refactor: refactor the prompt management mechanism (#17)

src/podcast/graph/script_writer_node.py

@@ -22,7 +22,7 @@ def script_writer_node(state: PodcastState):
     ).with_structured_output(Script, method="json_mode")
     script = model.invoke(
         [
-            SystemMessage(content=get_prompt_template("podcast_script_writer")),
+            SystemMessage(content=get_prompt_template("podcast/podcast_script_writer")),
             HumanMessage(content=state["input"]),
         ],
     )

src/ppt/graph/ppt_composer_node.py

@@ -21,7 +21,7 @@ def ppt_composer_node(state: PPTState):
     model = get_llm_by_type(AGENT_LLM_MAP["ppt_composer"])
     ppt_content = model.invoke(
         [
-            SystemMessage(content=get_prompt_template("ppt_composer")),
+            SystemMessage(content=get_prompt_template("ppt/ppt_composer")),
             HumanMessage(content=state["input"]),
         ],
     )

src/prompts/prose/prose_continue.md

@@ -0,0 +1,4 @@
+You are an AI writing assistant that continues existing text based on context from prior text.
+- Give more weight/priority to the later characters than the beginning ones.
+- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
+- Use Markdown formatting when appropriate

src/prompts/prose/prose_fix.md

@@ -0,0 +1,4 @@
+You are an AI writing assistant that fixes grammar and spelling errors in existing text. 
+- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
+- Use Markdown formatting when appropriate.
+- If the text is already correct, just return the original text.

src/prompts/prose/prose_improver.md

@@ -0,0 +1,3 @@
+You are an AI writing assistant that improves existing text.
+- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
+- Use Markdown formatting when appropriate.

src/prompts/prose/prose_longer.md

@@ -0,0 +1,2 @@
+You are an AI writing assistant that lengthens existing text.
+- Use Markdown formatting when appropriate.

src/prompts/prose/prose_shorter.md

@@ -0,0 +1,2 @@
+You are an AI writing assistant that shortens existing text.
+- Use Markdown formatting when appropriate.

src/prompts/prose/prose_zap.md

@@ -0,0 +1,3 @@
+You area an AI writing assistant that generates text based on a prompt. 
+- You take an input from the user and a command for manipulating the text."
+- Use Markdown formatting when appropriate.

src/prose/graph/prose_continue_node.py

@@ -7,24 +7,18 @@ from langchain.schema import HumanMessage, SystemMessage
 
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
+from src.prompts.template import get_prompt_template
 from src.prose.graph.state import ProseState
 
 logger = logging.getLogger(__name__)
 
-prompt = """
-You are an AI writing assistant that continues existing text based on context from prior text.
-- Give more weight/priority to the later characters than the beginning ones.
-- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
-- Use Markdown formatting when appropriate
-"""
-
 
 def prose_continue_node(state: ProseState):
     logger.info("Generating prose continue content...")
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_continue")),
             HumanMessage(content=state["content"]),
         ],
     )

src/prose/graph/prose_fix_node.py

@@ -7,24 +7,18 @@ from langchain.schema import HumanMessage, SystemMessage
 
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
+from src.prompts.template import get_prompt_template
 from src.prose.graph.state import ProseState
 
 logger = logging.getLogger(__name__)
 
-prompt = """
-You are an AI writing assistant that fixes grammar and spelling errors in existing text. 
-- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
-- Use Markdown formatting when appropriate.
-- If the text is already correct, just return the original text.
-"""
-
 
 def prose_fix_node(state: ProseState):
     logger.info("Generating prose fix content...")
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_fix")),
             HumanMessage(content=f"The existing text is: {state['content']}"),
         ],
     )

src/prose/graph/prose_improve_node.py

@@ -8,22 +8,17 @@ from langchain.schema import HumanMessage, SystemMessage
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
 from src.prose.graph.state import ProseState
+from src.prompts.template import get_prompt_template
 
 logger = logging.getLogger(__name__)
 
-prompt = """
-You are an AI writing assistant that improves existing text.
-- Limit your response to no more than 200 characters, but make sure to construct complete sentences.
-- Use Markdown formatting when appropriate.
-"""
-
 
 def prose_improve_node(state: ProseState):
     logger.info("Generating prose improve content...")
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_improver")),
             HumanMessage(content=f"The existing text is: {state['content']}"),
         ],
     )

src/prose/graph/prose_longer_node.py

@@ -7,22 +7,18 @@ from langchain.schema import HumanMessage, SystemMessage
 
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
+from src.prompts.template import get_prompt_template
 from src.prose.graph.state import ProseState
 
 logger = logging.getLogger(__name__)
 
-prompt = """
-You are an AI writing assistant that lengthens existing text.
-- Use Markdown formatting when appropriate.
-"""
-
 
 def prose_longer_node(state: ProseState):
     logger.info("Generating prose longer content...")
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_longer")),
             HumanMessage(content=f"The existing text is: {state['content']}"),
         ],
     )

src/prose/graph/prose_shorter_node.py

@@ -7,13 +7,10 @@ from langchain.schema import HumanMessage, SystemMessage
 
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
+from src.prompts.template import get_prompt_template
 from src.prose.graph.state import ProseState
 
 logger = logging.getLogger(__name__)
-prompt = """
-You are an AI writing assistant that shortens existing text.
-- Use Markdown formatting when appropriate.
-"""
 
 
 def prose_shorter_node(state: ProseState):
@@ -21,7 +18,7 @@ def prose_shorter_node(state: ProseState):
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_shorter")),
             HumanMessage(content=f"The existing text is: {state['content']}"),
         ],
     )

src/prose/graph/prose_zap_node.py

@@ -7,14 +7,10 @@ from langchain.schema import HumanMessage, SystemMessage
 
 from src.config.agents import AGENT_LLM_MAP
 from src.llms.llm import get_llm_by_type
+from src.prompts.template import get_prompt_template
 from src.prose.graph.state import ProseState
 
 logger = logging.getLogger(__name__)
-prompt = """
-You area an AI writing assistant that generates text based on a prompt. 
-- You take an input from the user and a command for manipulating the text."
-- Use Markdown formatting when appropriate.
-"""
 
 
 def prose_zap_node(state: ProseState):
@@ -22,7 +18,7 @@ def prose_zap_node(state: ProseState):
     model = get_llm_by_type(AGENT_LLM_MAP["prose_writer"])
     prose_content = model.invoke(
         [
-            SystemMessage(content=prompt),
+            SystemMessage(content=get_prompt_template("prose/prose_zap")),
             HumanMessage(
                 content=f"For this text: {state['content']}.\nYou have to respect the command: {state['command']}"
             ),