +2044,7 @@ async def _get_node_data(
             }
         )
 
-    text_units_context = []
-    for i, t in enumerate(use_text_units):
-        text_units_context.append(
-            {
-                "id": i + 1,
-                "content": t["content"],
-                "file_path": t.get("file_path", "unknown_source"),
-            }
-        )
-    return entities_context, relations_context, text_units_context
+    return entities_context, relations_context, use_text_units
 
 
 async def _find_most_related_text_unit_from_entities(
@@ -2167,23 +2133,21 @@ async def _find_most_related_text_unit_from_entities(
         logger.warning("No valid text units found")
         return []
 
-    tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer")
+    # Sort by relation counts and order, but don't truncate
     all_text_units = sorted(
         all_text_units, key=lambda x: (x["order"], -x["relation_counts"])
     )
-    all_text_units = truncate_list_by_token_size(
-        all_text_units,
-        key=lambda x: x["data"]["content"],
-        max_token_size=query_param.max_token_for_text_unit,
-        tokenizer=tokenizer,
-    )
 
-    logger.debug(
-        f"Truncate chunks from {len(all_text_units_lookup)} to {len(all_text_units)} (max tokens:{query_param.max_token_for_text_unit})"
-    )
+    logger.debug(f"Found {len(all_text_units)} entity-related chunks")
 
-    all_text_units = [t["data"] for t in all_text_units]
-    return all_text_units
+    # Add source type marking and return chunk data
+    result_chunks = []
+    for t in all_text_units:
+        chunk_data = t["data"].copy()
+        chunk_data["source_type"] = "entity"
+        result_chunks.append(chunk_data)
+
+    return result_chunks
 
 
 async def _find_most_related_edges_from_entities(
@@ -2485,21 +2449,16 @@ async def _find_related_text_unit_from_relationships(
         logger.warning("No valid text chunks after filtering")
         return []
 
-    tokenizer: Tokenizer = text_chunks_db.global_config.get("tokenizer")
-    truncated_text_units = truncate_list_by_token_size(
-        valid_text_units,
-        key=lambda x: x["data"]["content"],
-        max_token_size=query_param.max_token_for_text_unit,
-        tokenizer=tokenizer,
-    )
-
-    logger.debug(
-        f"Truncate chunks from {len(valid_text_units)} to {len(truncated_text_units)} (max tokens:{query_param.max_token_for_text_unit})"
-    )
+    logger.debug(f"Found {len(valid_text_units)} relationship-related chunks")
 
-    all_text_units: list[TextChunkSchema] = [t["data"] for t in truncated_text_units]
+    # Add source type marking and return chunk data
+    result_chunks = []
+    for t in valid_text_units:
+        chunk_data = t["data"].copy()
+        chunk_data["source_type"] = "relationship"
+        result_chunks.append(chunk_data)
 
-    return all_text_units
+    return result_chunks
 
 
 async def naive_query(
@@ -2527,13 +2486,33 @@ async def naive_query(
 
     tokenizer: Tokenizer = global_config["tokenizer"]
 
-    _, _, text_units_context = await _get_vector_context(
-        query, chunks_vdb, query_param, tokenizer
-    )
+    chunks = await _get_vector_context(query, chunks_vdb, query_param)
 
-    if text_units_context is None or len(text_units_context) == 0:
+    if chunks is None or len(chunks) == 0:
         return PROMPTS["fail_response"]
 
