refactor: implement ai_prompter library

open_notebook/graphs/ask.py

@@ -1,6 +1,7 @@
 import operator
 from typing import Annotated, List
 
+from ai_prompter import Prompter
 from langchain_core.output_parsers.pydantic import PydanticOutputParser
 from langchain_core.runnables import (
     RunnableConfig,
@@ -12,7 +13,6 @@ from typing_extensions import TypedDict
 
 from open_notebook.domain.notebook import vector_search
 from open_notebook.graphs.utils import provision_langchain_model
-from open_notebook.prompter import Prompter
 
 
 class SubGraphState(TypedDict):

open_notebook/graphs/chat.py

@@ -1,6 +1,7 @@
 import sqlite3
 from typing import Annotated, Optional
 
+from ai_prompter import Prompter
 from langchain_core.messages import SystemMessage
 from langchain_core.runnables import (
     RunnableConfig,
@@ -13,7 +14,6 @@ from typing_extensions import TypedDict
 from open_notebook.config import LANGGRAPH_CHECKPOINT_FILE
 from open_notebook.domain.notebook import Notebook
 from open_notebook.graphs.utils import provision_langchain_model
-from open_notebook.prompter import Prompter
 
 
 class ThreadState(TypedDict):

open_notebook/graphs/prompt.py

@@ -1,15 +1,13 @@
 from typing import Any, Optional
 
+from ai_prompter import Prompter
 from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_core.runnables import (
+from langchain_core.runnables import RunnableConfig
-    RunnableConfig,
-)
 from langgraph.graph import END, START, StateGraph
 from loguru import logger
 from typing_extensions import TypedDict
 
 from open_notebook.graphs.utils import provision_langchain_model
-from open_notebook.prompter import Prompter
 
 
 class PatternChainState(TypedDict):
@@ -22,7 +20,7 @@ class PatternChainState(TypedDict):
 def call_model(state: dict, config: RunnableConfig) -> dict:
     content = state["input_text"]
     system_prompt = Prompter(
-        prompt_text=state["prompt"], parser=state.get("parser")
+        template_text=state["prompt"], parser=state.get("parser")
     ).render(data=state)
     logger.warning(content)
     payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]

open_notebook/graphs/transformation.py

@@ -1,14 +1,12 @@
+from ai_prompter import Prompter
 from langchain_core.messages import HumanMessage, SystemMessage
-from langchain_core.runnables import (
+from langchain_core.runnables import RunnableConfig
-    RunnableConfig,
-)
 from langgraph.graph import END, START, StateGraph
 from typing_extensions import TypedDict
 
 from open_notebook.domain.notebook import Source
 from open_notebook.domain.transformation import DefaultPrompts, Transformation
 from open_notebook.graphs.utils import provision_langchain_model
-from open_notebook.prompter import Prompter
 
 
 class TransformationState(TypedDict):
@@ -25,14 +23,16 @@ def run_transformation(state: dict, config: RunnableConfig) -> dict:
     transformation: Transformation = state["transformation"]
     if not content:
         content = source.full_text
-    transformation_prompt_text = transformation.prompt
+    transformation_template_text = transformation.prompt
     default_prompts: DefaultPrompts = DefaultPrompts()
     if default_prompts.transformation_instructions:
-        transformation_prompt_text = f"{default_prompts.transformation_instructions}\n\n{transformation_prompt_text}"
+        transformation_template_text = f"{default_prompts.transformation_instructions}\n\n{transformation_template_text}"
 
-    transformation_prompt_text = f"{transformation_prompt_text}\n\n# INPUT"
+    transformation_template_text = f"{transformation_template_text}\n\n# INPUT"
 
-    system_prompt = Prompter(prompt_text=transformation_prompt_text).render(data=state)
+    system_prompt = Prompter(template_text=transformation_template_text).render(
+        data=state
+    )
     payload = [SystemMessage(content=system_prompt)] + [HumanMessage(content=content)]
     chain = provision_langchain_model(
         str(payload),

