Update README.md

README-zh.md

@@ -260,6 +260,11 @@ class QueryParam:
     If provided, this will be used instead of the global model function.
     This allows using different models for different query modes.
     """
+
+    user_prompt: str | None = None
+    """User-provided prompt for the query.
+    If proivded, this will be use instead of the default vaulue from prompt template.
+    """
 ```
 
 > top_k的默认值可以通过环境变量TOP_K更改。
@@ -527,128 +532,23 @@ response = rag.query(
 )
 ```
 
-### 自定义提示词
+### 自定义用户提示词
 
-LightRAG现在支持自定义提示，以便对系统行为进行精细控制。以下是使用方法：
+自定义用户提示词不影响查询内容，仅仅用于向LLM指示如何处理查询结果。以下是使用方法：
 
 ```python
 # 创建查询参数
 query_param = QueryParam(
-    mode="hybrid",  # 或其他模式："local"、"global"、"hybrid"、"mix"和"naive"
+    mode = "hybrid",  # 或其他模式："local"、"global"、"hybrid"、"mix"和"naive"
+    user_prompt = "Please create the diagram using the Mermaid syntax"
 )
 
-# 示例1：使用默认系统提示
+# 查询和处理
 response_default = rag.query(
-    "可再生能源的主要好处是什么？",
+    "Please draw a character relationship diagram for Scrooge",
     param=query_param
 )
 print(response_default)
-
-# 示例2：使用自定义提示
-custom_prompt = """
-您是环境科学领域的专家助手。请提供详细且结构化的答案，并附带示例。
----对话历史---
-{history}
-
----知识库---
-{context_data}
-
----响应规则---
-
-- 目标格式和长度：{response_type}
-"""
-response_custom = rag.query(
-    "可再生能源的主要好处是什么？",
-    param=query_param,
-    system_prompt=custom_prompt  # 传递自定义提示
-)
-print(response_custom)
-```
-
-### 关键词提取
-
-我们引入了新函数`query_with_separate_keyword_extraction`来增强关键词提取功能。该函数将关键词提取过程与用户提示分开，专注于查询以提高提取关键词的相关性。
-
-* 工作原理
-
-该函数将输入分为两部分：
-
-- `用户查询`
-- `提示`
-
-然后仅对`用户查询`执行关键词提取。这种分离确保提取过程是集中和相关的，不受`提示`中任何额外语言的影响。它还允许`提示`纯粹用于响应格式化，保持用户原始问题的意图和清晰度。
-
-* 使用示例
-
-这个`示例`展示了如何为教育内容定制函数，专注于为高年级学生提供详细解释。
-
-```python
-rag.query_with_separate_keyword_extraction(
-    query="解释重力定律",
-    prompt="提供适合学习物理的高中生的详细解释。",
-    param=QueryParam(mode="hybrid")
-)
-```
-
-### 插入自定义知识
-
-```python
-custom_kg = {
-    "chunks": [
-        {
-            "content": "Alice和Bob正在合作进行量子计算研究。",
-            "source_id": "doc-1"
-        }
-    ],
-    "entities": [
-        {
-            "entity_name": "Alice",
-            "entity_type": "person",
-            "description": "Alice是一位专门研究量子物理的研究员。",
-            "source_id": "doc-1"
-        },
-        {
-            "entity_name": "Bob",
-            "entity_type": "person",
-            "description": "Bob是一位数学家。",
-            "source_id": "doc-1"
-        },
-        {
-            "entity_name": "量子计算",
-            "entity_type": "technology",
-            "description": "量子计算利用量子力学现象进行计算。",
-            "source_id": "doc-1"
-        }
-    ],
-    "relationships": [
-        {
-            "src_id": "Alice",
-            "tgt_id": "Bob",
-            "description": "Alice和Bob是研究伙伴。",
-            "keywords": "合作 研究",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        },
-        {
-            "src_id": "Alice",
-            "tgt_id": "量子计算",
-            "description": "Alice进行量子计算研究。",
-            "keywords": "研究 专业",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        },
-        {
-            "src_id": "Bob",
-            "tgt_id": "量子计算",
-            "description": "Bob研究量子计算。",
-            "keywords": "研究 应用",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        }
-    ]
-}
-
-rag.insert_custom_kg(custom_kg)
 ```
 