+    # Process chunks using unified processing
+    processed_chunks = await process_chunks_unified(
+        query=query,
+        chunks=chunks,
+        query_param=query_param,
+        global_config=global_config,
+        source_type="vector",
+    )
+
+    logger.info(f"Final context: {len(processed_chunks)} chunks")
+
+    # Build text_units_context from processed chunks
+    text_units_context = []
+    for i, chunk in enumerate(processed_chunks):
+        text_units_context.append(
+            {
+                "id": i + 1,
+                "content": chunk["content"],
+                "file_path": chunk.get("file_path", "unknown_source"),
+            }
+        )
+
     text_units_str = json.dumps(text_units_context, ensure_ascii=False)
     if query_param.only_need_context:
         return f"""
@@ -2658,6 +2637,7 @@ async def kg_query_with_keywords(
     hl_keywords_str = ", ".join(hl_keywords) if hl_keywords else ""
 
     context = await _build_query_context(
+        query,
         ll_keywords_str,
         hl_keywords_str,
         knowledge_graph_inst,
@@ -2780,8 +2760,6 @@ async def query_with_keywords(
         f"{prompt}\n\n### Keywords\n\n{keywords_str}\n\n### Query\n\n{query}"
     )
 
-    param.original_query = query
-
     # Use appropriate query method based on mode
     if param.mode in ["local", "global", "hybrid", "mix"]:
         return await kg_query_with_keywords(
@@ -2808,3 +2786,131 @@ async def query_with_keywords(
         )
     else:
         raise ValueError(f"Unknown mode {param.mode}")
+
+
+async def apply_rerank_if_enabled(
+    query: str,
+    retrieved_docs: list[dict],
+    global_config: dict,
+    top_k: int = None,
+) -> list[dict]:
+    """
+    Apply reranking to retrieved documents if rerank is enabled.
+
+    Args:
+        query: The search query
+        retrieved_docs: List of retrieved documents
+        global_config: Global configuration containing rerank settings
+        top_k: Number of top documents to return after reranking
+
+    Returns:
+        Reranked documents if rerank is enabled, otherwise original documents
+    """
+    if not global_config.get("enable_rerank", False) or not retrieved_docs:
+        return retrieved_docs
+
+    rerank_func = global_config.get("rerank_model_func")
+    if not rerank_func:
+        logger.debug(
+            "Rerank is enabled but no rerank function provided, skipping rerank"
+        )
+        return retrieved_docs
+
+    try:
+        logger.debug(
+            f"Applying rerank to {len(retrieved_docs)} documents, returning top {top_k}"
+        )
+
+        # Apply reranking - let rerank_model_func handle top_k internally
+        reranked_docs = await rerank_func(
+            query=query,
+            documents=retrieved_docs,
+            top_k=top_k,
+        )
+        if reranked_docs and len(reranked_docs) > 0:
+            if len(reranked_docs) > top_k:
+                reranked_docs = reranked_docs[:top_k]
+            logger.info(
+                f"Successfully reranked {len(retrieved_docs)} documents to {len(reranked_docs)}"
+            )
+            return reranked_docs
+        else:
+            logger.warning("Rerank returned empty results, using original documents")
+            return retrieved_docs
+
+    except Exception as e:
+        logger.error(f"Error during reranking: {e}, using original documents")
+        return retrieved_docs
+
+
+async def process_chunks_unified(
+    query: str,
+    chunks: list[dict],
+    query_param: QueryParam,
+    global_config: dict,
+    source_type: str = "mixed",
+) -> list[dict]:
+    """
+    Unified processing for text chunks: deduplication, chunk_top_k limiting, reranking, and token truncation.
+
+    Args:
+        query: Search query for reranking
+        chunks: List of text chunks to process
+        query_param: Query parameters containing configuration
+        global_config: Global configuration dictionary
+        source_type: Source type for logging ("vector", "entity", "relationship", "mixed")
+
+    Returns:
+        Processed and filtered list of text chunks
+    """
+    if not chunks:
+        return []
+
+    # 1. Deduplication based on content
+    seen_content = set()
+    unique_chunks = []
+    for chunk in chunks:
+        content = chunk.get("content", "")
+        if content and content not in seen_content:
+            seen_content.add(content)
+            unique_chunks.append(chunk)
+
+    logger.debug(
+        f"Deduplication: {len(unique_chunks)} chunks (original: {len(chunks)})"
+    )
+
+    # 2. Apply reranking if enabled and query is provided
+    if global_config.get("enable_rerank", False) and query and unique_chunks:
+        rerank_top_k = query_param.chunk_rerank_top_k or len(unique_chunks)
+        unique_chunks = await apply_rerank_if_enabled(
+            query=query,
+            retrieved_docs=unique_chunks,
+            global_config=global_config,
+            top_k=rerank_top_k,
+        )
+        logger.debug(f"Rerank: {len(unique_chunks)} chunks (source: {source_type})")
+
+    # 3. Apply chunk_top_k limiting if specified
+    if query_param.chunk_top_k is not None and query_param.chunk_top_k > 0:
+        if len(unique_chunks) > query_param.chunk_top_k:
+            unique_chunks = unique_chunks[: query_param.chunk_top_k]
+            logger.debug(
+                f"Chunk top-k limiting: kept {len(unique_chunks)} chunks (chunk_top_k={query_param.chunk_top_k})"
+            )
+
+    # 4. Token-based final truncation
+    tokenizer = global_config.get("tokenizer")
+    if tokenizer and unique_chunks:
+        original_count = len(unique_chunks)
+        unique_chunks = truncate_list_by_token_size(
+            unique_chunks,
+            key=lambda x: x.get("content", ""),
+            max_token_size=query_param.max_token_for_text_unit,
+            tokenizer=tokenizer,
+        )
+        logger.debug(
+            f"Token truncation: {len(unique_chunks)} chunks from {original_count} "
+            f"(max tokens: {query_param.max_token_for_text_unit}, source: {source_type})"
+        )
+
+    return unique_chunks

