diff --git a/.env.example b/.env.example
index 2701335a..f70244e5 100644
--- a/.env.example
+++ b/.env.example
@@ -18,6 +18,7 @@
 
 ### Logging level
 LOG_LEVEL=INFO
+VERBOSE=False
 
 ### Optional Timeout
 TIMEOUT=300
@@ -27,14 +28,21 @@ 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
diff --git a/.gitignore b/.gitignore
index 2d074372..58c9e17e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,7 +5,7 @@ __pycache__/
 .eggs/
 *.tgz
 *.tar.gz
-*.ini  # Remove config.ini from repo
+*.ini
 
 # Virtual Environment
 .venv/
diff --git a/lightrag/api/README.md b/lightrag/api/README.md
index d48b6732..69023685 100644
--- a/lightrag/api/README.md
+++ b/lightrag/api/README.md
@@ -222,6 +222,7 @@ You can select storage  implementation by enviroment variables or command line a
 | --max-embed-tokens | 8192 | Maximum embedding token size |
 | --timeout | None | Timeout in seconds (useful when using slow AI). Use None for infinite timeout |
 | --log-level | INFO | Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL) |
+| --verbose | False | Verbose debug output (True, Flase) |
 | --key | None | API key for authentication. Protects lightrag server against unauthorized access |
 | --ssl | False | Enable HTTPS |
 | --ssl-certfile | None | Path to SSL certificate file (required if --ssl is enabled) |
diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index 751664f6..b7a83e04 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -133,8 +133,8 @@ def get_env_value(env_key: str, default: Any, value_type: type = str) -> Any:
     if value is None:
         return default
 
-    if isinstance(value_type, bool):
-        return value.lower() in ("true", "1", "yes")
+    if value_type is bool:
+        return value.lower() in ("true", "1", "yes", "t", "on")
     try:
         return value_type(value)
     except ValueError:
@@ -236,6 +236,8 @@ def display_splash_screen(args: argparse.Namespace) -> None:
     ASCIIColors.yellow(f"{ollama_server_infos.LIGHTRAG_MODEL}")
     ASCIIColors.white("    ├─ Log Level: ", end="")
     ASCIIColors.yellow(f"{args.log_level}")
+    ASCIIColors.white("    ├─ Verbose Debug: ", end="")
+    ASCIIColors.yellow(f"{args.verbose}")
     ASCIIColors.white("    └─ Timeout: ", end="")
     ASCIIColors.yellow(f"{args.timeout if args.timeout else 'None (infinite)'}")
 
@@ -565,6 +567,13 @@ def parse_args() -> argparse.Namespace:
         help="Prefix of the namespace",
     )
 
+    parser.add_argument(
+        "--verbose",
+        type=bool,
+        default=get_env_value("VERBOSE", False, bool),
+        help="Verbose debug output(default: from env or false)",
+    )
+
     args = parser.parse_args()
 
     # conver relative path to absolute path
@@ -768,6 +777,11 @@ temp_prefix = "__tmp_"  # prefix for temporary files
 
 
 def create_app(args):
+    # Initialize verbose debug setting
+    from lightrag.utils import set_verbose_debug
+
+    set_verbose_debug(args.verbose)
+
     global global_top_k
     global_top_k = args.top_k  # save top_k from args
 
diff --git a/lightrag/api/ollama_api.py b/lightrag/api/ollama_api.py
index 94703dee..7d9fe3b9 100644
--- a/lightrag/api/ollama_api.py
+++ b/lightrag/api/ollama_api.py
@@ -11,6 +11,7 @@ from fastapi.responses import StreamingResponse
 import asyncio
 from ascii_colors import trace_exception
 from lightrag import LightRAG, QueryParam
+from lightrag.utils import encode_string_by_tiktoken
 from dotenv import load_dotenv
 