 ### 插入
@@ -934,23 +834,160 @@ updated_relation = rag.edit_relation("Google", "Google Mail", {
 })
 ```
 
+所有操作都有同步和异步版本。异步版本带有前缀"a"（例如，`acreate_entity`，`aedit_relation`）。
+
 </details>
 
-所有操作都有同步和异步版本。异步版本带有前缀"a"（例如，`acreate_entity`，`aedit_relation`）。
+<details>
+<summary> <b>插入自定义知识</b> </summary>
 
-#### 实体操作
+```python
+custom_kg = {
+    "chunks": [
+        {
+            "content": "Alice和Bob正在合作进行量子计算研究。",
+            "source_id": "doc-1"
+        }
+    ],
+    "entities": [
+        {
+            "entity_name": "Alice",
+            "entity_type": "person",
+            "description": "Alice是一位专门研究量子物理的研究员。",
+            "source_id": "doc-1"
+        },
+        {
+            "entity_name": "Bob",
+            "entity_type": "person",
+            "description": "Bob是一位数学家。",
+            "source_id": "doc-1"
+        },
+        {
+            "entity_name": "量子计算",
+            "entity_type": "technology",
+            "description": "量子计算利用量子力学现象进行计算。",
+            "source_id": "doc-1"
+        }
+    ],
+    "relationships": [
+        {
+            "src_id": "Alice",
+            "tgt_id": "Bob",
+            "description": "Alice和Bob是研究伙伴。",
+            "keywords": "合作 研究",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        },
+        {
+            "src_id": "Alice",
+            "tgt_id": "量子计算",
+            "description": "Alice进行量子计算研究。",
+            "keywords": "研究 专业",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        },
+        {
+            "src_id": "Bob",
+            "tgt_id": "量子计算",
+            "description": "Bob研究量子计算。",
+            "keywords": "研究 应用",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        }
+    ]
+}
+
+rag.insert_custom_kg(custom_kg)
+```
+
+</details>
+
+<details>
+<summary> <b>其它实体与关系操作</b> </summary>
 
 - **create_entity**：创建具有指定属性的新实体
 - **edit_entity**：更新现有实体的属性或重命名它
 
-#### 关系操作
-
 - **create_relation**：在现有实体之间创建新关系
 - **edit_relation**：更新现有关系的属性
 
 这些操作在图数据库和向量数据库组件之间保持数据一致性，确保您的知识图谱保持连贯。
 
+</details>
+
+## 实体合并
+
+<details>
+<summary> <b>合并实体及其关系</b> </summary>
+
+LightRAG现在支持将多个实体合并为单个实体，自动处理所有关系：
+
+```python
+# 基本实体合并
+rag.merge_entities(
+    source_entities=["人工智能", "AI", "机器智能"],
+    target_entity="AI技术"
+)
+```
+
+使用自定义合并策略：
+
+```python
+# 为不同字段定义自定义合并策略
+rag.merge_entities(
+    source_entities=["约翰·史密斯", "史密斯博士", "J·史密斯"],
+    target_entity="约翰·史密斯",
+    merge_strategy={
+        "description": "concatenate",  # 组合所有描述
+        "entity_type": "keep_first",   # 保留第一个实体的类型
+        "source_id": "join_unique"     # 组合所有唯一的源ID
+    }
+)
+```
+
+使用自定义目标实体数据：
+
+```python
+# 为合并后的实体指定确切值
+rag.merge_entities(
+    source_entities=["纽约", "NYC", "大苹果"],
+    target_entity="纽约市",
+    target_entity_data={
+        "entity_type": "LOCATION",
+        "description": "纽约市是美国人口最多的城市。",
+    }
+)
+```
+
+结合两种方法的高级用法：
+
+```python
+# 使用策略和自定义数据合并公司实体
+rag.merge_entities(
+    source_entities=["微软公司", "Microsoft Corporation", "MSFT"],
+    target_entity="微软",
+    merge_strategy={
+        "description": "concatenate",  # 组合所有描述
+        "source_id": "join_unique"     # 组合源ID
+    },
+    target_entity_data={
+        "entity_type": "ORGANIZATION",
+    }
+)
+```
+
+合并实体时：
+
+* 所有来自源实体的关系都会重定向到目标实体
+* 重复的关系会被智能合并
+* 防止自我关系（循环）
+* 合并后删除源实体
+* 保留关系权重和属性
+
+</details>
+
 ## Token统计功能
+
 <details>
 <summary> <b>概述和使用</b> </summary>
 
@@ -1048,77 +1085,6 @@ rag.export_data("complete_data.csv", include_vector_data=True)
 * 关系数据（实体之间的连接）
 * 来自向量数据库的关系信息
 
-## 实体合并
-
-<details>
-<summary> <b>合并实体及其关系</b> </summary>
-
-LightRAG现在支持将多个实体合并为单个实体，自动处理所有关系：
-
-```python
-# 基本实体合并
-rag.merge_entities(
-    source_entities=["人工智能", "AI", "机器智能"],
-    target_entity="AI技术"
-)
-```
-
-使用自定义合并策略：
-
-```python
-# 为不同字段定义自定义合并策略
-rag.merge_entities(
-    source_entities=["约翰·史密斯", "史密斯博士", "J·史密斯"],
-    target_entity="约翰·史密斯",
-    merge_strategy={
-        "description": "concatenate",  # 组合所有描述
-        "entity_type": "keep_first",   # 保留第一个实体的类型
-        "source_id": "join_unique"     # 组合所有唯一的源ID
-    }
-)
-```
-
-使用自定义目标实体数据：
-
-```python
-# 为合并后的实体指定确切值
-rag.merge_entities(
-    source_entities=["纽约", "NYC", "大苹果"],
-    target_entity="纽约市",
-    target_entity_data={
-        "entity_type": "LOCATION",
-        "description": "纽约市是美国人口最多的城市。",
-    }
-)
-```
-
-结合两种方法的高级用法：
-
-```python
-# 使用策略和自定义数据合并公司实体
-rag.merge_entities(
-    source_entities=["微软公司", "Microsoft Corporation", "MSFT"],
-    target_entity="微软",
-    merge_strategy={
-        "description": "concatenate",  # 组合所有描述
-        "source_id": "join_unique"     # 组合源ID
-    },
-    target_entity_data={
-        "entity_type": "ORGANIZATION",
-    }
-)
-```
-
-合并实体时：
-
-* 所有来自源实体的关系都会重定向到目标实体
-* 重复的关系会被智能合并
-* 防止自我关系（循环）
-* 合并后删除源实体
-* 保留关系权重和属性
-
-</details>
-
 ## 缓存
 
 <details>

README.md

@@ -274,12 +274,6 @@ class QueryParam:
     max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
     """Maximum number of tokens allocated for entity descriptions in local retrieval."""
 
-    hl_keywords: list[str] = field(default_factory=list)
-    """List of high-level keywords to prioritize in retrieval."""
-
-    ll_keywords: list[str] = field(default_factory=list)
-    """List of low-level keywords to refine retrieval focus."""
-
     conversation_history: list[dict[str, str]] = field(default_factory=list)
     """Stores past conversation history to maintain context.
     Format: [{"role": "user/assistant", "content": "message"}].
@@ -296,6 +290,11 @@ class QueryParam:
     If provided, this will be used instead of the global model function.
     This allows using different models for different query modes.
     """
+
+    user_prompt: str | None = None
+    """User-provided prompt for the query.
+    If proivded, this will be use instead of the default vaulue from prompt template.
+    """
 ```
 
