Chapter 1: Understanding Your Unique Value Proposition

In this section, I'll explore how AI and LangChain can assist in articulating my "unique value proposition." My main goal is to develop an AI workflow that generates a comprehensive marketing strategy starting from the unique value proposition, extending to branding protocols, marketing channel definitions, and beyond. Tackling this challenge might seem straightforward, but it genuinely reflects a real-world scenario.

I've crafted a snippet of code that gathers context and utilizes that information to respond to queries. While I recognize there are more effective methods out there, I will clarify my approach as I outline the code that follows. Consider this not as the sole method but rather as a useful starting point for understanding how these components can work together to yield the desired results.

The positive aspect is that this approach has allowed me to begin drafting a more elaborate marketing plan. This task may even evolve into a future post focusing on "LangChain Agents."

import os

import requests

from langchain_community.llms import Ollama

from langchain_community.embeddings import OllamaEmbeddings

from langchain.text_splitter import CharacterTextSplitter

from langchain.schema.document import Document

from langchain_community.vectorstores import chroma

from langchain_core.output_parsers import StrOutputParser

from langchain_core.runnables import RunnablePassthrough

from langchain.prompts.prompt import PromptTemplate

from bs4 import BeautifulSoup

from dotenv import load_dotenv

This code snippet serves as a foundation for my exploration into how AI can identify my unique value proposition, which I can then utilize to craft a personal branding guide. The steps I plan to follow include:

1. Extracting text from my existing online content.
2. Formatting that content appropriately for the AI model.
3. Initializing the AI model.
4. Loading the extracted text as context.
5. Submitting my inquiry to the model.
6. Displaying the model's response.

To set this up, I'll create a dedicated working directory and establish a Python virtual environment, followed by the installation of necessary dependencies.

# Create the working directory

mkdir research

cd research

# Create and activate the virtual environment

python3 -m venv .venv

source .venv/bin/activate

# Install the required libraries

pip install langchain langchain_community beautifulsoup4 python-dotenv

# Create the application file

touch myapp.py

# Create the .env file:

echo "OPENAI_API_KEY=" >> .env

echo "OLLAMA_HOST=" >> .env

echo "OLLAMA_MODEL=" >> .env

By utilizing Ollama, I can run a large language model on my internal network, avoiding the typical restrictions of external services. The trade-off for this approach is slower response times, but I am willing to accept that in exchange for maintaining control over my data.

The next hurdle is ensuring LangChain connects with my local Ollama service. The Ollama class from langchain_community.llms facilitates this connection by allowing us to specify a base URL and model.

Now, let’s look at the video that illustrates how AI can supercharge your business by unlocking its potential.

Chapter 2: Automating Content Extraction and Processing

In this chapter, I will demonstrate how to extract text content from my blog posts using a programmatic approach, leveraging the BeautifulSoup library for efficient web scraping.

def extract_text_from(url):

html = requests.get(url).text

soup = BeautifulSoup(html, features="html.parser")

text = soup.get_text()

lines = (line.strip() for line in text.splitlines())

return 'n'.join(line for line in lines if line)

This function takes a URL, retrieves its content, extracts the text, and removes unnecessary whitespace and blank lines. The output is a cleaned-up string, ready for further processing.

Next, we will explore how to convert the text into tokens for the AI model's understanding, using the CharacterTextSplitter method from LangChain.

# Define a list of external resources

external_resources = [

'http://mydomain.com/',

'http://mydomain.com/about.html',

# Additional resources...

]

def get_documents():

resource_docs = []

for resource in external_resources:

print(f'>RESOURCE: {resource}')

text = extract_text_from(resource)

text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)

docs = [Document(page_content=x) for x in text_splitter.split_text(text)]

resource_docs.extend(docs)

return resource_docs

Finally, let’s watch another video that dives deeper into harnessing AI for business growth.

As we conclude, I hope this content and the accompanying resources will be beneficial in your journey to leverage AI for defining your unique value proposition and enhancing your marketing strategy.