diff --git a/lightrag/CHANGELOG.md b/lightrag/CHANGELOG.md
index 9136b8b0..d9f8a191 100644
--- a/lightrag/CHANGELOG.md
+++ b/lightrag/CHANGELOG.md
@@ -1,3 +1,9 @@
+## [0.0.0-alpha.16] - 2024-07-08
+
+### Fixed
+- Anthropic client message does not use system role. For now, we put the whole prompt as the first user message.
+- Update the `DEDEFAULT_LIGHTRAG_SYSTEM_PROMPT` to include 'You are a helpful assistant' as default <SYS> prompt.
+
 ## [0.0.0-alpha.15] - 2024-07-07
 
 ### Fixed
diff --git a/lightrag/lightrag/components/model_client/anthropic_client.py b/lightrag/lightrag/components/model_client/anthropic_client.py
index 53e51c5e..c53049ef 100644
--- a/lightrag/lightrag/components/model_client/anthropic_client.py
+++ b/lightrag/lightrag/components/model_client/anthropic_client.py
@@ -72,19 +72,26 @@ def parse_chat_completion(self, completion: Message) -> str:
         log.debug(f"completion: {completion}")
         return completion.content[0].text
 
+    # TODO: potentially use <SYS></SYS> to separate the system and user messages. This requires user to follow it. If it is not found, then we will only use user message.
     def convert_inputs_to_api_kwargs(
         self,
         input: Optional[Any] = None,
         model_kwargs: Dict = {},
         model_type: ModelType = ModelType.UNDEFINED,
     ) -> dict:
+        r"""Anthropic API messages separates the system and the user messages.
+
+        As we focus on one prompt, we have to use the user message as the input.
+
+        api: https://docs.anthropic.com/en/api/messages
+        """
         api_kwargs = model_kwargs.copy()
         if model_type == ModelType.LLM:
-            # api_kwargs["messages"] = [
-            #     {"role": "user", "content": input},
-            # ]
-            if input and input != "":
-                api_kwargs["system"] = input
+            api_kwargs["messages"] = [
+                {"role": "user", "content": input},
+            ]
+            # if input and input != "":
+            #     api_kwargs["system"] = input
         else:
             raise ValueError(f"Model type {model_type} not supported")
         return api_kwargs
diff --git a/lightrag/lightrag/core/default_prompt_template.py b/lightrag/lightrag/core/default_prompt_template.py
index 3355a8cf..6c8084b6 100644
--- a/lightrag/lightrag/core/default_prompt_template.py
+++ b/lightrag/lightrag/core/default_prompt_template.py
@@ -25,12 +25,12 @@
 User: {{input_str}}
 You:"""
 
-DEFAULT_LIGHTRAG_SYSTEM_PROMPT = r"""{% if task_desc_str or output_format_str or tools_str or examples_str or chat_history_str or context_str or steps_str %}
-<SYS>
-{% endif %}
+DEFAULT_LIGHTRAG_SYSTEM_PROMPT = r"""<SYS>
 {# task desc #}
 {% if task_desc_str %}
 {{task_desc_str}}
+{% else %}
+You are a helpful assistant.
 {% endif %}
 {# output format #}
 {% if output_format_str %}
@@ -68,9 +68,7 @@
 {{steps_str}}
 </STEPS>
 {% endif %}
-{% if task_desc_str or output_format_str or tools_str or examples_str or chat_history_str or context_str or steps_str %}
 </SYS>
-{% endif %}
 {% if input_str %}
 <User>
 {{input_str}}
diff --git a/lightrag/lightrag/core/generator.py b/lightrag/lightrag/core/generator.py
index 4c671cd3..0e078ea3 100644
--- a/lightrag/lightrag/core/generator.py
+++ b/lightrag/lightrag/core/generator.py
@@ -232,10 +232,11 @@ def call(
             }
             prompt_kwargs.update(trained_prompt_kwargs)
 
-        log.info(f"prompt_kwargs: {prompt_kwargs}")
-        log.info(f"model_kwargs: {model_kwargs}")
+        log.debug(f"prompt_kwargs: {prompt_kwargs}")
+        log.debug(f"model_kwargs: {model_kwargs}")
 
         api_kwargs = self._pre_call(prompt_kwargs, model_kwargs)
+        log.debug(f"api_kwargs: {api_kwargs}")
         output: GeneratorOutputType = None
         # call the model client
         try:
diff --git a/lightrag/pyproject.toml b/lightrag/pyproject.toml
index 9f8c7c81..bc8c91d7 100644
--- a/lightrag/pyproject.toml
+++ b/lightrag/pyproject.toml
@@ -1,7 +1,7 @@
 [tool.poetry]
 name = "lightrag"
 
-version = "0.0.0-alpha.15"
+version = "0.0.0-alpha.16"
 description = "The 'PyTorch' library for LLM applications. RAG=Retriever-Agent-Generator."
 authors = ["Li Yin <li@sylphai.com>"]
 readme = "README.md"