Content Writing Automation

1from langchain_openai import ChatOpenAI
2from langchain_core.prompts import ChatPromptTemplate
3from langchain_core.output_parsers import StrOutputParser
4
5llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.7)
6
7# Step 1: Generate outline
8outline_template = ChatPromptTemplate.from_messages([
9    ("system", """Ban la content strategist. Tao outline cho blog post voi:
10    - Hook introduction
11    - 4-5 main sections voi headings
12    - Key points cho moi section
13    - Conclusion voi CTA"""),
14    ("human", "Topic: {topic}\nAudience: {audience}\nTone: {tone}")
15])
16
17# Step 2: Write full post
18write_template = ChatPromptTemplate.from_messages([
19    ("system", """Ban la content writer chuyen nghiep. 
20    Viet blog post day du tu outline.
21    Su dung tone {tone}, do dai khoang {word_count} tu.
22    Format voi Markdown headings."""),
23    ("human", "Outline:\n{outline}\n\nViet bai hoan chinh:")
24])
25
26# Build chains
27outline_chain = outline_template | llm | StrOutputParser()
28write_chain = write_template | llm | StrOutputParser()
29
30# Execute
31outline = outline_chain.invoke({
32    "topic": "AI trong Marketing 2025",
33    "audience": "marketing managers",
34    "tone": "professional"
35})
36
37blog_post = write_chain.invoke({
38    "outline": outline,
39    "tone": "professional",
40    "word_count": "1000"
41})
42print(blog_post)

1from pydantic import BaseModel, Field
2from typing import List
3
4class EmailOutput(BaseModel):
5    subject: str = Field(description="Email subject line")
6    greeting: str = Field(description="Opening greeting")
7    body: str = Field(description="Main email body")
8    closing: str = Field(description="Closing and signature")
9
10email_template = ChatPromptTemplate.from_messages([
11    ("system", """Ban la email copywriter. Viet email {email_type}.
12    Tone: {tone}
13    Keep it concise and professional."""),
14    ("human", "Viet email cho {recipient} ve: {topic}")
15])
16
17structured_llm = llm.with_structured_output(EmailOutput)
18email_chain = email_template | structured_llm
19
20email = email_chain.invoke({
21    "email_type": "follow-up",
22    "tone": "friendly professional",
23    "recipient": "potential client",
24    "topic": "demo AI product"
25})
26
27print(f"Subject: {email.subject}")
28print(f"\n{email.greeting}\n\n{email.body}\n\n{email.closing}")

1class SocialPost(BaseModel):
2    platform: str
3    content: str
4    hashtags: List[str]
5    character_count: int
6
7class SocialPack(BaseModel):
8    posts: List[SocialPost]
9
10social_template = ChatPromptTemplate.from_messages([
11    ("system", """Tao social media content pack tu blog post.
12    Tao 1 post cho moi platform:
13    - LinkedIn (professional, 200-300 words)
14    - Twitter/X (concise, max 280 chars)
15    - Facebook (engaging, 100-200 words)
16    - Instagram caption (visual, include emojis)"""),
17    ("human", "Blog post:\n{blog_content}")
18])
19
20social_chain = social_template | llm.with_structured_output(SocialPack)
21
22pack = social_chain.invoke({"blog_content": blog_post})
23for post in pack.posts:
24    print(f"\n--- {post.platform} ---")
25    print(post.content)
26    print(f"Hashtags: {', '.join(post.hashtags)}")

1product_template = ChatPromptTemplate.from_messages([
2    ("system", """Ban la e-commerce copywriter.
3    Viet product description voi:
4    - Headline hap dan
5    - Key features (bullet points)
6    - Benefits (customer-focused)
7    - Technical specs
8    Tone: {tone}
9    Length: {length} tu"""),
10    ("human", """Product: {product_name}
11Category: {category}
12Features: {features}
13Target audience: {target}""")
14])
15
16product_chain = product_template | llm | StrOutputParser()
17
18description = product_chain.invoke({
19    "tone": "persuasive",
20    "length": "200",
21    "product_name": "Smart AI Assistant Pro",
22    "category": "Software",
23    "features": "voice control, task automation, calendar management",
24    "target": "busy professionals"
25})

1from langchain_core.runnables import RunnableParallel
2
3# Parallel repurposing
4repurpose = RunnableParallel(
5    twitter=twitter_chain,
6    linkedin=linkedin_chain,
7    email=email_chain,
8    video_script=script_chain
9)
10
11results = repurpose.invoke({"content": blog_post})