feat: move query-related settings to env file for better configuration

• Add env vars for token and chunk settings
• Add token count logging for prompts
• Add token count logging for context
• Move hardcoded values to env variables
• Improve logging clarity and consistency

.env.example

@@ -27,14 +27,21 @@ TIMEOUT=300
 
 ### RAG Configuration
 MAX_ASYNC=4
-MAX_TOKENS=32768
 EMBEDDING_DIM=1024
 MAX_EMBED_TOKENS=8192
-#HISTORY_TURNS=3
-#CHUNK_SIZE=1200
-#CHUNK_OVERLAP_SIZE=100
-#COSINE_THRESHOLD=0.2
-#TOP_K=60
+### Settings relative to query
+HISTORY_TURNS=3
+COSINE_THRESHOLD=0.2
+TOP_K=60
+MAX_TOKEN_TEXT_CHUNK = 4000
+MAX_TOKEN_RELATION_DESC = 4000
+MAX_TOKEN_ENTITY_DESC = 4000
+### Settings relative to indexing
+CHUNK_SIZE=1200
+CHUNK_OVERLAP_SIZE=100
+MAX_TOKENS=32768
+MAX_TOKEN_SUMMARY=500
+SUMMARY_LANGUAGE=English
 
 ### LLM Configuration (Use valid host. For local services, you can use host.docker.internal)
 ### Ollama example

lightrag/base.py

@@ -54,13 +54,15 @@ 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 = 4000
+    max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
     """Maximum number of tokens allowed for each retrieved text chunk."""
 
-    max_token_for_global_context: int = 4000
+    max_token_for_global_context: int = int(
+        os.getenv("MAX_TOKEN_RELATION_DESC", "4000")
+    )
     """Maximum number of tokens allocated for relationship descriptions in global retrieval."""
 
-    max_token_for_local_context: int = 4000
+    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)

lightrag/lightrag.py

@@ -263,10 +263,10 @@ class LightRAG:
     """Directory where logs are stored. Defaults to the current working directory."""
 
     # Text chunking
-    chunk_token_size: int = 1200
+    chunk_token_size: int = int(os.getenv("CHUNK_SIZE", "1200"))
     """Maximum number of tokens per text chunk when splitting documents."""
 
-    chunk_overlap_token_size: int = 100
+    chunk_overlap_token_size: int = int(os.getenv("CHUNK_OVERLAP_SIZE", "100"))
     """Number of overlapping tokens between consecutive text chunks to preserve context."""
 
     tiktoken_model_name: str = "gpt-4o-mini"
@@ -276,7 +276,7 @@ class LightRAG:
     entity_extract_max_gleaning: int = 1
     """Maximum number of entity extraction attempts for ambiguous content."""
 
-    entity_summary_to_max_tokens: int = 500
+    entity_summary_to_max_tokens: int = int(os.getenv("MAX_TOKEN_SUMMARY", "500"))
     """Maximum number of tokens used for summarizing extracted entities."""
 
     # Node embedding

lightrag/operate.py

@@ -642,9 +642,13 @@ async def kg_query(
         history=history_context,
     )
 
+
     if query_param.only_need_prompt:
         return sys_prompt
 
+    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    logger.info(f"[kg_query]Prompt Tokens: {len_of_prompts}")
+
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
@@ -730,6 +734,9 @@ async def extract_keywords_only(
         query=text, examples=examples, language=language, history=history_context
     )
 
+    len_of_prompts = len(encode_string_by_tiktoken(kw_prompt))
+    logger.info(f"[kg_query]Prompt Tokens: {len_of_prompts}")
+
     # 5. Call the LLM for keyword extraction
     use_model_func = global_config["llm_model_func"]
     result = await use_model_func(kw_prompt, keyword_extraction=True)
@@ -893,7 +900,9 @@ async def mix_kg_vector_query(
                     chunk_text = f"[Created at: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(c['created_at']))}]\n{chunk_text}"
                 formatted_chunks.append(chunk_text)
 
-            logger.info(f"Truncate {len(chunks)} to {len(formatted_chunks)} chunks")
+            logger.info(
+                f"Truncate text chunks from {len(chunks)} to {len(formatted_chunks)}"
+            )
             return "\n--New Chunk--\n".join(formatted_chunks)
         except Exception as e:
             logger.error(f"Error in get_vector_context: {e}")
@@ -926,6 +935,9 @@ async def mix_kg_vector_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
+    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    logger.info(f"[mix_kg_vector_query]Prompt Tokens: {len_of_prompts}")
+
     # 6. Generate response
     response = await use_model_func(
         query,
@@ -1031,7 +1043,7 @@ async def _build_query_context(
     if not entities_context.strip() and not relations_context.strip():
         return None
 