lightrag/rerank.py

@@ -0,0 +1,321 @@
+from __future__ import annotations
+
+import os
+import aiohttp
+from typing import Callable, Any, List, Dict, Optional
+from pydantic import BaseModel, Field
+
+from .utils import logger
+
+
+class RerankModel(BaseModel):
+    """
+    Pydantic model class for defining a custom rerank model.
+
+    This class provides a convenient wrapper for rerank functions, allowing you to
+    encapsulate all rerank configurations (API keys, model settings, etc.) in one place.
+
+    Attributes:
+        rerank_func (Callable[[Any], List[Dict]]): A callable function that reranks documents.
+            The function should take query and documents as input and return reranked results.
+        kwargs (Dict[str, Any]): A dictionary that contains the arguments to pass to the callable function.
+            This should include all necessary configurations such as model name, API key, base_url, etc.
+
+    Example usage:
+        Rerank model example with Jina:
+        ```python
+        rerank_model = RerankModel(
+            rerank_func=jina_rerank,
+            kwargs={
+                "model": "BAAI/bge-reranker-v2-m3",
+                "api_key": "your_api_key_here",
+                "base_url": "https://api.jina.ai/v1/rerank"
+            }
+        )
+
+        # Use in LightRAG
+        rag = LightRAG(
+            enable_rerank=True,
+            rerank_model_func=rerank_model.rerank,
+            # ... other configurations
+        )
+        ```
+
+        Or define a custom function directly:
+        ```python
+        async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+            return await jina_rerank(
+                query=query,
+                documents=documents,
+                model="BAAI/bge-reranker-v2-m3",
+                api_key="your_api_key_here",
+                top_k=top_k or 10,
+                **kwargs
+            )
+
+        rag = LightRAG(
+            enable_rerank=True,
+            rerank_model_func=my_rerank_func,
+            # ... other configurations
+        )
+        ```
+    """
+
+    rerank_func: Callable[[Any], List[Dict]]
+    kwargs: Dict[str, Any] = Field(default_factory=dict)
+
+    async def rerank(
+        self,
+        query: str,
+        documents: List[Dict[str, Any]],
+        top_k: Optional[int] = None,
+        **extra_kwargs,
+    ) -> List[Dict[str, Any]]:
+        """Rerank documents using the configured model function."""
+        # Merge extra kwargs with model kwargs
+        kwargs = {**self.kwargs, **extra_kwargs}
+        return await self.rerank_func(
+            query=query, documents=documents, top_k=top_k, **kwargs
+        )
+
+
+class MultiRerankModel(BaseModel):
+    """Multiple rerank models for different modes/scenarios."""
+
+    # Primary rerank model (used if mode-specific models are not defined)
+    rerank_model: Optional[RerankModel] = None
+
+    # Mode-specific rerank models
+    entity_rerank_model: Optional[RerankModel] = None
+    relation_rerank_model: Optional[RerankModel] = None
+    chunk_rerank_model: Optional[RerankModel] = None
+
+    async def rerank(
+        self,
+        query: str,
+        documents: List[Dict[str, Any]],
+        mode: str = "default",
+        top_k: Optional[int] = None,
+        **kwargs,
+    ) -> List[Dict[str, Any]]:
+        """Rerank using the appropriate model based on mode."""
+
+        # Select model based on mode
+        if mode == "entity" and self.entity_rerank_model:
+            model = self.entity_rerank_model
+        elif mode == "relation" and self.relation_rerank_model:
+            model = self.relation_rerank_model
+        elif mode == "chunk" and self.chunk_rerank_model:
+            model = self.chunk_rerank_model
+        elif self.rerank_model:
+            model = self.rerank_model
+        else:
+            logger.warning(f"No rerank model available for mode: {mode}")
+            return documents
+
+        return await model.rerank(query, documents, top_k, **kwargs)
+
+
+async def generic_rerank_api(
+    query: str,
+    documents: List[Dict[str, Any]],
+    model: str,
+    base_url: str,
+    api_key: str,
+    top_k: Optional[int] = None,
+    **kwargs,
+) -> List[Dict[str, Any]]:
+    """
+    Generic rerank function that works with Jina/Cohere compatible APIs.
+
+    Args:
+        query: The search query
+        documents: List of documents to rerank
+        model: Model identifier
+        base_url: API endpoint URL
+        api_key: API authentication key
+        top_k: Number of top results to return
+        **kwargs: Additional API-specific parameters
+
+    Returns:
+        List of reranked documents with relevance scores
+    """
+    if not api_key:
+        logger.warning("No API key provided for rerank service")
+        return documents
+
+    if not documents:
+        return documents
+
+    # Prepare documents for reranking - handle both text and dict formats
+    prepared_docs = []
+    for doc in documents:
+        if isinstance(doc, dict):
+            # Use 'content' field if available, otherwise use 'text' or convert to string
+            text = doc.get("content") or doc.get("text") or str(doc)
+        else:
+            text = str(doc)
+        prepared_docs.append(text)
+
+    # Prepare request
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+
+    data = {"model": model, "query": query, "documents": prepared_docs, **kwargs}
+
+    if top_k is not None:
+        data["top_k"] = min(top_k, len(prepared_docs))
+
+    try:
+        async with aiohttp.ClientSession() as session:
+            async with session.post(base_url, headers=headers, json=data) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    logger.error(f"Rerank API error {response.status}: {error_text}")
+                    return documents
+
+                result = await response.json()
+
+                # Extract reranked results
+                if "results" in result:
+                    # Standard format: results contain index and relevance_score
+                    reranked_docs = []
+                    for item in result["results"]:
+                        if "index" in item:
+                            doc_idx = item["index"]
+                            if 0 <= doc_idx < len(documents):
+                                reranked_doc = documents[doc_idx].copy()
+                                if "relevance_score" in item:
+                                    reranked_doc["rerank_score"] = item[
+                                        "relevance_score"
+                                    ]
+                                reranked_docs.append(reranked_doc)
+                    return reranked_docs
+                else:
+                    logger.warning("Unexpected rerank API response format")
+                    return documents
+
+    except Exception as e:
+        logger.error(f"Error during reranking: {e}")
+        return documents
+
+
+async def jina_rerank(
+    query: str,
+    documents: List[Dict[str, Any]],
+    model: str = "BAAI/bge-reranker-v2-m3",
+    top_k: Optional[int] = None,
+    base_url: str = "https://api.jina.ai/v1/rerank",
