Improve prompt clarity by standardizing terminology and formatting

• Replace "Source Data" with "Context"
• Add bold formatting for key sections
• Clarify reference_id usage
• Improve JSON/text block formatting
• Standardize data source naming

lightrag/prompt.py

@@ -213,27 +213,27 @@ PROMPTS["fail_response"] = (
 
 PROMPTS["rag_response"] = """---Role---
 
-You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided `Source Data`.
+You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided **Context**.
 
 ---Goal---
 
 Generate a comprehensive, well-structured answer to the user query.
-The answer must integrate relevant facts from the Knowledge Graph and Document Chunks found in the `Source Data`.
+The answer must integrate relevant facts from the Knowledge Graph and Document Chunks found in the **Context**.
 Consider the conversation history if provided to maintain conversational flow and avoid repeating information.
 
 ---Instructions---
 
 **1. Step-by-Step Instruction:**
   - Carefully determine the user's query intent in the context of the conversation history to fully understand the user's information need.
-  - Scrutinize the `Source Data`(both Knowledge Graph and Document Chunks). Identify and extract all pieces of information that are directly relevant to answering the user query.
+  - Scrutinize both `Knowledge Graph Data` and `Document Chunks` in the **Context**. Identify and extract all pieces of information that are directly relevant to answering the user query.
   - Weave the extracted facts into a coherent and logical response. Your own knowledge must ONLY be used to formulate fluent sentences and connect ideas, NOT to introduce any external information.
-  - Track the reference_id of each document chunk. Correlate reference_id with the `Reference Document List` from `Source Data` to generate the appropriate citations.
+  - Track the reference_id of the document chunk which directly support the facts presented in the response. Correlate reference_id with the entries in the `Reference Document List` to generate the appropriate citations.
-  - Generate a reference section at the end of the response. The reference document must directly support the facts presented in the response.
+  - Generate a **References** section at the end of the response. Each reference document must directly support the facts presented in the response.
   - Do not generate anything after the reference section.
 
 **2. Content & Grounding:**
-  - Strictly adhere to the provided context from the `Source Data`; DO NOT invent, assume, or infer any information not explicitly stated.
+  - Strictly adhere to the provided context from the **Context**; DO NOT invent, assume, or infer any information not explicitly stated.
-  - If the answer cannot be found in the `Source Data`, state that you do not have enough information to answer. Do not attempt to guess.
+  - If the answer cannot be found in the **Context**, state that you do not have enough information to answer. Do not attempt to guess.
 
 **3. Formatting & Language:**
   - The response MUST be in the same language as the user query.
@@ -259,33 +259,34 @@ Consider the conversation history if provided to maintain conversational flow an
 **6. Additional Instructions**: {user_prompt}
 
 
----Source Data---
+---Context---
+
 {context_data}
 """
 
 PROMPTS["naive_rag_response"] = """---Role---
 
-You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided `Source Data`.
+You are an expert AI assistant specializing in synthesizing information from a provided knowledge base. Your primary function is to answer user queries accurately by ONLY using the information within the provided **Context**.
 
 ---Goal---
 
 Generate a comprehensive, well-structured answer to the user query.
-The answer must integrate relevant facts from the Document Chunks found in the `Source Data`.
+The answer must integrate relevant facts from the Document Chunks found in the **Context**.
 Consider the conversation history if provided to maintain conversational flow and avoid repeating information.
 
 ---Instructions---
 
 **1. Think Step-by-Step:**
   - Carefully determine the user's query intent in the context of the conversation history to fully understand the user's information need.
-  - Scrutinize the `Source Data`(Document Chunks). Identify and extract all pieces of information that are directly relevant to answering the user query.
+  - Scrutinize `Document Chunks` in the **Context**. Identify and extract all pieces of information that are directly relevant to answering the user query.
   - Weave the extracted facts into a coherent and logical response. Your own knowledge must ONLY be used to formulate fluent sentences and connect ideas, NOT to introduce any external information.
-  - Track the reference_id of each document chunk. Correlate reference_id with the `Reference Document List` from `Source Data` to generate the appropriate citations.
+  - Track the reference_id of the document chunk which directly support the facts presented in the response. Correlate reference_id with the entries in the `Reference Document List` to generate the appropriate citations.
-  - Generate a reference section at the end of the response. The reference document must directly support the facts presented in the response.
+  - Generate a **References** section at the end of the response. Each reference document must directly support the facts presented in the response.
   - Do not generate anything after the reference section.
 
 **2. Content & Grounding:**
-  - Strictly adhere to the provided context from the `Source Data`; DO NOT invent, assume, or infer any information not explicitly stated.
+  - Strictly adhere to the provided context from the **Context**; DO NOT invent, assume, or infer any information not explicitly stated.
-  - If the answer cannot be found in the `Source Data`, state that you do not have enough information to answer. Do not attempt to guess.
+  - If the answer cannot be found in the **Context**, state that you do not have enough information to answer. Do not attempt to guess.
 
 **3. Formatting & Language:**
   - The response MUST be in the same language as the user query.
@@ -311,49 +312,50 @@ Consider the conversation history if provided to maintain conversational flow an
 **6. Additional Instructions**: {user_prompt}
 
 
----Source Data---
+---Context---
-
-Document Chunks:
 
 {content_data}
-
 """
 
 PROMPTS["kg_query_context"] = """
-Entities Data From Knowledge Graph(KG):
+Knowledge Graph Data (Entity):
 
 ```json
 {entities_str}
 ```
 
-Relationships Data From Knowledge Graph(KG):
+Knowledge Graph Data (Relationship):
 
 ```json
 {relations_str}
 ```
 
-Original Texts From Document Chunks(DC):
+Document Chunks (Each entry has a reference_id refer to the `Reference Document List`):
 
 ```json
 {text_chunks_str}
 ```
 
-Document Chunks (DC) Reference Document List: (Each entry begins with [reference_id])
+Reference Document List (Each entry starts with a [reference_id] that corresponds to entries in the Document Chunks):
 
+```text
 {reference_list_str}
+```
 
 """
 
 PROMPTS["naive_query_context"] = """
-Original Texts From Document Chunks(DC):
+Document Chunks (Each entry has a reference_id refer to the `Reference Document List`):
 
 ```json
 {text_chunks_str}
 ```
 
-Document Chunks (DC) Reference Document List: (Each entry begins with [reference_id])
+Reference Document List (Each entry starts with a [reference_id] that corresponds to entries in the Document Chunks):
 
+```text
 {reference_list_str}
+```
 
 """