 > default value of Top_k can be change by environment  variables  TOP_K.
@@ -571,76 +570,26 @@ response = rag.query(
 
 </details>
 
-### Custom Prompt Support
-
-LightRAG now supports custom prompts for fine-tuned control over the system's behavior. Here's how to use it:
+### Custom User Prompt Support
 
-<details>
-  <summary> <b> Usage Example </b></summary>
+Custom user prompts do not affect the query content; they are only used to instruct the LLM on how to handle the query results.  Here's how to use it:
 
 ```python
 # Create query parameters
 query_param = QueryParam(
-    mode="hybrid",  # or other mode: "local", "global", "hybrid", "mix" and "naive"
+    mode = "hybrid",  # 或其他模式："local"、"global"、"hybrid"、"mix"和"naive"
+    user_prompt = "Please create the diagram using the Mermaid syntax"
 )
 
-# Example 1: Using the default system prompt
+# Query and process
 response_default = rag.query(
-    "What are the primary benefits of renewable energy?",
+    "Please draw a character relationship diagram for Scrooge",
     param=query_param
 )
 print(response_default)
-
-# Example 2: Using a custom prompt
-custom_prompt = """
-You are an expert assistant in environmental science. Provide detailed and structured answers with examples.
----Conversation History---
-{history}
-
----Knowledge Base---
-{context_data}
-
----Response Rules---
-
-- Target format and length: {response_type}
-"""
-response_custom = rag.query(
-    "What are the primary benefits of renewable energy?",
-    param=query_param,
-    system_prompt=custom_prompt  # Pass the custom prompt
-)
-print(response_custom)
 ```
 
-</details>
-
-### Separate Keyword Extraction
-
-We've introduced a new function `query_with_separate_keyword_extraction` to enhance the keyword extraction capabilities. This function separates the keyword extraction process from the user's prompt, focusing solely on the query to improve the relevance of extracted keywords.
-
-**How It Works?**
-
-The function operates by dividing the input into two parts:
-
-- `User Query`
-- `Prompt`
-
-It then performs keyword extraction exclusively on the `user query`. This separation ensures that the extraction process is focused and relevant, unaffected by any additional language in the `prompt`. It also allows the `prompt` to serve purely for response formatting, maintaining the intent and clarity of the user's original question.
 