-    return f"""
+    result = f"""
 -----Entities-----
 ```csv
 {entities_context}
@@ -1045,6 +1057,15 @@ async def _build_query_context(
 {text_units_context}
 ```
 """
+    contex_tokens = len(encode_string_by_tiktoken(result))
+    entities_tokens = len(encode_string_by_tiktoken(entities_context))
+    relations_tokens = len(encode_string_by_tiktoken(relations_context))
+    text_units_tokens = len(encode_string_by_tiktoken(text_units_context))
+    logger.info(
+        f"Context Tokens - Total: {contex_tokens}, Entities: {entities_tokens}, Relations: {relations_tokens}, Chunks: {text_units_tokens}"
+    )
+
+    return result
 
 
 async def _get_node_data(
@@ -1089,7 +1110,7 @@ async def _get_node_data(
         ),
     )
     logger.info(
-        f"Local query uses {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} text units"
+        f"Local query uses {len(node_datas)} entites, {len(use_relations)} relations, {len(use_text_units)} chunks"
     )
 
     # build prompt
@@ -1222,6 +1243,10 @@ async def _find_most_related_text_unit_from_entities(
         max_token_size=query_param.max_token_for_text_unit,
     )
 
+    logger.info(
+        f"Truncate text chunks from {len(all_text_units_lookup)} to {len(all_text_units)}"
+    )
+
     all_text_units = [t["data"] for t in all_text_units]
     return all_text_units
 
@@ -1263,6 +1288,9 @@ async def _find_most_related_edges_from_entities(
         key=lambda x: x["description"],
         max_token_size=query_param.max_token_for_global_context,
     )
+
+    logger.info(f"Truncate relations from {len(all_edges)} to {len(all_edges_data)}")
+
     return all_edges_data
 
 
@@ -1310,11 +1338,13 @@ async def _get_edge_data(
     edge_datas = sorted(
         edge_datas, key=lambda x: (x["rank"], x["weight"]), reverse=True
     )
+    len_edge_datas = len(edge_datas)
     edge_datas = truncate_list_by_token_size(
         edge_datas,
         key=lambda x: x["description"],
         max_token_size=query_param.max_token_for_global_context,
     )
+    logger.info(f"Truncate relations from {len_edge_datas} to {len(edge_datas)}")
 
     use_entities, use_text_units = await asyncio.gather(
         _find_most_related_entities_from_relationships(
@@ -1325,7 +1355,7 @@ async def _get_edge_data(
         ),
     )
     logger.info(
-        f"Global query uses {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} text units"
+        f"Global query uses {len(use_entities)} entites, {len(edge_datas)} relations, {len(use_text_units)} chunks"
     )
 
     relations_section_list = [
@@ -1414,11 +1444,13 @@ async def _find_most_related_entities_from_relationships(
         for k, n, d in zip(entity_names, node_datas, node_degrees)
     ]
 
+    len_node_datas = len(node_datas)
     node_datas = truncate_list_by_token_size(
         node_datas,
         key=lambda x: x["description"],
         max_token_size=query_param.max_token_for_local_context,
     )
+    logger.info(f"Truncate entities from {len_node_datas} to {len(node_datas)}")
 
     return node_datas
 
@@ -1474,6 +1506,10 @@ async def _find_related_text_unit_from_relationships(
         max_token_size=query_param.max_token_for_text_unit,
     )
 
+    logger.info(
+        f"Truncate text chunks from {len(valid_text_units)} to {len(truncated_text_units)}"
+    )
+
     all_text_units: list[TextChunkSchema] = [t["data"] for t in truncated_text_units]
 
     return all_text_units
@@ -1541,7 +1577,8 @@ async def naive_query(
         logger.warning("No chunks left after truncation")
         return PROMPTS["fail_response"]
 
-    logger.info(f"Truncate {len(chunks)} to {len(maybe_trun_chunks)} chunks")
+    logger.info(f"Truncate text chunks from {len(chunks)} to {len(maybe_trun_chunks)}")
+
     section = "\n--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
 
     if query_param.only_need_context:
@@ -1564,6 +1601,9 @@ async def naive_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
+    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    logger.info(f"[naive_query]Prompt Tokens: {len_of_prompts}")
+
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
@@ -1706,6 +1746,9 @@ async def kg_query_with_keywords(
     if query_param.only_need_prompt:
         return sys_prompt
 
+    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    logger.info(f"[kg_query_with_keywords]Prompt Tokens: {len_of_prompts}")
+
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,