 
@@ -111,18 +112,9 @@ class OllamaTagResponse(BaseModel):
 
 
 def estimate_tokens(text: str) -> int:
-    """Estimate the number of tokens in text
-    Chinese characters: approximately 1.5 tokens per character
-    English characters: approximately 0.25 tokens per character
-    """
-    # Use regex to match Chinese and non-Chinese characters separately
-    chinese_chars = len(re.findall(r"[\u4e00-\u9fff]", text))
-    non_chinese_chars = len(re.findall(r"[^\u4e00-\u9fff]", text))
-
-    # Calculate estimated token count
-    tokens = chinese_chars * 1.5 + non_chinese_chars * 0.25
-
-    return int(tokens)
+    """Estimate the number of tokens in text using tiktoken"""
+    tokens = encode_string_by_tiktoken(text)
+    return len(tokens)
 
 
 def parse_query_mode(query: str) -> tuple[str, SearchMode]:
diff --git a/lightrag/base.py b/lightrag/base.py
index d9a63d26..6a7374ac 100644
--- a/lightrag/base.py
+++ b/lightrag/base.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import os
+from dotenv import load_dotenv
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import (
@@ -9,12 +10,12 @@ from typing import (
     TypedDict,
     TypeVar,
 )
-
 import numpy as np
-
 from .utils import EmbeddingFunc
 from .types import KnowledgeGraph
 
+load_dotenv()
+
 
 class TextChunkSchema(TypedDict):
     tokens: int
@@ -54,13 +55,15 @@ 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)
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index a7c5920e..8fbf4d29 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -268,10 +268,10 @@ 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"
@@ -281,7 +281,7 @@ 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
diff --git a/lightrag/llm/openai.py b/lightrag/llm/openai.py
index e6d00377..024f7f52 100644
--- a/lightrag/llm/openai.py
+++ b/lightrag/llm/openai.py
@@ -40,9 +40,10 @@ __version__ = "1.0.0"
 __author__ = "lightrag Team"
 __status__ = "Production"
 
-
+from ..utils import verbose_debug, VERBOSE_DEBUG
 import sys
 import os
+import logging
 
 if sys.version_info < (3, 9):
     from typing import AsyncIterator
@@ -110,6 +111,11 @@ async def openai_complete_if_cache(
         "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_8) LightRAG/{__api_version__}",
         "Content-Type": "application/json",
     }
+
+    # Set openai logger level to INFO when VERBOSE_DEBUG is off
+    if not VERBOSE_DEBUG and logger.level == logging.DEBUG:
+        logging.getLogger("openai").setLevel(logging.INFO)
+
     openai_async_client = (
         AsyncOpenAI(default_headers=default_headers, api_key=api_key)
         if base_url is None
@@ -125,13 +131,11 @@ async def openai_complete_if_cache(
     messages.extend(history_messages)
     messages.append({"role": "user", "content": prompt})
 
-    # 添加日志输出
-    logger.debug("===== Query Input to LLM =====")
+    logger.debug("===== Sending Query to LLM =====")
     logger.debug(f"Model: {model}   Base URL: {base_url}")
     logger.debug(f"Additional kwargs: {kwargs}")
-    logger.debug(f"Query: {prompt}")
-    logger.debug(f"System prompt: {system_prompt}")
-    # logger.debug(f"Messages: {messages}")
+    verbose_debug(f"Query: {prompt}")
+    verbose_debug(f"System prompt: {system_prompt}")
 
     try:
         if "response_format" in kwargs:
diff --git a/lightrag/llm/zhipu.py b/lightrag/llm/zhipu.py
index 9f5d9ca5..5a73f41d 100644
--- a/lightrag/llm/zhipu.py
+++ b/lightrag/llm/zhipu.py
@@ -43,6 +43,7 @@ __status__ = "Production"
 import sys
 import re
 import json
+from ..utils import verbose_debug
 
 if sys.version_info < (3, 9):
     pass
@@ -119,7 +120,7 @@ async def zhipu_complete_if_cache(
     # Add debug logging
     logger.debug("===== Query Input to LLM =====")
     logger.debug(f"Query: {prompt}")
-    logger.debug(f"System prompt: {system_prompt}")
+    verbose_debug(f"System prompt: {system_prompt}")
 
     # Remove unsupported kwargs
     kwargs = {
diff --git a/lightrag/operate.py b/lightrag/operate.py
index 5c80a4d1..cc5dffe7 100644
--- a/lightrag/operate.py
+++ b/lightrag/operate.py
@@ -687,6 +687,9 @@ async def kg_query(
     if query_param.only_need_prompt:
         return sys_prompt
 
