src/blogGenerator/nodes/content_generator_node.py

from blogGenerator.state.state import State
from pydantic import BaseModel, Field

from langchain_core.messages import SystemMessage, HumanMessage


class BlogContent(BaseModel):
    title: str = Field(description="Title of the Blog")
    content: str = Field(description="Content of the Blog")


class ContentGeneratorNode:
    def __init__(self, model):
        self.llm = model

    def process(self, state: State):

        # print(f"Node Called : ContentGeneratorNode ---> {state['decision']}")

        blog_llm = self.llm.with_structured_output(BlogContent)

        system_prompt = f"""
                You are an expert SEO content writer specializing in generating high-quality, search-engine-optimized blog posts. Your goal is to create engaging, well-structured, and informative content that ranks well on search engines. Ensure the following best practices:

                <Instruction>
                    - Structure the blog into well-defined paragraphs.
                    - Use bullet points where appropriate for clarity.
                    - Keep sentences concise and easy to read.
                    - Maintain a professional yet engaging tone.
                    - Ensure natural integration of relevant keywords for SEO optimization.
                    - Address common user queries to match search intent.
                    - Include a strong conclusion with a clear call to action.
                    - Ensure the blog is original, well-researched, and free from AI-detectable patterns. The word count should typically be between 800-1500 words, depending on the topic.
                </Instruction>
            """

        if state["decision"] == "topic":
            human_prompt = (
                f"Generate an SEO-optimized blog post on {state['user_message']}"
            )

        elif state["decision"] == "youtube":
            human_prompt = f"Generate an SEO-optimized blog post from this YouTube transcript: {state['yt_transcript']}."

        blog = blog_llm.invoke(
            [SystemMessage(content=system_prompt), HumanMessage(content=human_prompt)]
        )

        # print(f"### blog : {blog}")
        # print(f"### blog_title : {blog.title}")
        # print(f"### blog_content : {blog.content}")

        return {
            "blog_title": blog.title,
            "blog_content": blog.content,
            "final_content": "\n\n---\n\n".join([blog.title, blog.content]),
        }