-<details>
-  <summary> <b> Usage Example </b></summary>
-
-This `example` shows how to tailor the function for educational content, focusing on detailed explanations for older students.
-
-```python
-rag.query_with_separate_keyword_extraction(
-    query="Explain the law of gravity",
-    prompt="Provide a detailed explanation suitable for high school students studying physics.",
-    param=QueryParam(mode="hybrid")
-)
-```
-
-</details>
 
 ### Insert
 
@@ -725,70 +674,6 @@ rag.insert(text_content.decode('utf-8'))
 
 </details>
 
-<details>
-  <summary> <b> Insert Custom KG </b></summary>
-
-```python
-custom_kg = {
-    "chunks": [
-        {
-            "content": "Alice and Bob are collaborating on quantum computing research.",
-            "source_id": "doc-1"
-        }
-    ],
-    "entities": [
-        {
-            "entity_name": "Alice",
-            "entity_type": "person",
-            "description": "Alice is a researcher specializing in quantum physics.",
-            "source_id": "doc-1"
-        },
-        {
-            "entity_name": "Bob",
-            "entity_type": "person",
-            "description": "Bob is a mathematician.",
-            "source_id": "doc-1"
-        },
-        {
-            "entity_name": "Quantum Computing",
-            "entity_type": "technology",
-            "description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
-            "source_id": "doc-1"
-        }
-    ],
-    "relationships": [
-        {
-            "src_id": "Alice",
-            "tgt_id": "Bob",
-            "description": "Alice and Bob are research partners.",
-            "keywords": "collaboration research",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        },
-        {
-            "src_id": "Alice",
-            "tgt_id": "Quantum Computing",
-            "description": "Alice conducts research on quantum computing.",
-            "keywords": "research expertise",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        },
-        {
-            "src_id": "Bob",
-            "tgt_id": "Quantum Computing",
-            "description": "Bob researches quantum computing.",
-            "keywords": "research application",
-            "weight": 1.0,
-            "source_id": "doc-1"
-        }
-    ]
-}
-
-rag.insert_custom_kg(custom_kg)
-```
-
-</details>
-
 <details>
   <summary><b>Citation Functionality</b></summary>
 
@@ -992,12 +877,78 @@ updated_relation = rag.edit_relation("Google", "Google Mail", {
 
 All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`).
 
-#### Entity Operations
+</details>
+
+<details>
+  <summary> <b> Insert Custom KG </b></summary>
+
+```python
+custom_kg = {
+    "chunks": [
+        {
+            "content": "Alice and Bob are collaborating on quantum computing research.",
+            "source_id": "doc-1"
+        }
+    ],
+    "entities": [
+        {
+            "entity_name": "Alice",
+            "entity_type": "person",
+            "description": "Alice is a researcher specializing in quantum physics.",
+            "source_id": "doc-1"
+        },
+        {
+            "entity_name": "Bob",
+            "entity_type": "person",
+            "description": "Bob is a mathematician.",
+            "source_id": "doc-1"
+        },
+        {
+            "entity_name": "Quantum Computing",
+            "entity_type": "technology",
+            "description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
+            "source_id": "doc-1"
+        }
+    ],
+    "relationships": [
+        {
+            "src_id": "Alice",
+            "tgt_id": "Bob",
+            "description": "Alice and Bob are research partners.",
+            "keywords": "collaboration research",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        },
+        {
+            "src_id": "Alice",
+            "tgt_id": "Quantum Computing",
+            "description": "Alice conducts research on quantum computing.",
+            "keywords": "research expertise",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        },
+        {
+            "src_id": "Bob",
+            "tgt_id": "Quantum Computing",
+            "description": "Bob researches quantum computing.",
+            "keywords": "research application",
+            "weight": 1.0,
+            "source_id": "doc-1"
+        }
+    ]
+}
+
+rag.insert_custom_kg(custom_kg)
+```
+
+</details>
+
+<details>
+  <summary> <b>Other Entity and Relation Operations</b></summary>
 