open_notebook/prompter.py

@@ -1,102 +0,0 @@
-"""
-A prompt management module using Jinja to generate complex prompts with simple templates.
-"""
-
-import os
-from dataclasses import dataclass
-from datetime import datetime
-from typing import Any, Optional, Union
-
-from jinja2 import Environment, FileSystemLoader, Template
-
-current_dir = os.path.dirname(os.path.abspath(__file__))
-
-project_root = os.path.dirname(current_dir)
-
-env = Environment(
-    loader=FileSystemLoader(
-        os.path.join(project_root, os.environ.get("PROMPT_PATH", "prompts"))
-    )
-)
-
-
-@dataclass
-class Prompter:
-    """
-    A class for managing and rendering prompt templates.
-
-    Attributes:
-        prompt_template (str, optional): The name of the prompt template file.
-        prompt_variation (str, optional): The variation of the prompt template.
-        prompt_text (str, optional): The raw prompt text.
-        template (Union[str, Template], optional): The Jinja2 template object.
-    """
-
-    prompt_template: Optional[str] = None
-    prompt_variation: Optional[str] = "default"
-    prompt_text: Optional[str] = None
-    template: Optional[Union[str, Template]] = None
-    parser: Optional[Any] = None
-
-    def __init__(self, prompt_template=None, prompt_text=None, parser=None):
-        """
-        Initialize the Prompter with either a template file or raw text.
-
-        Args:
-            prompt_template (str, optional): The name of the prompt template file.
-            prompt_text (str, optional): The raw prompt text.
-        """
-        self.prompt_template = prompt_template
-        self.prompt_text = prompt_text
-        self.parser = parser
-        self.setup()
-
-    def setup(self):
-        """
-        Set up the Jinja2 template based on the provided template file or text.
-        Raises:
-            ValueError: If neither prompt_template nor prompt_text is provided.
-        """
-        if self.prompt_template:
-            self.template = env.get_template(f"{self.prompt_template}.jinja")
-        elif self.prompt_text:
-            self.template = Template(self.prompt_text)
-        else:
-            raise ValueError("Prompter must have a prompt_template or prompt_text")
-
-        assert self.prompt_template or self.prompt_text, "Prompt is required"
-
-    @classmethod
-    def from_text(cls, text: str):
-        """
-        Create a Prompter instance from raw text, which can contain Jinja code.
-
-        Args:
-            text (str): The raw prompt text.
-
-        Returns:
-            Prompter: A new Prompter instance.
-        """
-        return cls(prompt_text=text)
-
-    def render(self, data) -> str:
-        """
-        Render the prompt template with the given data.
-
-        Args:
-            data (dict): The data to be used in rendering the template.
-
-        Returns:
-            str: The rendered prompt text.
-
-        Raises:
-            AssertionError: If the template is not defined or not a Jinja2 Template.
-        """
-        data["current_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        if self.parser:
-            data["format_instructions"] = self.parser.get_format_instructions()
-        assert self.template, "Prompter template is not defined"
-        assert isinstance(
-            self.template, Template
-        ), "Prompter template is not a Jinja2 Template"
-        return self.template.render(data)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "open-notebook"
-version = "0.2.0"
+version = "0.2.1"
 description = "An open source implementation of a research assistant, inspired by Google Notebook LM"
 authors = [
     {name = "Luis Novo", email = "lfnovo@gmail.com"}
@@ -41,6 +41,7 @@ dependencies = [
     "podcastfy",
     "nest-asyncio>=1.6.0",
     "content-core>=1.0.0",
+    "ai-prompter>=0.3",
 ]
 
 [tool.setuptools]

uv.lock

@@ -20,16 +20,16 @@ resolution-markers = [
 
 [[package]]
 name = "ai-prompter"
-version = "0.2.3"
+version = "0.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2" },
     { name = "pip" },
     { name = "pydantic" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/39/ff/cf13c31b88c06e11a1ffeed505601c167293b23d3e2e4e02adac93cc9300/ai_prompter-0.2.3.tar.gz", hash = "sha256:40f55c18f87df250a13f84d0cf7a4e8b31815a01f27666039386d6592849694b", size = 72955 }
+sdist = { url = "https://files.pythonhosted.org/packages/88/1a/263b2fb49a485d1b394ead887361cb8855ab28daa20a184cef0d2a0f8f2c/ai_prompter-0.3.0.tar.gz", hash = "sha256:3369555345386c6b9eebb7edbbb96df268977ab2657acb2890c217290bf92569", size = 74091 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/5f/11/9e3712b8393dbef152258c68617baec343040c3d08b372d77b57e51d8e5d/ai_prompter-0.2.3-py3-none-any.whl", hash = "sha256:e8c0becbb3c8bdff399e372830e2c0a3cc3292e02d67921e2b255871329ee477", size = 7345 },
+    { url = "https://files.pythonhosted.org/packages/90/ae/cc493d9d37cd1501e442154aa7265fa05814d0e8519ddf549ebd2f5fcb1b/ai_prompter-0.3.0-py3-none-any.whl", hash = "sha256:b70569bf6a64258ab3453e1ff99a7a4cd1c7709296093dc2a35127230d408e7b", size = 8419 },
 ]
 
 [[package]]
@@ -2701,9 +2701,10 @@ wheels = [
 
 [[package]]
 name = "open-notebook"
-version = "0.2.0"
+version = "0.2.1"
 source = { editable = "." }
 dependencies = [
+    { name = "ai-prompter" },
     { name = "content-core" },
     { name = "google-generativeai" },
     { name = "groq" },
@@ -2752,6 +2753,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
+    { name = "ai-prompter", specifier = ">=0.3" },
     { name = "content-core", specifier = ">=1.0.0" },
     { name = "google-generativeai", specifier = ">=0.8.3" },
     { name = "groq", specifier = ">=0.12.0" },