+    len_of_prompts = len(encode_string_by_tiktoken(query + sys_prompt))
+    logger.debug(f"[kg_query]Prompt Tokens: {len_of_prompts}")
+
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
@@ -772,6 +775,9 @@ 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.debug(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)
@@ -935,7 +941,9 @@ 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.debug(
+                f"Truncate chunks from {len(chunks)} to {len(formatted_chunks)} (max tokens:{query_param.max_token_for_text_unit})"
+            )
             return "\n--New Chunk--\n".join(formatted_chunks)
         except Exception as e:
             logger.error(f"Error in get_vector_context: {e}")
@@ -968,6 +976,9 @@ 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.debug(f"[mix_kg_vector_query]Prompt Tokens: {len_of_prompts}")
+
     # 6. Generate response
     response = await use_model_func(
         query,
@@ -1073,7 +1084,7 @@ 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}
@@ -1087,6 +1098,15 @@ 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.debug(
+        f"Context Tokens - Total: {contex_tokens}, Entities: {entities_tokens}, Relations: {relations_tokens}, Chunks: {text_units_tokens}"
+    )
+
+    return result
 
 
 async def _get_node_data(
@@ -1130,8 +1150,19 @@ async def _get_node_data(
             node_datas, query_param, knowledge_graph_inst
         ),
     )
+
+    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.debug(
+        f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})"
+    )
+
     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
@@ -1264,6 +1295,10 @@ async def _find_most_related_text_unit_from_entities(
         max_token_size=query_param.max_token_for_text_unit,
     )
 
+    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})"
+    )
+
     all_text_units = [t["data"] for t in all_text_units]
     return all_text_units
 
@@ -1305,6 +1340,11 @@ async def _find_most_related_edges_from_entities(
         key=lambda x: x["description"],
         max_token_size=query_param.max_token_for_global_context,
     )
+
+    logger.debug(
+        f"Truncate relations from {len(all_edges)} to {len(all_edges_data)} (max tokens:{query_param.max_token_for_global_context})"
+    )
+
     return all_edges_data
 
 
@@ -1352,11 +1392,15 @@ 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.debug(
+        f"Truncate relations from {len_edge_datas} to {len(edge_datas)} (max tokens:{query_param.max_token_for_global_context})"
+    )
 
     use_entities, use_text_units = await asyncio.gather(
         _find_most_related_entities_from_relationships(
@@ -1367,7 +1411,7 @@ 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 = [
@@ -1456,11 +1500,15 @@ 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.debug(
+        f"Truncate entities from {len_node_datas} to {len(node_datas)} (max tokens:{query_param.max_token_for_local_context})"
+    )
 
     return node_datas
 
@@ -1516,6 +1564,10 @@ async def _find_related_text_unit_from_relationships(
         max_token_size=query_param.max_token_for_text_unit,
     )
 
+    logger.debug(
+        f"Truncate chunks from {len(valid_text_units)} to {len(truncated_text_units)} (max tokens:{query_param.max_token_for_text_unit})"
+    )
+
     all_text_units: list[TextChunkSchema] = [t["data"] for t in truncated_text_units]
 
     return all_text_units
@@ -1583,7 +1635,10 @@ 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.debug(
+        f"Truncate chunks from {len(chunks)} to {len(maybe_trun_chunks)} (max tokens:{query_param.max_token_for_text_unit})"
+    )
+
     section = "\n--New Chunk--\n".join([c["content"] for c in maybe_trun_chunks])
 
     if query_param.only_need_context:
@@ -1606,6 +1661,9 @@ 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,
@@ -1748,6 +1806,9 @@ 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.debug(f"[kg_query_with_keywords]Prompt Tokens: {len_of_prompts}")
+
     response = await use_model_func(
         query,
         system_prompt=sys_prompt,
diff --git a/lightrag/utils.py b/lightrag/utils.py
index fd8e0adc..e5d29824 100644
--- a/lightrag/utils.py
+++ b/lightrag/utils.py
@@ -20,6 +20,23 @@ import tiktoken
 
 from lightrag.prompt import PROMPTS
 
+VERBOSE_DEBUG = os.getenv("VERBOSE", "false").lower() == "true"
+
+
+def verbose_debug(msg: str, *args, **kwargs):
+    """Function for outputting detailed debug information.
+    When VERBOSE_DEBUG=True, outputs the complete message.
+    When VERBOSE_DEBUG=False, outputs only the first 30 characters.
+    """
+    if VERBOSE_DEBUG:
+        logger.debug(msg, *args, **kwargs)
+
+
+def set_verbose_debug(enabled: bool):
+    """Enable or disable verbose debug output"""
+    global VERBOSE_DEBUG
+    VERBOSE_DEBUG = enabled
+
 
 class UnlimitedSemaphore:
     """A context manager that allows unlimited access."""