 - **create_entity**: Creates a new entity with specified attributes
 - **edit_entity**: Updates an existing entity's attributes or renames it
 
-#### Relation Operations
 
 - **create_relation**: Creates a new relation between existing entities
 - **edit_relation**: Updates an existing relation's attributes
@@ -1006,6 +957,77 @@ These operations maintain data consistency across both the graph database and ve
 
 </details>
 
+## Entity Merging
+
+<details>
+<summary> <b>Merge Entities and Their Relationships</b> </summary>
+
+LightRAG now supports merging multiple entities into a single entity, automatically handling all relationships:
+
+```python
+# Basic entity merging
+rag.merge_entities(
+    source_entities=["Artificial Intelligence", "AI", "Machine Intelligence"],
+    target_entity="AI Technology"
+)
+```
+
+With custom merge strategy:
+
+```python
+# Define custom merge strategy for different fields
+rag.merge_entities(
+    source_entities=["John Smith", "Dr. Smith", "J. Smith"],
+    target_entity="John Smith",
+    merge_strategy={
+        "description": "concatenate",  # Combine all descriptions
+        "entity_type": "keep_first",   # Keep the entity type from the first entity
+        "source_id": "join_unique"     # Combine all unique source IDs
+    }
+)
+```
+
+With custom target entity data:
+
+```python
+# Specify exact values for the merged entity
+rag.merge_entities(
+    source_entities=["New York", "NYC", "Big Apple"],
+    target_entity="New York City",
+    target_entity_data={
+        "entity_type": "LOCATION",
+        "description": "New York City is the most populous city in the United States.",
+    }
+)
+```
+
+Advanced usage combining both approaches:
+
+```python
+# Merge company entities with both strategy and custom data
+rag.merge_entities(
+    source_entities=["Microsoft Corp", "Microsoft Corporation", "MSFT"],
+    target_entity="Microsoft",
+    merge_strategy={
+        "description": "concatenate",  # Combine all descriptions
+        "source_id": "join_unique"     # Combine source IDs
+    },
+    target_entity_data={
+        "entity_type": "ORGANIZATION",
+    }
+)
+```
+
+When merging entities:
+
+* All relationships from source entities are redirected to the target entity
+* Duplicate relationships are intelligently merged
+* Self-relationships (loops) are prevented
+* Source entities are removed after merging
+* Relationship weights and attributes are preserved
+
+</details>
+
 ## Token Usage Tracking
 
 <details>
@@ -1112,78 +1134,6 @@ All exports include:
 * Relation data (connections between entities)
 * Relationship information from vector database
 
-
-## Entity Merging
-
-<details>
-<summary> <b>Merge Entities and Their Relationships</b> </summary>
-
-LightRAG now supports merging multiple entities into a single entity, automatically handling all relationships:
-
-```python
-# Basic entity merging
-rag.merge_entities(
-    source_entities=["Artificial Intelligence", "AI", "Machine Intelligence"],
-    target_entity="AI Technology"
-)
-```
-
-With custom merge strategy:
-
-```python
-# Define custom merge strategy for different fields
-rag.merge_entities(
-    source_entities=["John Smith", "Dr. Smith", "J. Smith"],
-    target_entity="John Smith",
-    merge_strategy={
-        "description": "concatenate",  # Combine all descriptions
-        "entity_type": "keep_first",   # Keep the entity type from the first entity
-        "source_id": "join_unique"     # Combine all unique source IDs
-    }
-)
-```
-
-With custom target entity data:
-
-```python
-# Specify exact values for the merged entity
-rag.merge_entities(
-    source_entities=["New York", "NYC", "Big Apple"],
-    target_entity="New York City",
-    target_entity_data={
-        "entity_type": "LOCATION",
-        "description": "New York City is the most populous city in the United States.",
-    }
-)
-```
-
-Advanced usage combining both approaches:
-
-```python
-# Merge company entities with both strategy and custom data
-rag.merge_entities(
-    source_entities=["Microsoft Corp", "Microsoft Corporation", "MSFT"],
-    target_entity="Microsoft",
-    merge_strategy={
-        "description": "concatenate",  # Combine all descriptions
-        "source_id": "join_unique"     # Combine source IDs
-    },
-    target_entity_data={
-        "entity_type": "ORGANIZATION",
-    }
-)
-```
-
-When merging entities:
-
-* All relationships from source entities are redirected to the target entity
-* Duplicate relationships are intelligently merged
-* Self-relationships (loops) are prevented
-* Source entities are removed after merging
-* Relationship weights and attributes are preserved
-
-</details>
-
 ## Cache
 
 <details>