+    api_key: Optional[str] = None,
+    **kwargs,
+) -> List[Dict[str, Any]]:
+    """
+    Rerank documents using Jina AI API.
+
+    Args:
+        query: The search query
+        documents: List of documents to rerank
+        model: Jina rerank model name
+        top_k: Number of top results to return
+        base_url: Jina API endpoint
+        api_key: Jina API key
+        **kwargs: Additional parameters
+
+    Returns:
+        List of reranked documents with relevance scores
+    """
+    if api_key is None:
+        api_key = os.getenv("JINA_API_KEY") or os.getenv("RERANK_API_KEY")
+
+    return await generic_rerank_api(
+        query=query,
+        documents=documents,
+        model=model,
+        base_url=base_url,
+        api_key=api_key,
+        top_k=top_k,
+        **kwargs,
+    )
+
+
+async def cohere_rerank(
+    query: str,
+    documents: List[Dict[str, Any]],
+    model: str = "rerank-english-v2.0",
+    top_k: Optional[int] = None,
+    base_url: str = "https://api.cohere.ai/v1/rerank",
+    api_key: Optional[str] = None,
+    **kwargs,
+) -> List[Dict[str, Any]]:
+    """
+    Rerank documents using Cohere API.
+
+    Args:
+        query: The search query
+        documents: List of documents to rerank
+        model: Cohere rerank model name
+        top_k: Number of top results to return
+        base_url: Cohere API endpoint
+        api_key: Cohere API key
+        **kwargs: Additional parameters
+
+    Returns:
+        List of reranked documents with relevance scores
+    """
+    if api_key is None:
+        api_key = os.getenv("COHERE_API_KEY") or os.getenv("RERANK_API_KEY")
+
+    return await generic_rerank_api(
+        query=query,
+        documents=documents,
+        model=model,
+        base_url=base_url,
+        api_key=api_key,
+        top_k=top_k,
+        **kwargs,
+    )
+
+
+# Convenience function for custom API endpoints
+async def custom_rerank(
+    query: str,
+    documents: List[Dict[str, Any]],
+    model: str,
+    base_url: str,
+    api_key: str,
+    top_k: Optional[int] = None,
+    **kwargs,
+) -> List[Dict[str, Any]]:
+    """
+    Rerank documents using a custom API endpoint.
+    This is useful for self-hosted or custom rerank services.
+    """
+    return await generic_rerank_api(
+        query=query,
+        documents=documents,
+        model=model,
+        base_url=base_url,
+        api_key=api_key,
+        top_k=top_k,
+        **kwargs,
+    )
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    async def main():
+        # Example usage
+        docs = [
+            {"content": "The capital of France is Paris."},
+            {"content": "Tokyo is the capital of Japan."},
+            {"content": "London is the capital of England."},
+        ]
+
+        query = "What is the capital of France?"
+
+        result = await jina_rerank(
+            query=query, documents=docs, top_k=2, api_key="your-api-key-here"
+        )
+        print(result)
+
+    asyncio.run(main())

lightrag_webui/src/stores/settings.ts

@@ -111,7 +111,7 @@ const useSettingsStoreBase = create<SettingsState>()(
         mode: 'global',
         response_type: 'Multiple Paragraphs',
         top_k: 10,
-        max_token_for_text_unit: 4000,
+        max_token_for_text_unit: 6000,
         max_token_for_global_context: 4000,
         max_token_for_local_context: 4000,
         only_need_context: false,

tests/test_graph_storage.py

@@ -10,6 +10,7 @@
 - Neo4JStorage
 - MongoDBStorage
 - PGGraphStorage
+- MemgraphStorage
 """
 
 import asyncio